ORIGINAL_ARTICLE
A hierarchical approach for designing the downstream segment for a supply chain of petroleum production systems
Strategic decisions in a supply chain are the most important decisions for petroleum production systems. These decisions, due to high costs of transportation and storing, are costly and affected by the tactical and operational decisions in uncertain situations. In this article, we focus on designing a downstream segment for a supply chain of petroleum production systems. For this purpose, we will propose a two- stage approach considering a hierarchical structure, including the mathematical optimization model for determining strategic decisions in a leader problem and a simulation model for determining tactical and operational decisions in a follower problem. In the first stage, strategic decisions are made by solving a new mathematical model to obtain the location of depots and their capacities, transportation facilities, the volume of annual production, annual flow from refinery to depots and from depots to markets regions. In the second stage, we face some queuing systems where we aim to determine the number of loading and unloading platforms and order volume. Finally, the proposed model is applied in a real-world problem. The results show the suitable performance of the proposed model.
https://www.jise.ir/article_10533_81d2e19e2f345214a6396241d0f123c4.pdf
2015-10-01
1
17
Supply chain
petroleum production systems
Simulation-based optimization
Vahid
Ghezavati
v_ghezavati@azad.ac.ir
1
School of Industrial Engineering, Islamic Azad University., South Tehran Branch
LEAD_AUTHOR
M
Ghaffarpour
m.h.ghafar@gmail.com
2
School of Industrial Engineering, Islamic Azad University., South Tehran Branch
AUTHOR
Mohammad
Salimian
mohammad.salimian@metu.edu.tr
3
Middle East Technical University (METU) Ankara, Turkey
AUTHOR
Cafaro, D. C.; Cerda, J. (2008a) Efficient Tool for the Scheduling of Multiproduct Pipelines and Terminal Operations.Ind. Eng. Chem. Res. 47, 9941–9956. (14)
1
Cafaro, d. c., &Cerda, j. (2008b).dynamic scheduling of multiproduct pipelines with multiple delivery due dates. Computers & Chemical Engineering, 32, 728–753.
2
Cafaro, d. c., &cerda, j. (2004).optimal scheduling of multiproduct pipeline systems using a non-discrete milp formulation. Computers & Chemical Engineering, 28, 2053–2068.
3
Dempster, M. A. H., Pedron, N. H., Medova, E. A., Scott, J. E., &Sembos, A. (2000). Planning logistics operations in the oil industry.Journal of Operational Research Society, 51(11), 1271–1288.
4
Escudero, L. F., Quintana, F. J., &Salmeron, J. (1999). CORO, a modeling and an algorithmic framework for oil supply, transformation and distribution optimization under uncertainty. European Journal of Operational Research, 114(3), 638–656.
5
Ghassemi-Tari, F.; Olfat, L., (2007): Development of a Set of Algorithms for the Multi-Project Scheduling Problems, Journal of Industrial and Systems Engineering, 1(1), PP. 23-36.
6
Kabirian, A., (2009), Hybrid Probabilistic Search Methods for Simulation Optimization, Journal of Industrial and Systems Engineering, 2(4), PP: 259-270.
7
Kim, Y., Yun, C., Park, S. B., Park, S., & Fan, L. T. (2008).An integrated model of supply network and production planning for multiple fuel products of multi-site refineries.Computers & Chemical Engineering, 32(11), 2529-2535.
8
MirHassani SA.(2008), An operational planning model for petroleum products logistics under uncertainty.Applied Mathematics and Computation,196:744,51.
9
MirHassani, S.A.,Noori, R.,(2011), Implications of capacity expansion under uncertainty in oil industry, Journal of Petroleum Science and Engineering, vol. 77 pp.194–199.
10
Neiro S.M.S, Pinto J.M.(2004), A general modeling framework for the operational planning of petroleum supply chains. Computers and Chemical Engineering, 28:871,96.
11
Nikandish, N.; Eshghi, K.,; and Torabi, S.A. (2009), Integrated Procurement, Production and Delivery Scheduling in a Generalized three Stage Supply Chain, Journal of Industrial and Systems Engineering, 3(3), PP. 189-212.
12
Pinto, J. M., Joly, M., & Moro, L. F. L. (2000). Planning and scheduling models for refinery operations. Computers and Chemical Engineering, 24, 2259–2276.
13
Pitty, Suresh S., Li,Wenkai, Adhitya, A, and Srinivasan,R., and Karimi, I.A., (2008), Decision support for integrated refinery supply chains, Part 1. Dynamic simulation, Computers and Chemical Engineering, 32 2767–2786.
14
Sajadijfar,M; Pourghannad, B., (2011), An Integrated Model for a Two-supplier Supply Chain with Uncertainty in the Supply, Journal of Industrial and Systems Engineering, 5(3), PP. 154-174.
15
Sear TN.(1993), Logistics planning in the downstream oil industry. Journal of Operations Research Society, 44 (1):9e17
16
RezaFarahibilavi,Designing mathematical model of production planning for shiraz refinery (Fuzzy approach), ,TarbiatModares University , Humanist Sciences branch, 2010.
17
ORIGINAL_ARTICLE
Supplier selection with multi-criteria group decision making based on interval-valued intuitionistic fuzzy sets (case study on a project-based company)
Supplier selection can be considered as a complicated multi criteria decision-making problem.In this paper the problem of supplier selection is studied in the presence of conflicting evaluations and insufficient information about the criteria and different attitudes of decision makers towards the risk. Most of fuzzy approaches used in multi-criteria group decision making (MCGDM) are non-intuitionistic, which significantly restricts their application areas. Because of considering belongingness and non-belongingness of the issue in a same time, intuitionistic fuzzy sets can better encounter with a real supplier selection problem. Also to deal with different attitudes of decision makers toward the risk, the proposed approach in this paper employs a new decision function to participate this factor in decision process. In order to integrate fuzzy information, interval-valued intuitionistic fuzzy ordered weighted aggregation (IIFOWA) is applied to aggregate the obtained preferences. The influence of unfair arguments in final results can be reduced by assigning low weights to the “optimistic” or “pessimistic” discretions. Ranking process is based on the two indices, weighted score function and weighted accuracy function. To demonstrate the efficiency of the proposed approach, it is implemented to supplier selection in a project-based company.
https://www.jise.ir/article_9073_28d41b07367fed1dc6074c2b33fe6bf2.pdf
2015-10-01
18
37
multi-criteria group decision making
Supplier selection
Interval-Valued Intuitionistic Fuzzy Set
Aggregation Operator
Risk Attitude
Decision Function
Ahmad
Makui
amakui@iust.ac.ir
1
3Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
LEAD_AUTHOR
Mohammad
Gholamian
gholamian@iust.ac.ir
2
2Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
AUTHOR
Seyed Erfan
Mohammadi
erfanmohammadi@ind.iust.ac.ir
3
3Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
AUTHOR
ORIGINAL_ARTICLE
Coordination of pricing and cooperative advertising for perishable products in a two-echelon supply chain: A bi-level programming approach
In this article the coordination of pricing and cooperative advertising decisions in one-manufacturer one-retailer decentralized supply chain with different market power for channel members is studied. The products are both perishable and substitutable. The problem is modeled as a nonlinear bi-level programming problem to consider both retailer and manufacturer decisions about prices and advertisement expenditure as well as the amount of retailer’s purchase. An Improved Particle Swarm Optimization through combining PSO by local search and diversification is proposed to solve the model. Finally, a numerical example is presented to analyze the effect of market scale. Also the role of the values of coefficient of price elasticity on decisions is studied via the numerical example. Numerical results indicate that to raise profit when the consumers are more price-sensitive, both the manufacturer and the retailer should decrease their prices and increase their advertising expenditure. In the larger market scale, the manufacturer and the retailer are even permitted to increase their prices to gain more profit.
https://www.jise.ir/article_10942_e61a13daba318bd6584d90b31865ddf4.pdf
2015-10-01
38
58
Bi-level programming
pricing
Multi-product supply chain
Substitutable and perishable products
Cooperative advertising
Market Power
Seyed Hessameddin
Zegordi
zegordi@modares.ac.ir
1
Department of Industrial Engineering, TarbiatModares University, Tehran, Iran.
LEAD_AUTHOR
Maryam
Mokhlesian
maryam.mokhlesian@modares.ac.ir
2
Department of Industrial Engineering, TarbiatModares University, Tehran, Iran.
AUTHOR
Abad, P.L.(1996). Optimal pricing and lot-sizing under conditions of perishability and partial backordering.Management Science,42(8), 1093–1104.
1
Aust, G., &Buscher, U.(2012). Vertical cooperative advertising and pricing decisions in a manufacturer-retailer supply chain: A game-theoretic approach.European Journal of Operational Research,223, 473-482.
2
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3
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4
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5
Chew, E.P., Lee, C.,& Liu, R.(2009). Joint inventory allocation and pricing decisions for perishable products.International Journal of Production Economics,120, 139–150.
6
Chew, E.P., Lee, C., Liu, R., Hong, K.S.,& Zhang, A.(2014). Optimal dynamic pricing and ordering decisions for perishable products.International Journal of Production Economics. 157, 39-48.
7
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8
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9
Crimmins, E.C.(1970).A management guide to cooperative advertising. Association of national advertisers: New York.
10
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11
Ertek, G.,& Griffin, P.M.(2002). Supplier- and buyer-driven channels in a two-stage supply chain.IIE Transactions,34(8), 691–700.
12
Gallego, G.,&van Ryzin, G.(1994). Optimal dynamic pricing of inventories with stochastic demand over finite horizons.Management Science,40(2),999–1020.
13
Gao, Y., Zhang, G., Lu, J.,& Wee, H.M.(2011). Particle swarm optimization for bi-level pricing problems in supply chains.Journal of Global Optimization,51,245-254.
14
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15
Hu, T., Guo, X., Fu, X.,&Lv, Y.(2010). A neural network approach for solving linear bilevel programming problem.Knowledge-Based Systems,23,239–242.
16
Huang, Z.,&Li, S.X.(2001). Co-op advertising models in manufacturer–retailer supply chains: A game theory approach.European Journal of Operational Research,135,527–544.
17
Huang, Z., Li, S.X.,& Mahajan, V.(2002). An analysis of manufacturer–retailer supply chain coordination in cooperative advertising.Decision Sciences,33,469–494.
18
Hutchins, M.S.(1953).Cooperative advertising. Ronald Press: New York.
19
Ingene, C.A.,& Parry, M.E.(2007). Bilateral monopoly, identical distributors, and game-theoretic analyses of distribution channels.Journal of the Academy of Marketing Science,35, 586-602.
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Jia, J.,&Hu, Q.(2011). Dynamic ordering and pricing for a perishable goods supply chain.Computers & Industrial Engineering,60,302–309.
21
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22
Kuo, R.J.,&Huang, C.C.(2009). Application of particle swarm optimization algorithm for solving bi-level linear programming problem.Computers and Mathematics with Applications,58,678-685.
23
Lan, K. M., Wen, U. P., Shih, H. S.,& Lee, E.S.(2007).A hybrid neural network approach to bilevel programming problems.Applied Mathematics Letters,20,880–884.
24
Li, G., Xiong, Z., Zhou, Y.,&Xiong, Y.(2013). Dynamic pricing for perishable products with hybriduncertainty in demand.Applied Mathematics and Computation,219(20), 10366-10377.
25
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26
Mokhlesian, M., &Zegordi, S.H. (2014). Application of multidivisional bi-level programming to coordinate pricing and inventory decisions in a multiproduct competitive supply chain. Int J AdvManufTechnol, 71, 1975–1989.
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33
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35
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38
Xie, J.,&Neyret, A.(2009). Co-op advertising and pricing models in manufacturer–retailer supply chains.Computers & Industrial Engineering,56,1375–1385.
39
Xie, J.,&Wei, J.(2009). Coordinating advertising and pricing in a manufacturer–retailer channel.European Journal of Operational Research,197,785–791.
40
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41
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42
Zhang, J., Gou, Q., Liang, L.,& Huang, Z.(2013). Supply chain coordination through cooperative advertising with reference price effect.Omega,41(2),345-353.
43
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44
ORIGINAL_ARTICLE
Maximizing service level in a β-robust job shop scheduling model
In the realm of scheduling problems, different sources of uncertainty such as probabilistic durations of jobs or stochastic breakdowns of machines can arise. Given this, one highly desirable characteristic of an intelligent schedule is to bring better punctuality with less efficiency-loss because a dominant factor in customer appreciation is punctuality. It is also one of the most intangible topics for managers when a due date is predetermined to deliver jobs. In this paper, we address the β-robust job shop scheduling problem when the processing time of each operation is a normal random variable. We intend to minimize the deviation of makespan from a common due date for all jobs which corresponds to maximizing the service level, defined as probability of the makespan not exceeding the given due date. We develop a branch-and-bound algorithm to solve the problem. Using a set of generated benchmark instances, the performance of the developed algorithm has been evaluated.
https://www.jise.ir/article_10943_ae2e96f70890edc951d33adbb2d9f07c.pdf
2015-10-01
59
71
Job shop
Stochastic scheduling
Branch-and-Bound Algorithm
Seyed-Morteza
Khatamia
sm_khatami@stu.um.ac.ir
1
Department of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
AUTHOR
Mohammad
Ranjbara
m_ranjbar@um.ac.ir
2
Department of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
LEAD_AUTHOR
Morteza
Davari
morteza.davari@econ.kuleuven.be
3
Research group for Operations Management, KU Leuven, Belgium.
AUTHOR
Adams J, Balas E and Zawack D (1988). The shifting bottleneck procedure for job-shop scheduling. Management Science 34: 391-401.
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Carlier J and Pinson E (1989). An algorithm for solving the job-shop problem. Management Science 35: 164-176.
5
Daniels RL and Carrillo JE (1997). Beta-Robust scheduling for single-machine systems with uncertain processing times. IIE Transactions 29: 977-985.
6
Golenko-Ginzburg D and Gonik A (2002). Optimal job-shop scheduling with random operations and cost objectives. International Journal of Production Economics 76: 147-154.
7
Graham RL, Lawler EL, Lenstra JK and RinnooyKan AHG (1979). Optimization and approximation in deterministic sequencing and scheduling theory: a survey. Annals of Discrete Mathematics 5: 287-326.
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9
Lin S W, Chou S Y and Ying K C (2007). A sequential exchange approach for minimizing earliness–tardiness penalties of single-machine scheduling with a common due date. European Journal of Operational Research 177: 1294-1301.
10
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11
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Pardalos P, Shylo O, Vazacopoulos A (2010). Solving job shop scheduling problems utilizing the properties of backbone and “big valley”. Computational Optimization and Applications 47: 61-76.
13
Pinedo ML (2014). Scheduling: theory, algorithms, and systems. Springer.
14
Ranjbar M and NajafianRazavi M (2012). A hybrid metaheuristic for concurrent layout and scheduling problem in a job shop environment. Advanced Manufacturing Technology 62: 1249-1260.
15
Ranjbar M, Davari M and Leus R (2012a). Two branch-and-bound algorithms for the robust parallel machine scheduling problem. Computers & Operations Research 39: 1652-1660.
16
Ranjbar M, Khalilzadeh M, Kianfar F, Etminani K (2012b). An optimal procedure for minimizing total weighted resource tardiness penalty costs in the resource-constrained project scheduling problem. Computers & Industrial Engineering 62: 264-270.
17
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Sarin SC, Nagarajan B, Liao L (2014). Stochastic scheduling: expectation-variance analysis of a schedule. Cambridge University Press.
19
Singer M and Pinedo M (1998). A computational study of branch and bound techniques for minimizing the total weighted tardiness in job shops. IIE Transactions 30: 109-118.
20
Spanos AC, Ponis ST, Tatsiopoulos IP, Christou IT and Rokou E (2014). A new hybrid parallel genetic algorithm for the job-shop scheduling problem. International Transaction in Operational Research 21: 479-499.
21
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22
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23
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24
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25
Zhang CY, Li PG, Rao YQ and Guan ZL (2008). A very fast TS/SA algorithm for the job shop scheduling problem. Computers & Operations Research 35: 282-294.
26
ORIGINAL_ARTICLE
Proposing an approach to calculate headway intervals to improve bus fleet scheduling using a data mining algorithm
The growth of AVL (Automatic Vehicle Location) systems leads to huge amount of data about different parts of bus fleet (buses, stations, passenger, etc.) which is very useful to improve bus fleet efficiency. In addition, by processing fleet and passengers’ historical data it is possible to detect passenger’s behavioral patterns in different parts of the day and to use it in order to improve fleet plans. In this research, a new approach is developed to use AVL data to investigate relationship between headway change and passenger downfall rate. For this purpose, a new method is developed that is called Intelligent Headway Selection (IHS) approach. The aim of this approach is finding similar days from passengers’ behavior perspective in the dataset and by focusing on unusual patterns of each group, headway changes effects on passenger downfall rate is being studied. In this approach, in the first step, each day is classified into specific time periods (like half of hours) and the passengers’ behavior pattern is detected for each day during the specified time periods. Then, in the K-Means algorithm, Euclidian distance measure is replaced with Dynamic Time Warping (DTW) algorithm to enable the K-Means to compare time series. The modified K-Means algorithm is used to compare days in the dataset and categorize similar days in the same clusters. Then, headway – passenger per minute plot is created for each time period to detect unusual patterns. Then, a Headway Interval Detection Procedure (HIDP) is developed to use these unusual patterns to find suitable headway values for each time period. Afterwards, these plots merged and the final headways are calculated.
https://www.jise.ir/article_11225_a7b49d61824ee6086279fa72da20fc90.pdf
2015-10-01
72
86
Headway
AVL
Dynamic Time Warping (DTW)
Data mining
K-means Algorithm
Bus scheduling
Seyyed-Mahdi
Hosseini-Motlagh
motlagh@iust.ac.ir
1
Iran University of Science and Technology
LEAD_AUTHOR
Peyman
Ahadpour
peyman_ahadpour@ind.iust.ac.ir
2
Iran University of Science and Technology
AUTHOR
Abdorrahman
Haeri
ahaeri@iust.ac.ir
3
Iran University of Science and Technology
AUTHOR
Lobel, A., 1999. Solving large-scale multiple-depot vehicle scheduling problems. In: Wilson, N.H.M. (Ed.), Computer-Aided Transit Scheduling. Lecture Notesin Economics and Mathematical Systems, vol. 471. Springer-Verlag, pp. 193–220.447–458.
1
Kwan, R.S.K., Rahin, M.A., 1999. Object oriented bus vehicle scheduling – the BOOST system. In: Wilson, N.H.M. (Ed.), Computer-Aided Transit Scheduling.Lecture Notes in Economics and Mathematical Systems, vol. 471. Springer-Verlag, pp. 177–191.
2
Mesquita, M., Paixao, J.M.P., 1999. Exact algorithms for the multi-depot vehicle scheduling problem based on multicommodity network flow typeformulations. In: Wilson, N.H.M. (Ed.), Computer-Aided Transit Scheduling Lecture Notes in Economics and Mathematical Systems, vol. 471. Springer-Verlag, pp. 221–243.
3
Banihashemi, M., Haghani, A., 2000. Optimization model for large-scale bus transit scheduling problems. Transportation Research Record 1733, 23–30.
4
Freling, R., Wagelmans, A.P.M., Paixao, J.M.P., 2001. Models and algorithms for single-depot vehicle scheduling. Transportation Science 35 (2), 165–180.
5
Haghani, A., Banihashemi, M., 2002. Heuristic approaches for solving large-scale bus transit vehicle scheduling problem with route time constraints.Transportation Research 36A, 309–333.
6
Haghani, A., Banihashemi, M., Chiang, K.H., 2003. A comparative analysis of bus transit vehicle scheduling models. Transportation Research 37B, 301–322.(Eds.), Computer-Aided Transit Scheduling. Lecture Notes in Economics and Mathematical Systems, vol. 430. Springer-Verlag, pp. 115–129.
7
Huisman, D., Freling, R., Wagelmans, A.P.M., 2004. A robust solution approach to the dynamic vehicle scheduling problem. Transportation Science 38 (4),447–458.
8
Shangyao Yan,2007, Intercity Bus Scheduling Model Incorporating Variable Market Share, Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions,Volume: 37, Issue: 6,921 – 932
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Zhu Chang-sheng,2010, the research in public transit scheduling based on the improved genetic simulated annealing algorithm, Computational Intelligence and Natural Computing Proceedings (CINC), Second International Conference on, Volume 2, 273 – 276
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Zhiwei Yang,2008, Research on Bus Scheduling Based on Artificial Immune Algorithm, Wireless Communications, Networking and Mobile Computing. WiCOM '08. 4th International Conference on, 1 - 4
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Yanhong Li, Wangtu Xu, Shiwei He,2013,Expected value model for optimizing the multiple bus headways, Applied Mathematics and Computation, Volume 219, Issue 11, Pages 5849–5861
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26
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28
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29
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30
ORIGINAL_ARTICLE
Impact of government’s policies on competition of two closed-loop and regular supply chains
With progressing technologies and new features of production, new products compete with older ones in markets. Indeed, new products initiate contest with olden ones and this process repeats in different productions lifetime several times. In this situation recycling the olden products seems to be significant for supply chains. Governments often levy special tariffs for these products as a control tool which aims to incentive production recovery. In the real world, government purposes financial incentive plans for recoverable productions and also punitive plans for unrecoverable products. This paper tries to model the competition of a closed-loop supply chain and an ordinary supply chain using a game theory approach. In next step, the effects of persuasive and punitive governmental plans are modeled. Finally optimal retail and wholesale prices of the products are found in two chains. Numerical examples including sensitivity analysis of some key parameters will compare the results between different models of this study.
https://www.jise.ir/article_11226_48649025c45908b27bfe80c520be1121.pdf
2015-10-01
87
105
game theory
competition
Closed-loop supply chain
government intervention
Ashkan
Hafezalkotob
a_hafez@azad.ac.ir
1
Industrial Engineering college, Islamic Azad university, South Tehran Branch
LEAD_AUTHOR
Tina
Hadi
tina.hadi66@gmail.com
2
Knowledge Engineering and Decision Sciences Department, Kharazmi University
AUTHOR
Bowen, F. E., Cousins, P. D., Lamming, R. C., & Farukt, A. C. (2001). The role of supply management capabilities in green supply. Production and operations management, 10(2), 174-189.
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3
Hafezalkotob, A. (2015). Competition of two green and regular supply chains under environmental protection and revenue seeking policies of government. Computers & Industrial Engineering, 82, 103-114.
4
Hafezalkotob, A., Alavi, A., & Makui, A. (2015). Government financial intervention in green and regular supply chains: Multi-level game theory approach. International Journal of Management Science and Engineering Management, (ahead-of-printd), 1-11.
5
Hjaila, K.,Puigjaner, L,. Espuna A.(2015). Scenario-Based Price Negotiations vs. Game Theory in the Optimization of Coordinated Supply Chains. Computer Aided Chemical Engineering, Volume 37, 2015, Pages 1859-1864
6
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7
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8
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9
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10
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11
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12
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19
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20
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21
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22
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23
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25
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26
ORIGINAL_ARTICLE
A data mining approach to employee turnover prediction (case study: Arak automotive parts manufacturing)
Training and adaption of employees are time and money consuming. Employees’ turnover can be predicted by their organizational and personal historical data in order to reduce probable loss of organizations. Prediction methods are highly related to human resource management to obtain patterns by historical data. This article implements knowledge discovery steps on real data of a manufacturing plant. We consider many characteristics of employees such as age, technical skills and work experience. Different data mining methods are compared based on their accuracy, calculation time and user friendliness. Furthermore the importance of data features is measured by Pearson Chi-Square test. In order to reach the desired user friendliness, a graphical user interface is designed specifically for the case study to handle knowledge discovery life cycle.
https://www.jise.ir/article_10857_380ab2c2c84e1525e1f53647b46d6879.pdf
2015-10-01
106
121
Employees’ turnover
Data mining
Human Resource Management
recruitment decision support system
Amir
Esmaieeli Sikaroudi
amir_esmaieeli@iust.ac.ir
1
Iran University of Science and Technology
LEAD_AUTHOR
Rouzbeh
Ghousi
ghousi@iust.ac.ir
2
Iran University of Science and Technology
AUTHOR
Ali
Sikaroudi
aesmaieelisikaroudi@fsu.edu
3
Industrial & Manufacturing Engineering Department Florida State University,USA
AUTHOR
Beach, L. R. (1990). Image Theory: Decision Making in Personal and Organizational Contexts. European Journal of Operational Research, 47(2), xv, 254 p. doi: 10.1016/0377-2217(90)90287-l
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