Simulation of the performance of production strategies for the ELSP under different scenarios of complexity depending on the number of items and utilization
Submitted: 2014-06-29
|Accepted: 2014-12-21
|Published: 2014-12-23
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Keywords:
ELSP, Complexity, Uncertainty, Simulation, Scheduling
Supporting agencies:
Abstract:
This study analyzes the behavior of various production strategies for the ELSP, depending on the context of complexity and uncertainty in which they are used. We analyzed the performance of various heuristics designed for the classical ELSP problem.We evaluated total cost in different scenarios with respect to the number of items and the utilization level. The simulation results show a direct relationship between the performance of the strategies, and therefore their suitability, and the production environment. Furthermore, we have proven that the variation in performance does not follow the same pattern for each strategy, which reinforces the significance of this study in the process of designing a production strategy for the ELSP.
References:
Allahverdi, A.; Ng, C. T.; Cheng, T. C. E.; Kovalyov, M. Y. (2008). A survey of scheduling problems with setup times or costs. European Journal of Operational Research, Vol. 187, No. 3, pp. 985-1032. http://dx.doi.org/10.1016/j.ejor.2006.06.060
Bomberger, E. E. (1966). A dynamic programming approach to a lot size scheduling problem. Management Science, Vol. 12, No. 11, p. 778. http://dx.doi.org/10.1287/mnsc.12.11.778
Brander, P.; Forsberg, R. (2006). Determination of safety stocks for cyclic schedules with stochastic demands. International Journal of Production Economics, Vol. 104, No. 2, pp. 271-295. http://dx.doi.org/10.1016/j.ijpe.2004.11.009
Brander, P.; Leven, E.; Segerstedt, A. (2005). Lot sizes in a capacity constrained facility - a simulation study of stationary stochastic demand. International Journal of Production Economics, Vol. 93-94, pp. 375-386. http://dx.doi.org/10.1016/j.ijpe.2004.06.034
Chan, H. K.; Chung, S. H.; Lim, M. K. (2013). Recent research trend of economic-lot scheduling problems. Journal of Manufacturing Technology Management, Vol. 24, No. 3, pp. 465-482. http://dx.doi.org/10.1108/17410381311318936
Frizelle, G. D. M. (1996). Getting the measure of complexity. Manufacturing Engineer, Vol. 75, No. 6, pp. 268-270. http://dx.doi.org/10.1049/me:19960606
Hsu, W. L. (1983). On the General Feasibility Test of Scheduling Lot Sizes for Several Products on One Machine. Management Science, Vol. 29, No. 1, pp. 93-105. http://dx.doi.org/10.1287/mnsc.29.1.93
Magee, J. F.; Boodman, D. M. (1967). Production Planning and inventory control, Second ed. Mcgraw Hill
Soman, C. A.; Pieter van Donk, D.; Gaalman, G. (2006). Comparison of dynamic scheduling policies for hybrid make-to-order and make-to-stock production systems with stochastic demand. International Journal of Production Economics, Vol. 104, No. 2, pp. 441-453. http://dx.doi.org/10.1016/j.ijpe.2004.08.002
Sox, C. R.; Jackson, P. L.; Bowman, A.; Muckstadt, J. A. (1999). A review of the stochastic lot scheduling problem. International Journal of Production Economics, Vol. 62, No. 3, pp. 181-200. http://dx.doi.org/10.1016/S0925-5273(98)00247-3
Vaughan, T. S. (2007). Cyclical schedules vs. dynamic sequencing: Replenishment dynamics and inventory efficiency. International Journal of Production Economics, Vol. 107, No. 2, pp. 518-527. http://dx.doi.org/10.1016/j.ijpe.2006.10.010
Vergin, R. C.; Lee, T. N. (1978). Scheduling Rules for Multiple Product Single Machine System with Stochastic Demand. Infor, Vol. 16, No. 1, pp. 64-73.
Winands, E. M. M.; Adan, I. J. B. F.; van Houtum, G. J. (2011). The stochastic economic lot scheduling problem: A survey. European Journal of Operational Research, Vol. 210, No. 1, pp. 1-9. http://dx.doi.org/10.1016/j.ejor.2010.06.011
Yao, M. J.; Huang, J. X. (2005). Solving the economic lot scheduling problem with deteriorating items using genetic algorithms. Journal of Food Engineering, Vol. 70, No. 3, pp. 309-322. http://dx.doi.org/10.1016/j.jfoodeng.2004.05.077
