Enhanced Simulated Annealing for Solving Aggregate Production Planning

Abstract

Simulated annealing (SA) has been an effective means that can address difficulties related to optimisation problems.SAis now a common discipline for research with several productive applications such as production planning. Due to the fact that aggregate production planning (APP) is one of the most considerable problems in production planning, in this paper, we present multiobjective linear programming model for APP and optimised bySA. During the course of optimising for the APP problem, it uncovered that the capability ofSAwas inadequate and its performance was substandard, particularly for a sizable controlledAPPproblem with many decision variables and plenty of constraints. Since this algorithm works sequentially then the current state will generate only one in next state that will make the search slower and the drawback is that the search may fall in local minimum which represents the best solution in only part of the solution space. In order to enhance its performance and alleviate the deficiencies in the problem solving, a modifiedSA(MSA) is proposed. We attempt to augment the search space by starting withN+1solutions, instead of one solution. To analyse and investigate the operations of the MSA with the standardSAand harmony search (HS), the real performance of an industrial company and simulation are made for evaluation. The results show that, compared toSAandHS,MSAoffers better quality solutions with regard to convergence and accuracy.