Simulation-GA approach for production-delivery scheduling in supply chain

Abstract

Most companies have made significant changes for efficient supply chain management. The coordination of activities along different stages of a supply chain has received many attentions in production and operation management research area. The purpose of this paper is to generate realistic production scheduling in the supply chain. The scheduling model determines the best schedule using operation sequences and machine and strongly satisfies the due dates of customer order. The model is NP-hard in the strong sense in general. And, real system can be happened various kinds of uncertain factors such as queuing, breakdowns and repairing time in the manufacturing supply chain. To solve this problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. Such an approach has not been treated in the literature. The GA is employed in order to quickly generate feasible production and delivery schedules. But, the optimal solution in a GA procedure cannot correctly represent the stochastic behaviour of a real operation. The simulation is used to minimize the maximum completion time for the production and delivery plan with last sequence with fixed schedules from the GA model. More realistic production and delivery schedules with an optimal completion time by performing the iterative hybrid approach can be obtained. This proposed approach generates: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, (3) minimizing the makespan for each order. The results of computational experiments for a simple example of the supply chain are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production delivery scheduling in the manufacturing supply chain.