Just-in-time scheduling for a distributed concrete precast flow shop system

Abstract

Abstract This paper focuses on the distributed concrete precast flow shop scheduling problem to minimize total weighted earliness and tardiness. It is crucial for a precast manufacturer to schedule jobs effectively to meet shipping dates because of tight due date, limited inventory capacity and huge tardiness penalties. In order to respond quickly to customer demands, many concrete precast manufacturers have adopted the distributed-multiple-factory mode in which job assignment and scheduling have to be decided jointly. To solve this problem, through analyzing the characteristics of distributed precast production, we first develop a novel mixed integer nonlinear programming (MINLP) model and then transform it into an effective mixed integer linear programming (MILP) model by linearization techniques. Additionally, since the problem is more complex than the classical distributed flow shop scheduling problem, which is NP-hard in most cases, we propose a hybrid iterated greedy algorithm (HIG) by integrating due date related NEH-based heuristics, problem-specific knowledge, different local search methods, and mechanism of destruction and reconstruction. Subsequently, we propose a hybrid tabu search and iterated greedy (HTS_IG) in which a hybrid tabu search (HTS) is run at first and then an iterated greedy (IG) starts from the best solution obtained by HTS. Aimed at improving search efficiency, some structural properties of the schedules are explored and integrated into the local search steps of HIG and HTS_IG. We also develop a hybrid genetic algorithm and variable neighborhood search (HGA_VNS) and a two-phase heuristic method (TPHM) for comparison. Finally, extensive experiments and deep analysis are conducted on instances with different combination of problem parameters. Computational results show the effectiveness of the MILP model and the proposed algorithms. The computational analysis indicates that, on average, HTS_IG performs best among all the proposed metaheuristics. The effectivenesses of local search based on problem-specific knowledge (PSK) in HIG and HTS_IG are also verified.