In the“shared manufacturing”environment,based on fairness,shared manufacturing platforms often require manufacturing service enterprises to arrange production according to the principle of“order first,finish first...In the“shared manufacturing”environment,based on fairness,shared manufacturing platforms often require manufacturing service enterprises to arrange production according to the principle of“order first,finish first”which leads to a series of scheduling problems with fixed processing sequences.In this paper,two two-machine hybrid flow-shop problems with fixed processing sequences are studied.Each job has two tasks.The first task is flexible,which can be processed on either of the two machines,and the second task must be processed on the second machine after the first task is completed.We consider two objective functions:to minimize the makespan and tominimize the total weighted completion time.First,we show the problem for any one of the two objectives is ordinary NP-hard by polynomial-time Turing Reduction.Then,using the Continuous ProcessingModule(CPM),we design a dynamic programming algorithm for each case and calculate the time complexity of each algorithm.Finally,numerical experiments are used to analyze the effect of dynamic programming algorithms in practical operations.Comparative experiments show that these dynamic programming algorithms have comprehensive advantages over the branch and bound algorithm(a classical exact algorithm)and the discrete harmony search algorithm(a high-performance heuristic algorithm).展开更多
Given the challenges of manufacturing resource sharing and competition in the modern manufacturing industry,the coordinated scheduling problem of parallel machine production and transportation is investigated.The prob...Given the challenges of manufacturing resource sharing and competition in the modern manufacturing industry,the coordinated scheduling problem of parallel machine production and transportation is investigated.The problem takes into account the coordination of production and transportation before production as well as the disparities in machine spatial position and performance.A non-cooperative game model is established,considering the competition and self-interest behavior of jobs from different customers for machine resources.The job from different customers is mapped to the players in the game model,the corresponding optional processing machine and location are mapped to the strategy set,and the makespan of the job is mapped to the payoff.Then the solution of the scheduling model is transformed into the Nash equilibrium of the non-cooperative game model.A Nash equilibrium solution algorithm based on the genetic algorithm(NEGA)is designed,and the effective solution of approximate Nash equilibrium for the game model is realized.The fitness function,single-point crossover operator,and mutation operator are derived from the non-cooperative game model’s characteristics and the definition of Nash equilibrium.Rules are also designed to avoid the generation of invalid offspring chromosomes.The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes.Compared with other algorithms such as heuristic algorithms(FCFS,SPT,and LPT),the simulated annealing algorithm(SA),and the particle swarm optimization algorithm(PSO),experimental results show that the proposed NE-GA algorithm has obvious performance advantages.展开更多
Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tas...Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tasks and resources.Compared with the traditional mode,shared manufacturing offers more abundant manufacturing resources and flexible configuration options.This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment,and the characteristics of shared manufacturing resource allocation.The execution of manufacturing tasks,in which candidate manufacturing resources enter or exit at various time nodes,enables the dynamic allocation of manufacturing tasks and resources.Then non-dominated sorting genetic algorithm(NSGA-II)and multi-objective particle swarm optimization(MOPSO)algorithms are designed to solve the model.The optimal parameter settings for the NSGA-II and MOPSO algorithms have been obtained according to the experiments with various population sizes and iteration numbers.In addition,the proposed model’s efficiency,which considers the entries and exits of manufacturing resources in the shared manufacturing environment,is further demonstrated by the overlap between the outputs of the NSGA-II and MOPSO algorithms for optimal resource allocation.展开更多
Efficient utilization of the equipment distributed in different enterprises and optimal allocation of these resources is an important concern for networked manufacturing. The third party based equipment sharing approa...Efficient utilization of the equipment distributed in different enterprises and optimal allocation of these resources is an important concern for networked manufacturing. The third party based equipment sharing approach is put forward to optimize the utilization of distributed equipment for networked manufacturing; Taking advantage of the shared equipment offered by equipment providers by means of lease agreement, the third party carries out production by establishing networked virtual factory. Operational mechanism of the third party based equipment sharing is discussed, and characteristics of this approach in achieving resource allocation are analyzed. Shared equipment planning is formulated as an optimization problem with the objective of maximizing profits for equipment coordinator, a mathematical model for shared equipment planning is developed. Finally a case study is discussed to show the effectiveness of the planning model.展开更多
The structural changes that the Chinese economy has been experiencing since its working-age population began to decline pose challenges for its further growth.First,as it loses its comparative advantage in labor-inten...The structural changes that the Chinese economy has been experiencing since its working-age population began to decline pose challenges for its further growth.First,as it loses its comparative advantage in labor-intensive activities,the share of manufacturing in its GDP has shrunk.Second,unproductive enterprises that are reluctant to exit the market tend to seek policy protection,which leads to the immobility of resource allocation.Third,the reallocation of the labor force from the highly productive manufacturing sector to the low-productivity service sector leads to the degradation of resource allocation.The inadequate exploitation of the potential of resource reallocation implies that the decline in manufacturing is premature.It is therefore important to combine market competition policy,industrial policy,and social protection policy to stabilize the development of manufacturing.展开更多
基金This work was partially supported by the Zhejiang Provincial Philosophy and Social Science Program of China(Grant No.19NDJC093YB)the National Social Science Foundation of China(Grant No.19BGL001)+1 种基金the Natural Science Foundation of Zhejiang Province of China(Grant No.LY19A010002)the Natural Science Foundation of Ningbo of China(The design of algorithms and cost-sharing rules for scheduling problems in shared manufacturing,Acceptance No.20211JCGY010241).
文摘In the“shared manufacturing”environment,based on fairness,shared manufacturing platforms often require manufacturing service enterprises to arrange production according to the principle of“order first,finish first”which leads to a series of scheduling problems with fixed processing sequences.In this paper,two two-machine hybrid flow-shop problems with fixed processing sequences are studied.Each job has two tasks.The first task is flexible,which can be processed on either of the two machines,and the second task must be processed on the second machine after the first task is completed.We consider two objective functions:to minimize the makespan and tominimize the total weighted completion time.First,we show the problem for any one of the two objectives is ordinary NP-hard by polynomial-time Turing Reduction.Then,using the Continuous ProcessingModule(CPM),we design a dynamic programming algorithm for each case and calculate the time complexity of each algorithm.Finally,numerical experiments are used to analyze the effect of dynamic programming algorithms in practical operations.Comparative experiments show that these dynamic programming algorithms have comprehensive advantages over the branch and bound algorithm(a classical exact algorithm)and the discrete harmony search algorithm(a high-performance heuristic algorithm).
基金supported in part by the Project of Liaoning BaiQianWan Talents ProgramunderGrand No.2021921089the Science Research Foundation of EducationalDepartment of Liaoning Province under Grand No.LJKQZ2021057 and WJGD2020001the Key Program of Social Science Planning Foundation of Liaoning Province under Grant L21AGL017.
文摘Given the challenges of manufacturing resource sharing and competition in the modern manufacturing industry,the coordinated scheduling problem of parallel machine production and transportation is investigated.The problem takes into account the coordination of production and transportation before production as well as the disparities in machine spatial position and performance.A non-cooperative game model is established,considering the competition and self-interest behavior of jobs from different customers for machine resources.The job from different customers is mapped to the players in the game model,the corresponding optional processing machine and location are mapped to the strategy set,and the makespan of the job is mapped to the payoff.Then the solution of the scheduling model is transformed into the Nash equilibrium of the non-cooperative game model.A Nash equilibrium solution algorithm based on the genetic algorithm(NEGA)is designed,and the effective solution of approximate Nash equilibrium for the game model is realized.The fitness function,single-point crossover operator,and mutation operator are derived from the non-cooperative game model’s characteristics and the definition of Nash equilibrium.Rules are also designed to avoid the generation of invalid offspring chromosomes.The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes.Compared with other algorithms such as heuristic algorithms(FCFS,SPT,and LPT),the simulated annealing algorithm(SA),and the particle swarm optimization algorithm(PSO),experimental results show that the proposed NE-GA algorithm has obvious performance advantages.
基金This work was supported by the Key Program of Social Science Planning Foundation of Liaoning Province under Grant L21AGL017.
文摘Shared manufacturing is recognized as a new point-to-point manufac-turing mode in the digital era.Shared manufacturing is referred to as a new man-ufacturing mode to realize the dynamic allocation of manufacturing tasks and resources.Compared with the traditional mode,shared manufacturing offers more abundant manufacturing resources and flexible configuration options.This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment,and the characteristics of shared manufacturing resource allocation.The execution of manufacturing tasks,in which candidate manufacturing resources enter or exit at various time nodes,enables the dynamic allocation of manufacturing tasks and resources.Then non-dominated sorting genetic algorithm(NSGA-II)and multi-objective particle swarm optimization(MOPSO)algorithms are designed to solve the model.The optimal parameter settings for the NSGA-II and MOPSO algorithms have been obtained according to the experiments with various population sizes and iteration numbers.In addition,the proposed model’s efficiency,which considers the entries and exits of manufacturing resources in the shared manufacturing environment,is further demonstrated by the overlap between the outputs of the NSGA-II and MOPSO algorithms for optimal resource allocation.
基金This project is supported by National Hi-tech Research and Development Program of China (863 Program, No. 2005AA411040)Chongqing University Graduate Innovation Foundation, China (No. 200506Z1B0270134)
文摘Efficient utilization of the equipment distributed in different enterprises and optimal allocation of these resources is an important concern for networked manufacturing. The third party based equipment sharing approach is put forward to optimize the utilization of distributed equipment for networked manufacturing; Taking advantage of the shared equipment offered by equipment providers by means of lease agreement, the third party carries out production by establishing networked virtual factory. Operational mechanism of the third party based equipment sharing is discussed, and characteristics of this approach in achieving resource allocation are analyzed. Shared equipment planning is formulated as an optimization problem with the objective of maximizing profits for equipment coordinator, a mathematical model for shared equipment planning is developed. Finally a case study is discussed to show the effectiveness of the planning model.
文摘The structural changes that the Chinese economy has been experiencing since its working-age population began to decline pose challenges for its further growth.First,as it loses its comparative advantage in labor-intensive activities,the share of manufacturing in its GDP has shrunk.Second,unproductive enterprises that are reluctant to exit the market tend to seek policy protection,which leads to the immobility of resource allocation.Third,the reallocation of the labor force from the highly productive manufacturing sector to the low-productivity service sector leads to the degradation of resource allocation.The inadequate exploitation of the potential of resource reallocation implies that the decline in manufacturing is premature.It is therefore important to combine market competition policy,industrial policy,and social protection policy to stabilize the development of manufacturing.
