Abstract:
In the context of globalization and decentralized economies, the distributed manufacturing and scheduling systems have become emerging in large enterprises. This article ...Show MoreMetadata
Abstract:
In the context of globalization and decentralized economies, the distributed manufacturing and scheduling systems have become emerging in large enterprises. This article addresses the distributed hybrid flow-shop scheduling problem (DHFSP) with heterogeneous factories to minimize makespan. Different from single-factory scheduling problem, DHFSP contains several strongly coupled sub-problems, i.e., factory assignment of jobs and the job sequencing in each hybrid flow-shop. In this article, we present a mixed integer linear programming model for the DHFSP and make a first attempt to propose a bi-population cooperative memetic algorithm (BCMA) for solving such a strongly NP-hard problem. The proposed optimization framework comprises collaborative initialization, bi-population cooperation and local intensification. To generate diverse solutions with small makespan, two knowledge-based heuristics are designed for collaborative initialization by utilizing the lower bound of the problem and the historic information of elite solutions during search process. To balance exploration and exploitation, two populations are evolved in a cooperative way. To further enhance the optimization capability, intensification search with multiple problem-specific operators is incorporated. The effect of parameter setting is investigated and extensive computational tests are carried out. The comparative results show that the BCMA is more effective than both the Math solver Gurobi and the existing iterated greedy algorithm, and the effectiveness of each specific design is also verified in solving DHFSP.
Published in: IEEE Transactions on Emerging Topics in Computational Intelligence ( Volume: 5, Issue: 6, December 2021)
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- IEEE Keywords
- Index Terms
- Hybrid Genetic Algorithm ,
- Hybrid Flow Shop ,
- Hybrid Flow Shop Scheduling ,
- Distributed Hybrid Flow Shop ,
- Distributed Hybrid Flow Shop Scheduling ,
- Lower Bound ,
- Product Distribution ,
- Local Intensity ,
- Manufacturing Systems ,
- Large Enterprises ,
- Scheduling System ,
- Cooperative Way ,
- Job Sequence ,
- Job Assignment ,
- Strongly NP-hard ,
- Processing Time ,
- Running Time ,
- Random Selection ,
- Cooperative Strategy ,
- Decoding Method ,
- Variable Neighbourhood Search ,
- Parallel Machines ,
- Values Of The Response Variables ,
- Binary Index ,
- Search Operations ,
- Maximum Completion Time ,
- Reduced Search Space ,
- Local Refinement ,
- Job Processing Times
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Hybrid Genetic Algorithm ,
- Hybrid Flow Shop ,
- Hybrid Flow Shop Scheduling ,
- Distributed Hybrid Flow Shop ,
- Distributed Hybrid Flow Shop Scheduling ,
- Lower Bound ,
- Product Distribution ,
- Local Intensity ,
- Manufacturing Systems ,
- Large Enterprises ,
- Scheduling System ,
- Cooperative Way ,
- Job Sequence ,
- Job Assignment ,
- Strongly NP-hard ,
- Processing Time ,
- Running Time ,
- Random Selection ,
- Cooperative Strategy ,
- Decoding Method ,
- Variable Neighbourhood Search ,
- Parallel Machines ,
- Values Of The Response Variables ,
- Binary Index ,
- Search Operations ,
- Maximum Completion Time ,
- Reduced Search Space ,
- Local Refinement ,
- Job Processing Times
- Author Keywords