Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has bec...Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has become a research hot topic in the field of scheduling because its production is closer to reality.The research of DFJSP is of great significance to the organization and management of actual production process.To solve the heterogeneous DFJSP with minimal completion time,a hybrid chemical reaction optimization(HCRO)algorithm is proposed in this paper.Firstly,a novel encoding-decoding method for flexible manufacturing unit(FMU)is designed.Secondly,half of initial populations are generated by scheduling rule.Combined with the new solution acceptance method of simulated annealing(SA)algorithm,an improved method of critical-FMU is designed to improve the global and local search ability of the algorithm.Finally,the elitist selection strategy and the orthogonal experimental method are introduced to the algorithm to improve the convergence speed and optimize the algorithm parameters.In the experimental part,the effectiveness of the simulated annealing algorithm and the critical-FMU refinement methods is firstly verified.Secondly,in the comparison with other existing algorithms,the proposed optimal scheduling algorithm is not only effective in homogeneous FMUs examples,but also superior to existing algorithms in heterogeneous FMUs arithmetic cases.展开更多
The fuzzy goal flexible job-shop scheduling problem （FGFJSP） is the extension of FJSP. Compared with the convention JSP, it can solve the fuzzy goal problem and meet suit requirements of the key job. The multi-objec...The fuzzy goal flexible job-shop scheduling problem （FGFJSP） is the extension of FJSP. Compared with the convention JSP, it can solve the fuzzy goal problem and meet suit requirements of the key job. The multi-object problem, such as the fuzzy cost, the fuzzy due-date, and the fuzzy makespan, etc, can be solved by FGFJSP. To optimize FGFJSP, an individual optimization and colony diversity genetic algorithm （IOCDGA） is presented to accelerate the convergence speed and to avoid the earliness. In IOCDGA, the colony average distance and the colony entropy are defined after the definition of the encoding model. The colony diversity is expressed by the colony average distance and the colony entropy. The crossover probability and the mutation probability are controlled by the colony diversity. The evolution emphasizes that sigle individual or a few individuals evolve into the best in IOCDGA, but not the all in classical GA. Computational results show that the algorithm is applicable and the number of iterations is less.展开更多
In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objectiv...In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.展开更多
The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worke...The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.展开更多
The flexible job shop scheduling problem(FJSP) is considered as an important problem in the modern manufacturing system. It is known to be an NP-hard problem. Most of the algorithms used in solving FJSP problem are ca...The flexible job shop scheduling problem(FJSP) is considered as an important problem in the modern manufacturing system. It is known to be an NP-hard problem. Most of the algorithms used in solving FJSP problem are categorized as metaheuristic methods. Some of these methods normally consume more CPU time and some other methods are more complicated which make them di cult to code and not easy to reproduce. This paper proposes a modified iterated greedy(IG) algorithm to deal with FJSP problem in order to provide a simpler metaheuristic, which is easier to code and to reproduce than some other much more complex methods. This is done by separating the classical IG into two phases. Each phase is used to solve a sub-problem of the FJSP: sequencing and routing sub-problems. A set of dispatching rules are employed in the proposed algorithm for the sequencing and machine selection in the construction phase of the solution. To evaluate the performance of proposed algorithm, some experiments including some famous FJSP benchmarks have been conducted. By compared with other algorithms, the experimental results show that the presented algorithm is competitive and able to find global optimum for most instances. The simplicity of the proposed IG provides an e ective method that is also easy to apply and consumes less CPU time in solving the FJSP problem.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 problem of simultaneous scheduling of machines and vehicles in flexible manufacturing system (FMS) was addressed.A spreadsheet based genetic algorithm (GA) approach was presented to solve the problem.A domain inde...The problem of simultaneous scheduling of machines and vehicles in flexible manufacturing system (FMS) was addressed.A spreadsheet based genetic algorithm (GA) approach was presented to solve the problem.A domain independent general purpose GA was used,which was an add-in to the spreadsheet software.An adaptation of the propritary GA software was demonstrated to the problem of minimizing the total completion time or makespan for simultaneous scheduling of machines and vehicles in flexible manufacturing systems.Computational results are presented for a benchmark with 82 test problems,which have been constructed by other researchers.The achieved results are comparable to the previous approaches.The proposed approach can be also applied to other problems or objective functions without changing the GA routine or the spreadsheet model.展开更多
A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a ...A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a collaborative population-based search, which isinspired by the social behavior of bird flocking. It combines local search (by self experience) andglobal search (by neighboring experience), possessing high search efficiency. Simulated annealing(SA) employs certain probability to avoid becoming trapped in a local optimum and the search processcan be controlled by the cooling schedule. By reasonably combining these two different searchalgorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, isdeveloped. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated byapplying it to some benchmark job-shop scheduling problems and comparing results with otheralgorithms in literature. Comparing results indicate that PSO-based algorithm is a viable andeffective approach for the job-shop scheduling problem.展开更多
An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal ...An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.展开更多
The standard genetic algorithm has limitations of a low convergence rate and premature convergence in solving the job-shop scheduling problem.To overcome these limitations,this paper presents a new improved hybrid gen...The standard genetic algorithm has limitations of a low convergence rate and premature convergence in solving the job-shop scheduling problem.To overcome these limitations,this paper presents a new improved hybrid genetic algorithm on the basis of the idea of graft in botany.Through the introduction of a grafted population and crossover probability matrix,this algorithm accelerates the convergence rate greatly and also increases the ability to fight premature convergence.Finally,the approach is tested on a set of standard instances taken from the literature and compared with other approaches.The computation results validate the effectiveness of the proposed algorithm.展开更多
The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied....The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.Targeting this problem,the process state model of a mixed-flow production line is analyzed.On this basis,a mathematical model of a mixed-flow job-shop scheduling problem with combined processing constraints is established based on the traditional FJSP.Then,an improved genetic algorithm with multi-segment encoding,crossover,and mutation is proposed for the mixed-flow production line problem.Finally,the proposed algorithm is applied to the production workshop of missile structural components at an aerospace institute to verify its feasibility and effectiveness.展开更多
This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-object...This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.展开更多
Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of th...Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem(MOFJSP)considering transportation time.Design/methodology/approach–A hybrid genetic algorithm(GA)approach is integrated with simulated annealing to solve the MOFJSP considering transportation time,and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.Findings–The performance of the proposed algorithm is tested on different MOFJSP taken from literature.Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution,especially when the number of jobs and the flexibility of the machine increase.Originality/value–Most of existing studies have not considered the transportation time during scheduling of jobs.The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs.Meanwhile,GA is one of primary algorithms extensively used to address MOFJSP in literature.However,to solve the MOFJSP,the original GA has a possibility to get a premature convergence and it has a slow convergence speed.To overcome these problems,a new hybrid GA is developed in this paper.展开更多
Meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable timeframe.By combining domain knowledge of the specific optimization problem,the search efficiency and ...Meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable timeframe.By combining domain knowledge of the specific optimization problem,the search efficiency and quality of meta-heuristic algorithms can be significantly improved,making it crucial to identify and summarize domain knowledge within the problem.In this paper,we summarize and analyze domain knowledge that can be applied to meta-heuristic algorithms in the job-shop scheduling problem(JSP).Firstly,this paper delves into the importance of domain knowledge in optimization algorithm design.After that,the development of different methods for the JSP are reviewed,and the domain knowledge in it for meta-heuristic algorithms is summarized and classified.Applications of this domain knowledge are analyzed,showing it is indispensable in ensuring the optimization performance of meta-heuristic algorithms.Finally,this paper analyzes the relationship among domain knowledge,optimization problems,and optimization algorithms,and points out the shortcomings of the existing research and puts forward research prospects.This paper comprehensively summarizes the domain knowledge in the JSP,and discusses the relationship between the optimization problems,optimization algorithms and domain knowledge,which provides a research direction for the metaheuristic algorithm design for solving the JSP in the future.展开更多
基金This work was supported by the National Natural Science Foundation of China(Nos.61973120,62076095,61673175,and 61573144).
文摘Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode.The distributed flexible job-shop scheduling problem(DFJSP)has become a research hot topic in the field of scheduling because its production is closer to reality.The research of DFJSP is of great significance to the organization and management of actual production process.To solve the heterogeneous DFJSP with minimal completion time,a hybrid chemical reaction optimization(HCRO)algorithm is proposed in this paper.Firstly,a novel encoding-decoding method for flexible manufacturing unit(FMU)is designed.Secondly,half of initial populations are generated by scheduling rule.Combined with the new solution acceptance method of simulated annealing(SA)algorithm,an improved method of critical-FMU is designed to improve the global and local search ability of the algorithm.Finally,the elitist selection strategy and the orthogonal experimental method are introduced to the algorithm to improve the convergence speed and optimize the algorithm parameters.In the experimental part,the effectiveness of the simulated annealing algorithm and the critical-FMU refinement methods is firstly verified.Secondly,in the comparison with other existing algorithms,the proposed optimal scheduling algorithm is not only effective in homogeneous FMUs examples,but also superior to existing algorithms in heterogeneous FMUs arithmetic cases.
文摘The fuzzy goal flexible job-shop scheduling problem （FGFJSP） is the extension of FJSP. Compared with the convention JSP, it can solve the fuzzy goal problem and meet suit requirements of the key job. The multi-object problem, such as the fuzzy cost, the fuzzy due-date, and the fuzzy makespan, etc, can be solved by FGFJSP. To optimize FGFJSP, an individual optimization and colony diversity genetic algorithm （IOCDGA） is presented to accelerate the convergence speed and to avoid the earliness. In IOCDGA, the colony average distance and the colony entropy are defined after the definition of the encoding model. The colony diversity is expressed by the colony average distance and the colony entropy. The crossover probability and the mutation probability are controlled by the colony diversity. The evolution emphasizes that sigle individual or a few individuals evolve into the best in IOCDGA, but not the all in classical GA. Computational results show that the algorithm is applicable and the number of iterations is less.
文摘In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.
基金supported by the Natural Science Foundation of Anhui Province(Grant Number 2208085MG181)the Science Research Project of Higher Education Institutions in Anhui Province,Philosophy and Social Sciences(Grant Number 2023AH051063)the Open Fund of Key Laboratory of Anhui Higher Education Institutes(Grant Number CS2021-ZD01).
文摘The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.
基金Supported by National Natural Science Foundation of China(Grant Nos.51825502,51775216)Hubei Provincial Natural Science Foundation of China(Grant No.2018CFA078)Program for HUST Academic Frontier Youth Team
文摘The flexible job shop scheduling problem(FJSP) is considered as an important problem in the modern manufacturing system. It is known to be an NP-hard problem. Most of the algorithms used in solving FJSP problem are categorized as metaheuristic methods. Some of these methods normally consume more CPU time and some other methods are more complicated which make them di cult to code and not easy to reproduce. This paper proposes a modified iterated greedy(IG) algorithm to deal with FJSP problem in order to provide a simpler metaheuristic, which is easier to code and to reproduce than some other much more complex methods. This is done by separating the classical IG into two phases. Each phase is used to solve a sub-problem of the FJSP: sequencing and routing sub-problems. A set of dispatching rules are employed in the proposed algorithm for the sequencing and machine selection in the construction phase of the solution. To evaluate the performance of proposed algorithm, some experiments including some famous FJSP benchmarks have been conducted. By compared with other algorithms, the experimental results show that the presented algorithm is competitive and able to find global optimum for most instances. The simplicity of the proposed IG provides an e ective method that is also easy to apply and consumes less CPU time in solving the FJSP problem.
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 problem of simultaneous scheduling of machines and vehicles in flexible manufacturing system (FMS) was addressed.A spreadsheet based genetic algorithm (GA) approach was presented to solve the problem.A domain independent general purpose GA was used,which was an add-in to the spreadsheet software.An adaptation of the propritary GA software was demonstrated to the problem of minimizing the total completion time or makespan for simultaneous scheduling of machines and vehicles in flexible manufacturing systems.Computational results are presented for a benchmark with 82 test problems,which have been constructed by other researchers.The achieved results are comparable to the previous approaches.The proposed approach can be also applied to other problems or objective functions without changing the GA routine or the spreadsheet model.
基金This project is supported by National Natural Science Foundation of China (No.70071017).
文摘A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a collaborative population-based search, which isinspired by the social behavior of bird flocking. It combines local search (by self experience) andglobal search (by neighboring experience), possessing high search efficiency. Simulated annealing(SA) employs certain probability to avoid becoming trapped in a local optimum and the search processcan be controlled by the cooling schedule. By reasonably combining these two different searchalgorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, isdeveloped. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated byapplying it to some benchmark job-shop scheduling problems and comparing results with otheralgorithms in literature. Comparing results indicate that PSO-based algorithm is a viable andeffective approach for the job-shop scheduling problem.
基金Supported by the National Natural Science Foundation of China(51175262)the Research Fund for Doctoral Program of Higher Education of China(20093218110020)+2 种基金the Jiangsu Province Science Foundation for Excellent Youths(BK201210111)the Jiangsu Province Industry-Academy-Research Grant(BY201220116)the Innovative and Excellent Foundation for Doctoral Dissertation of Nanjing University of Aeronautics and Astronautics(BCXJ10-09)
文摘An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.
文摘The standard genetic algorithm has limitations of a low convergence rate and premature convergence in solving the job-shop scheduling problem.To overcome these limitations,this paper presents a new improved hybrid genetic algorithm on the basis of the idea of graft in botany.Through the introduction of a grafted population and crossover probability matrix,this algorithm accelerates the convergence rate greatly and also increases the ability to fight premature convergence.Finally,the approach is tested on a set of standard instances taken from the literature and compared with other approaches.The computation results validate the effectiveness of the proposed algorithm.
基金supported by the National Key Research and Development Program of China (No.2020YFB1710500)the National Natural Science Foundation of China(No.51805253)the Fundamental Research Funds for the Central Universities(No. NP2020304)
文摘The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.Targeting this problem,the process state model of a mixed-flow production line is analyzed.On this basis,a mathematical model of a mixed-flow job-shop scheduling problem with combined processing constraints is established based on the traditional FJSP.Then,an improved genetic algorithm with multi-segment encoding,crossover,and mutation is proposed for the mixed-flow production line problem.Finally,the proposed algorithm is applied to the production workshop of missile structural components at an aerospace institute to verify its feasibility and effectiveness.
基金supported by the National Key Research and Development Program of China(2016YFD0700605)the Fundamental Research Funds for the Central Universities(JZ2016HGBZ1035)the Anhui University Natural Science Research Project(KJ2017A891)
文摘This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.
基金supported by National Social Science Foundation of China under the project of 18BGL003.
文摘Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem(MOFJSP)considering transportation time.Design/methodology/approach–A hybrid genetic algorithm(GA)approach is integrated with simulated annealing to solve the MOFJSP considering transportation time,and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.Findings–The performance of the proposed algorithm is tested on different MOFJSP taken from literature.Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution,especially when the number of jobs and the flexibility of the machine increase.Originality/value–Most of existing studies have not considered the transportation time during scheduling of jobs.The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs.Meanwhile,GA is one of primary algorithms extensively used to address MOFJSP in literature.However,to solve the MOFJSP,the original GA has a possibility to get a premature convergence and it has a slow convergence speed.To overcome these problems,a new hybrid GA is developed in this paper.
基金supported by the National Natural Science Foundation of China(Nos.U21B2029 and 51825502).
文摘Meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable timeframe.By combining domain knowledge of the specific optimization problem,the search efficiency and quality of meta-heuristic algorithms can be significantly improved,making it crucial to identify and summarize domain knowledge within the problem.In this paper,we summarize and analyze domain knowledge that can be applied to meta-heuristic algorithms in the job-shop scheduling problem(JSP).Firstly,this paper delves into the importance of domain knowledge in optimization algorithm design.After that,the development of different methods for the JSP are reviewed,and the domain knowledge in it for meta-heuristic algorithms is summarized and classified.Applications of this domain knowledge are analyzed,showing it is indispensable in ensuring the optimization performance of meta-heuristic algorithms.Finally,this paper analyzes the relationship among domain knowledge,optimization problems,and optimization algorithms,and points out the shortcomings of the existing research and puts forward research prospects.This paper comprehensively summarizes the domain knowledge in the JSP,and discusses the relationship between the optimization problems,optimization algorithms and domain knowledge,which provides a research direction for the metaheuristic algorithm design for solving the JSP in the future.
