Simulated annealing (SA) has been an effective means that can address difficulties related to optimisation problems. SA is 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 by SA. During the course of optimising for the APP problem, it uncovered that the capability of SA was inadequate and its performance was substandard, particularly for a sizable controlled APP problem 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 modified SA (MSA) is proposed. We attempt to augment the search space by starting with + 1 solutions, instead of one solution. To analyse and investigate the operations of the MSA with the standard SA and harmony search (HS), the real performance of an industrial company and simulation are made for evaluation. The results show that, compared to SA and HS, MSA offers better quality solutions with regard to convergence and accuracy.

Aggregate production planning (APP) is one of the most significant and complicated problems in production planning and aim to set overall production levels for each product category to meet fluctuating or uncertain demand in future and to set decision concerning hiring, firing, overtime, subcontract, carrying inventory level. In this paper, we present a simulated annealing (SA) for multi-objective linear programming to solve APP. SA is considered to be a good tool for imprecise optimization problems. The proposed model minimizes total production and workforce costs. In this study, the proposed SA is compared with particle swarm optimization (PSO). The results show that the proposed SA is effective in reducing total production costs and requires minimal time.