Integrated optimization of production planning and scheduling for a kind of job-shop

Abstract

This paper addresses an integrated job-shop production planning and scheduling problem with setup time and batches. It not only considers the setup cost, work-in-process inventory, product demand, and the load of equipment, but also the detailed scheduling constraints. That is a way different from the traditional hierarchical production planning method. The hierarchical methods do not consider the detailed scheduling constraints, so it cannot guarantee to obtain a feasible production plan. Here the integrated problem is formulated as a nonlinear mixed integer program model. And in order to simultaneously optimize the production plan and the schedule, an improved hybrid genetic algorithm (HGA) is given. In the model, the detailed scheduling constraints are used to compute the accurate load of a device in order to obtain a feasible production plan. The heuristic scheduling rules such as the shortest processing time (SPT) and the longest processing time (LPT) are used to generate a better initial solution. Also, a subsection coding strategy is offered to convert the planning and scheduling solution into a chromosome. At last, a comparison is made between the hybrid algorithm and a hierarchical production planning and scheduling method, showing that the hybrid algorithm can solve the problem effectively.