Many-Objective Flexible Job Shop Scheduling Problem with Green Consideration

Abstract

With the increasingly customized product requirements of customers, the manufactured products have the characteristics of multi-variety and small-batch production. A high-quality production scheduling scheme can reduce energy consumption, improve production capacity and processing quality of the enterprise. The high-dimensional many-objective green flexible job shop scheduling problem (Ma-OFJSSP) urgently needs to be solved. However, the existing optimization method are difficult to effectively optimize the Ma-OFJSSP. This study proposes a many-objective flexible job shop scheduling model. An optimization method SV-MA is designed to effectively optimize the Ma-OFJSSP model. The SV-MA memetic algorithm combines an improved strength Pareto evolution method (SPEA2) and the variable neighborhood search method. To effectively distinguish the better solutions and increase the selection pressure of the non-dominated solutions, the fitness calculation method based on the shift-based density estimation strategy is adopted. The SV-MA algorithm designs the variable neighborhood strategy which combines with scheduling knowledge. Finally, in the workshop scheduling benchmarks and the machining workshop engineering case, the feasibility and effectiveness of the proposed model and SV-MA algorithm are verified by comparison with other methods. The production scheduling scheme obtained by the proposed model and SV-MA optimization algorithm can improve production efficiency and reduce energy consumption in the production process.

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

“…NSGA-II [50], MOPSO [51], SPEA2 [52], MOEA/D [53], NSGA-III [54], and INSGA-II, respectively, are used in the MATLAB 2022a environment to solve the Pareto front solutions of 6 biased constrained test functions, including ZDT1, ZDT2, ZDT3 and DTLZ1, DTLZ2, DTLZ3 [55], etc., to verify the feasibility of the algorithms. Each algorithm has been configured with a population size of 100 and a maximum number of 500 iterations.…”

“…In this section, NSGA-II [50], MOPSO [51], SPEA2 [52], MOEA/D [53], NSGA-III [54], and INSGA-II are used to solve the test functions with full constraints on the independent variables, respectively, the test functions are shown in Table 3, and all objective functions are to be minimized. The Pareto fronts of INSGA-II are shown in Figures 11 and 12.…”

Research on sustainable collaborative scheduling problem of multi-stage mixed flow shop for crankshaft components

“…Wang et al 4 studied the blocking flow shop scheduling problem to optimize its maximum completion time and energy consumption. Sang and Tan 5 designed an optimization method called SV-MA to solve the high-dimensional multi-objective green flexible job shop scheduling problem, aiming to improve the production capacity and processing quality while reducing energy consumption. Qin et al 6 investigated the green job shop scheduling problem with variable processing speeds and designed a mixed-integer linear programming model and a knowledge-based multi-objective memory algorithm (MOMA) to solve the problem.…”