Multi-period, Multi-product, Aggregate Production Planning under demand uncertainty by considering Wastage Cost and Incentives

Aggregate production planning (APP) involves the simultaneous determination of company’s production, inventory and employment levels which fall between the broad decisions of long range planning and detailed short range planning. A mathematical model is formulated to investigate the optimal decision on each planning period. The goal is to minimize the total relevant costs considering time varying demand, unstable production capacity and work forces, inventory control, wastage reduction, and proper incentive for work force. Genetic Algorithm Optimization (GAO) approach and Big M method are used for solving a real time multi-product, multi-period aggregate production planning (APP) decision problem. The practicality of the proposed model is demonstrated through its application in solving an APP decision problem in an industrial case study. Required values of decision variables are obtained by both Big M method and genetic algorithm optimization model using TORA version 2.00, Feb. 2006 software and MATLAB R2011a software respectively. According to cost minimization objective of Aggregate production planning, genetic algorithm optimization results better than Big M method.