A modified bottleneck-based （MB） heuristic for large-scale job-shop scheduling problems with a welldefined bottleneck is suggested, which is simpler but more tailored than the shifting bottleneck （SB） procedure. I...A modified bottleneck-based （MB） heuristic for large-scale job-shop scheduling problems with a welldefined bottleneck is suggested, which is simpler but more tailored than the shifting bottleneck （SB） procedure. In this algorithm, the bottleneck is first scheduled optimally while the non-bottleneck machines are subordinated around the solutions of the bottleneck schedule by some effective dispatching rules. Computational results indicate that the MB heuristic can achieve a better tradeoff between solution quality and computational time compared to SB procedure for medium-size problems. Furthermore, it can obtain a good solution in a short time for large-scale jobshop scheduling problems.展开更多
The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborativ...The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.展开更多
To solve job shop scheduling problem, a new approach-DNA computing is used in solving job shop scheduling problem. The approach using DNA computing to solve job shop scheduling is divided into three stands. Finally, o...To solve job shop scheduling problem, a new approach-DNA computing is used in solving job shop scheduling problem. The approach using DNA computing to solve job shop scheduling is divided into three stands. Finally, optimum solutions are obtained by sequencing A small job shop scheduling problem is solved in DNA computing, and the ＂operations＂ of the computation were performed with standard protocols, as ligation, synthesis, electrophoresis etc. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.展开更多
基金the National Natural Science Foundation of China (6027401360474002)Shanghai Development Found for Science and Technology (04DZ11008).
文摘A modified bottleneck-based （MB） heuristic for large-scale job-shop scheduling problems with a welldefined bottleneck is suggested, which is simpler but more tailored than the shifting bottleneck （SB） procedure. In this algorithm, the bottleneck is first scheduled optimally while the non-bottleneck machines are subordinated around the solutions of the bottleneck schedule by some effective dispatching rules. Computational results indicate that the MB heuristic can achieve a better tradeoff between solution quality and computational time compared to SB procedure for medium-size problems. Furthermore, it can obtain a good solution in a short time for large-scale jobshop scheduling problems.
基金supported by the National Key R&D Program of China(2018AAA0101700)the Program for HUST Academic Frontier Youth Team(2017QYTD04).
文摘The flexible job shop scheduling problem(FJSP),which is NP-hard,widely exists in many manufacturing industries.It is very hard to be solved.A multi-swarm collaborative genetic algorithm(MSCGA)based on the collaborative optimization algorithm is proposed for the FJSP.Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA.Good operators are adopted and designed to ensure this algorithm to achieve a good performance.Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA.The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.
基金This Project was supported by the National Nature Science Foundation (60274026 ,30570431) China Postdoctoral Sci-ence Foundation Natural +1 种基金Science Foundation of Educational Government of Anhui Province of China Excellent Youth Scienceand Technology Foundation of Anhui Province of China (06042088) and Doctoral Foundation of Anhui University of Scienceand Technology
文摘To solve job shop scheduling problem, a new approach-DNA computing is used in solving job shop scheduling problem. The approach using DNA computing to solve job shop scheduling is divided into three stands. Finally, optimum solutions are obtained by sequencing A small job shop scheduling problem is solved in DNA computing, and the ＂operations＂ of the computation were performed with standard protocols, as ligation, synthesis, electrophoresis etc. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.
