ORIGINAL_ARTICLE
Structural Relationship of Sustainable Manufacturing Divers and Incentives
This paper is to study the factors that encourage, drive or force companies to alter manufacturing processes in a way that simultaneously minimizes their environmental and social effects and is cost-efficient for the companies. The principal focus of this research is on the relationships of these drivers, and their mutual influence on each other. This research is descriptive, and a case study is conducted in automotive plastic parts industry in Iran. After recognizing the drivers of sustainable manufacturing, they were localized, and finally ten drivers were approved. Then the relationships between the drivers were analyzed applying Grey-DEMATEL method. The most effective and important drivers in the macro-environment are laws and media, and in the micro-environment, competitors and customers are the key drivers. Business benefits and partners transfer the influences of cause drivers to effect divers. Managers, owners and personnel are the most effected drivers.
https://jieng.ut.ac.ir/article_62208_324e4bfc25b79a9a47529b0b0dabe937.pdf
2017-06-22T11:23:20
2020-08-08T11:23:20
133
146
10.22059/jieng.2017.62208
Grey DEMATEL
Sustainability drivers and incentives
Sustainable manufacturing
Mohammad aslam
Hosseinbor
m.a.hosseinbor@gmail.com
true
1
Department of Industrial Management, University of Mazandaran, Iran
Department of Industrial Management, University of Mazandaran, Iran
Department of Industrial Management, University of Mazandaran, Iran
AUTHOR
Abdolhamid
Safaei Qadikolaei
ab.safaei@umz.ac.ir
true
2
Department of Industrial Management, University of Mazandaran, Iran
Department of Industrial Management, University of Mazandaran, Iran
Department of Industrial Management, University of Mazandaran, Iran
LEAD_AUTHOR
Mehrdad
Madhooshi
madhoshi@umz.ac.ir
true
3
Department of Industrial Management, University of Mazandaran, Iran
Department of Industrial Management, University of Mazandaran, Iran
Department of Industrial Management, University of Mazandaran, Iran
AUTHOR
Mittal, V. K. and Sangwan, K. S. (2011). “Development of an interpretive structural model of obstacles to environmentally conscious technology adoption in Indian industry”, Glocalized Solutions for Sustainability in Manufacturing, Springer, Berlin Heidelberg, PP. 448-453 .
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38
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39
ORIGINAL_ARTICLE
A Genetic Algorithm for Integration of Vehicle Routing Problem and Production Scheduling in Supply Chain (Case Study: Medical Equipment Supply Chain)
This paper studies a model for integration of vehicle routing problem (VRP) in a supply chain with order assignment to the suppliers and determining their production sequence. The considered supply chain consists of some suppliers, vehicles and a manufacturer. It is assumed that manufacturer purchases identify the raw material demand of suppliers in wholesale all at once. This provides the opportunity of receiving discounts and consequently decreasing final price. A transportation fleet composed of some vehicles, each of which may have a different speed and different transport capacity, is responsible for transporting purchased raw materials to suppliers and gathering completed parts from them aiming at minimizing the total tardiness of all jobs. After presenting the mathematical model of the problem, a dynamic genetic algorithm with two dimensional structures is proposed. The algorithm was applied to the supply chain of a medical equipment manufacturer and the results were compared with real results beforehand. Findings show that applying dynamic genetic algorithm results in improving the average of tardiness from 9.44 days to 2.11 days. Also the comparison of dynamic genetic algorithm with the optimum solution for the small size problems, and the algorithm proposed for the nearest problem in the literature to our problem shows the high efficiency of dynamic genetic algorithm.
https://jieng.ut.ac.ir/article_62209_eb0ebd187484e516059ac15f305e75b0.pdf
2017-06-22T11:23:20
2020-08-08T11:23:20
147
160
10.22059/jieng.2017.62209
Genetic Algorithm
Medical equipment
Router
Scheduling
Supply Chain
Mohammad Ali
Beheshtiniya
beheshtinia@semnan.ac.ir
true
1
Faculty of Engineering, Semnan University, Iran
Faculty of Engineering, Semnan University, Iran
Faculty of Engineering, Semnan University, Iran
LEAD_AUTHOR
Atena
Aarabi
aarabi.atena@gmail.com
true
2
Faculty of Engineering, Semnan University, Iran
Faculty of Engineering, Semnan University, Iran
Faculty of Engineering, Semnan University, Iran
AUTHOR
1. Nasiri, M. and PourmohamadZia, N. (2015). “A Hybrid model for supplier selection and order allocation in supply chain”, Journal of Industrial Engineering, Vol. 49, No. 1, PP. 117–128.
1
2. Omrani, H. and Adabi, F. (2016). “A multi objective planning for supply chain network design with efficient manufacturers and distributers”, Journal of Industrial Engineering, Vol. 50, No. 2, PP. 261–278.
2
3. Lee, Y. H., Jeong, C. S. and Moon, C. (2002). “Advanced planning and scheduling with outsourcing in manufacturing supply chain”, Computers and Industrial Engineering, Vol. 43, No. 1–2, PP. 351–374.
3
4. Berning, G., et al. (2004). “Integrating collaborative planning and supply chain optimization for the chemical process industry (I)—methodology”, Computers & Chemical Engineering, Vol. 28, No. 6–7, PP. 913–927.
4
5. Naso, D. et al. (2007). “Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete”, European Journal of Operational Research, Vol. 177, No. 3, PP. 2069–2099.
5
6. Averbakh, I. and Xue, Z. (2007). “On-line supply chain scheduling problems with preemption." European Journal of Operational Research, Vol. 181, No. 1, PP. 500–504.
6
7. Zegordi, S. and Beheshti Nia, M. (2009). “Integrating production and transportation scheduling in a two-stage supply chain considering order assignment”, The International Journal of Advanced Manufacturing Technology, Vol. 44, No. 9–10, PP. 928–939.
7
8. Sawik, T. (2009). “Coordinated supply chain scheduling." International Journal of Production Economics, Vol. 120, No. 2, PP. 437–451.
8
9. Su, C.-S., Pan, J.C.-H. and Hsu, T.-S. (2009). "A new heuristic algorithm for the machine scheduling problem with job delivery coordination”, Theoretical Computer Science, Vol. 410, No. 27–29, PP. 2581–2591.
9
10. 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.
10
11. 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.
11
12. Yimer, A.D. and Demirli, K. (2010). “A genetic approach to two-phase optimization of dynamic supply chain scheduling”, Computers and Industrial Engineering, Vol. 58, No. 3, PP. 411–422.
12
13. Fahimnia, B., Luong, L.and Marian, R. (2012). “Genetic algorithm optimisation of an integrated aggregate production–distribution plan in supply chains”, International Journal of Production Research, Vol. 50, No. 1, PP. 81–96.
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. Selvarajah, E. and Zhang, R. (2014). “Supply chain scheduling at the manufacturer to minimize inventory holding and delivery costs”, International Journal of Production Economics, Vol. 147, Part A, No. 0, PP. 117–124.
15
16. Low, C., et al. (2014). “Coordination of production scheduling and delivery problems with heterogeneous fleet”, International Journal of Production Economics, Vol. 153, No. 1, PP. 139–148.
16
17. Cheng, B., Yang, Y. and Hu, X. (2015). “Supply chain scheduling with batching, production and distribution”, International Journal of Computer Integrated Manufacturing, Vol., No. ahead-of-print, PP. 1–12.
17
18. Chang, Y.-C., Chang, K.-H. and Kang, T.-C. (2015). “Applied Variable Neighborhood Search-Based Approach to Solve Two-Stage Supply Chain Scheduling Problems”, Journal of Testing and Evaluation, Vol. 44, No. 3, PP. 1337-1349.
18
19. Tasan, A. S. and Gen, M. (2012). “A genetic algorithm based approach to vehicle routing problem with simultaneous pick-up and deliveries”, Computers and Industrial Engineering, Vol. 62, No. 3, PP. 755–761.
19
ORIGINAL_ARTICLE
An Efficient Imperialist Competitive Algorithm for Resource Constrained Project Scheduling Problem
In this paper, a new algorithm based on the framework of the imperialist competitive algorithm for solving resource constrained project scheduling problem (RCPSP) will be proposed. In this problem, the activities are scheduled based on the resource and precedence relationships constraints in a way that the makes pan will be minimized. In order to model the assimilation process, a uniform crossover has been used, and to avoid premature convergence of the proposed algorithm, two revolution operators including one point revolution and multi-point revolution will be introduced. Also, in order to enhance the exploitation ability, a combined local search including permutation based local search (PBLS) and forward-backward improvement (FBI) is performed. The algorithm parameters are determined by designing Taguchi experiment, and the efficiency of proposed ICA is demonstrated by solving PSPLIB problems. Computational results and comparisons with some existing algorithms show that the proposed algorithm can produce near-optimal solution for small problems and competitive solution for large ones.
https://jieng.ut.ac.ir/article_62210_c4f5cca97cf4ea25be4b2a6cb19cb261.pdf
2017-06-22T11:23:20
2020-08-08T11:23:20
161
174
10.22059/jieng.2017.62210
Imperialist competitive algorithm
Optimization Algorithm
Resource constrained project scheduling problem
Iman
Panahi
iman.panahi.c@gmail.com
true
1
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
AUTHOR
Nasim
Nahavandi
n_nahavandi@modares.ac.ir
true
2
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
1. Blazewicz, J., Lenstra, J. and Rinnoy Kan, A. H. (1983). “Scheduling subject to resource classification and complexity constraint.”, Discret. Appl. Math., Vol. 5, No. 1, PP. 11–24.
1
2. Hartmann, S. (1997). Scheduling medical research experiments: an application of project scheduling methods, Technical Report, University Kiel, Germany.
2
3. Alba, E. and Francisco Chicano, J. (2007). “Software project management with Gas”, Information Sciences, Vol. 177, No. 11, PP. 2380–2401.
3
4. Dodin, B., Elimam, A. A. and Rolland, E. (1998). “Tabu search in audit scheduling.”, European Journal of Operational Research, Vol. 106, No. 2–3, PP. 373–392.
4
5. Sprecher, A. (1994). “Special cases”, In Resource-constrained project scheduling: Exact methods for the multi-mode case,1th Ed,PP. 10–18, Springer, Berlin, Germany.
5
6. Demeulemeester, E. L. and Herroelen, W. S. (2002). The Resource-Constrained Project Scheduling Problem, In Project Scheduling: A Research Handbook, 1th Ed, PP. 203–342, Springer, Berlin, Germany.
6
7. Hartmann, S. (1998). “A competitive genetic algorithm for resource-constrained project scheduling”, Naval Research Logistics, Vol. 45, No. 6, PP. 733–750.
7
8. Hartmann, S. (2002). “A self-adapting genetic algorithm for project scheduling under resource constraints”, Naval Research Logistics, Vol. 49, No. 5, PP. 433–448
8
9. Bouleimen, K. and Lecocq, H. (2003). “A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version”, European Journal of Operational Research, Vol. 149, No. 2, PP. 268–281.
9
10. Valls, V., Ballestín, F. and Quintanilla, S. (2008). “A hybrid genetic algorithm for the resource-constrained project scheduling problem”, European Journal of Operational Research, Vol. 185, No. 2, PP. 495–508.
10
11. Ziarati, K., Akbari, R. and Zeighami, V. (2011). “On the performance of bee algorithms for resource-constrained project scheduling problem”, Applied Soft Computing, Vol. 11, No. 4, PP. 3720–3733.
11
12. Fang, C. and Wang, L. (2012). “An effective shuffled frog-leaping algorithm for resource-constrained project scheduling problem”, Computers & Operations Research, Vol. 39, No. 5, PP. 890–901.
12
13. Fahmy, A., Hassan, T. M. and Bassioni, H. (2014). “Improving RCPSP solutions quality with Stacking Justification—Application with particle swarm optimization”, Expert Systems with Applications, Vol. 41, No. 13, PP. 5870–5881.
13
14. Zheng, X. and Wang, L. (2015). “A multi-agent optimization algorithm for resource constrained project scheduling problem”, Expert Systems with Applications, Vol. 42, No. 15–16, PP. 6039–6049.
14
15. Atashpaz-Gargari, E. and Lucas, C. (2007). “Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition”, IEEE Congress on Evolutionary Computation, PP. 4661–4667.
15
16. Hosseini, S. and Al Khaled, A. (2014). “A survey on the Imperialist Competitive Algorithm metaheuristic: Implementation in engineering domain and directions for future research”, Applied Soft Computing, Vol. 24, PP. 1078–1094.
16
17. Kolisch, R. (1996). “Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation”, European Journal of Operational Research, Vol. 90, No. 95, PP. 320–333.
17
18. Li, K. Y. and Willis, R. J. (1992). “An iterative scheduling technique for resource-constrained project scheduling”, European Journal of Operational Research, Vol. 56, No. 3, PP. 370–379.
18
19. Kolisch, R. and Sprecher, A. (1997). “PSPLIB - A project scheduling problem library”, European Journal of Operational Research, Vol. 96, No. 1, PP. 205–216.
19
20. Kolisch, R. and Drexl, A. (1996). “Adaptive search for solving hard project scheduling problems”, Naval Research Logistics, Vol. 43, No. 1, PP. 23–40.
20
21. Schirmer, A. (2000). “Case-based reasoning and improved adaptive search for project scheduling. Naval Research Logistics”, Naval Research Logistics, Vol. 47, No. 3, PP. 201–222.
21
22. Coelho, J. and Tavares, L. (2005). “Comparative analysis of metaheuristics for the resource constrained project scheduling problem”, European Journal of Operational Research, Vol. 165, PP. 375–386.
22
23. Agarwal, A., Colak, S. and Erenguc, S. (2011). “A Neurogenetic approach for the resource-constrained project scheduling problem”, Computers & Operations Research, Vol. 38, No. 1, PP. 44–50.
23
24. Kolisch, R. and Hartmann, S. (2006). “Experimental investigation of heuristics for resource-constrained project scheduling: An update”, European Journal of Operational Research, Vol. 174, No. 1, PP. 23–37.
24
ORIGINAL_ARTICLE
A Hybrid Algorithm to Solve a Bi-objective Location Routing Inventory Problem in a Supply Chain under Stochastic Demand
Nowadays, fierce competition in global markets has forced companies to improve the design and management of supply chains, and provide competitive advantages. Decision integrity is one of the main factors which highly lead to a considerable reduction of supply chain costs, and higher costumer’s satisfaction. Distribution network design is based on three major problems: location allocation, vehicle routing and inventory control. Since the effective role of reducing distribution costs in the survival of the supply chain is clear to all, in this paper, these three problems will be incorporated into an integrated model under demand uncertainty. This approach leads to the significant reduction of distribution costs, higher customer satisfaction, and also providing an efficient supply chain. Also in this study, in addition to minimizing the total cost including fixed cost of establishing depots, transportation costs and inventory costs, the customers’ satisfaction will increase by reducing their waiting time. So, a bi-objective mixed integer non-linear model is presented by using chance constrained programming, where customer demands are assumed to have a normal distribution. Then, to solve the model, a hybrid algorithm based on simulated annealing and genetic algorithm is proposed, and is evaluated on a set of instances. The computational results illustrate the algorithm efficiency to solve a wide range of problems with different sizes.
https://jieng.ut.ac.ir/article_62211_4561cb6c7e7c43d14ccb23b068367616.pdf
2017-06-22T11:23:20
2020-08-08T11:23:20
175
193
10.22059/jieng.2017.62211
Facility location
Integrated supply chain
Inventory Control
Metaheuristic algorithms
Vehicle routing
Ebrahim
Teymouri
teimoury@iust.ac.ir
true
1
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Fatemeh
Aboutorabiyan
aboutorabian@ut.ac.ir
true
2
Faculty of Engineering, University of Tehran, Iran
Faculty of Engineering, University of Tehran, Iran
Faculty of Engineering, University of Tehran, Iran
AUTHOR
Mohammad Hosein
Babaei
true
3
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Salhi, S. and Rand, G. K. (1989). “The effect of ignoring routes when locating depots”, European Journal of Operational Research, Vol. 39, No. 2, PP. 150–156.
1
Ahmadi Javid, A. and Seddighi, A. H. (2012). “A location-routing-inventory model for designing multisource distribution networks”, Engineering Optimization, Vol. 44, No. 6, PP. 637–656.
2
Teimoury, E., Modarres, M., Ghasemzadeh, F. and Fathi, M. (2010). “A queueing approach to production-inventory planning for supply chain with uncertain demands: Case study of PAKSHOO Chemicals Company”, Journal of Manufacturing Systems, Vol. 29, No. 2, PP 55–62.
3
Min, H., Jayaraman, V. and Srivastava, R. (1998). “Combined location-routing problems: A synthesis and future research directions”, European Journal of Operational Research, Vol. 108, No. 1, PP. 1–15.
4
Nagy, G. and Salhi, S. (2007). “Location-routing: Issues, models and methods”, European Journal of Operational Research, Vol. 177, No. 2, PP. 649–672.
5
Quintero‐Araujo, C. L., Caballero‐Villalobos, J. P., Juan, A. A. and Montoya‐Torres, J. R. (2016). “A biased‐randomized metaheuristic for the capacitated location routing problem”, International Transactions in Operational Research, Vol. 24, No. 5, PP. 1079-1098.
6
Baita, F., Ukovich, W., Pesenti, R. and Favaretto, D. (1998). “Dynamic routing-and-inventory problems: a review”, Transportation Research Part A: Policy and Practice, Vol. 32, No. 8, PP. 585–598.
7
Jaillet, P., Bard, J. F., Huang, L. and Dror, M. (2002). “Delivery cost approximations for inventory routing problems in a rolling horizon framework”, Transportation Science, Vol. 36, No. 3, PP. 292–300.
8
Kleywegt, A. J., Nori, V. S. and Savelsbergh, M. W. (2002). “The stochastic inventory routing problem with direct deliveries”, Transportation Science, Vol. 36, No. 1, PP. 94–118.
9
Adelman, D. (2004). “A price-directed approach to stochastic inventory/routing”, Operations Research, Vol. 52, No. 4, PP. 499–514.
10
Gaur, V. and Fisher, M. L. (2004). “A periodic inventory routing problem at a supermarket chain.” Operations Research, Vol. 52, No. 6, PP. 813–822.
11
Zhao, Q. H., Chen, S. and Zang, C. X. (2008). “Model and algorithm for inventory/routing decision in a three-echelon logistics system”, European Journal of Operational Research, Vol. 191, No. 3, PP. 623–635.
12
Yu, Y., Chen, H. and Chu, F. (2008). “A new model and hybrid approach for large scale inventory routing problems”, European Journal of Operational Research, Vol. 189, No. 3, PP. 1022–1040.
13
Day, J. M., Wright, P. D., Schoenherr, T., Venkataramanan, M. and Gaudette, K. (2009). “Improving routing and scheduling decisions at a distributor of industrial gasses”, Omega, Vol. 37, No. 1, PP. 227–237.
14
Andersson, H., Christiansen, M. and Desaulniers, G. (2016). “A new decomposition algorithm for a liquefied natural gas inventory routing problem”, International Journal of Production Research, Vol. 54, No. 2, PP. 564–578.
15
Daskin, M. S., Coullard, C. R. and Shen, Z. J. M. (2002). “An inventory-location model: Formulation, solution algorithm and computational results”, Annals of Operations Research, Vol. 110, No. 1, PP. 83–106.
16
Shen, Z. J. M., Coullard, C. and Daskin, M. S. (2003). “A joint location-inventory model”, Transportation Science, Vol. 37, No. 1, PP. 40–55.
17
Diabat, A., Richard, J. P. and Codrington, C. W. (2013). “A Lagrangian relaxation approach to simultaneous strategic and tactical planning in supply chain design”, Annals of Operations Research, Vol. 203, No. 1, PP. 55-80
18
Puga, M. S. and Tancrez, J. S. (2017). “A heuristic algorithm for solving large location–inventory problems with demand uncertainty”, European Journal of Operational Research, Vol. 259, No. 2, PP. 413–423.
19
Liu, S. C. and Lee, S. B. (2003). “A two-phase heuristic method for the multi-depot location routing problem taking inventory control decisions into consideration”, The International Journal of Advanced Manufacturing Technology, Vol. 22, No. 11, PP. 941–950.
20
Liu, S. C. and Lin, C. C. (2005). “A heuristic method for the combined location routing and inventory problem”, The International Journal of Advanced Manufacturing Technology, Vol. 26, No. 4, PP. 372–381.
21
Shen, Z. J. M. and Qi, L. (2007). “Incorporating inventory and routing costs in strategic location models”, European Journal of Operational Research, Vol. 179, No. 2, PP. 372–389.
22
Javid, A. A. and Azad, N. (2010). “Incorporating location, routing and inventory decisions in supply chain network design”, Transportation Research Part E: Logistics and Transportation Review, Vol. 46, No. 5, PP. 582–597.
23
Wang, C., Ma, Z. and Li, H. (2008, October). “Stochastic dynamic location-routing-inventory problem in closed-loop logistics system for reusing end-of-use products.” In Intelligent Computation Technology and Automation (ICICTA), 2008 International Conference (Vol. 2, PP. 691–695). IEEE.
24
Sajjadi, S. R. and Cheraghi, S. H. (2011). “Multi-products location–routing problem integrated with inventory under stochastic demand”, International Journal of Industrial and Systems Engineering, Vol. 7, No. 4, PP. 454–476.
25
Guerrero, W. J., Prodhon, C., Velasco, N. and Amaya, C. A. (2013). “Hybrid heuristic for the inventory location-routing problem with deterministic demand”, International Journal of Production Economics, Vol. 146, No. 1, PP. 359–370.
26
Tavakkoli-Moghaddam, R., Forouzanfar, F. and Ebrahimnejad, S. (2013). “Incorporating location, routing, and inventory decisions in a bi-objective supply chain design problem with risk-pooling”, Journal of Industrial Engineering International, Vol. 9, No. 1, PP. 9-19
27
Tavakkoli-Moghaddam, R. and Raziei, Z. (2016). “A New Bi-Objective Location-Routing-Inventory Problem with Fuzzy Demands”, IFAC-PapersOnLine, Vol. 49, No. 12, PP. 1116–1121.
28
Turan, B., Minner, S. and Hartl, R. F. (2017). “A VNS approach to multi-location inventory redistribution with vehicle routing”, Computers and Operations Research, Vol. 78, February 2017,PP. 526–536.
29
Hiassat, A., Diabat, A. and Rahwan, I. (2017). “A genetic algorithm approach for location-inventory-routing problem with perishable products”, Journal of Manufacturing Systems, Vol. 42, January 2017,PP. 93–103.
30
Norouzi, N., Tavakkoli, M. R., Sadegh, A. M. and Khaefi, S. (2015). “New mathematical modeling for a facilities location and vehicle routing problem solving by a hybrid imperialist competitive algorithm.” Vol. 49, No. 1, PP. 129–137.
31
Gholami, M. and Honarvar, M. (2015). “Developing a Mathematical Model for Vendor Managed Inventory Considering Deterioration and Amelioration Items in a Three-Level Supply Chain”, Journal of Industrial Engineering???, Vol. 49, No. 2, PP. 237–256.
32
Kiani, M., Seidgar, H., Mahdavi, I. and Tavakkoli, M. R. (2015), “An Efficient Genetic Algorithm for a Vehicle Routing Problem Considering the Competency of Working Teams”, Journal of Industrial Engineering, Vol. 49, No. 2, PP. 257–271.
33
Cordeau, J. F., Laporte, G., Savelsbergh, M. W. P. and Vigo, D. (2006). “Vehicle Routing”, in Handbook of Operations Research and Management Science, Transportation, C. Barnhart and G. Laporte, Editors, Elsevier: Amsterdam, PP. 367–428.
34
Megiddo, N. and Supowit, K. J. (1984). “On the complexity of some common geometric location problems”, SIAM Journal on Computing, Vol. 13, No. 1, PP. 182–196.
35
Derbel, H., Jarboui, B., Hanafi, S. and Chabchoub, H. (2012). “Genetic algorithm with iterated local search for solving a location-routing problem”, Expert Systems with Applications, Vol. 39, No. 3, PP. 2865–2871.
36
Vincent, F. Y., Lin, S. W., Lee, W. and Ting, C. J. (2010). “A simulated annealing heuristic for the capacitated location routing problem”, Computers and Industrial Engineering, Vol. 58, No. 2, PP. 288–299.
37
Ting, C. J. and Chen, C. H. (2013). “A multiple ant colony optimization algorithm for the capacitated location routing problem”, International Journal of Production Economics, Vol. 141, No. 1, PP. 34–44.
38
Barreto, S., Ferreira, C., Paixao, J. and Santos, B. S. (2007). “Using clustering analysis in a capacitated location-routing problem”, European Journal of Operational Research, Vol. 179, No. 3, PP. 968–977.
39
Prins, C., Prodhon, C. and Calvo, R. W. (2006, April). “A memetic algorithm with population management (MA| PM) for the capacitated location-routing problem.” In European Conference on Evolutionary Computation in Combinatorial Optimization (PP. 183–194). Springer Berlin Heidelberg.
40
Contardo, C., Cordeau, J. F. and Gendron, B. (2014). “A GRASP+ ILP-based metaheuristic for the capacitated location-routing problem”, Journal of Heuristics, Vol. 20, No. 1, PP. 1–38.
41
ORIGINAL_ARTICLE
A Simultaneous Location, Routing and Scheduling Model for Transporting Evacuees with Time Window and Multiple Depots
After natural disasters and unexpected events, one of the most vital actions of disaster response phase is to transport evacuees from disaster areas to safe places. In this paper, decisions of the location of shelters and routing and scheduling of relief vehicles at the same time are modeled for a two-level network including depots of vehicles, affected areas, and shelters. In the evacuation operation, the possibility of servicing to evacuees in each affected area by several vehicles, existence of multiple depots of heterogeneous vehicles and time window constraints are considered. To solve the proposed model and demonstrate its efficiency, a numerical example was solved by exact method, and it was done the sensitivity analysis on the problem main parameters. Results show that the number of shelters to locate evacuees and capacity of relief vehicles effects on total times for vehicles to get to affected areas and shelters.
https://jieng.ut.ac.ir/article_62212_e6a3ca6605f81882bc347b721371de47.pdf
2017-06-22T11:23:20
2020-08-08T11:23:20
195
206
10.22059/jieng.2017.62212
Disaster management
Location of shelters
Routing
Scheduling
Fateme
Sabouhi
sabouhi@ind.iust.ac.ir
true
1
Faculty of Engineering, Iran University of Science and Technology, Tehran, Iran
Faculty of Engineering, Iran University of Science and Technology, Tehran, Iran
Faculty of Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Ali
Bozorgi-Amiri
alibozorgi@ut.ac.ir
true
2
Faculty of Engineering, University of Tehran, Iran
Faculty of Engineering, University of Tehran, Iran
Faculty of Engineering, University of Tehran, Iran
LEAD_AUTHOR
Mahdi
Heydari
mheydari@iust.ac.ir
true
3
Faculty of Engineering, Iran University of Science and Technology, Tehran, Iran
Faculty of Engineering, Iran University of Science and Technology, Tehran, Iran
Faculty of Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
1- Ngueveu, S.U., Prins, C. and Calvo, R. W. (2010). “An effective memetic algorithm for the cumulative capacitated vehicle routing problem”, Computers & Operations Research., Vol. 37, No. 11, PP. 1877–1885.
1
2- Ribeiro, G. M. and Laporte, G. (2012). “An adaptive large neighborhood search heuristic for the cumulative capacitated vehicle routing problem.” Computers and Operations Research., Vol. 39, No. 3, PP. 728–735.
2
3- Ke, L. and Feng, Z. (2013). “A two-phase metaheuristic for the cumulative capacitated vehicle routing problem”, Computers and Operations Research., Vol. 40, No. 2, PP. 633–638.
3
4- Ozsoydan, F. B. and Sipahioglu, A. (2013). “Heuristic solution approaches for the cumulative capacitated vehicle routing problem”, Optimization., Vol. 62, No. 10, PP. 1321–1340.
4
5- Özdamar, L., Aksu, D.T. and Ergüneş, B. (2014). “Coordinating debris cleanup operations in post disaster road networks”, Socio-Economic Planning Sciences., Vol. 48, No. 4, PP. 249–262.
5
6- Wohlgemuth, S., Oloruntoba, R. and Clausen, U. (2012). “Dynamic vehicle routing with anticipation in disaster relief”, Socio-Economic Planning Sciences., Vol. 46, No. 4, PP. 261–271.
6
7- Lee, K., Lei, L., Pinedo, M. and Wang, S. (2013a). “Operations scheduling with multiple resources and transportation considerations”, International Journal of Production Research., Vol. 51, No. 23-24, PP. 7071-7090.
7
8- Lee, K., Lei, L. and Dong, H. (2013b). “A Solvable Case of Emergency Supply Chain Scheduling Problem with Multi-stage Lead Times”, Journal of Supply Chain and Operations Management., Vol. 11, No. 2, PP. 30–45.
8
9- Gan, X., Wang, Y., Kuang, J., Yu, Y. and Niu, B. (2014). “Emergency Vehicle Scheduling Problem with Time Utility in Disasters”, Mathematical Problems in Engineering, Vol. 2015, PP. 1-7.
9
10- Pramudita, A., Taniguchi, E. and Qureshi, A.G. (2014). “Location and Routing Problems of Debris Collection Operation after Disasters with Realistic Case Study”, Procedia-Social and Behavioral Sciences., Vol. 125, PP. 445–458.
10
11- Wex, F., Schryen, G., Feuerriegel, S. and Neumann, D. (2014). “Emergency response in natural disaster management: Allocation and scheduling of rescue units.” European Journal of Operational Research., Vol. 235, No. 3, PP. 697–708.
11
12- Bish, D. R. (2011). “Planning for a bus-based evacuation”, OR Spectrum., Vol. 33, No. 3, PP. 629–654.
12
13- Abdelgawad, H. and Abdulhai, B. (2011). “Large-scale evacuation using subway and bus transit: approach and application in city of Toronto”, Journal of Transportation Engineering, Vol. 138, No. 10, PP. 1215–1232.
13
14- Hamedi, M., Haghani, A. and Yang, S. (2012). “Reliable transportation of humanitarian supplies in disaster response: model and heuristic”, Procedia-Social and Behavioral Sciences., Vol. 54, PP. 1205–1219.
14
15- Rath, S. and Gutjahr, W. J. (2014). “A math-heuristic for the warehouse location–routing problem in disaster relief”, Computers and Operations Research., Vol. 42, PP. 25–39.
15
16- Gan, X., Wang, Y., Yu, Y. and Niu, B. (2013). “An emergency vehicle scheduling problem with time utility based on particle swarm optimization”, In Proceedings of the 9th international conference on Intelligent Computing Theories and Technology, PP. 614–623.
16
17- Wex, F., Schryen, G. and Neumann, D. (2012). “Operational emergency response under informational uncertainty: a fuzzy optimization model for scheduling and allocating rescue units”, In Proceedings of the 9th International ISCRAM Conference, PP. 1-10.
17
18- Talarico, L., Meisel, F. and Sorensen, K. (2015) “Ambulance routing for disaster response with patient groups”, Computers and Operations Research, Vol. 56, PP. 120–133.
18
19- Caunhye, A. M., Zhang, Y., Li, M. and Nie, X. (2016). “A location-routing model for prepositioning and distributing emergency supplies”, Transportation Research Part E: Logistics and Transportation Review, Vol. 90, PP. 161–176.
19
ORIGINAL_ARTICLE
A Hierarchical Approach for Lot-sizing and Production Scheduling of Complementary Product Packages
The lot sizing and scheduling problems for quick response to the diverse customers’ demands through the optimal utilization of resources and reducing the costs has a particular importance. In this paper, it is investigated the lot sizing and scheduling problem for complementary products. Each package consists of several complementary products with certain portions and different processing times, producing on the parallel production lines in a make-to-stock environment. To solve the problem, it is proposed a hierarchical approach with the objectives of minimizing the package costs, bound and stock, and maximizing the capacity utilization at the first level, and the aim of minimizing the completion time of complementary products at the second level. The second level model is difficult-to-solve in the large-sized instances; therefore, a rolling horizon heuristic solution algorithm is developed whose comparing performance to the exact solution as well as a proposed lower bound in different numerical examples, show the solution quality and its appropriate computation time. To validate the model, the actual data of a tile factory have been employed. Results show that the production plan, costs and times to complete the packages are improved, compared to the current process in the factory.
https://jieng.ut.ac.ir/article_62213_8c125ff52fc29f3ae6cf04621ec0c150.pdf
2017-06-22T11:23:20
2020-08-08T11:23:20
207
222
10.22059/jieng.2017.62213
Complementary product package
Heuristic algorithm
Hierarchical planning
Lot-sizing
Production Scheduling
Najmeh
Abbasi Hafshejani
najmeh.abbasi246@gmail.com
true
1
Department of Industrial Engineering, Yazd University, Iran
Department of Industrial Engineering, Yazd University, Iran
Department of Industrial Engineering, Yazd University, Iran
AUTHOR
Mohammad Mahdi
Lotfi
lotfi@yazd.ac.ir
true
2
Department of Industrial Engineering, Yazd University, Iran
Department of Industrial Engineering, Yazd University, Iran
Department of Industrial Engineering, Yazd University, Iran
LEAD_AUTHOR
Mahboobe
Honarvar
mhonarvar@yazd.ac.ir
true
3
Department of Industrial Engineering, Yazd University, Iran
Department of Industrial Engineering, Yazd University, Iran
Department of Industrial Engineering, Yazd University, Iran
AUTHOR
1. Ramezanian, R., Mehrabad, M. S. and Teimoury, E. (2013). “A mathematical model for integrating lot-sizing and scheduling problem in capacitated flow shop environments”, Intrnational Journal Of Advanced Manufacturing Technology, Vol. 66, No. 1, PP. 347–361.
1
2. Baker, K. R. (1974). Introduction to sequencing and scheduling, John Wiley and Sons, New York.
2
3. Hax, A. C. and Meal, H. C. (1973). Hierarchical integration of production planning and scheduling, Massachusetts Institute of Technology, Operations Research Center.
3
4. 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.
4
5. Sawik, T. (2006). “Hierarchical approach to production scheduling in make-to-order assembly”, International Journal of Production Research, Vol. 44, No. 4, PP. 801–830.
5
6. Omar, M. K. and Teo, S. C. (2007). “Hierarchical production planning and scheduling in a multi-product, batch process environment”, International Journal of Production Research, Vol. 45, No. 5, PP. 1029–1047.
6
7. Ebadian, M., Rabbani M., Torabi, S. A. and Jolai, F. (2009). “Hierarchical production planning and scheduling in make-to-order environments: reaching short and reliable delivery dates”, Internationl Journal of Production Research, Vol. 47, No. 20, PP. 5761–5789.
7
8. Bang, J. Y. J. and Kim, Y. D. Y. (2010). “Hierarchical production planning for semiconductor wafer fabrication based on linear programming and discrete event simulation,” IEEE Transactions on Automation Science & Engineering., Vol. 7, No. 2, PP. 326–336.
8
9. 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 lot-sizing problem with sequence-dependent setups”, Journal of Intelligent Manufacturing, Vol. 21, No. 4, PP. 501–510.
9
10. Mohammadi, M. (2010). “Integrating lot sizing, loading, and scheduling decisions in flexible flow shops”, International Journal of Advanced Manufacturing Technology, Vol. 50, No. 9, PP. 1165–1174.
10
11. Kwak, I. S. and Jeong, I. J. (2011). “A hierarchical approach for the capacitated lot-sizing and scheduling problem with a special structure of sequence-dependent setups”, International Journal of Production Research, Vol. 49, No. ?24, PP. 7425–7439.
11
12. Camargo V. C. B., Toledo, F. M. B. and Almada Lobo, B. (2012). “Three time-based scale formulations for the two-stage lot sizing and scheduling in process industries”, Journal of the Operational Research Society, Vol. ?63, No. 11, PP. 1613–1630.
12
13. Seeanner, F. and Meyr, H. (2013). “Multi-stage simultaneous lot-sizing and scheduling for flow line production”, OR Spectrum, Vol. 35, No. 1, PP. 33–73.
13
14. Sereshti, N. and Bijari, M. (2013). “Profit maximization in simultaneous lot-sizing and scheduling Problem”, Applied Mathematical Modelling, Vol. 37, No. 23, PP. 9516–9523.
14
15. Babaei, M., Mohammadi, M. and Fatemi Ghomi, S. M. T. (2014). “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, PP. 125–134.
15
16. Guimaraes, L., Klabjan, D. and Almada Lobo, B. (2014). “Modeling lot sizing and scheduling problems with sequence dependent setups”, European Journal of Operational Research, Vol. 239, No. 3, PP. 644–662.
16
17. Merce, C. and Fontan, G. (2003). “MIP-based heuristics for capacitated lot sizing problems”, International Journal of Production Economic, Vol. 41, No. 2, PP. 97–111.
17
ORIGINAL_ARTICLE
Location-Routing Problems: A Review of Concepts, Models, Methods and Research Gaps
A location-routing problem is a kind of location problem with the routing aspects. Although the basic idea of simultaneously solving the two problems started on 1961, and it has been done a lot of researches on this issue, but a comprehensive review of the problem literature in this paper, has identified research gaps, which indicates the potentiality of this problem in new studies. This paper surveys 303 related published researches. in which the large number of survey focuses on the location-routing problem in different periods, in this research, based on a comprehensive review of the problem definition, it is studied the different aspects and indexes, type of LRPs, type of objectives, categories of LRPs and solution methods with the authors’ proposed reforms. Finally, research gaps and recommendations for future studies are explained.
https://jieng.ut.ac.ir/article_62214_bcec3390ae6b4060671a16ab75e135ce.pdf
2017-06-22T11:23:20
2020-08-08T11:23:20
223
250
10.22059/jieng.2017.62214
Depot
Location-routing problem
Vehicles
Atefeh
Kahfi-Ardakani
atefehkahfi2009@gmail.com
true
1
Faculty of Industrial Engineering, Payame Noor University, Tehran, Iran
Faculty of Industrial Engineering, Payame Noor University, Tehran, Iran
Faculty of Industrial Engineering, Payame Noor University, Tehran, Iran
AUTHOR
Seyed Mohammad
Seyyed-Hosseini
seyedhosseini@iust.ac.ir
true
2
Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Reza
Tavakkoli-Moghaddam
tavakoli@ut.ac.ir
true
3
Faculty of Engineering, University of Tehran, Iran
Faculty of Engineering, University of Tehran, Iran
Faculty of Engineering, University of Tehran, Iran
LEAD_AUTHOR
1- Weber, A. (1962). Theory of the Location of Industries. C. J. Friedrich (Ed.). Chicago, Ill, USA: University of Chicago Press.
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2
3- Montoya-Torres, J.R., Franco, J.L., Isaza, S. N., Jiménez, H.F. and Herazo-Padilla, N., (2015). “A literature review on the vehicle routing problem with multiple depots”, Computers & Industrial Engineering, Vol. 79, No. 1, PP. 115–129.
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4- Lahyani, R., Khemakhem, M., Semet, F., (2015). “Rich vehicle routing problems: From a taxonomy to a definition” European Journal of Operational Research, Vol. 241, No. 1, PP. 1–14.
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35- Karaoglan, I., Altiparmak, F., Kara, I., Dengiz, B. (2011). “A branch and cut algorithm for the location-routing problem with simultaneous pickup and delivery”, European Journal of Operational Research, Vol. 211, No. 2, PP. 318–332.
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ORIGINAL_ARTICLE
Minimizing Net Present Value of Costs in Lot-Sizing in a Two-Echelon Inventory System
In this paper, a two-echelon supplier-manufacturer system has been studied through net present value (NPV) approach. The production rate is finite and constant in both echelons. Also it is assumed that there is a lead-time between the first echelon and it is getting to the second echelon. It is also assumed that the lot-size of manufacturer (second echelon) is m times larger than the supplier’s factors (first echelon), and the supplier can receive wares (the raw material) from the manufacturer in a cycle through several shipments, due to the point that shortage is not allowed. So, it is supposed that the supplier’s production rate is greater than manufacturer’s. The aim is to determine the optimal lot-size of each echelon such that the NPV of the total cost of system is minimized. After approximating the NPV objective function via Maclaurin expansion in both zero and non-zero lead-time cases, an exact algorithm is presented to find optimal solution of the presented model. Based on the results, the two approaches of average cost and NPV do not lead to a same result, and non-equivalency is occurred in this case.
https://jieng.ut.ac.ir/article_62215_c56b55a1c4c3e8bdba30d2cc8dc1b5d3.pdf
2017-06-22T11:23:20
2020-08-08T11:23:20
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10.22059/jieng.2017.62215
Economic production quantity
Lead-time
Time values of money
Two-Echelon inventory system
Yaser
Malekiyan
malekiyan@gmail.com
true
1
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
AUTHOR
Seyed Hamid
Mirmohammadi
h_mirmohammadi@cc.iut.ac.ir
true
2
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
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