Multi-objective assembly permutation flow shop scheduling problem: a mathematical model and a meta-heuristic algorithm

This study is devoted to schedule a three‐stage manufacturing system including machining, assembly and batch processing stages. The system is supposed to be capable of manufacturing a variation of products. At the first stage, the need for machining raw parts causes the manufacturer to deal with a flow shop scheduling problem. In the next stage, processed parts should be assembled together in order to form desired products. It is noteworthy that several operations are not allowed to be executed simultaneously on the same machine. Second stage should be considered as a single‐assembly line or a single team of operators, and finally the manufacturing processing stage. The considered objectives are to minimize completion time of all products (makespan) and sum of the earliness and tardiness costs, simultaneously. First, the proposed scheduling problem is formulated into a mixed‐integer mathematical model, and then owing to the NP‐hardness of the concluded model a meta‐heuristic approach is applied. A hybrid algorithm is modified to create a powerful method in searching the discrete solution space of this problem by taking advantage of superiorities of both Genetic Algorithm and Particle Swarm Optimization methods. Numerical experiments are designed to evaluate the performance of the proposed algorithm.