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

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.

[1]  Tien-Fu Liang,et al.  Application of fuzzy multi-objective linear programming to aggregate production planning , 2004, Comput. Ind. Eng..

[2]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[3]  Adil Baykasoğlu MOAPPS 1.0: Aggregate production planning using the multiple-objective tabu search , 2001 .

[4]  Yi-Feng Hung,et al.  Solving mixed integer programming production planning problems with setups by shadow price information , 1998, Comput. Oper. Res..

[5]  D. Simchi-Levi Designing And Managing The Supply Chain , 2007 .

[6]  Jack R. Meredith,et al.  Operations Management for MBAs , 2002 .

[7]  C. Hwang,et al.  An aggregate production planning model and application of three multiple objective decision methods , 1980 .

[8]  Romeo M. Marian,et al.  Optimisation of distribution networks using Genetic Algorithms. Part 2 - the Genetic Algorithm and Genetic Operators , 2008, Int. J. Manuf. Technol. Manag..

[9]  Kin Keung Lai,et al.  A stochastic programming approach for multi-site aggregate production planning , 2006, J. Oper. Res. Soc..

[10]  Shu-Cherng Fang,et al.  A genetics-based approach for aggregated production planning in a fuzzy environment , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[11]  Philip M. Kaminsky,et al.  Designing and managing the supply chain : concepts, strategies, and case studies , 2007 .

[12]  Heinrich Kuhn,et al.  Flexible Manufacturing Systems: Decision Support for Design and Operation , 1993 .