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An improved ant colony optimization for solving the flexible job shop scheduling problem with multiple time
Shaofeng Yan1, Guohui Zhang1, Jinghe Sun2
1College of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China.
Mathematical Biosciences and Engineering : MBE
|May 10, 2023
Summary
This study introduces an improved ant colony optimization for the many-objective flexible job shop scheduling problem (MaOFJSP) with complex time constraints. The developed algorithm effectively solves MaOFJSP instances, optimizing multiple objectives simultaneously.
Area of Science:
- Operations Research
- Combinatorial Optimization
- Production Management
Background:
- The flexible job shop scheduling problem (FJSP) is a complex optimization challenge with significant implications for production management.
- Existing research often simplifies FJSP by not fully addressing multiple objectives and various time constraints.
Purpose of the Study:
- To address the many-objective flexible job shop scheduling problem (MaOFJSP) incorporating setup, transportation, and delivery time constraints.
- To develop an effective optimization algorithm for minimizing multiple objectives including maximum completion time, total workload, critical machine workload, and earliness/tardiness penalties.
Main Methods:
- An improved ant colony optimization (ACO) algorithm is proposed, featuring a distributed coding approach tailored to MaOFJSP characteristics.
- Novel initialization methods, iterative machine assignment updates, and neighborhood search enhance solution quality and diversity.
- Entropy weight method and non-dominated sorting are employed for filtering, complemented by mutation and closeness operations to further improve solution diversity.
Main Results:
- The proposed algorithm was evaluated on 28 benchmark instances, demonstrating its capability to handle MaOFJSP.
- Experimental results confirm the algorithm's effectiveness in solving the complex MaOFJSP with multiple time constraints and objectives.
Conclusions:
- The developed improved ant colony optimization provides an effective approach for solving the many-objective flexible job shop scheduling problem.
- The algorithm's ability to manage multiple objectives and time constraints offers practical benefits for production management and scheduling optimization.
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