Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem

Abstract

The integrated process planning and scheduling (IPPS) problem is of critical importance in achieving desirable performance for complex manufacturing systems. The IPPS problem is often categorized into two types, i.e., Type-I and Type-II, depending on how the process plan is represented. In recent years, several approaches have been proposed to solve the IPPS problem in the literature. However, due to the complexity of the problem, optimal solutions of some benchmark datasets still cannot be obtained in a reasonable time, and few of them can be used to simultaneously address both types of IPPS problem. To this end, this study constructs a constraint programming (CP) model considering both types of IPPS problem, and proposes two basic logic-based Benders decomposition (LBBD) algorithms: one for each type of IPPS problem. In order to ensure computational efficiency, an enhanced LBBD algorithm is designed for both types of IPPS problem with three effective enhancement strategies. The performance of proposed methods is rigorously evaluated and compared with the existing approaches in the literature based on thirteen datasets. The results show that our methods significantly outperform these approaches.