ORIGINAL_ARTICLE
Robust Estimation in Nonlinear Modeling of Volatility Transmission in Stock Market
Volatility transmission means the connection between different markets in a way that volatility can be transmitted from one market to another. The volatility of oil price in global markets is one of the factors which influence the capital markets of the countries of which their economy is based on oil revenues. Most of these markets have long-run memory characteristic which should be considered in modeling and estimation. In this paper the long memory effect in BEKK model which is one of the main Multivariate models of volatility spillover is considered and the Boudt & Croux (2010) approache is used for stable estimation of the model. The data used in this paper are daily returns of stock prices and oil prices in time interval December 2006 to January 2012. The paper investigate the influence of world oil price index on Dubai and Tehran stock markets in the strategic region of Middle East and also the mutual transmission between the two main trading partner of Iran and Emirates. The results indicate the volatility transmission from world oil market to Dubai and Tehran markets and also the transmission from Dubai market to Tehran market.
https://aie.ut.ac.ir/article_60722_b90b1b6d4c041754d0dfd03eba9144b0.pdf
2016-09-22
165
176
10.22059/jieng.2016.60722
Long memory
pricing
Return
Robust estimation
Volatility transmission
Seyed Babak
Ebrahimi
b_ebrahimi@kntu.ac.ir
1
Department of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran
LEAD_AUTHOR
1. Boudt, K. and Croux, C. (2010). “Robust M- estimation of multivariate GARCH models”, Computational Statistics & Data Analysis, Vol. 54, No. 11, PP. 2459- 2469.
1
2. Hamilton, J. D. (1983). “Oil and Macro- Economy since world war II”, Journal of Political Economy, Vol.91, No. 2, PP. 228– 248.
2
3. Hamilton, J. D. (2003). “What is an oil shock?”, Journal of Econometrics, Vol.113, No. 2, PP. 363– 398.
3
4. Kilian, L. (2008b). “Exogenous oil supply shocks: How big are they and how much do they matter for the U.S. economy?”, Review ofEconomics and Statistics, Vol.90, No. 2, PP. 216– 240.
4
5. Kilian, L. (2008a). “The economic effects of energy price shocks”, Journal of Economic Literature, Vol.46, No. 4, PP. 871– 1009.
5
6. Tansuchat, R., Chang, C. L. and McAleer, M. (2010). “Conditional correlation and volatility spillovers between crude oil and stock index returns”No.25, PP.116– 138.
6
7. Yu, J., and Hasan, M. K. (2008). “Global and regional integration of the Middle East and North African (MENA) stock markets”, The Quarterly Review of Economics and Finance, Vol.48, No. 3, PP. 482– 504.
7
8. Malik, F. and Hammoudeh, S. (2007). “Shock and volatility transmission in the oil, US and Gulf equity markets”, International Review of Economics and Finance, Vol. 16, No. 3, PP. 357- 368.
8
9. Aloui, C. and Jammazi, R. (2009). “The effects of crude oil shocks on stock market shifts behavior: A regime switching approach”, Energy Economics, Vol.31, No. 5, PP. 789- 799.
9
10. Arouri, M. and Nguyen, D. K. (2010). “Oil prices, stock markets and portfolio investment: Evidence from sector analysis in Europe over the last decade”, Energy Policy, Vol.38, No. 8, PP. 4528- 4539.
10
11. Wei, Y., Wang, Y. and Huang, D. (2010). “Forecasting crude oil market volatility: Further evidence using GARCH- class models”, Energy Economics, Vol. 32, No. 6, PP. 1477- 1484.
11
12. Mahmoudi, V., Mohammadi, S. and Chitsazan, H. (2010). “A study of long memory trend for international oil markets”, Economic Research Modelling, Vol. 1, No. 1, PP. 29- 48.
12
13. Mohammadi, S. and Chitsazan, H. (2011). “Analysing long run memory in Tehran stock exchange”, Journal of Economic Research, Vol. 45, No. 97, PP. 207- 226.
13
14. Mohammadi, H. and Sue, L. (2010). International evidence on crude oil price dynamics: Application of ARIMA-GARCH models, Energy Economics, Vol. 32, No. 5, PP. 1001-1008.
14
15. Filis, G., Degiannakis, S. and Floros, C. (2011). “Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries”, International Review of Financial Analysis, Vol.20, No. 3, PP. 152- 164.
15
16. Van Nguyen, T. (2013). “The stable relationship between crude oil price and petrol price: Evidence from multivariate GARCH model”, The Empirical Econometrics and Quantitative Economics Letters, Vol. 2, No. 2.
16
17. Ghorbel, A., Boujelbène, M. and Boujelbène, Y. (2012). “Volatility spillovers and dynamic conditional correlation between crude oil and stock market returns.” International Journal of Managerial and Financial Accounting (IJMFA), Vol. 4, No. 2, PP. 177- 194.
17
18. Girardi, G. and Tolga Ergün, A. (2013). “Systemic risk measurement: Multivariate GARCH estimation of CoVaR”, Journal of Banking & Finance, Vol. 37, No. 8, PP. 3169- 3180.
18
19. Nazlioglu, S., Erdem, C., & Soytas, U. (2013). Volatility spillover between oil and agricultural commodity markets. Energy Economics, Vol.36, PP.658-665. 20. Miralles- Marcelo, J. L. and Miralles-Quirós, M. D. M. (2013). “Multivariate GARCH models and risk minimizing portfolios: The importance of medium and small firms”, The Spanish Review of Financial Economics, Vol. 11, No. 1, PP. 29- 38.
19
21. Chang, C. L., McAleer, M. J. and Tansuchat, R. (2013). “Conditional correlations and volatility spillovers between crude oil and stock index returns”, North American Journal of Economics and Finance, Vol. 25, No.1, PP. 116–138.
20
22. Wang, Y., Wu, C. and Yang, L. (2016). “Forecasting crude oil market volatility: A Markov switching multifractal volatility approach”, International Journal of Forecasting, Vol. 32, No. 1, PP. 1- 9.
21
23. Chan, J. C. and Grant, A. L. (2016). “Modeling energy price dynamics: GARCH versus stochastic volatility”, Energy Economics, Vol. 54, No. 1, PP. 182- 189.
22
24. Serletis, A. and Xu, L. (2016). “Volatility and a century of energy markets dynamics”, Energy Economics, Vol. 55, No. 1, PP. 1- 9.
23
25. Palma, W, (2007). Long-memory time series, theory and methods, John Wiley & Sons, Inc, New Jersey.
24
26. Seyyed Hosseini S. M. and Ebrahimi S. B. (2013). “Comparing of volatility transmission model with consideration of long memory effect; Case study: Three selected industry index”, Journal of Financial Research, Vol. 15, No. 1, PP. 74- 51.
25
27. Seyyed Hosseini, S. M., Babakhani, M. and Ebrahimi, S. B. (2012). Introduction to volatility transmission models in stock market, Bours publication.
26
28. Jeantheau, T. (1998), “Strong consistency of estimators for multivariate ARCH models”, Econometric Theory, Vol. 14, No. 1, PP. 70- 86.
27
29. Muler, N. and Yohai, V. J. (2002). “Robust estimates for ARCH processes”, Journal of Time Series Analysis, Vol. 23, No. 3, PP. 341- 375.
28
30. Muler, N. and Yohai, V. J. (2008). “Robust estimates for GARCH models”, Journal of Statistical Planning and Inference, Vol. 138, No. 10, PP. 2918- 2940.
29
31. Pafka, S. and Matyas, L. (2001). “Multivariat diagonal FIGARCH: Specification, Estimation and application to modelling exchange rate volatility”, Available at http://ideas.repec.org. PP. 5- 7.
30
ORIGINAL_ARTICLE
Truck Sequencing and Dock Assignment in a Cross-docking System
In a supply chain, cross-docking is one of the most innovative systems for ameliorating the operational performance at distribution centers. Cross-docking is a logistics strategy in which freight is unloaded from inbound trucks and (almost) directly loaded into outbound trucks, with little or no storage in between, thus no inventory remains at the distribution center. In this paper, we consider the scheduling problem of inbound and outbound trucks with multiple dock doors, which the aim is to minimize the makespan. In this research, a mathematical model is derived to find the optimal solution. Also a Simulated Annealing algorithm is adapted to find near optimal solution, as the mathematical model will not be applicable for large scale problems. Numerical examples are presented in order to specify the efficiency of the proposed algorithm in comparison with mathematical model.
https://aie.ut.ac.ir/article_60723_f9da93f08d6bffa5c41d8dd4941fb70b.pdf
2016-09-22
177
189
10.22059/jieng.2016.60723
Cross-docking
Door assignment
Simulated annealing algorithm
Truck scheduling
Jamal
Arkat
j.arkat@uok.ac.ir
1
Department of Industrial Engineering, University of Kurdistan, Iran
LEAD_AUTHOR
Parak
Qods
parak.qods@gmail.com
2
Department of Industrial Engineering, University of Kurdistan, Iran
AUTHOR
Fardin
Ahmadizar
f.ahmadizar@uok.ac.ir
3
Department of Industrial Engineering, University of Kurdistan, Iran
AUTHOR
1. Apte, U. M. and Viswanathan S. (2000). “Effective cross docking for improving distribution efficiencies”, International Journal of Logistics: Research and Applications, Vol. 3, No. 3, PP. 291– 302.
1
2. Yu, W. and Egbelu, J. P. (2013). “Development of dispatching strategy for inbound and outbound trucks in cross docking system”, Journal of the Korea Safety Management and Science, Vol. 15, No. 2, PP. 167-184..
2
3. Konur, D. and Golias, M. M. (2013). “Analysis of different approaches to cross-dock truck scheduling with truck arrival time uncertainty”, Computers & Industrial Engineering, Vol. 65, No. 4,, PP. 663– 672.
3
4. Lim, A., Ma, H. and Miao, Z. (2006). “Truck dock assignment problem with time windows and capacity constraint in transshipment network through crossdocks”, In: Computational Science and Its Applications-ICCSA, Springer Berlin Heidelberg, PP. 688- 697.
4
5. Lim, A., Ma, H. and Miao, Z. (2006). “Truck dock assignment problem with operational time constraint within crossdocks”, In: Advances in applied artificial intelligence, Springer Berlin Heidelberg, PP. 262- 271.
5
6. Miao, Z., Lim, A. and Ma, H. (2009). “Truck dock assignment problem with operational time constraint within crossdocks”, European Journal of Operational Research, Vol. 192, No. 1, PP. 105- 115.
6
7. Boysen, N. (2010). “Truck scheduling at zero-inventory cross docking terminals”, Computers & Operations Research, Vol. 37, No. 1, PP. 32- 41.
7
8. Lee, K., Kim, B. S. and Joo, C. M. (2012). “Genetic algorithms for door-assigning and sequencing of trucks at distribution centers for the improvement of operational performance”, Expert Systems with Applications, Vol. 39, No. 17, PP. 12975- 12983.
8
9. Joo, C. M. and Kim, B. S. (2013). “Scheduling compound trucks in multi-door cross-docking terminals”, The International Journal of Advanced Manufacturing Technology, Vol. 64, No. 5- 8, PP. 977- 988.
9
10. Shakeri, M., Low, M. Y. H. and Li, Z. (2008). “A generic model for crossdock truck scheduling and truck-to-door assignment problems”, In Industrial Informatics, 6th IEEE International Conference on. IEEE, PP. 857- 864.
10
11. Li, Z. P., Low, M. Y. H., Shakeri, M. and Lim, Y. G. (2009). “Crossdocking planning and scheduling: Problems and algorithms”, SIMTech Technical Reports, Vol. 10, No. 3, PP. 159-167..
11
12. Liao, T. W., Egbelu, P. J. and Chang, P. C. (2012). “Two hybrid differential evolution algorithms for optimal inbound and outbound truck sequencing in cross docking operations”, Applied Soft Computing, Vol. 12, No. 11, PP. 3683- 3697.
12
13. Arabani, A. B., Ghomi, S. F. and Zandieh, M. (2011). “Metaheuristics implementation for scheduling of trucks in a cross-docking system with temporary storage”, Expert Systems with Applications, Vol. 38, No. 3, PP. 1964- 1979.
13
14. Yu, W. and Egbelu, P. J. (2008). “Scheduling of inbound and outbound trucks in cross docking systems with temporary storage”, European Journal of Operational Research, Vol. 184, No. 1, PP. 377- 396.
14
15. Kuo, Y. (2013). “Optimizing truck sequencing and truck dock assignment in a cross docking system”, Expert Systems with Applications, Vol. 40, No. 14, PP. 5532- 5541.
15
16. Mohtashami, A. (2015). “Scheduling trucks in cross docking systems with temporary storage and repetitive pattern for shipping trucks”, Applied Soft Computing Journal, Vol. 36, PP. 468- 486.
16
17. Yu, W. (2002). Operational strategies for cross docking systems,Ph.D. thesis, Iowa State University.
17
18. Yu, W. (2015). “Truck scheduling for cross docking systems with multiple receiving and shipping docks”, International Journal of Shipping and Transport Logistic, Vol. 7, No. 2, PP. 174– 196.
18
19. Assadi, M. T. and Bagheri, M. (2016). “Scheduling trucks in a multiple-door cross docking system with unequal ready times”, European Journal of Industrial Engineering, Vol. 10, No. 1, PP. 103- 125.
19
20. Li, J., Ye, Y. and Fu, H. (2016). “Research on truck scheduling with preemption in cross-docking systems”, Proceedings of the 22nd International Conference on Industrial Engineering and Engineering Management 2015, PP. 147- 156.
20
21. Assadi, M. T. and Bagheri, M. (2016). “Differential evolution and Population-based simulated annealing for truck scheduling problem in multiple door cross-docking systems”, Computers & Industrial Engineering, Vol. 96, PP. 149- 161.
21
22. Arkat, J., Qods, P. and Ahmadizar, F. (2016). “Truck scheduling problem in a cross-docking system with release time constraint”, Journal of Industrial and Systems Engineering, In Press.
22
23. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. and Teller, E. (1953). “Equation of state calculations by fast computing machines”, The Journal of Chemical Physics, Vol. 21, No. 6, PP. 1087-1092.
23
24. Kirkpatrick, S., Gelatt, C. D. and Vecchi, M. P. (1983). “Optimization by simulated annealing”, Science, Vol. 220, No. 4598, PP. 671- 680.
24
ORIGINAL_ARTICLE
Production and Transportation Scheduling and Allocation of Orders in the Supply Chain
In this paper a scheduling problem in a 2-stage supply chain is discussed. Suppliers are in the first stage and in the second stage, there are vehicles which carry orders to a manufacturing center. The purpose is to allocate orders to suppliers, sequence the suppliers’ production, allocate orders to transport vehicles and prioritize orders that should be carried by vehicles to minimize the total time of the process and transportation. This issue has not yet been discussed in the literature. First, a mixed integer programming mathematical model is presented. Then, in order to solve the problem, a new algorithm is proposed which is a new combination of genetic and Simulated Annealing Algorithms. To evaluate the performance of the algorithm, it is compared with one of the algorithms presented in the literature, genetic algorithm and simulated annealing algorithm, separately. Comparison results indicate the advantage of the proposed algorithm in comparison with other algorithms.
https://aie.ut.ac.ir/article_60724_11379d783dddb7946a8fd820932ea8d3.pdf
2016-09-22
191
203
10.22059/jieng.2016.60724
Genetic Algorithm
Scheduling
Simulated annealing algorithm
Supply Chain
Transport Planning
Mohammad Ali
Beheshtinia
beheshtinia@semnan.ac.ir
1
Faculty of Engineering, University of Semnan, Iran
LEAD_AUTHOR
Amir
Ghasemi
ghasemii.amir@gmail.com
2
Faculty of Engineering, University of Semnan, Iran
AUTHOR
Moein
Farokhnia
moein.farokhnia@gmail.com
3
Faculty of Engineering, University of Semnan, Iran
AUTHOR
1- Chang, Y. and Lee, C. (2004). “Machine scheduling with job delivery coordination”, European Journal of Operational Research, Vol. 158, No. 2, PP. 470– 487.
1
2- Li, H. and Womer K. (2008). “Modeling the supply chain configuration problem with resource constraints”, International Journal of Project Management, Vol. 26, No. 6, PP. 646– 654.
2
3- Sawik, T. (2009). “Coordinated supply chain scheduling”, Int. J. Production Economics, Vol. 120, No. 2, PP. 437– 451.
3
4- Zegordi, S. H. and Beheshti Nia, M. (2009). “Integrating production and transportation scheduling in a two-stage supply chain considering order assignment”, International Journal of Advanced Manufacturing Technology, Vol. 44, No. 9-10, PP. 928- 939.
4
5- Averbakh, I. (2010). “On-line integrated production–distribution scheduling problems with capacitated deliveries”, European Journal of Operational Research, Vol. 200, No. 2, PP. 377– 384.
5
6- Scholz-Reiter, B., Frazzon, E. M. and Makuschewitz, T. (2010). “Integrating manufacturing and logistic systems along global supply chains”, CIRP Journal of Manufacturing Science and Technology, Vol. 2, No. 3, PP. 216– 223.
6
7- Bhatnagar, R., Mehta, P. and Teo, C. C. (2011). “Coordination of planning and scheduling decisions in global supply chains with dual supply modes”, Int. J. Production Economics, Vol. 131, No. 2, PP. 473– 482.
7
8- Yeung, W., Choi, T. and Cheng, T. C. E. (2011). “Supply chain scheduling and coordination with dual delivery modes and inventory storage cost”, Int. J. Production Economics, Vol. 132, No. 2, PP. 223–229.
8
9- Mehravaran, Y. and Logendran, R. (2012). “Non-permutation flow shop scheduling in a supply chain with sequence-dependent setup times”, Int. J. Production Economics, Vol. 135, No. 2, PP. 953– 963.
9
10- Osman, H. and Demirli, K. (2012). “Economic lot and delivery scheduling problem for multi-stage supply chains”, Int. J. Production Economics, Vol. 136, No. 2, PP. 275– 286.
10
11- Averbakh, I. and Baysan, M. (2013). “Approximation algorithm for the on-line multi-customer two-level supply chain scheduling problem”, Operations Research Letters, Vol. 41, No. 6, PP. 710– 714.
11
12- Kabra, S., Shaik, M. A. and Rathore, A. S. (2013). “Multi-period scheduling of a multi-stage multi-product bio-pharmaceutical process”, Computers and Chemical Engineering, Vol. 57, No. 1, PP. 95– 103.
12
13- Shaik, M. A. and Floudas, C. A. (2007). “Improved unit-specific event-based continuous-time model for short-term scheduling of continuous processes: Rigorous treatment of storage requirements”, Industrial and Engineering Chemistry Research, Vol. 46, No. 6, PP. 1764– 1779.
13
14- Ullrich, C. A. (2013). “Integrated machine scheduling and vehicle routing with time windows”, European Journal of Operational Research, Vol. 227, No. 1, PP. 152– 165.
14
15- Thomas, A., Venkateswaran, J., Singh, G. and Krishnamoorthy, M. (2013). “Resource constrained scheduling problem with multiple independent producers and a single linking constraint: A coal supply chain example”, European Journal of Operational Research, Vol. 236, No. 3, PP. 946– 957.
15
16- Selvarajah, E. and Zhang, R. (2014). “Supply chain scheduling at the manufacturer to minimize inventory holding and delivery costs”, Int. J. Production Economics, Vol. 147, No. 1, PP. 117– 124.
16
17- Holland, J. H. (1975). Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor.
17
18- Kirkpatrick, S., Gelatt Jr, C. D., Vecchi, M. P. (1983). “Optimization by Simulated Annealing”, Science, Vol. 220, No. 4598, PP. 671– 680.
18
ORIGINAL_ARTICLE
Robust Model for Designing a Dynamic Closed-loop Supply Chain with Adjustable Capacity
In this paper, firstly by using a mixed linear programming a new model of locating facilities with limited capacity is presented to design a closed-loop supply chain in a multi-product and multi-period mode. Then, using a robust optimization approach, the proposed model decreases in non-deterministic expansion. The results show that the proposed model can handle facility capacity in a closed loop logistics network. In addition, the results showed that the cost and time of test problems for the robust model is higher than the deterministic model.
https://aie.ut.ac.ir/article_60725_e927e1714efe1fbe00df98eab2990df4.pdf
2016-09-22
205
220
10.22059/jieng.2016.60725
Capacity planning
Closed-Loop network
Robust optimization
Supply chain design
uncertainty
Siamak
Jebreilzade
siamak.jebreilzade@yahoo.com
1
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
AUTHOR
Behnam
Vahdani
b.vahdani@gmail.com
2
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
LEAD_AUTHOR
Seyed Meysam
Mousavi
mousavi.sme@gmail.com
3
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
AUTHOR
1. Lee, D. and Dong, M. (2008). “A heuristic approach to logistics network design for end-of-lease computer products recovery”, Transportations Research, Vol. 44, No. 3, PP. 455– 474.
1
2. Chopra, S. and Meindel, P. (2004). Supply chain management: Strategy planning and operations, Chapter 11, Prentice- Hall Inc.
2
3. Fleischmann, M., Beullens, P., Bloemhof-Ruwaard, J., Van Wassenhove, L. (2001). “The impact of product recovery on logistics network design”, Production and Operations Management, Vol. 10, No. 2, PP. 156– 173.
3
4. Salema, M. I. G., Barbosa-Pavoa, A. P. and Navais, A. Q. (2007). “An optimization model for the design of a capacitated multi- ptoduct revers logistics network with uncertaintiv”, Eur. J. Res., Vol. 179, No. 3, PP. 1063- 1077.
4
5. Üster, H., Easwaran, G., Akçali, E. and Çetinkaya, S. (2007). “Benders decomposition with alternative multiple cuts for a multi-product closed-loop supply chain network design model”, Naval Research. Logistics, Vol. 54, No. 8, PP. 890– 907.
5
6. Pishvaee, M. R., Farahani, R. Z. and Dullaert, W. (2010). “A memetic algorithm for bi-objective integrated forward/reverse logistics network design”, Computer Operational Research, Vol. 37, No. 6, PP. 1100– 1112.
6
7. Pishvaee, M. S., Rabbani, M. and Torabi, S. A. (2011). “A robust optimization approach to closed-loop supply chain network design under uncertainty”, Applied Mathematical Modeling, Vol. 35, No. 2, PP. 637– 649.
7
8. Hasanzadeh, Amin., S. and Zhang, G. (2012). “An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach”, Expert Systems with Applications, Vol. 39, No. 8, PP. 6782– 6791.
8
9. Ramezani, M., Bashiri, M. and Tavdkoli Moghaddam, R. (2012). “A new multi-objective stochastic model for a forward/ reverse logistic network design with responsiveness and quality level”, Applied Mathematical Modeling, Vol. 37, No. 1, PP. 328– 344.
9
10. Hasanzadeh Amin, S. and Zhang, G. (2013). “A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return”, Applied Mathematical Modeling, Vol. 37, No. 6, PP. 4165– 4176.
10
11. Ramezani, M., Bashiri, M. and Tavakkoli-Moghaddam, R. (2013). “A robust design for a closed-loop supply chain network under an uncertain environment”, International Journal of Advanced Manufacturing Technology, Vol. 66, No. 5- 8, PP. 825- 843.
11
12. Ko, H. J. and Evans, G. W. (2007). “A genetic-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs”, Computer Operational Research, Vol. 34, No. 2, PP. 346– 366.
12
13. Min, H. and Ko, H. J. (2008). “The dynamic design of a reverse logisticsnetwork from the perspective of third-party logistics service providers”, Int J Prod Econ, Vol. 113, No. 1, PP. 176– 192.
13
14. Lee, D. H. and Dong, M. (2009). “Dynamic network design for reverse logistics operations under uncertainty”, Transportation Research, Vol. 45, No. 1, PP. 61– 71.
14
15. El-Sayed, M., Afia, N. and El-Kharbotly, A. (2010). “A stochastic model for forward–reverse logistics network design under risk”, Computer Industrial Engineering, Vol. 58, No. 3, PP. 423– 431.
15
16. Vahdani, B. (2014). “Vehicle positioning in cell manufacturing systems via robust optimization”, Applied Soft Computing, Vol. 24, No. 1, PP. 78- 85.
16
17. Ben-Tal, A. and Nemirovski, A. (1998). “Robust convex optimization”, Mathematics of Operations Research, Vol. 23, No. 4, PP. 769– 805.
17
18. Ben-Tal, A. and Nemirovski, A. (2000). “Robust solutions of linear programming problems contaminated with uncertain data”, Mathematical Programming, Vol. 88, No. 3, PP. 411– 424.
18
19. Ben-Tal, A., Golany, B., Nemirovski, A. and Vial, J. P. (20005). “Retailer-supplier flexible commitments contracts: A robust optimization approach”, Manufacturing and Service Operations Management, Vol. 7, No. 3, PP. 248– 271.
19
20. Ben-Tal, A., El-Ghaoui, L. and Nemirovski, A. (2009). Robust Optimization, Princeton University Press.
20
ORIGINAL_ARTICLE
A Bi-objective Mathematical Model Toward Staff Planning Considering Cross-training
In this paper, the staff assignment problem considering cross-training of caregivers in health care systems is addressed to determine which staff should be cross-trained for each service and how they should be assigned to services. A bi-objective non-linear mathematical programming model is presented where the first objective function aims to minimize workload balancing, cross-training as well as maintenance and transportation costs, while the second objective function is concerned with maximization of caregivers’ satisfaction level. Several constraints with respect to budget capacity, staff absenteeism, maximum allowable consecutive shifts, multi-functionality and redundancy level and maximum allowable distance for transportation are taken into account to build a service plan. The behavior of the various elements and features of the model is evaluated in a real-world HC provider and the results reveal that the caregivers’ workload is relatively balanced and the caregivers’ preferences are satisfied.
https://aie.ut.ac.ir/article_60726_c5852cbe63bea0d0c2da4c6044ed1ba5.pdf
2016-09-22
221
233
10.22059/jieng.2016.60726
Cross-training
Health care systems
Optimization
Staff assignment
Hamed
Habibnejad
h_habibnejad@ut.ac.ir
1
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
masoud
Rabbani
mrabani@ut.ac.ir
2
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Babak
Javadi
babakjavadi@ut.ac.ir
3
Department of Industrial Engineering, College of Farabi, University of Tehran, Qom, Iran
AUTHOR
Nastaran
Ghorbani-Kutenaie
n.ghorbani@student.alzahra.ac.ir
4
Department of Industrial Engineering, School of Engineering, Alzahra University, Tehran, Iran
AUTHOR
1. Rasmussen, M. S., Justesen, T., Dohn, A. and Larsen, J. (2012). “The home care crew scheduling problem: Preference-based visit clustering and temporal dependencies”, European Journal of Operational Research, Vol. 219, No. 3, PP. 598- 610.
1
2. Allaoua, H., Borne, S., Létocart, L. and Calvo, R. W. (2013). “A metaheuristic approach for solving a home health care problem”, Electronic Notes in Discrete Mathematics, Vol. 41, PP. 471- 478.
2
3. Borsani, V., Matta, A., Beschi, G. and Sommaruga, F. (2006). “A home care scheduling model for human resources”, Service Systems and Service Management, International Conference on. IEEE, PP. 449- 454.
3
4. Benzarti, E., Sahin, E. and Dallery, Y. (2013). “Operations management applied to home care services: Analysis of the districting problem”, Decision Support Systems, Vol. 55, No. 2, PP. 587- 598.
4
5. Lanzarone, E. and Matta, A. (2014). “Robust nurse-to-patient assignment in home care services to minimize overtimes under continuity of care”, Operations Research for Health Care, Vol. 3, No. 2, PP. 48- 58.
5
6. Lanzarone, E. and Matta, A. (2012). “A cost assignment policy for home care patients”, Flexible Services and Manufacturing Journal, Vol. 24, No. 4, PP. 465- 495.
6
7. Denton, M., Brookman, C., Zeytinoglu, I., Plenderleith, J. and Barken, R. (2014). “Task shifting in the provision of home and social care in Ontario, Canada: Implications for quality of care”, Health & social care in the community, Vol. 23, No. 5, PP. 485-492.
7
8. Koeleman, P., Bhulai, S. and Van Meersbergen, M. (2012). “Optimal patient and personnel scheduling policies for care-at-home service facilities”, European Journal of Operational Research, Vol. 219, No. 3, PP. 557- 563.
8
9. Olivella, J., Corominas, A. and Pastor, R. (2013). “Task assignment considering cross-training goals and due dates”, International Journal of Production Research, Vol. 51, No. 3, PP. 952- 962.
9
10. Slomp, J., Bokhorst, J. A. and Molleman, E. (2005). “Cross-training in a cellular manufacturing environment”, Computers & Industrial Engineering, Vol. 48, No. 3, PP. 609- 624.
10
11. Slomp, J. and Molleman, E. (2002). “Cross-training policies and team performance”, International Journal of Production Research, Vol. 40, No. 5, PP. 1193- 1219.
11
12. Marentette, K. A., Johnson, A. W. and Mills, L. (2009). “A measure of cross-training benefit versus job skill specialization”, Computers & Industrial Engineering, Vol. 57, No. 3, PP. 937- 940.
12
13. Wilke, H. and Meertens, R. (1994). “Group performance”, International Series on Communication Skills.
13
14. Bokhorst, J. A. and Slomp, J. (2007). “Design and operation of a cross-trained workforce”, Workforce cross training, In: D. Nembhard (ed.), CRC Press, Taylor & Francis Group, Boca Raton, FL, PP. 3- 63.
14
15. Akjiratikarl, C., Yenradee, P. and Drake, P. R. (2007). “PSO-based algorithm for home care worker scheduling in the UK”, Computers & Industrial Engineering, Vol. 53, No. 4, PP. 559- 583.
15
16. Rabeh, R., Saïd, K. and Eric, M. (2011). “Collaborative model for planning and scheduling caregiver’s activities in homecare”, 18th IFAC World Congress, PP. 2877- 2882.
16
17. Mankowska, D. S., Meisel, F. and Bierwirth, C. (2014). “The home health care routing and scheduling problem with interdependent services”, Health Care Management Science, Vol. 17, No. 1, PP. 15- 30.
17
18. Duque, P. M., Castro, M., Sörensen, K. and Goos, P. (2014). “Home care service planning. The case of Landelijke Thuiszorg”, European Journal of Operational Research, Vol. 243, No. 1, PP. 292- 301.
18
19. Campbell, G. M. and Diaby, M. (2002). “Development and Evaluation of an Assignment Heuristic for Allocating Cross-trained Workers”, European Journal of Operational Research, Vol. 138, No. 1, PP. 9- 20.
19
20. Campbell, G. M. (2010). “A two-stage stochastic program for scheduling and allocating cross-trained workers”, Journal of the Operational Research Society, Vol. 62, No. 6, PP. 1038- 1047.
20
21. Bokhorst, J. A., Slomp, J. and Molleman, E. (2004). “Development and evaluation of cross-training policies for manufacturing teams”, Iie Transactions, Vol. 36, No. 10, PP. 969- 984.
21
22. Yue, H., Slomp, J., Molleman, E. and Van Der Zee, D. (2008). “Worker flexibility in a parallel dual resource constrained job shop”, International Journal of Production Research, Vol. 46, No. 2, PP. 451- 467.
22
23. Valls, V., Pérez, Á. and Quintanilla, S. (2009). “Skilled workforce scheduling in service centres”, European Journal of Operational Research, Vol. 193, No. 3, PP. 791- 804.
23
24. Easton, F. F. (2011). “Cross-training performance in flexible labor scheduling environments”, Iie Transactions, Vol. 43, No. 8, PP. 589- 603.
24
25. Li, Q., Gong, J., Fung, RY. and Tang, J. (2012). “Multi-objective optimal cross-training configuration models for an assembly cell using non-dominated sorting genetic algorithm-II”, International Journal of Computer Integrated Manufacturing, Vol. 25, No. 11, PP. 981- 995.
25
26. Liu, C., Yang, N., Li, W., Lian, J., Evans, S. and Yin, Y. (2013). “Training and assignment of multi-skilled workers for implementing seru production systems”, The International Journal of Advanced Manufacturing Technology, Vol. 69, No. 5-8, PP. 937- 959.
26
27. Xu, Z., Ming, X. G., Zheng, M., Li, M., He, L. and Song, W. (2015). “Cross-trained workers scheduling for field service using improved NSGA-II”, International Journal of Production Research, Vol. 53, No. 4, PP. 1255- 1272.
27
28. Mavrotas, G. (2009). “Effective implementation of the ε-constraint method in multi-objective mathematical programming problems”, Applied Mathematics and Computation, Vol. 213, No. 2, PP. 455- 465.
28
29. Rafiei, H. and Ghodsi, R. (2013). “A bi-objective mathematical model toward dynamic cell formation considering labor utilization”, Applied Mathematical Modelling, Vol. 37, No. 4, PP. 2308- 2316.
29
ORIGINAL_ARTICLE
Economic Order Quantity for Deteriorating Items with Imperfect Quality, Destructive Testing Acceptance Sampling, and Inspection Errors
Now a days, management and control of perishable inventories is important in many units and industrial enterprises. Basically perishable inventory management and control is more complex and challenging than inventories with unlimited lifetime. Hence determining the optimal inventory policies for these products is very important. In this paper, the optimal inventory policy for perishable items given to the a single acceptance sampling plan with destructive testing and inspection errors is adopted .After developing a model for the problem and obtaining the objective function, first an exact solution and a simple and efficient algorithm to find the optimal values is proposed. Then, the model will be validated with a numerical example and senility analysis.
https://aie.ut.ac.ir/article_60727_d795e9a313994506d1007af8c81bf41b.pdf
2016-09-22
235
246
10.22059/jieng.2016.60727
Acceptance sampling
Destructive testing
EOQ
Inspection
Perishable items
Javad
Hasanpour Rodbaraki
javad10049@yahoo.com
1
Industrial Engineering, Quchan university of Advanced Technology, Quchan, Iran
LEAD_AUTHOR
Ebrahim
Sharifi
esharifi1372@gmail.com
2
Industrial Engineering, Quchan university of Advanced Technology, Quchan, Iran
AUTHOR
Harris, F. W. (1913). “How many parts to make at once, Factory”, The Magazine of Management, Vol. 38, No. 6, 10 (2), PP. 135– 136.
1
Whitin, T. M. (1953). The theory of inventory management, Princeton University Press, Princeton, NJ, USA.
2
Ghare, P. N. and Schrader, G. F. (1963). “A model for exponentially decaying inventories”, The Journal of Industrial Engineering, Vol. 1, No. 14, PP. 238– 243.
3
Elionand, S. and Mallaya, R. V. (1996). “Issuing and pricing policy of semi-perishables”, Proceedings of the 4th Internationam Conference on Operational Research, Wiley Inter science, New York.
4
Goyal, S. K. and Giri, B. C. (2001). “Recent trends in modeling of deteriorating inventory”, European Journal of Operational Research, Vol. 1, No. 134, PP. 1- 16.
5
Nahmias, S. (1982). “Perishable inventory theory: A review”, Operations Research, Vol. 30, No. 4, PP. 680-708.
6
Raafat, F. (1991). “Survey of literature on continuously deteriorating inventory models”, Journal of the Operational Research Society , Vol. 42, No. 1, PP. 27– 37.
7
Pahl, J., Voss, S. and Woodruff, D. L. (2007). “Production planning with deterioration constraints: A survey”, In: 19th International Conference on Production Research, P. 6.
8
Akkerman, R., Farahani, P. and Grunow. M. (2010). “Quality, safety and sustainability in food distribution: A review of quantitative operations management approaches and challenges”, OR Spectrum , Vol. 32, No. 4, PP. 863– 904.
9
10. Kempf, K. G., Keskinocak, P. and Uzsoy, R. (2011). “Planning production and inventories in the extended enterprise”, A State of the Art Handbook, Vol. 1, No. 151, PP. 393– 436.
10
11. Li, R., Lan, H. and Mawhinney, J. R. (2010). “A review on deteriorating inventory study”, Journal of Service Science and Management, Vol. 3, No. 1, PP. 117– 129.
11
12. Bakker, M., Riezebos, J. and Teunter, R. H. (2012). “Review of inventory systems with deterioration since 2001”, European Journal of Operational Research, Vol. 2, No. 221, PP. 275– 284.
12
13. Jolai, F., Rabbani, M. and Honarvar, M. (2006). “Continuous review inventory model for deteriorating itemswith no shortage, stochastic demand, and expedited ordering”, Journal of University College of Engineering, University of Tehran, Vol. 40, No. 40, PP. 487- 494.
13
14. Mirzazadeh, A., Seyed Esfehani, M. and Fatemi, M. (2006). “Determining economic order policy for deteriorating items with time-dependent inflation”, Journal of University College Of Engineering, Vol. 40, No. 40, PP. 585- 595.
14
15. Salameh, M. K. and Jaber, M. Y. (2000). “Economic production quantity model for items with imperfect quality”, International Journal of Production Economics, Vol. 64, No. 1-3, PP. 59- 64.
15
16. Goyal, S. K. and Cardenas-Barron, L. E. (2002). “Note on: Economic production quantity model for items with imperfect quality – A practical approach”, International Journal of Production Economics, Vol. 77, No. 1, PP. 85- 87.
16
17. Papachristos, S. and Konstantaras, I. (2006). “Economic ordering quantity models for items with imperfect quality”, International Journal of Production Economics, Vol. 100, No. 1, PP. 148- 154.
17
18. Tsou, J. (2007). “Economic order quantity model and Taguchi’s cost of poor quality”, Applied Mathematical Modeling , Vol. 31, No. 2, PP. 283- 291.
18
19. Hsu, W. K. and Yu, H. (2009). “EOQ model for imperfective items under a one-time-only discount”, Omega, Vol. 37, No. 5, PP. 1018- 1026.
19
20. Maddah, B. and Jaber, M. Y. (2008). “Economic order quantity for items with imperfect quality: Revisited”, International Journal of ProductionEconomics, Vol. 112, No. 2, PP. 808- 815.
20
21. Jaber, M. Y., Bonney M. and Moualek, I. (2009). “An economic order quantity model for an imperfect poduction process with entropy cost”, International Journal of Production Economics, Vol. 118, No. 1, PP. 26- 33.
21
22. Wang, X., Tang, W. and Zhao, R. (2007). “Random fuzzy EOQ model with imperfect quality items”, Fuzzy Optimization and Decision Making, Vol. 6, No. 2, PP. 139- 153.
22
23. Rezaei, J. (2005). “Economic order quantity model with backorder for imperfect quality items”, Proceedings of the IEEE International Engineering Management Conference, 2005, PP. 466- 470.
23
24. Wee, H. M., Yu, J. and Chen, M. C. (2007). “Optimal inventory model for items with imperfect quality and shortage backordering”, Omega, Vol. 35, No. 1, PP. 7- 11.
24
25. Eroglu A. and Ozdemir, G. (2007). “An economic order quantity model with defective items and shortages”, International Journal of Production Economics, Vol. 106, No. 2, PP. 544- 549.
25
26. Khan, M., Jaber, M. Y. and Wahab, M. I. M. (2010). “Economic order quantity model for items with imperfect quality with learning in inspection”, International Journal Of Production Economics, Vol. 124, No. 1, PP. 87- 96.
26
27. Salamah, M. (2011). “Economic order quantity with imperfect quality items, destructive testing acceptance sampling and Inspection errors”, Advances in management & applied Economics, Vol1.1, No. 2, PP. 59- 75.
27
ORIGINAL_ARTICLE
Providing a New Mathematical Model for School Service Routing with Considering Gender Separation
In our country, school bus routes are determined by experiments of driver without considering the scientific optimum route and location. Traversing additional routes will always result an increase in vehicle movements and fuel consumption and enormous costs. Hence, this paper will study the school bus routing in Tehran considering special students and a model will be presented to minimize traveling distance and to prevent repetitive crossings through the bus stops and to determine the shortest routes by presenting a way to propel several students to a bus stop. The proposed model will solve via GAMS software. Because the model is NP-Hard, the Genetic algorithm is used to solve the large scale problem. The contribution of this paper is to consider gender separation in schools and buses. To solve this problem, an integer linear programming model is developed. The conclusion indicates a decrease in transportation time.
https://aie.ut.ac.ir/article_60728_5265038df07073e4a97017b276f73d79.pdf
2016-09-22
247
259
10.22059/jieng.2016.60728
Gender separation
Integer linear programming
School bus routing problem
Special students
Alireza
Rashidi Komijan
rashidi@azad.ac.ir
1
Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
LEAD_AUTHOR
Peyman
Ghasemi
peiman.ghasemi@aut.ac.ir
2
Department of Industrial Engineering, Faculty of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Houda, D., Bassem, J., Saïd, H. and Habib, C. (2012). “Genetic algorithm with iterated local search for solving a location-routing problem”, Journal of Expert Systems with Applications, Vol. 39, No. 3, PP. 2865– 2871.
1
Norouzi, N., Tavakkoli-Moghaddam, R., Ghazanfari, M., Alinaghian, M. and Salamatbakhsh, A. (2012). “A new multiobjective competitive open vehicle routing problem solved by particle swarm optimization”, Networks and Spatial Economics, Vol. 14, No.4, PP. 603– 633.
2
Fügenschuh, A. (2009). “Solving a school bus scheduling problem with integer programming”, European Journal of Operational Research, Vol. 193, No. 3, PP. 867- 884.
3
Newton, R. M. and Thomas, W. H. (1969). “Design of school bus routes by computer”, Socio-Economic Planning Sciences, Vol. 3, No. 1, PP. 75- 85.
4
Banos, R., Ortega, J., Gil, C., Marquez, A. L. and Toro, F. D. (2013). “A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows”, Comput. Ind. Eng, Vol. 65, No.2, PP. 286- 296.
5
Nahum, O. E., Hadas, Y. and Spiegel, U. (2014). “Multi-objective vehicle routing problems with time windows: A vector evaluated artificial bee colony approach”, Int. J. Comput. Inf. Technol, Vol. 3, No. 1, PP. 41- 47.
6
Park, J., Tae, H., Kim. and B. I. (2012). “A post-improvement procedure for the mixed load school bus routing problem”, European Journal of Operational Research, Vol. 217, No.1, PP. 204- 213.
7
Park, J., and Kim, B. I. (2010). “The school bus routing problem: A review”, European Journal of Operational Research, Vol. 202, No.2, PP. 311- 319.
8
Naseri, A. and Mansouri, E. (2012). “Two-stage algorithm for the taxi in dynamic mode”, Journal of Transportation, Vol. 9, No. 2, PP. 137- 152.
9
Santos, D, Xavier, E. (2015). “Taxi and ride sharing: A dynamic dial-a-ride problem with money as an incentive”, Expert Systems with Applications, Vol. 42, No. 19, PP. 6728– 6737.
10
Liu, M., Luo, Z. and Lim, A. (2015). “A branch-and-cut algorithm for a realistic dial-a-ride problem”, Transportation Research Part B: Methodological, Vol. 81, No. 1, PP. 267– 288.
11
Molenbruch, Y., Braekers, K., Caris, A. and Berghe, G. (2017). “Multi-directional local search for a bi-objective dial-a-ride problem in patient transportation”, Computers & Operations Research,Vol. 77, No. 1, PP. 58– 71.
12
Chen, X., Kong, Y., Dang, L., Yane, H. and Xinyue, Y. (2015). “Exact and metaheuristic approaches for a bi-objective school bus scheduling problem”, Vol. 11, No. 4, PP. 1-2.
13
Kang, M., Kim, S., Felan, T., Choi, H. and Cho, M. (2015). “Development of a genetic algorithm for the school bus routing problem”, International Journal of Software Engineering and Its Applications, Vol. 9, No. 5, PP. 107- 126
14
William, A., Ellegood, F. and Campbell, J. (2015). “Continuous approximation models for mixed load school bus routing”, Transportation Research Part B, Vol. 77, No. 3, PP. 182– 198.
15
Lima, F., Pereira, D., Samuel, C. and Nilson, N. (2016). “A mixed load capacitated rural school bus routing problem with heterogeneous fleet: Algorithms for the Brazilian context”, Expert Systems with Applications,Vol. 56, No. 2, PP. 320– 334.
16
ORIGINAL_ARTICLE
A Multiple Objective Programming Model for Designing of Supply Chain Network with Efficient Manufacturers and Distributers
One of the most important decisions in supply chain network (SCN) design is choosing the optimal location for the facilities. The facilities in SCN have different efficiency according to their locations. In this paper, efficiency of facilities is added to the supply chain network design by using data envelopment analysis model, and a multi-objective model is presented for the design of efficient supply chain network. The proposed model chooses the most appropriate place for manufacturers and distributors thereby decreases the total cost of the supply chain and simultaneously increases the efficiency. The desired supply chain has several raw materials and products, with four layers of suppliers, manufacturers, distributors and customers. The proposed model is locating manufacturers and distributors, and planning the purchase of any suppliers. The results of numerical example show that adding efficiency promotes the supply chain network model. Namely, with regard to the tradeoff between cost and efficiency objectives, SCN design with efficient facilities is better than networks based only on cost objective function.
https://aie.ut.ac.ir/article_60729_570250aa3169026140b84ea374a1145d.pdf
2016-09-22
261
278
10.22059/jieng.2016.60729
Efficiency
Data Envelopment Analysis
Multiple objective programming
Supply chain network design
Hashem
Omrani
omrani57@alumni.iust.ac.ir
1
Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran
LEAD_AUTHOR
Farzaneh
Adabi
fanar_adabi@yahoo.com
2
Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran
AUTHOR
Castillo-Villar, K. K., Smith, N. R. and Herbert-Acero, J. F. (2014). “Design and optimization of capacitated supply chain networks including quality measures”, Mathematical Problems in Engineering, Vol. 2014, 17, doi:10.1155/2014/218913.
1
Bala, K. (2014). “Supply chain management: Some issues and challenges-a review”, International Journal of Current Engineering and Technology, Vol. 4, No. 2, PP. 947- 953.
2
S. Chopra, P. Meindl, (2007). Supply Chain Management: Strategy, Planning and Operations. Prentice Hall, New Jersey.
3
Melo, M. T. S. N and da Gama, F. S. (2005). “Dynamic multi-commodity capacitated facility location: A mathematical modeling framework for strategic supply chain planning”, Computers and Operations Research, Vol. 33, No.1, PP. 181- 208.
4
Melo, M. T., Nickel, S. and Saldanha-Da-Gama, F. (2009). “Facility location and supply chain management–A review”, European Journal of Operational Research, Vol. 196, No. 2, PP. 401- 412.
5
Correia, I., Melo, T. and Saldanha-da-Gama, F. (2013). “Comparing classical performance measures for a multi-period, two-echelon supply chain network design problem with sizing decisions”, Computers & Industrial Engineering, Vol. 64, No. 1, PP. 366- 380.
6
Solo, C. J. (2009). Multi-objective, integrated supply chain design and operation under uncertainty. Ph.D thesis, The Pennsylvania State University, https://etda.libraries.psu.edu/paper/9709/5222
7
Pan, F. and Nagi, R. (2013). “Multi-echelon supply chain network design in agile manufacturing”, Omega, Vol. 41, No. 6, PP. 969- 983.
8
Seuring, S. (2013). “A review of modeling approaches for sustainable supply chain management”, Decision Support Systems, Vol. 54, No. 4, PP. 1513- 1520.
9
10. Tang, O. and Nurmaya Musa, S. (2011). “Identifying risk issues and research advancements in supply chain risk management”, International Journal of Production Economics, Vol. 133, No.1, PP. 25- 34.
10
11. Minner, S. (2003). “Multiple-supplier inventory models in supply chain management: A review”, International Journal of Production Economics, Vol. 81, PP. 265- 279.
11
12. Sarkis, J., Zhu, Q. and Lai, K. H. (2011). “An organizational theoretic review of green supply chain management literature”, International Journal of Production Economics, Vol. 130, No. 1, PP. 1- 15.
12
13. Meixell, M. J. and Gargeya, V. B. (2005). “Global supply chain design: A literature review and critique”, Transportation Research Part E: Logistics and Transportation Review, Vol. 41, No. 6, PP. 531- 550.
13
Gan, M., Li, Z. and Chen, S. (2014). “On the transformation mechanism for formulating a multiproduct two-layer supply chain network design problem as a network flow model”, Mathematical Problems in Engineering, Vol. 2014, doi:10.1155/2014/480127.
14
15. Xu, N. and Nozick, L. (2009). “Modeling supplier selection and the use of option contracts for global supply chain design”, Computers & Operations Research, Vol. 36, No. 10, PP. 2786- 2800.
15
16. Syam, S. S. and Côté, M. J. (2010). “A location–allocation model for service providers with application to not-for-profit health care organizations”, Omega, Vol. 38, No. 3, PP. 157- 166.
16
17. Altiparmak, F., Gen, M., Lin, L. and Paksoy, T. (2006). “A genetic algorithm approach for multi-objective optimization of supply chain networks”, Computers & Industrial Engineering, Vol. 51, No. 1, PP. 196- 215.
17
18. Altiparmak, F., Gen, M., Lin, L. and Karaoglan, I. (2009). “A steady-state genetic algorithm for multi-product supply chain network design”, Computers & Industrial Engineering, Vol. 56, No. 2, PP. 521- 537.
18
19. Sahraeian, R., Bashiri, M. and Ramezani, M. (2010). “A Stochastic multi-product, multi-stage supply chain design considering products waiting time in the queue”, International Conference of Industrial Engineering and Operations Management Dhaka, Bangladesh.
19
20. Bashiri, M. et al., (2010). Facilities Planning II: Applications & research areas, Shahed University Pub. Co., Tehran.
20
21. Georgiadis, M. C., Tsiakis, P., Longinidis, P. and Sofioglou, M. K. (2011). “Optimal design of supply chain networks under uncertain transient demand variations”, Omega, Vol. 39, No. 3, PP. 254- 272.
21
22. Baghalian, A., Rezapour, S. and Farahani, R. Z. (2013). “Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case”, European Journal of Operational Research, Vol. 227, No. 1, PP. 199- 215.
22
23. Mirzapour Al-E-Hashem, S. M. J., Malekly, H. and Aryanezhad, M. B. (2011). “A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty”, International Journal of Production Economics, Vol. 134, No. 1, PP. 28- 42.
23
24. Pishvaee, M. S., Rabbani, M. and Torabi, S. A. (2011). “A robust optimization approach to closed-loop supply chain network design under ncertainty”, Applied Mathematical Modeling, Vol. 35, No. 2, PP. 637-649.
24
25. Pishvaee, M. S. and Torabi, S. A. (2010). “A possibilistic programming approach for closed-loop supply chain network design under uncertainty”, Fuzzy sets and systems, Vol. 161, No. 20, PP. 2668- 2683.
25
26. Pishvaee, M. S., Razmi, J. and Torabi, S. A. (2012). “Robust possibilistic programming for socially responsible supply chain network design: A new approach”, Fuzzy Sets and Systems, Vol. 206, PP. 1- 20.
26
27. Tavana, M., Mirzagoltabar, H., Mirhedayatian, S. M., Saen, R. F. and Azadi, M. (2013). “A new network epsilon-based DEA model for supply chain performance evaluation”, Computers & Industrial Engineering, Vol. 66, No. 2, PP. 501- 513.
27
28. Parmigiani, A., Klassen, R. D. and Russo, M. V. (2011). “Efficiency meets accountability: Performance implications of supply chain configuration, control, and capabilities”, Journal of Operations Management, Vol. 29, No. 3, PP. 212- 223.
28
29. Shafiee, M., Lotfi, F. H. and Saleh, H. (2014). “Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach.” Applied Mathematical Modelling, Vol. 38, No. 21, PP. 5092-5112.
29
30. Charnes, A., Cooper, W. W. and Rhodes, E. (1978). “Measuring the efficiency of decision making units”, European Journal of Operational Research, Vol. 2, No. 6, PP. 429- 444.
30
31. Klimberg, R. K. and Ratick, S. J. (2008). “Modeling data envelopment analysis (DEA) efficient location/allocation decisions”, Computers & Operations Research, Vol. 35, No. 2, PP. 457- 474.
31
32. Moheb-Alizadeh, H., Rasouli, S. M. and Tavakkoli-Moghaddam, R. (2011). “The use of multi-criteria data envelopment analysis (MCDEA) for location–allocation problems in a fuzzy environment”, Expert Systems with Applications, Vol. 38, No. 5, PP. 5687- 5695.
32
33. Ganeshan, R. and Harrison, T. P. (1995). “An introduction to supply chain management”, Department of Management Science and Information Systems, Vol. 1, No.1, PP. 1-7.
33
34. Shen, Z. (2007). “Integrated supply chain design models: A survey and future research directions”, Journal of Industrial and Management Optimization, Vol. 3, No. 1, PP. 1- 12.
34
35. Pierce, N. A. and Giles, M. B. (1997). “Preconditioned multi-grid methods for compressible flow calculations on stretched meshes”, Journal of Computational Physics, Vol. 136, No. 2, PP. 425- 445.
35
36. Porembski, M., Breitenstein, K. and Alpar, P. (2005). “Visualizing efficiency and reference relations in data envelopment analysis with an application to the branches of a German bank”, Journal of Productivity Analysis, Vol. 23, No. 2, PP. 203- 221.
36
37. Salema, M. I. G., Barbosa-Povoa, A. P. and Novais, A. Q. (2007). “An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty”, European Journal of Operational Research, Vol. 179, No. 3, PP. 1063- 1077.
37
38. Koski, J. and Silvennoinen, R. (1987). “Norm methods and partial weighting in multi-criterion optimization of structures”, Int. J. Numer. Methods Eng., Vol. 24, No.6, PP. 1101– 1121.
38
39. Yoon, K. P. and Hwang, C. L. (1995). Multiple Attribute Decision Making, an Introduction, Sage Publications, London.
39
40. Messac, A., Sukam, C. P. and Melachrinoudis, E. (2000). “Aggregate objective functions and Pareto frontiers: Required relationships and practical implications”, Optim. Eng., Vol. 1, No.2, PP. 171– 188.
40
41. Messac, A. and Hattis, P. (1996). “Physical programming design optimization for high speed civil transport (HSCT)”, J. Aircr., Vol. 33, No.2, PP. 446– 449.
41
42. Das, I. and Dennis, J. E. (1998). “Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multi-criteria optimization problems”, SIAM J. Optim., Vol. 8, No.3, PP. 631– 657.
42
43. Messac, A., Sundararaj, G. J., Tappeta, R. V. and Renaud, J. E. (2000). “Ability of objective functions to generate points on nonconvex Pareto frontiers”, AIAA J., Vol. 38, No.6, PP. 1084– 1091.
43
44. Das, I. and Dennis, J. E. (1997). “A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multi-criteria optimization problems”, Struct. Optim., Vol. 14, No.1, PP. 63– 69.
44
45. Miettinen, K. (1999). Nonlinear Multi-objective Optimization, Kluwer Academic Publishers, Boston.
45
46. Ruiz-Canales, P. and Rufian-Lizana, A. (1995). “A characterization of weakly efficient points”, Math. Program., Vol. 68, No.1, PP. 205– 212.
46
47. Chankong, V. and Haimes, Y. Y. (1983). Multi-objective Decision Making Theory and Methodology, Elsevier Science Publishing, New York.
47
48. Carmichael, D. G. (1980). “Computation of Pareto optima in structural design”, Int. J. Numer. Methods Eng., Vol. 15, No.6, PP. 925– 952.
48
49. Laumanns, M., Thiele, L. and Zitzler, E. (2006). “An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method.”, European Journal of Operational Research, Vol. 169, No. 3, PP. 932- 942.
49
50. Bérubé, J. F., Gendreau, M. and Potvin, J. Y. (2009). “An exact ϵ-constraint method for bi-objective combinatorial optimization problems: Application to the traveling salesman problem with profits”, European Journal of Operational Research, Vol. 194. No. 1, PP. 39- 50.
50
ORIGINAL_ARTICLE
Stochastic Cell Formation Problem within Queuing Theory and Considering Reliability
In this study, the stochastic cell formation problem with developing model within queuing theory with stochastic demand, processing time and reliability has been presented. Machine as server and part as customer are assumed where servers should service to customers. Since, the cell formation problem is NP-Hard, therefore, deterministic methods need a long time to solve this model. In this study, genetic algorithm and modified particle swarm optimization algorithm are presented to solve problems. Because the metaheurstic algorithms quality depends strongly on selected operators and parameters, design of experiment is done for set parameters. The deterministic method of branch and bound algorithm is used to evaluate the results of modified particle swarm optimization algorithm and the genetic algorithm.Evaluates indicate better performance of the proposed algorithms in quality the metaheurstic algorithms final solution and solving time in comparing with the method of Lingo software’s branch and bound. Ultimately, the results of numerical examples indicate that considering reliability has significant effect on block structures of machine-part matrixes.
https://aie.ut.ac.ir/article_60730_88b27cf96284d59b88d9c2e2abf42bd2.pdf
2016-09-22
279
293
10.22059/jieng.2016.60730
Cell formation problem
Queuing theory
reliability
Metaheurstic algorithm
Parviz
Fattahi
pfattahi@gmail.com
1
Department of Industrial Engineering, Alzahra University, Tehran, Iran
LEAD_AUTHOR
Amir Saman
Kheirkhah
kheirkhah@basu.ac.ir
2
Department of Industrial Engineering, Bu-Ali Sina University, Hamedan, Iran
AUTHOR
Bahman
Esmailnezhad
bahman.ismailnezhad@yahoo.com
3
Department of Industrial Engineering, Bu-Ali Sina University, Hamedan, Iran
AUTHOR
1. Singh, N. and Rajamani, D. (1996). Cellular manufacturing systems: design, planning and control, 1th Ed, Chapter 1, Chapman & Hall Pub. Co., London.
1
2. Ahi, A., Aryanezhad, M. B., Ashtiani, B. and Makui, A. (2009). “A novel approach to determine cell formation, intracellular machine layout and cell layout in the CMS problem based on TOPSIS method”, Computers & Operations Research,Vol. 36, No. 5, PP. 1478- 1496.
2
3. Papaioannou, G. and Wilson, J. M. (2010). “The evolution of cell formation problem methodologies based on recent studies (1997-2008): Review and directions for future research”, European Journal of Operational Research,Vol. 206, No. 3, PP. 509- 521.
3
4. Ghezavati, V. R. and Saidi-Mehrabad, M. (2010). “Designing integrated cellular manufacturing systems with scheduling considering stochastic processing time”, The International Journal of Advanced Manufacturing Technology, Vol. 48, No. 5- 8, PP. 701- 717.
4
5. Ghezavati, V. R. and Saidi-Mehrabad, M. (2011). “An efficient hybrid self-learning method for stochastic cellular manufacturing problem: A queuing-based analysis”, Expert Systems with Applications, Vol. 38, No. 3, PP. 1326- 1335.
5
6. Das, K., Lashkari, R. S. and Sengupta, S. (2007a). “Machine reliability and preventive maintenance planning for cellular manufacturing systems”, European Journal of Operational Research, Vol. 183, No. 1, PP. 162-180.
6
7. Das, K., Lashkari, R. S. and Sengupta, S. (2007b). “Reliability consideration in the design and analysis of cellular manufacturing systems”, International Journal of Production Economics, Vol. 105, No1, PP. 243-262.
7
8. Das, K. (2008). “A comparative study of exponential distribution vs Weibull distribution in machine reliability analysis in a CMS design”, Computers & Industrial Engineering, Vol. 54, No. 1, PP. 12- 33.
8
9. Ameli, M. S. J., Arkat, J. and Barzinpour, F. (2008). “Modelling the effects of machine breakdowns in the generalized cell formation problem”, The International Journal of Advanced Manufacturing Technology, Vol. 39, No. 7- 8, PP. 838- 850.
9
10. Ameli, M. S. J. and Arkat, J. (2008). “Cell formation with alternative process routings and machine reliability consideration”, The International Journal of Advanced Manufacturing Technology, Vol. 35, No. 7-8, PP. 761- 768.
10
11. Chung, S. H., Wu, T. H. and Chang, C. C. (2011). “An efficient tabu search algorithm to the cell formation problem with alternative routings and machine reliability considerations”, Computers & Industrial Engineering, Vol. 60, No. 1, PP. 7- 15.
11
12. Rafiee, K., Rabbani, M., Rafiei, H. and Rahimi-Vahed, A. (2011). “A new approach towards integrated cell formation and inventory lot sizing in an unreliable cellular manufacturing system”, Applied Mathematical Modelling, Vol. 35, No. 4, PP. 1810- 1819.
12
13. Asgharpour, M. J. and Javadian, N. (2004). “Solving a stochastic cellular manufacturing model by using genetic algorithms”, International Journal of Engineering Transactions A, Vol. 17, No. 2, PP. 145- 156.
13
14. Tavakkoli-Moghaddam, R., Javadian, N., Javadi, B. and Safaei, N. (2007). “Design of a facility layout problem in cellular manufacturing systems with stochastic demands”, Applied Mathematics and Computation, Vol. 184, No. 2, PP. 721- 728.
14
15. Egilmez, G., Suer, G. A. and Huang, J. (2012). “Stochastic cellular manufacturing system design subject to maximum acceptable risk level”, Computers & Industrial Engineering, Vol. 63, No. 4, PP. 842- 854.
15
16. Frederick, G. J. L. and HillIer, S. (2001). Introduction to Operations Research, 7th Ed., Chapter 17, McGraw-Hill Pub. Co., New York.
16
17. Duran, O., Rodriguez, N. and Consalter, L. A. (2010). “Collaborative particle swarm optimization with a data mining technique for manufacturing cell design”, Expert Systems with Applications,Vol. 37, No. 2, PP. 1563- 1567.
17
18. Kennedy, J. and Eberhart, R. (1995). “Particle swarm optimization”, In Proceedings of the IEEE International Conference on Neural Networks IV., Perth, WA, Vol. 4, PP. 1942- 1948.
18
19. Eberhart, R. and Kennedy, J. (1995). “A new optimizer using particle swarm theory”, In Proceedings of the sixth international symposium on micro machine and human science., Nagoya, Japan. PP. 39- 43.
19
20. Mahdavi, I., Paydar, M. M., Solimanpur, M. and Heidarzade, A. (2009). “Genetic algorithm approach for solving a cell formation problem in cellular manufacturing”, Expert Systems with Applications, Vol. 36, No. 3, PP. 6598- 6604.
20
ORIGINAL_ARTICLE
Simoltaneous Lot-sizing and Scheduling in Hybrid Flow Shop Production Environment with Resource Constraint
The aim of this Paper is to study a multi-product, multi-period production systems in a hybrid flow shop so that lot-sizing and scheduling will be detemined simultaneously. A new mixed-integer programming model is proposed to formulate the studied problem. The objective function in this investigation includes the total cost of production, inventory and external supply. In the case of not satisfying the demand of customers, this demand should be met by foreign suppliers with higher price. The simultaneous lot-sizing and scheduling problem are classified in strongly NP-hard class. Due to the high computational complexity of the studied problem, particle swarm optimization (PSO) and imperialist competitive algorithms (ICA) are implemented for solving the considered problem. The algorithms explore the solution space for both lot-sizing and scheduling and find a combination of production plan and sequence that is feasible and close to optimum. First, the implemented algorithms are used for solving randomly generated instances with different sizes. Then, these methods are used to solve the case of tile industry and the obtained results by two methods are compared with each other. Computational experiences show that the algorithms are able to achieve good-quality solutions for the problem in a reasonable time. Also, the results of ICA are better than PSO results for the mentioned case study.
https://aie.ut.ac.ir/article_60731_27bc5cda97841378ac5689d8e2c327cb.pdf
2016-09-22
295
310
10.22059/jieng.2016.60731
Hybrid flow shop production system
Imperialist competitive algorithm
Machine capacity constraint
mathematical modeling
Particle swarm optimization
Simultaneous lot-sizing and scheduling
Sahar
Fallah Sanami
fallah.sahar@gmail.com
1
Department of Economic and Management, Semnan University, Semnan, Iran
AUTHOR
Reza
Ramezanian
ramezanian@kntu.ac.ir
2
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
LEAD_AUTHOR
Mohsen
Shafiei Nikabadi
mohsenshnaj@yahoo.com
3
Department of Economic and Management, Semnan University, Semnan, Iran
AUTHOR
Wagner, H. M. and Whithin, T. M. (1958). “Dynamic version of the economic lot size model”, Management Science, Vol. 5, No. 1, PP. 89– 96.
1
Gupta, D. and Magnusson, T. (2005). “The capacitated lot-sizing and scheduling problem with sequence-dependent setup costs and setup times”, Computers & Operations Research, Vol. 32, No. 4, PP. 727– 747.
2
Almada-Lobo, B., Klabjan, D., Carravilla, M. A. and Oliveira, J. (2007). “Single machine multiproduct capacitated lotsizing with sequence-dependent setups”, International Journal of Production Research, Vol. 45, No. 20, PP. 4873– 4894.
3
James, R. J. W. and Almada-Lobo, B. (2011). “Single and parallel machine capacitated lotsizing and scheduling: New iterative MIP-based neighborhood search heuristics”, Computers & Operations Research, Vol. 38, No. 12, PP. 1816- 1825.
4
Buschkühl, L., Sahling, F., Helber, S. and Tempelmeier, H. (2010). “Dynamic capacitated lot-sizing problems: A classification and review of solution approaches”, OR Spectrum, Vol. 32, No. 2, PP. 231– 261.
5
Kimms, A. (1996). “Multi-level, single-machine lotsizing and scheduling (with initial inventory)”, European Journal of Operational Research, Vol. 89, No. 1, PP. 86– 99.
6
Kimms, A. and Drexl, A. (1998). “Some insights into proportional lotsizing and scheduling”, Journal of the Operational Research, Vol. 49, No. 11, PP. 1196- 1205.
7
Fandel, G. and Stammen-Hegene, C. (2006). “Simultaneous lot sizing and scheduling for multi-product multi-level production”, International Journal of Production Economics, Vol. 104, No. 2, PP. 308– 316.
8
Mohammadi, M., Fatemi Ghomi, S. M. T., Karimi, B. and Torabi, S. A. (2010). “Rolling-horizon and fix-and-relax heuristics for the multi-product multi-level capacitated lotsizing problem with sequence-dependent setups”, Journal of Intelligent Manufacturing, Vol. 21, No. 4, PP. 501– 510.
9
Mohammadi, M., Karimi, B., Fatemi Ghomi, S. M. T. and Torabi, S. A. (2010). “A new algorithmic approach for capacitated lot-sizing problem in flow shops with sequence-dependent setups”, International Journal of Advanced Manufacturing Technology, Vol. 49, No. 1, PP. 201– 211.
10
Clark, A. R. and Clark, S. J. (2000). “Rolling-horizon lot-sizing when setup times are sequence-dependent”, International Journal of Production Research, Vol. 38, No. 10, PP. 2287– 2308.
11
Mohammadi. M., Fatemi Ghomi. S. M. T. and Jafari, N. (2011). “A genetic algorithm for simultaneous lotsizing and sequencing of the permutation flow shops with sequence-dependent setups”, International Journal of Computer Integrated Manufacturing, Vol. 24, No. 1, PP. 87– 93.
12
Ramezanian, R., Saidi-Mehrabad, M. and Fattahi, P. (2013). “Integrated lot-sizing and scheduling with overlapping for multi-level capacitated production system”, International Journal of Computer Integrated Manufacturing, Vol. 26, No. 7, PP. 681- 695.
13
Ramezanian, R., Saidi-Mehrabad, M. and Fattahi, P. (2013). “MIP formulation and heuristics for multi-stage capacitated lot-sizing and scheduling problem with availability constraints”, Journal of Manufacturing Systems, Vol. 32, No. 2, PP. 392- 401.
14
Ramezanian, R., Shafiei-Nikabadi, M. and Fallah, S. (2014). “Particle swarm optimization algorithm for integrated lot-sizing and scheduling in the flow shop production environment”, Journal of Industrial Engineering (University of Tehran), Vol. 48, No. 2, PP. 215- 228.
15
Babaei, M. Affiliated withDepartment of Industrial Engineering, Faculty of Engineering, Kharazmi University, Mohammadi, M. and Fatemi Ghomi, S. M. T. (2013). “A genetic algorithm for the simultaneous lot sizing and scheduling problem in capacitated flow shop with complex setups and backlogging”, International Journal of Advanced Manufacturing Technology, Vol. 70, No. 1- 4, PP. 125- 134.
16
Urrutia, E. D. G., Aggoune, R. and Dauzère-Pérès, S. (2014). “Solving the integrated lot-sizing and job-shop scheduling problem”, International Journal of Production Research, Vol. 52, No. 17, PP. 5236– 5254.
17
Wolosewicz, C., Dauzère-Pérès, S. and Aggoune, R. (2015). “A lagrangian heuristic for an integrated lot-sizing and fixed scheduling problem”, European Journal of Operational Research, Vol. 244, No. 1, PP. 3–12.
18
Glover, F. and Kochenberger, G. (2005). Handbook of metaheuristics, Kluwer Academic Publishers, Norwell.
19
Eberhart, R. and Kennedy, J. (1995). “A New Optimizer Using Particles Swarm Theory”, Proc. Sixth International Symposium on Micro Machine and Human Science (Nagoya, Japan), IEEE Service Center, Piscataway, NJ, PP. 39- 43.
20
Kennedy, J. and Eberhart, R. (1995). “Particle Swarm Optimization”, IEEE Conference on Neural Networks, (Perth, Australia), Piscataway, NJ, IV, PP. 1942- 1948.
21
Shi, Y. and Eberhart, R. C. (1998). “A modified particle swarm optimizer”, In Proceedings of the IEEE Congress on Evolutionary Computation, PP. 69– 73.
22
Pinedo, M. (2008). Scheduling: Theory, algorithms and systems, 3rd edition, Springer.
23
Atashpaz-Gargari E. and Lucas, C. (2007). “Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition”, IEEE Congress on Evolutionary Computation Singapore, PP. 4661–4667.
24
Riane, F. (1998). Scheduling hybrid flowshops: Algorithms and applications, PhD thesis, CREGI-FUCaM, Belgium.
25
ORIGINAL_ARTICLE
A Hierarchical Multimodal Hub Location Model with Maximum Allowable Delay (Case Study: Iran)
In this study, a hierarchical hub location problem by considering different modes of transportation and maximum allowed delay is investigated. This study is done on Iran’s express postal service with 2 days maximum delay in delivery considering ground and airport hubs. The problem is of the type of location and single allocation for collection, transfer and distribution of the shipments. All the hubs are of limited capacity and there is a penalty for violation of the maximum allowed delay. The strategic goal of the model is to determine the location of ground and airport hubs and the allocation of links to these hubs by which the delay is minimized and the total cost of the system is optimized. The model is linear mixed-integer programming. Finally, a case study to implement the model in Iran’s postal service is conducted, and a sensitivity analysis is done using GAMS software.
https://aie.ut.ac.ir/article_60732_22d3590a189ba164da31b71bfd4e2250.pdf
2016-09-22
311
325
10.22059/jieng.2016.60732
Capacitated hubs
Hierarchical hub location problem
Hub location problem
Single allocation
Firoozeh
Kaveh
f.kaveh@ut.ac.ir
1
University of Tehran, Iran
AUTHOR
Jafar
Razmi
jrazmi@hotmail.com
2
University of Tehran, Iran
LEAD_AUTHOR
Thomadsen, T. and Larsen, J. (2007). “A hub location problem with fully interconnected backbone and access networks”, Computers & Operations Research, Vol. 34, No. 8, PP. 2520- 2531.
1
Contreras, I. (2015). “Hub Location Problems”, Location Science, Springer, PP. 311- 344.
2
Teixeria, J. C. and Antunes, P. A. (2008). “A hierarchical location model for public facility planning”, European Journal of Operational Research, Vol. 185, No. 1, PP. 92- 104.
3
Yaman, H. (2009). “The Hierarchical hub median problem with single assignment”, Transportation Research Part B, Vol. 43, No. 6, PP. 643- 658.
4
Davari, S. and Zarandi, M. H. F. (2012). “The single-allocation hierarchical hub median location problem with fuzzy demands”, African Journal of Business Management, Vol. 6, No. 1, PP. 347- 360.
5
Alumur, S. A., Yaman, H. and Kara, B. Y. (2012). “Hierarchical multimodal hub location problem with time-definite deliveries”, Transportation Research Part E, Vol. 48, No. 6, PP. 1107- 1120.
6
Yaman, H. and Elloumi, S. (2012). “Star P-Hub center problem and star P-Hub median problem with bounded path lengths”, Computers & Operations Research, Vol. 39, No. 11, PP. 2725- 2732.
7
de sa, E. M., De Camargo, R. S. and de Miranda, G. (2013). “Descret optimization an improved benders decomposition algorithm for the tree of hubs location problem”, European Journal of Operation Research, Vol. 266, PP. 185- 202.
8
Figueiredo, R. M. A., O'Kelly, M. E. and Pizzolato, N. D. (2014). “A two-stage hub location method for air transportation in Brazil”, International Transactions in Operational Research, Vol. 21, No. 2, PP. 275– 289.
9
Zanjirani Farahani, R., Hassani, A., Mousavi, S. M. and Bakhshayeshi Baygi, M. (2014). “A hybrid artificial bee colony for disruption in a hierarchical maximal covering location problem”, Computers & Industrial Engineering, Vol. 75, No. 6, PP. 129– 141.
10
Sahin, G. and Sural, H. (2007). “A review of hierarchical facility location models”, Computers & Operations Research, Vol. 34, No. 8, PP. 2310- 2331.
11
Lin, CH. CH. & Chen, SH. H. (2008). “An integral constrained generalized hub-and-spoke network design problem”, Transportation Research Part E, Vol. 44, No. 6, PP. 986- 1003.
12
Chen, SH. H. (2010). “A heuristic algorithm for hierarchical hub-and-spoke network of time-definite common carrier operation planning problem”, Networks and Spatial Economics. Springer Science, Vol. 10, No. 4, PP. 509- 523.
13
Ayed, O. B. (2011). “Parcel distribution network design problem”, Operational Research, Vol. 3, No. 2, PP. 139- 149.
14
Sender, J. and Clausen, U. (2011). “A new hub location model for network design of wagonload traffic”, Procedia Social and Behavioral Sciences, Vol. 20, No. 8, PP. 90- 99.
15
Chi, T. H., Yang, H. and Hsiao, H. M. (2011). “A new hierarchical facility location model and genetic algorithm for humanitarian relief”, Information Science And Service Science (NISS). Conference Publications, Vol. 2, No. 3, PP. 367- 374.
16
Manzour-al-Ajdad, S. M., Torabi, S. A. and Eshghi, K. (2012). “Single-source capacitated multi-facility weber problem-an iterative two phase heuristic algorithm”, Computers and Operations Research, Vol. 39, No. 7, PP. 1465- 1476.
17
Sheu, J. B. and Lin, A. Y. S. (2012). “Hierarchical facility network planning model for global logistics network configurations”, Applied Mathematical Modelling, Vol. 36, No. 7, PP. 3053- 3066.
18
Saboury, A., Ghaffari-Nasab, N., Barzinpour, F. and Jabalameli, M. S. (2013). “Applying two efficient hybrid heuristics for hub location problem with fully inter connected backbone and access networks”, Computers & Operations Research, Vol. 40, No. 10, PP. 2493- 2507.
19
Ryerson, M. S. and Kim H. (2013). “Integrating airline operational practices in to passenger airline hub definition”, Journal of Transport Geography, Vol. 31, No. 12, PP. 84- 93.
20
Torkestani, S. (2013). “A new hierarchical hub location model with limited demand for network design”, MA thesis, Engineering school, Iran University of Science & Technology.
21
Karimi, M., Eydi, A. R. and Korani, E. (2014). “Modeling of the capacitated single allocation hub location problem with a hierarchical approch”, IJE Transactions A: Basics Vol. 27, No. 4, PP. 573- 586.
22
Adibi A. and Razmi J. (2015), 2-Stage stochastic programming approach for hub location problem under uncertainty: A case study of air network of Iran, J. of Air Transport Management, Vol. 47, PP. 172-178
23
Razmi J. and Rahmanniya F. (2013), Design of distribution network using hub location model with regard to capacity constraint and service level, Int. J. Logistics Systems and Management, Vol. 16, No. 4, pp. 386-398
24
ORIGINAL_ARTICLE
Sustainable Construction Project Portfolio Selection under Interval- Valued Type-2 Fuzzy Sets
Choosing the right set of projects is the first step of project oriented firms in strategic project portfolio management. Since economic growth depends on environmental and social issues, sustainable development has been an essential part of firms' plans to keep their competitive advantage. Moreover, market conditions, fast worldwide changes and other similar issues have given these issue ever-growing uncertain elements. As a result, this paper provides a method of sustainable construction-project portfolio selection under interval-valued type-2 fuzzy sets. This method consists of two main parts. The first level evaluates the proposed projects and omits the unsuitable ones. Then, in the second level, the portfolio is selected by means of mathematical programming. Eventually, to display applicability of the method, an application example is presented and solved.
https://aie.ut.ac.ir/article_60733_296e69c6bb13dc2b8ea019a5bd878185.pdf
2016-09-22
327
340
10.22059/jieng.2016.60733
Construction projects
Mathematical Programming
Project Portfolio Selection
Sustainable Development
Type-2 fuzzy sets
Vahid
Mohagheghi
v.mohagheghi@gmail.com
1
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
AUTHOR
Seyed Meysam
Mousavi
mousavi.sme@gmail.com
2
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
LEAD_AUTHOR
Behnam
Vahdani
b.vahdani@gmail.com
3
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
AUTHOR
Better, M. and Glover, F. (2006). “Selecting project portfolios by optimizing simulations”, Engineering Economist, Vol. 51, No. 2, PP. 81- 97.
1
Zou, P. X., Zhang, G. and Wang, J. (2007). “Understanding the key risks in construction projects in China”, International Journal of Project Management, Vol. 25, No. 6, PP. 601-614.
2
Smith, N. J., Merna, T. and Jobling, P. (2009). Managing risk: in construction projects, John Wiley & Sons.
3
Alidi, A. S. (1996). “Use of the analytic hierarchy process to measure the initial viability of industrial projects”, International Journal of Project Management, Vol. 14, No. 4, PP. 205- 208.
4
Khalili-Damghani, K., Sadi-Nezhad, S., Lotfi, F. H. and Tavana, M. (2013). “A hybrid fuzzy rule-based multi-criteria framework for sustainable project portfolio selection”, Information Sciences, Vol. 220 , PP. 442-462.
5
Robert, K. W., Parris, T. M. and Leiserowitz, A. A. (2005). “What is sustainable development? Goals, indicators, values, and practice”, Environment: Science and Policy for Sustainable Development, Vol. 47, No. 3, PP. 8- 21.
6
Hutchins, M. J. and Sutherland, J. W. (2008). “An exploration of measures of social sustainability and their application to supply chain decisions”, Journal of Cleaner Production, Vol. 16, No. 15, PP. 1688- 1698.
7
Mavrotas, G. and Pechak, O. (2013). “Combining mathematical programming and Monte Carlo simulation to deal with uncertainty in energy project portfolio selection”, In Assessment and Simulation Tools for Sustainable Energy Systems, Vol. 129, Springer, London, PP. 333- 356.
8
Mohanty, R. P. (1992). “Project selection by a multiple-criteria decision-making method: An example from a developing country”, International Journal of Project Management, Vol. 10, No. 1, PP. 31- 38.
9
Martinsuo, M. (2013). “Project portfolio management in practice and in context”, International Journal of Project Management, Vol. 31, No. 6, PP. 794- 803.
10
Silvius, A. J. and Schipper, R. P. (2014). “Sustainability in project management: A literature review and impact analysis”, Social Business, Vol. 4, No. 1, PP. 63- 96.
11
Altuntas, S. and Dereli, T. (2015). “A novel approach based on DEMATEL method and patent citation analysis for prioritizing a portfolio of investment projects”, Expert Systems with Applications, Vol. 42, No. 3, PP. 1003- 1012.
12
Ebrat, M. and Ghodsi, R. (2011). “Risk assessment of construction projects using network based adaptive fuzzy system”, International Journal of Academic Research, Vol. 3, No. 1.
13
Ebrahimnejad, S., Mousavi, S. M., Tavakkoli-Moghaddam, R., Hashemi, H. and Vahdani, B. (2012). “A novel two-phase group decision making approach for construction project selection in a fuzzy environment”, Applied Mathematical Modelling, Vol. 36, No. 9, PP. 4197- 4217.
14
Abbasianjahromi, H. and Rajaie, H. (2012). “Developing a project portfolio selection model for contractor firms considering the risk factor”, Journal of Civil Engineering and Management, Vol. 18, No. 6, PP. 879- 889.
15
Vahdani, B., Mousavi, S. M., Hashemi, H., Mousakhani, M. and Ebrahimnejad, S. (2014). “A new hybrid model based on least squares support vector machine for project selection problem in construction industry”, Arabian Journal for Science and Engineering, Vol. 39, No. 5, PP. 4301- 4314.
16
Bezdek, J. C. (2013). Pattern recognition with fuzzy objective function algorithms, Springer Science & Business Media.
17
Celikyilmaz, A. and Turksen, I. B. (2009). “Modeling uncertainty with fuzzy logic”, Studies in Fuzziness and soft Computing, Vol. 240 , PP. 149-215.
18
Türkşen, I. B. (1999). “Type I and Type II fuzzy system modeling”, Fuzzy Sets and Systems, Vol. 106, No. 1, PP. 11- 34.
19
Mendel, J. M. (2003). “Type-2 fuzzy sets: some questions and answers”, IEEE Connections, Newsletter of the IEEE Neural Networks Society, Vol. 1, PP. 10-13.
20
Mendel, J. M., John, R. and Liu, F. (2006). “Interval type-2 fuzzy logic systems made simple”, Fuzzy Systems, IEEE Transactions on, Vol. 14, No. 6, PP. 808- 821.
21
Chen, S. M. and Lee, L. W. (2010). “Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method”, Expert Systems with Applications, Vol. 37, No. 4, PP. 2790- 2798.
22
Chen, T. Y. (2013). “A linear assignment method for multiple-criteria decision analysis with interval type-2 fuzzy sets”, Applied Soft Computing, Vol. 13, No. 5, PP. 2735- 2748.
23
Zhang, Z. and Zhang, S. (2013). “A novel approach to multi attribute group decision making based on trapezoidal interval type-2 fuzzy soft sets”, Applied Mathematical Modelling, Vol. 37, No. 7, PP. 4948- 4971.
24
Deng, H. (2014). “Comparing and ranking fuzzy numbers using ideal solutions”, Applied Mathematical Modelling, Vol. 38, No. 5, PP. 1638- 1646.
25
Wei, C. C. and Chang, H. W. (2011). “A new approach for selecting portfolio of new product development projects”, Expert Systems with Applications, Vol. 38, No. 1, PP. 429-434.
26
Sari, I. U. and Kahraman, C. (2015). “Interval type-2 fuzzy capital budgeting”, International Journal of Fuzzy Systems, Vol. 17, No. 4, PP. 635- 646.
27
Mohagheghi, V., Mousavi, S. M., Vahdani, B. and Shahriari, M. R. (2016). “R&D project evaluation and project portfolio selection by a new interval type-2 fuzzy optimization approach”, Neural Computing and Applications, Article in press, DOI: 10.1007/s00521-016-2262-3
28
ORIGINAL_ARTICLE
Solving a Fuzzy Multi-objective Aggregate Production Planning Model with Learning and Deterioration Effects by Using Genetic and Tabu Search Algorithms
In this paper a non linear integrated fuzzy multi-objective production planning model with the labor learning and machines deterioration effects is presented. The objective function consists of two quantitative objectives namely increase profits and reduces the cost of system failure and a qualitative objective namely increases the satisfaction rate of the customers. Different weights for objectives and modification of the objectives by using fuzzy goal programming method are considered to convert the fuzzy multi-objective model to a deterministic single-objective model and the obtained model is solved by Genetic algorithm and Tabu search algorithm. Finally, the solution obtained from two algorithms compared together by using hypothesis test of equality of means. Experimental results show the proposed Genetic algorithm for solving the model has higher performance than the Tabu search algorithm.
https://aie.ut.ac.ir/article_60734_08fef3e3514607ea944baf6ac5f0316a.pdf
2016-09-22
341
354
10.22059/jieng.2016.60734
Aggregate production planning
Fuzzy goal programming
Genetic Algorithm
Multi-Objective Programming
Tabu search algorithm
Esmaeil
Mehdizadeh
emqiau@yahoo.com
1
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Iran
LEAD_AUTHOR
Rasa
Ghazizadeh
rasa_gh_87@yahoo.com
2
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Iran
AUTHOR
Gen. M, Tsujimura. Y and Ida. K. (1992). “Method for solving multi-objective aggregate production planning problem with fuzzy parameters”, Computers and Industrial Engineering, Vol. 23, No. I- 4, PP. 117- 120.
1
Wang, R. C. and Fang, H. H. (2001). “Aggregate production planning with multiple objectives in a fuzzy environment”, European journal of operational research, Vol. 133, No. 3, PP. 521- 536.
2
Wang, R. Chen, Liang, T. F. (2004). “Application of fuzzy multi-objective linear programming to aggregate production planning”, Computers & Industrial Engineering, Vol. 46, No. 1, PP. 4617– 41.
3
Wang, R. C. and Liang, T. F. (2005). “Applying possibilistic linear programming to aggregate production planning”, International Journal of Production Economics, Vol. 98, No. 3, PP. 328- 341.
4
Chao, F. H., Chang-J. T. and Shao Y. L. (2007). “A fuzzy goal programming approach to multi-objective optimization problem with priorities”, European Journal of Operational Research, Vol. 176, No. 3, PP. 1319–1333.
5
Shaoyuan, L. and Chaofang, H. (2009). “Satisfying optimization method based on goal programming for fuzzy multiple objective optimization problems”, Europe an Journal of Operational Research, Vol. 197, No. 2, PP. 675– 684.
6
Jamalnia, A. and Soukhakian, M. A. (2009). “A hybrid fuzzy goal programming approach with different goal priorities to aggregate production planning”, Computers &Industrial Engineering, Vol. 56, No. 4, PP. 1474–1486.
7
Ozcana, U. and Toklu, B. (2009). “Multiple-criteria decision-making in two-sided assembly line balancing: A goal programming and a fuzzy goal programming models”, Computers &Operations Research, Vol. 36, No. 6, PP. 1955 – 1965.
8
Baykasoglu, A. and Gocken, T. (2010). “Multi-objective aggregate production planning with fuzzy parameters”, Advances in Engineering Software, Vol. 41, No. 9, PP. 1124– 1131.
9
Phruksaphanrat, B. (2011). “Preemptive possibilistic linear programming: Application to aggregate production planning”, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, Vol. 5, No. 8, PP. 1592- 1599.
10
Ramezanian, R. Rahmani, D. and Barzinpour, F. (2012). “An aggregate production planning model for two phase production systems: Solving with genetic algorithm and tabusearch”, Expert Systems with Applications, Vol. 39, No. 1, PP. 1256– 1263.
11
Hung, Y. F. and Hu, Y. C. (1998). “Solving mixed integer programming production planning problems with setups by shadow price information”, Computers and Operations Research, Vol. 25, No. 12, PP. 1027– 1042.
12
Mortezaei, N., Zulkifli, N., Hong, T. S. and Yusuff, R. M. (2013). “Multi-objective aggregate production planning model with fuzzy parameters and its solving methods”, Life Science Journal, Vol. 10, No. 4, PP. 2406- 2414.
13
Kaveh, K. D. and Ayda, S. (2014). “Solving a new multi-period multi-objective multi-product aggregate production planning problem using fuzzy goal programming”, Industrial Engineering and Management Systems, Vol. 13, No. 4, PP. 369- 382.
14
Madadi, N. and Wong, K. Y. (2014). “A multi objective fuzzy aggregate production planning model considering real capacity and quality of products”, Mathematical Problems in Engineering, Vol. 2014, Article ID 313829, 15 pages, doi:10.1155/2014/313829.
15
Gholamian, N., Mahdavi I., Tavakkoli-Moghaddam R. and Mahdavi-Amiri, N. (2015). “Comprehensive fuzzy multi-objective multi-product multi-site aggregate production planning decisions in a supply chain under uncertainty”, Applied Soft Computing, Vol. 37, PP. 585– 607.
16
Chen Z. and Sarker B. R. (2015). “Aggregate production planning with learning effect and uncertain demand: A case based study”, Journal of Modelling in Management, Vol. 10, No. 3, PP. 296– 324.
17
Aneirson, F. Da S. and Fernando, A. S. M. (2014). “A fuzzy goal programming model for solving aggregate production-planning problems under uncertainty: A case study in a Brazilian sugar mill”, Energy Economics, Vol. 45, , PP. 196– 204.
18
Azadeh, A., Habibnejad-Ledari, H., Abdolhossein Zadeh, S. and Hosseinabadi Farahani, M. (2017). “A single-machine scheduling problem with learning effect, deterioration and non-monotonic time-dependent processing times”, International Journal of Computer Integrated Manufacturing, Vol. 30, No.2-3, PP. 292-304.
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