Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment
As a well-known NP-hard problem, the dynamic job shop scheduling problem has significant practical value, so this paper proposes an Improved Heuristic Kalman Algorithm to solve this problem. In Improved Heuristic Kalman Algorithm, the cellular neighbor network is introduced, together with the bounda...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/8/909 |
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| author | Hankun Zhang Borut Buchmeister Xueyan Li Robert Ojstersek |
| author_facet | Hankun Zhang Borut Buchmeister Xueyan Li Robert Ojstersek |
| author_sort | Hankun Zhang |
| collection | DOAJ |
| description | As a well-known NP-hard problem, the dynamic job shop scheduling problem has significant practical value, so this paper proposes an Improved Heuristic Kalman Algorithm to solve this problem. In Improved Heuristic Kalman Algorithm, the cellular neighbor network is introduced, together with the boundary handling function, and the best position of each individual is recorded for constructing the cellular neighbor network. The encoding method is introduced based on the relative position index so that the Improved Heuristic Kalman Algorithm can be applied to solve the dynamic job shop scheduling problem. Solving the benchmark example of dynamic job shop scheduling problem and comparing it with the original Heuristic Kalman Algorithm and Genetic Algorithm-Mixed, the results show that Improved Heuristic Kalman Algorithm is effective for solving the dynamic job shop scheduling problem. The convergence rate of the Improved Heuristic Kalman Algorithm is reduced significantly, which is beneficial to avoid the algorithm from falling into the local optimum. For all 15 benchmark instances, Improved Heuristic Kalman Algorithm and Heuristic Kalman Algorithm have obtained the best solution obtained by Genetic Algorithm-Mixed. Moreover, for 9 out of 15 benchmark instances, they achieved significantly better solutions than Genetic Algorithm-Mixed. They have better robustness and reasonable running time (less than 30 s even for large size problems), which means that they are very suitable for solving the dynamic job shop scheduling problem. According to the dynamic job shop scheduling problem applicability, the integration-communication protocol was presented, which enables the transfer and use of the Improved Heuristic Kalman Algorithm optimization results in the conventional Simio simulation environment. The results of the integration-communication protocol proved the numerical and graphical matching of the optimization results and, thus, the correctness of the data transfer, ensuring high-level usability of the decision-making method in a real-world environment. |
| first_indexed | 2024-03-10T12:10:21Z |
| format | Article |
| id | doaj.art-2cff12893fe94d7c903c4fdc68a64830 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T12:10:21Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-2cff12893fe94d7c903c4fdc68a648302023-11-21T16:12:47ZengMDPI AGMathematics2227-73902021-04-019890910.3390/math9080909Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling EnvironmentHankun Zhang0Borut Buchmeister1Xueyan Li2Robert Ojstersek3School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, ChinaFaculty of Mechanical Engineering, University of Maribor, 2000 Maribor, SloveniaSchool of Management, Beijing Union University, Beijing 100101, ChinaFaculty of Mechanical Engineering, University of Maribor, 2000 Maribor, SloveniaAs a well-known NP-hard problem, the dynamic job shop scheduling problem has significant practical value, so this paper proposes an Improved Heuristic Kalman Algorithm to solve this problem. In Improved Heuristic Kalman Algorithm, the cellular neighbor network is introduced, together with the boundary handling function, and the best position of each individual is recorded for constructing the cellular neighbor network. The encoding method is introduced based on the relative position index so that the Improved Heuristic Kalman Algorithm can be applied to solve the dynamic job shop scheduling problem. Solving the benchmark example of dynamic job shop scheduling problem and comparing it with the original Heuristic Kalman Algorithm and Genetic Algorithm-Mixed, the results show that Improved Heuristic Kalman Algorithm is effective for solving the dynamic job shop scheduling problem. The convergence rate of the Improved Heuristic Kalman Algorithm is reduced significantly, which is beneficial to avoid the algorithm from falling into the local optimum. For all 15 benchmark instances, Improved Heuristic Kalman Algorithm and Heuristic Kalman Algorithm have obtained the best solution obtained by Genetic Algorithm-Mixed. Moreover, for 9 out of 15 benchmark instances, they achieved significantly better solutions than Genetic Algorithm-Mixed. They have better robustness and reasonable running time (less than 30 s even for large size problems), which means that they are very suitable for solving the dynamic job shop scheduling problem. According to the dynamic job shop scheduling problem applicability, the integration-communication protocol was presented, which enables the transfer and use of the Improved Heuristic Kalman Algorithm optimization results in the conventional Simio simulation environment. The results of the integration-communication protocol proved the numerical and graphical matching of the optimization results and, thus, the correctness of the data transfer, ensuring high-level usability of the decision-making method in a real-world environment.https://www.mdpi.com/2227-7390/9/8/909metaheuristic algorithmImproved Heuristic Kalman Algorithmcellular neighbor networksimulation modelingdecision-makingdynamic job shop scheduling |
| spellingShingle | Hankun Zhang Borut Buchmeister Xueyan Li Robert Ojstersek Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment Mathematics metaheuristic algorithm Improved Heuristic Kalman Algorithm cellular neighbor network simulation modeling decision-making dynamic job shop scheduling |
| title | Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment |
| title_full | Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment |
| title_fullStr | Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment |
| title_full_unstemmed | Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment |
| title_short | Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment |
| title_sort | advanced metaheuristic method for decision making in a dynamic job shop scheduling environment |
| topic | metaheuristic algorithm Improved Heuristic Kalman Algorithm cellular neighbor network simulation modeling decision-making dynamic job shop scheduling |
| url | https://www.mdpi.com/2227-7390/9/8/909 |
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