In this paper, we give a mathematical model for earliness-tardiness job scheduling problem with a common due window on parallel and non-identical machines. Because the job scheduling problem discussed in the paper con...In this paper, we give a mathematical model for earliness-tardiness job scheduling problem with a common due window on parallel and non-identical machines. Because the job scheduling problem discussed in the paper contains a problem of minimizing make-span, which is NP-complete on parallel and uniform machines, a heuristic algorithm is presented to find an approximate solution for the scheduling problem after proving an important theorem. Two numerical examples illustrate that the heuristic algorithm is very useful and effective in obtaining the near-optimal solution.展开更多
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
The classical job shop scheduling problem(JSP) is the most popular machine scheduling model in practice and is known as NP-hard.The formulation of the JSP is based on the assumption that for each part type or job ther...The classical job shop scheduling problem(JSP) is the most popular machine scheduling model in practice and is known as NP-hard.The formulation of the JSP is based on the assumption that for each part type or job there is only one process plan that prescribes the sequence of operations and the machine on which each operation has to be performed.However,JSP with alternative machines for various operations is an extension of the classical JSP,which allows an operation to be processed by any machine from a given set of machines.Since this problem requires an additional decision of machine allocation during scheduling,it is much more complex than JSP.We present a domain independent genetic algorithm(GA) approach for the job shop scheduling problem with alternative machines.The GA is implemented in a spreadsheet environment.The performance of the proposed GA is analyzed by comparing with various problem instances taken from the literatures.The result shows that the proposed GA is competitive with the existing approaches.A simplified approach that would be beneficial to both practitioners and researchers is presented for solving scheduling problems with alternative machines.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Zhejiang Provincial Natural Science Foundation (No. 698069) and High-Tech. Research andDevelopment Program (No. 863-511-945-002)
文摘In this paper, we give a mathematical model for earliness-tardiness job scheduling problem with a common due window on parallel and non-identical machines. Because the job scheduling problem discussed in the paper contains a problem of minimizing make-span, which is NP-complete on parallel and uniform machines, a heuristic algorithm is presented to find an approximate solution for the scheduling problem after proving an important theorem. Two numerical examples illustrate that the heuristic algorithm is very useful and effective in obtaining the near-optimal solution.
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.
文摘The classical job shop scheduling problem(JSP) is the most popular machine scheduling model in practice and is known as NP-hard.The formulation of the JSP is based on the assumption that for each part type or job there is only one process plan that prescribes the sequence of operations and the machine on which each operation has to be performed.However,JSP with alternative machines for various operations is an extension of the classical JSP,which allows an operation to be processed by any machine from a given set of machines.Since this problem requires an additional decision of machine allocation during scheduling,it is much more complex than JSP.We present a domain independent genetic algorithm(GA) approach for the job shop scheduling problem with alternative machines.The GA is implemented in a spreadsheet environment.The performance of the proposed GA is analyzed by comparing with various problem instances taken from the literatures.The result shows that the proposed GA is competitive with the existing approaches.A simplified approach that would be beneficial to both practitioners and researchers is presented for solving scheduling problems with alternative machines.
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.
