Modeling and Optimization of Aggregate Production Planning - A Genetic Algorithm Approach
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Modeling and Optimization of Aggregate Production Planning - A Genetic Algorithm Approach

Authors: B. Fahimnia, L.H.S. Luong, R. M. Marian

Abstract:

The Aggregate Production Plan (APP) is a schedule of the organization-s overall operations over a planning horizon to satisfy demand while minimizing costs. It is the baseline for any further planning and formulating the master production scheduling, resources, capacity and raw material planning. This paper presents a methodology to model the Aggregate Production Planning problem, which is combinatorial in nature, when optimized with Genetic Algorithms. This is done considering a multitude of constraints of contradictory nature and the optimization criterion – overall cost, made up of costs with production, work force, inventory, and subcontracting. A case study of substantial size, used to develop the model, is presented, along with the genetic operators.

Keywords: Aggregate Production Planning, Costs, and Optimization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333780

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References:


[1] Meredith, J. R. & Shafer, S. M. "Operations Management for Mbas", John Wiley & Sons Inc., New York, 2001.
[2] Tempelmeier, H. & Kuhn, H. "Flexible Manufacturing Systems: Decision Support for Design and Operation", Wiley, New York, 1993.
[3] Masud, A. S. M. & Hwang, C. L., "An Aggregate Production Planning Model and Application of Three Multiple Objective Decision Methods", International Journal Of Production Research, 18, 741 - 752, 1980.
[4] Y. F. Hung, & Y. C. Hu, "Solving Mixed Integer Programming Production Planning Problems With Setups By Shadow Price Information", Computer Operations Research, 25, 1027-1042, 1998.
[5] A. Baykasoglu, "Aggregate Production Planning Using the Multiple- Objective Tabu Search", Int J Prod Res, 39, 3685-3702, 2001.
[6] Wang, D. & Fang, S. C. "A Genetics-based Approach for Aggregated Production Planning in a Fuzzy Environment". Ieee Transactions On Systems, Man, And Cybernetics, Part A: Systems & Humans, 27(5), 1997.
[7] Wang, R. C. & Liang, T. F. "Application of Fuzzy Multi-Objective Linear Programming to Aggregate Production Planning", Pergamon Press Inc, 2004.
[8] Leung, S. C. H., Wu, Y. & Lai, K. K. "A Stochastic Programming Approach for Multi-Site Aggregate Production Planning". Journal of the Operational Research Society, 57, 123 - 132, 2005.
[9] Simchi-Levi, D., Kaminsky, P. & Simchi-Levi, E. "Designing and Managing the Supply Chain: Concepts, Strategies and Case Studies", Mcgraw-Hill Publishers, New York, 2003.
[10] M. Gen, & R. Cheng, "Genetic Algorithms and Engineering Optimization", Wiley, New York, 2000.
[11] Marian, R. M. "Optimization of Assembly Sequences Using Genetic Algorithms", Advanced Manufacturing and Mechanical Engineering, Adelaide, Australia, University Of South Australia, 2003.
[12] Marian, R. M., Luong, L. H. S. & Akararungruangkul, R. "Optimization of Distribution Networks Using Genetic Algorithms", Part 2, The Genetic Algorithm and Genetic Operators, International Journal of Manufacturing and Technology Management, Accepted, In Press, 2006.