Proceedings of the
The Nineteenth International Conference on Computational Intelligence and Security (CIS 2023)
December 1 – 4, 2023, Haikou, China
Solving the Multi-Objective Flexible Job Shop Scheduling Problem with Improved NSGA-II
1Department of Computer Science, Shantou University, China.
2Business School, Shantou University, China.
ABSTRACT
Job scheduling involves arranging the production sequence and resource allocation for different tasks to ensure that all tasks are completed within specified timeframes. This paper addresses job scheduling problems and accomplishes the following tasks: (1) Designing improved strategies for the second-generation non-dominated sorting genetic algorithms (NSGA-II), including the process order crossover (POX) strategy, on-demand layering strategy, adaptive selection strategy, and early termination strategy. It provides a systematic introduction to the implementation process of solving the multi-objective flexible job shop scheduling problem (MOFJSP) using the enhanced NSGA-II. Numerical experiments indicate that, in comparison to traditional algorithms, the improved algorithm presented in this paper exhibits higher efficiency.
Keywords: Operation scheduling, Flexible job-shop scheduling problem, NSGA-II, Multi-objective optimization.
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1Department of Computer Science, Shantou University, China.
2Business School, Shantou University, China.
ABSTRACT
Job scheduling involves arranging the production sequence and resource allocation for different tasks to ensure that all tasks are completed within specified timeframes. This paper addresses job scheduling problems and accomplishes the following tasks: (1) Designing improved strategies for the second-generation non-dominated sorting genetic algorithms (NSGA-II), including the process order crossover (POX) strategy, on-demand layering strategy, adaptive selection strategy, and early termination strategy. It provides a systematic introduction to the implementation process of solving the multi-objective flexible job shop scheduling problem (MOFJSP) using the enhanced NSGA-II. Numerical experiments indicate that, in comparison to traditional algorithms, the improved algorithm presented in this paper exhibits higher efficiency.
Keywords: Operation scheduling, Flexible job-shop scheduling problem, NSGA-II, Multi-objective optimization.
Download PDF