Constraint programing for solving four complex flexible shop scheduling problems

Abstract

In recent years, with the advent of robust solvers such as Cplex and Gurobi, constraint programing (CP) has been widely applied to a variety of scheduling problems. This paper presents CP models for formulating four scheduling problems with minimal makespan and complex constraints: the no-wait hybrid flow shop scheduling problem, the hybrid flow shop scheduling problem with sequence-dependent setup times, the flexible job shop scheduling problem with worker flexibility and the semiconductor final testing problem. The advantages of CP method in solving these four complex scheduling problems are explored. Finally, a set of benchmark instances are adopted to demonstrate the effectiveness and efficiency of the CP method. Experiment results show that the proposed CP models outperform existing algorithms; in particular, several best-known solutions of benchmark instances are improved by our CP method.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

A Novel Constraint Programming Decomposition Approach for the Total Flow Time Fixed Group Shop Scheduling Problem

“…Their VNS makes use of a probabilistic learning mechanism to self-tune a parameter that balance the generation of active or non-delay solutions. More recently, in [29] the authors proposed four CP formulations to tackle four complex flexible shop scheduling problems (i.e., the no-wait hybrid flow shop scheduling problem, the hybrid flow shop scheduling problem with sequence-dependent setup times, the flexible job shop scheduling problem with worker flexibility and the semiconductor final testing problem). Their experimental results report that the CP models outperform previously proposed solution methods.…”