A Decision Support System Based on Genetic Algorithm (Case Study: Scheduling in Supply Chain)

Document Type : Research Paper

Authors

1 Assistant Prof., Faculty of Industrial Engineering, Semnan University, Semnan, Iran

2 MSc. Student of Science in Business Administration, Faculty of Industrial Engineering, Semnan University, Semnan, Iran

Abstract

Nowadays, the application of effective and efficient decisions on complex issues require the use of decision support systems. This Paper provided a decision support system based on the genetic algorithm for production and transportation scheduling problem in a supply chain. It is assumed that there are number of orders that should be produced by suppliers and should be transported to the plant by a transportation fleet. The aim is to assign orders to the suppliers, specify the order of their production, allocate processed orders to the vehicles for transport and to arrange them in a way that minimizes the total delivery time. It has been shown that the complexity of the problem was related to Np-hard and there was no possibility of using accurate methods to solve the problem in a reasonable time. So, the genetic algorithm was used in this paper to solve the problem. By using this decision support system, a new approach to supply chain management was proposed. The analysis of the approach proposed in this study compared to the conventional approaches by the decision support system indicated the preference of our proposed approach

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Main Subjects


Agrell, P., Hatami, J. & Marbini, A. (2013). Frontier-based performance analysis models for supply chain management: State of the art and research directions. Computers & Industrial Engineering, 66 (3): 567-583.
Averbakh, I. (2010). On-line integrated production–distribution scheduling problems with capacitated deliveries. European Journal of Operational Research, 200(2): 377-384.
Averbakh, I. & Baysan, M. (2013). Approximation algorithm for the on-line multi-customer two-level supply chain scheduling problem. Operations Research Letters, 41(6): 710-714.
Bagherinezhad, J., Sadegh Amal Nik, M. (2011). Introducing a model for selecting the most appropriate third party logistics companies in Iran. 2nd international & 4rd national Logistics & Supply Chain Conference, Tehran: 22/11/2011.
Beheshtinia, M. A., (2009). The integration of timing for supply and transportation in the car manufactoring industry supply chain. Doctoral dissertation, Tarbiat Modarres University.
Bhatnagar, R., Mehta, P. & Teo, C. C. (2011). Coordination of planning and scheduling decisions in global supply chains with dual supply modes. International Journal of Production Economics, 131(2): 473-482.
Holland, J.H. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. USA: Michigan Press.
Kabra, S., Shaik, M. A. &  Rathore, A. S. (2013). Multi-period scheduling of a multi-stage multi-product bio-pharmaceutical process. Computers & Chemical Engineering, 57: 95-103.
Kabaranzadeh Ghadim, M. R. ,Rofougar Astaneh, H., (2009). Designing a Decision Support System (DSS) schema with Applying Genetic Algorithm for Survey of Resource Leveling Problem-(Vehicles), Journal of information technology management, 3(2): 69-78. (in Persian)
Lejeune, M. A. (2006). A variable neighborhood decomposition search method for supply chain management planning problems. European Journal of Operational Research, 175(2): 959-976.
Li, H. & Womer, K. (2008). Modeling the supply chain configuration problem with resource constraints. International Journal of Project Management, 26(6): 646-654.
Liu, S.C. & Chen, A.Z. (2012). Variable neighborhood search for the inventory routing and scheduling problem in a supply chain. Expert Systems with Applications, 39(4): 4149-4159.
Mahdavi Mazdeh, M., Sarhadi, M. & Hindi, Kh. S. (2008). A branch-and-bound algorithm for single-machine scheduling with batch delivery and job release times. Computers & Operations Research, 35(4): 1099-1111.
Maravelias, C. T. & Sung, C. (2009). Integration of production planning and scheduling: Overview, challenges and opportunities. Computers & Chemical Engineering, 33(12): 1919-1930.
Mehravaran, Y. & Logendran, R. (2012). Non-permutation flowshop scheduling in a supply chain with sequence-dependent setup times. International Journal of Production Economics, 135(2): 953-963.
Murdick. R.G. & Munson, J.C. (1986). MIS Concepts & Design. 2nd ed. London: Prentic– Hall.
Osman, H. & Demirli, K. (2012). Economic lot and delivery scheduling problem for multi-stage supply chains. International Journal of Production Economics, 136(2): 275-286.
Ren, J., Du, D. & Xu, D. (2013). The complexity of two supply chain scheduling problems. Information Processing Letters, 113(17): 609-612.
Rostamian Delavar, M., Hajiaghaei-Keshteli, M. & Molla-Alizadeh-Zavardehi, S. (2010). Genetic algorithms for coordinated scheduling of production and air transportation. Expert Systems with Applications, 37(12): 8255-8266.
Sawik, T. (2014). Joint supplier selection and scheduling of customer orders under disruption risks: Single vs. dual sourcing. Omega, 43: 83-95.
Selvarajah, E., & Zhang, R. (2014). Supply chain scheduling at the manufacturer to minimize inventory holding and delivery costs. International Journal of Production Economics, 147, Part A: 117-124.
Scholz-Reiter, B., Frazzon, E.M. & Makuschewitz, T. (2010). Integrating manufacturing and logistic systems along global supply chains. CIRP Journal of Manufacturing Science and Technology, 2(3): 216-223.
Shaik, M. A. & Floudas, C. A. (2007). Resource-Task-Network Framework For Short-Term Scheduling Of Batch Plants Using Unit-Specific Event-Based Continuous-Time Approach. AIChE Annual Meeting 2007.
Simchi-Levi, D. & Kaminsky, P. (2000) Designing and managing the supply chain, New York: Mc Graw Hill.
Su, C.S., Pan, J. C.H. & Hsu, T. S. (2009). A new heuristic algorithm for the machine scheduling problem with job delivery coordination. Theoretical Computer Science, 410(27-29): 2581-2591.
Tavana, M., Abtahi, A. R. & Khalili-Damghani, K. (2014). A new multi-objective multi-mode model for solving preemptive time–cost–quality trade-off project scheduling problems. Expert Systems with Applications, 41(4): 1830-1846.
Thomas, A., Venkateswaran, J., Singh, G. & Krishnamoorthy, M. (2014). A resource constrained scheduling problem with multiple independent producers and a single linking constraint: A coal supply chain example. European Journal of Operational Research, 236(3): 946-956.
Ullrich, C. A. (2013). Integrated machine scheduling and vehicle routing with time windows. European Journal of Operational Research, 227(1): 152-165.
Wang, X. and T. C. E. Cheng (2009). Production scheduling with supply and delivery considerations to minimize the makespan. European Journal of Operational Research, 194(3): 743-752.
Yao, J. & Liu, L. (2009). Optimization analysis of supply chain scheduling in mass customization. International Journal of Production Economics, 117(1): 197-211.
Yeung, W.K., Choi, T.M. & Cheng, T. C. E. (2011). Supply chain scheduling and coordination with dual delivery modes and inventory storage cost. International Journal of Production Economics, 132(2): 223-229.
Yimer, A.D. & Demirli, K. (2010). A genetic approach to two-phase optimization of dynamic supply chain scheduling. Computers & Industrial Engineering, 58(3), 411-422.
Zegordi, S. H., Kamal Abadi, I.N. & Beheshti-Nia, M.A. (2010). A novel genetic algorithm for solving production and transportation scheduling in a two-stage supply chain. Computers & Industrial Engineering, 58(3): 373-381.