An Iterated Greedy Algorithm for Distributed Hybrid Flowshop Scheduling Problem with Total Tardiness Minimization

Abstract

The trend of globalization has facilitated the development of distributed manufacturing and research on distributed scheduling problem. This paper first attempts to study the distributed hybrid flowshop scheduling problem (DHFSP) with total tardiness minimization. To solve such a complex scheduling problem, a mixed integer linear programming (MILP) model and a novel iterated greedy (IG) algorithm are presented. To minimize total tardiness, an objective-driven decoding method is proposed and a modified Nawaz-Enscore-Ham (NEH) heuristic is presented for initialization. To enhance exploitation, a variable neighborhood search based local intensification is designed, which uses several problem-specific neighborhood structures. Computational results and comparisons demonstrate the effectiveness of the designed decoding method and local intensification. Moreover, it is shown that the proposed IG is effective to solve DHFSP with total tardiness minimization.