Content area
The agricultural equipment market has the characteristics of rapid demand changes and high demand for machine models, etc., so multi-variety, small-batch, and customized production methods have become the mainstream of agricultural machinery enterprises. The flexible job shop scheduling problem (FJSP) in the context of agricultural machinery and equipment manufacturing is addressed, which involves multiple resources including machines, workers, and automated guided vehicles (AGVs). The aim is to optimize two objectives: makespan and the maximum continuous working hours of all workers. To tackle this complex problem, a Multi-Objective Discrete Grey Wolf Optimization (MODGWO) algorithm is proposed. The MODGWO algorithm integrates a hybrid initialization strategy and a multi-neighborhood local search to effectively balance the exploration and exploitation capabilities. An encoding/decoding method and a method for initializing a mixed population are introduced, which includes an operation sequence vector, machine selection vector, worker selection vector, and AGV selection vector. The solution-updating mechanism is also designed to be discrete. The performance of the MODGWO algorithm is evaluated through comprehensive experiments using an extended version of the classic Brandimarte test case by randomly adding worker and AGV information. The experimental results demonstrate that MODGWO achieves better performance in identifying high-quality solutions compared to other competitive algorithms, especially for medium- and large-scale cases. The proposed algorithm contributes to the research on flexible job shop scheduling under multi-resource constraints, providing a novel solution approach that comprehensively considers both workers and AGVs. The research findings have practical implications for improving production efficiency and balancing multiple objectives in agricultural machinery and equipment manufacturing enterprises.
Details
Mathematical models;
Algorithms;
Workers;
Workshops;
Agricultural technology;
Optimization;
Productivity;
Job shops;
Agriculture;
Multiple objective analysis;
Manufacturing;
Automated guided vehicles;
Tax regulations;
Energy consumption;
Climate change;
Farmworkers;
Efficiency;
Scheduling;
Agricultural equipment;
Genetic algorithms;
Manufacturing industry;
Flexibility;
Resource scheduling;
Encoding-Decoding;
Methods;
Working hours;
Constraints;
Farm machinery;
Production methods;
Job shop scheduling
; Yu, Zipeng 2 ; Niu, Renzhong 3
; Zhao, Qilong 2 ; Li, Zhigang 2 1 College of Information Science and Technology, Shihezi University, Shihezi 832000, China;
2 College of Information Science and Technology, Shihezi University, Shihezi 832000, China;
3 College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China;