A hybrid genetic algorithm for the job shop scheduling problem with practical considerations for manufacturing costs: Investigations motivated by vehicle production

This paper studies a job shop scheduling problem with two new objective functions based on the setup and synergy costs besides the traditional total weighted tardiness criterion. The background is found in the real-world situation of a commercial vehicle producer, where the reduction of manufacturing costs has become a significant concern like in many heavy industry firms. The cost-related objective functions have been modeled in a quite general way so that they can also be applied to other similar types of production. To tackle this multi-objective scheduling problem, the paper presents a Pareto-based genetic algorithm incorporating a local search module, which utilizes the neighborhood properties specifically developed for each objective function. The computational experiments on both real-world and randomly generated scheduling instances verify the effectiveness of the proposed approach. The research presented in this paper could shed some light on the modeling and heuristic solving of practical production scheduling problems.