Research on Green Reentrant Hybrid Flow Shop Scheduling Problem Based on Improved Moth-Flame Optimization Algorithm

Abstract

To address the green reentrant hybrid flow shop-scheduling problem (GRHFSP), we performed lifecycle assessments for evaluating the comprehensive impact of resources and the environment. An optimization model was established to minimize the maximum completion time and reduce the comprehensive impact of resources and the environment, and an improved moth-flame optimization algorithm was developed. A coding scheme based on the number of reentry layers, stations, and machines was designed, and a hybrid population initialization strategy was developed, according to a situation wherein the same types of nonequivalent parallel machines were used. Two different update strategies were designed for updating the coding methods of processes and machines. The population evolution strategy was adopted to improve the local search ability of the proposed algorithm and the quality of the solution. Through simulation experiments based on different datasets, the effectiveness of the proposed algorithm was verified, and comparative evaluations revealed that the proposed algorithm could solve the GRHFSP more effectively than other well-known algorithms.

Evolutionary Process for Engineering Optimization in Manufacturing Applications: Fine Brushworks of Single-Objective to Multi-Objective/Many-Objective Optimization

“…Relatively less optimization constraints in the nature of the PFSP will simplify the problems (more complicated cases, such as job shop) or discrete problems in real world applications, whose solution spaces may be broken up by temporal type restrictions on tasks and/or jobs or resources and/or machines, making the traversal of the solution spaces quite confounded. Thus, the PFSP, which also has the building blocks that are straightforward, could serve as a nice starting point/platform [64,65] for the investigation into the scheduling applications or discrete problems, which may be extended for reentrant scheduling problems.…”

Hybrid Flow-Shop Scheduling Problems with Missing and Re-Entrant Operations Considering Process Scheduling and Production of Energy Consumption

“…Eskandadi et al [14] generalized heuristics based on several basic scheduling rules and proposed a variable neighborhood search (VNS) to solve the problem of sequence-dependent setup times and unrelated parallel machines. Xu et al [15] developed an improved moth-flame optimization algorithm to solve the green re-entrant hybrid flow shop scheduling problem. Qin et al [16] proposed a rescheduling-based ant colony algorithm to solve the HFSP with uncertain processing time and introduced the concept of due date deviation to design a rolling-horizon-driven strategy that compressed the path of ant movement and reduced the cycle time in which to find a new solution.…”