eng
University of Tehran
Advances in Industrial Engineering
2423-6896
2423-6888
2012-03-20
46
1
1
13
10.22059/jieng.2012.29616
29616
A Quantitative Outsourcing Model Considering Dependency among Components
A. Ahmadi Dastjerdi
dastjerdi@mailinator.com
1
ali shahandeh nookabadi
ali-nook@cc.iut.ac.ir
2
Today, changes in the competitive environment have led to creating organizations with a flexible structure and extensive supply networks, so that they are able to make the necessary changes coordinated with their environment. In this regard, organizations are trying to concentrate their efforts on their competing activities and outsource the other activities to other companies. Therefore, the decisions considering what activities are necessary to be done within the company and what activities to be outsourced and also which suppliers to be selected are important for organizations. This paper proposes a combination model of analytical hierarchy process and binary integer programming to respond to these questions.
Since previous researches considered the issue of the hidden cost rarely, this study uses the concept of well-known loss function for the hidden cost of dependencies between components. There are also various criteria with different scales to be considered in evaluating suppliers, Taguchi loss function method is applied to measure those criteria in the form of losses. The losses are taken into account in the proposed mathematical model. Finally, the proposed model was implemented in a case study and its results were analyzed.
https://jieng.ut.ac.ir/article_29616_28d98fc91de227b7a881ef491690d046.pdf
Binary integer programming
Dependency between parts
Hidden cost
Hierarchical Analysis
Outsourcing
Process analysis
Taguchi loss function
eng
University of Tehran
Advances in Industrial Engineering
2423-6896
2423-6888
2012-03-20
46
1
15
26
10.22059/jieng.2012.29617
29617
Genetic Algorithm and Hybrid Method to Minimize Total Distribution Cost in Multi-level Supply Chain
Mohammad Jafar Tarokh
mjtarokh@kntu.ac.ir
1
A. Naseri
naseri@mailinator.com
2
In this paper, the distribution network for multi-level supply chain has been studied. Products produced in factories are sent to customers through warehouses and distribution centers based on specific demands. Warehouses as holding inventory facilities are located close to factories and the distribution centers are placed in the most accessible locations for services near customers. Each item is sent from factories to customers through warehouses and distribution centers. Therefore, a model was designed to minimize the total distribution costs in a multi-level supply chain network. The main goal of this paper is offering a model to determine a replenishment program, to determine the values of inventory distributed to reduce the cost of lost sales, and also to determine delivery routes to reduce transport costs and determine the values stored to reduce the holding costs. A mixed integer programming for the suggested model is formulated. Therefore, the objective function of this model is to minimize the total costs of distribution network including holding cost, lost sale cost, replenishment cost and transportation costs. The model shows that the problem is Np-Hard and thus cannot be solved by LINGO for large size problems. Hence, two Meta-heuristics methods for solving the model have been used. In the first part, we have used the genetic algorithm that according to the specification of the suggested model was programmed to earn high quality solution in short run time. Secondly we have used a hybrid algorithm that simultaneously takes advantage of genetic and simulation annealing algorithms. For the hybrid algorithm, the initial solution was earned through the implementation of genetic algorithms and then this solution was improved using simulated annealing algorithm. Computational results indicate the superiority of the hybrid algorithm for small and medium size problems but for larger problems it is recommended to use the genetic algorithm alone.
https://jieng.ut.ac.ir/article_29617_9c24a1c5775183bb4ef6028b25396b56.pdf
Facility capacity
Hybrid Genetic–Simulated annealing algorithm
Inventory - distribution models
Lost sale cost
Supply Chain Management
eng
University of Tehran
Advances in Industrial Engineering
2423-6896
2423-6888
2012-03-20
46
1
27
38
10.22059/jieng.2012.29618
29618
The Role of Opinion Leaders in Marketing Based On Social Networks: A Literature Review and Analysis (Technical note)
N. Jafari Momtaz
momtaz@mailinator.com
1
Abdollah Aghaie
aaghaie@kntu.ac.ir
2
In this paper, considering the importance of opinion leadership and use of social networks for marketing, over 90 articles are studied and a comprehensive review of different studies in this area has been done. Studies show that different titles such as opinion leader, influential people, market maven and key player are used to refer to influential groups in social networks. Notwithstanding the similarity of these titles, in concept and identifying factors, a comprehensive classification for introducing such people’s properties is not done before. Hence, in this paper considering all the properties presented for the opinion leaders, influential people, market mavens and key players, three general categories including structural, relational and personal characteristics are presented. Furthermore, considering that in the literature, many methods have been introduced for the identification and selection of opinion leaders in social networks, in this article based on the studied opinion leader identification methods, appropriate parameters, including the input, identification and selection, and output are extracted in a comprehensive chart to accurately assess these methods. Comprehensive study, classifications and analysis of these studies are the main achievements of this article.
https://jieng.ut.ac.ir/article_29618_1e56074f0dca517becdc821f2c6168aa.pdf
Marketing
Opinion leader
Social network
Social network analysis
Word-of-Mouth
eng
University of Tehran
Advances in Industrial Engineering
2423-6896
2423-6888
2012-03-20
46
1
39
51
10.22059/jieng.2012.29619
29619
Waiting Time Improvement in Human Queues by Using First-best Customer Priority Rule Algorithm
Abbas Dideban
adideban@semnan.ac.ir
1
M. Kiani
kiani@mailinator.com
2
In this paper we introduced a new priority rule algorithm in human queues. The suggested method is applicable in human lines with different demands (serving times) for different customers. In this method, the waiting time for each customer is related to the volume of demand. A common example of such a system is a bakery line. The usual priority rule algorithm in this kind of queue is First-In-First-Out where its waiting time is not optimal. In this new priority rule algorithm, the login time is saved for each customer and for choosing the customer for the service at each turn, the ratio of waiting time to the service time is calculated for each customer, and the customer with the highest value of this ratio is selected for the service. A mathematical model was extracted to simulate the algorithm and the model indicated that waiting time could be optimized up to 25%. In addition, in our case study the waiting time was reduced by 20 % after the implementation of the algorithm. Another advantage of our approach is the prediction of waiting time by an intuitive method. Moreover, the volume of demand is regulated automatically by using this algorithm. This algorithm was developed in a queue ordering system and the results are presented. The result proved the advantages of this algorithm. The customer satisfaction was also measured that showed improvements.
https://jieng.ut.ac.ir/article_29619_98e88daa46a9d188c02feb71aecffda9.pdf
optimization
Queue
Queue ordering
waiting time
eng
University of Tehran
Advances in Industrial Engineering
2423-6896
2423-6888
2012-03-20
46
1
53
62
10.22059/jieng.2012.29620
29620
Presenting a New Model for Inventory Control of Multi-item Economic Production Quantity (EPQ) with Fuzzy Random Demand
A. Kazemi
abkaazemi@qiau.ac.ir
1
M. R. Malekian
malekian@mailinator.com
2
K. Sarrafha
sarrafha@mailinator.com
3
Inventory Control Problem is one of the problems in decision making and management that contains non-crisp parameters in real world. Economic Production Quantity (EPQ) model in fuzzy environment has been studied so far by many researchers. One of the main assumptions in all previous researches was neglecting the inventory shortage. In this research, a new multi-item EPQ model with fuzzy random demand allowing shortage and space constraint has been studied. In this model, the quantity of demand is employed using a trapezoidal fuzzy random number and the inventory shortage is considered as backorder The production and consumption are continuous throughout the period and the objective function is to minimize the total annual inventory costs. This is a non-linear objective function with a single constraint. To solve the model, the Lagrangian Relaxation method is used and using this method the space constraint is added to the objective function by a constant factor and the constraint is removed. To defuzzify the results the center of gravity method is used and finally to validate the model, a large numerical example (100 items) is provided.
https://jieng.ut.ac.ir/article_29620_cd854be047d6789942589b695f60ea3f.pdf
Backorder
EPQ
Fuzzy random demand
eng
University of Tehran
Advances in Industrial Engineering
2423-6896
2423-6888
2012-03-20
46
1
63
75
10.22059/jieng.2012.29621
29621
An Energy Supply Model of Iran Aiming to Reduce Greenhouse Gases
Aliyeh Kazemi
aliyehkazemi@ut.ac.ir
1
M.R. Mehregan
mehregan@mailinator.com
2
H. Shakouri. G
shakouri@mailinator.com
3
This research presents a mathematical model for the optimal allocation of oil and gas to different sectors in Iran using operations research techniques. Sectors are residential, commercial, transportation, industries, agriculture, exports, injection to oil reservoirs and power plants as a secondary energy producer. Optimal allocation of energy resources to end-users from 2011 to 2021 has been done using a linear programming model aiming to reduce greenhouse gases. Actual data of Iran from 1967 to 2008 are used to forecast the energy demand of different sectors and to illustrate capability of the approach in this regard. The results provide scientific basic for the optimal allocation of energy resources in Iran.
https://jieng.ut.ac.ir/article_29621_4b3d011758f98de38aab6f6675329ec0.pdf
energy resources allocation
Greenhouse gases reduction
Linear programming
eng
University of Tehran
Advances in Industrial Engineering
2423-6896
2423-6888
2012-03-20
46
1
77
89
10.22059/jieng.2012.29622
29622
An Extended Particle Swarm Optimization Algorithm to Solve Integrated Model for Production Planning and Dynamic Cellular Manufacturing System
A. Kahfi ardakani
ardakani@mailinator.com
1
Farnaz Barzin pour
barzinpour@iust.ac.ir
2
R. Tavakkoli-Moghaddam
tavakkoli@mailinator.com
3
Cellular manufacturing system is one of the most important applications of group technology. Design of this system involves many structural and operational issues, in which the cell formation and production planning are two important steps. In this paper, a new mathematical model is proposed for integration of cell formation and production planning problems with the aim of minimizing the overall costs such as machine, inter-cell and intra-cell movements, reconfiguration, tool consumption inventory holding, backorders and partial subcontracting based on tooling available in dynamic condition. Since the cell formation problem is NP-hard, an extended particle swarm optimization is presented. In the proposed algorithm, we use the local best for updating the particle position and re-initialize the worst particles positions to increase diversity and prevent premature convergence. Comparison of the proposed algorithm with LINGO 8.0 software in small size problem and with the standard particle swarm optimization in large size problem shows the efficiency of the presented approach.
https://jieng.ut.ac.ir/article_29622_b5f07db1a00534e3868fcfcb1e91a361.pdf
cell formation
Dynamic cell formation
Particle Swarm Optimization Algorithm
Production Planning
eng
University of Tehran
Advances in Industrial Engineering
2423-6896
2423-6888
2012-03-20
46
1
91
104
10.22059/jieng.2012.29623
29623
A Trust-based Credit Scoring Model Using Neural Network
M. S. Mirtalaie
m.mirtalaei@gmail.com
1
M.A. Azadeh
azadeh@mailinator.com
2
M. Saberi
saberi@mailinator.com
3
B. Ashjari
ashjari@mailinator.com
4
Credit decisions are extremely vital for any type of financial institution because it can stimulate huge financial losses generated from defaulters. Credit scoring models are decision support systems that take a set of predictor variables as input and provide a score as output and creditors use these models to justify who will get credit and who will not.
Many different credit scoring models have been developed by the banks and researchers in order to solve the classification problems (i.e. distinguishing the good credit customers from the bad ones). Almost all these methods categorize the customers into two groups: the Good Credits and the Bad Credits. But regarding to the rapid growth in the number of credit applicants and also the intense competition between financial institutions, developing the models which are able to classify credit applicants into more groups (e.g. 6 or more), seems to be necessary.
The purpose of this study is to propose an ANN- based algorithm which is capable of classifying the customers into 6 levels, regarding to their trust values. Till now, almost all of the studies in credit scoring are trying to improve the accuracy rate of the proposed algorithms and this is the first time that trust’s concept is used in credit scoring domain. On the other hand, categorizing customers into more groups, will lead to make fast, easy, certain and fair credit lending decisions.
https://jieng.ut.ac.ir/article_29623_9bd2349d1f1dd12244e9ce99dad226ff.pdf
Artificial Neural Network
Credit scoring
trust
eng
University of Tehran
Advances in Industrial Engineering
2423-6896
2423-6888
2012-03-20
46
1
105
117
10.22059/jieng.2012.29624
29624
A Framework for Bullwhip Measurement in Two and Three-echelon Supply Chains with more than One Product
L. Nazari
lea.nazari@gmail.com
1
A. Aghaie
aghaie@mailinator.com
2
Supply chain management is one of the main factors of successful organization. One of the important causes of its’ ineffectiveness is “Bullwhip Effect”. Most of previous studies were about its’ effect on one-product and two-echelon chains. In this paper, the authors try to study bullwhip effect on two and three-echelon chains which includes more than one product according to some assumptions. The main aim of the paper is to provide a measure for bullwhip effect that enables the analysis and reduction of this phenomenon in three-echelon supply chains with two products and determine its’ effective factors and reach to some ways to decrease bullwhip effect in these chains. To do this, Time Series; a suitable pattern for demand modeling in a two product supply chain; was utilized and Moving Average was used for lead-time demand forecasting. To prove that the expression for bullwhip effect measurement is true, the finding were compared by real information and results of simulation that in which, estimation error can be neglected. Finally, the finding showed that by reducing lead time and optimum selection of forecasting periods, the bullwhip effect can be reduced.
https://jieng.ut.ac.ir/article_29624_d24908c6817525aebb6765e473022a3a.pdf
Bullwhip effect measurement
Supply Chain Management
Three-echelon supply chain with two products
time series