It is known that the problem of minimizing total weighted completion time on a series-batching machine is NP-hard. We consider a series-batching bicriteria scheduling problem of minimizing makespan and total weighted ...It is known that the problem of minimizing total weighted completion time on a series-batching machine is NP-hard. We consider a series-batching bicriteria scheduling problem of minimizing makespan and total weighted completion time with equal length job simultaneously. A batching machine can handle up to b jobs in a batch, where b is called the batch capacity of the machine. We study the unbounded model with b ≥ n, where n denotes the number of jobs. A dynamic programming algorithm is proposed to solve the unbounded model, which can find all Pareto optimal schedules in O(n3) time.展开更多
This paper discusses design and comparison of Simulated Annealing Algorithm and Greedy Randomized Adaptive Search Procedure (GRASP) to minimize the makespan in scheduling n single operation independent jobs on m unrel...This paper discusses design and comparison of Simulated Annealing Algorithm and Greedy Randomized Adaptive Search Procedure (GRASP) to minimize the makespan in scheduling n single operation independent jobs on m unrelated parallel machines. This problem of minimizing the makespan in single machine scheduling problem with uniform parallel machines is NP hard. Hence, heuristic development for such problem is highly inevitable. In this paper, two different Meta-heuristics to minimize the makespan of the assumed problem are designed and they are compared in terms of their solutions. In the first phase, the simulated annealing algorithm is presented and then GRASP (Greedy Randomized Adaptive Search procedure) is presented to minimize the makespan in the single machine scheduling problem with unrelated parallel machines. It is found that the simulated annealing algorithm performs better than GRASP.展开更多
In the dynamic, complex and unbounded Grid systems, failures of Grid resources caused by malicious attacks and hardware failures are inevitable and have an adverse effect on the execution of tasks. To mitigate this pr...In the dynamic, complex and unbounded Grid systems, failures of Grid resources caused by malicious attacks and hardware failures are inevitable and have an adverse effect on the execution of tasks. To mitigate this problem, a makespan and reliability driven （MRD） sufferage scheduling algorithm is designed and implemented. Different from the traditional Grid scheduling algorithms, the algorithm addresses the makespan as well as reliability of tasks. The simulation experimental results show that the MRD sufferage scheduling algorithm can increase reliability of tasks and can trade off reliability against makespan of tasks by adjusting the weighting parameter in its cost function. So it can be applied to the complex Grid computing environment well.展开更多
This paper proposes a heuristic algorithm, called list-based squeezing branch and bound algorithm, for solving a machine-fixed, machining-assembly flowshop scheduling problem to minimize makespan. The machine-fixed, m...This paper proposes a heuristic algorithm, called list-based squeezing branch and bound algorithm, for solving a machine-fixed, machining-assembly flowshop scheduling problem to minimize makespan. The machine-fixed, machining-assembly flowshop consists of some parallel two-machine flow lines at a machining stage and one robot at an assembly stage. Since an optimal schedule for this problem is not always a permutation schedule, the proposed algorithm first finds a promising permutation schedule, and then searches better non-permutation schedules near the promising permutation schedule in an enumerative manner by elaborating a branching procedure in a branch and bound algorithm. The results of numerical experiments show that the proposed algorithm can efficiently provide an optimal or a near-optimal schedule with high accuracy such as mean relative error being less than 0.2% and the maximum relative error being at most 3%.展开更多
The objective of the research discussed in this paper has been to find an optimal schedule on mixed mass production lines of two and three machines. Johnson's rule on flow shops is generalized to mixed mass produ...The objective of the research discussed in this paper has been to find an optimal schedule on mixed mass production lines of two and three machines. Johnson's rule on flow shops is generalized to mixed mass production lines. Scheduling methods on three-machine lines are proposed for six special kinds of data of processing times of jobs. The scheduling method on two-machine lines is also proposed.展开更多
The problem of scheduling jobs on uniform parallel machines with makespan ( C max ) minimization objective was studied. Due to its non-deterministic polynomial-time ( N ) hard nature,it is difficult to achieve an opti...The problem of scheduling jobs on uniform parallel machines with makespan ( C max ) minimization objective was studied. Due to its non-deterministic polynomial-time ( N ) hard nature,it is difficult to achieve an optimal solution with traditional optimization methods. For this reason,a novel modified differential evolution ( MDE) was proposed to tackle the uniform parallel machine scheduling problem ( UPMSP ) ,taking out the mutation operation and introducing the roulette wheel selection strategy into conventional differential evolution ( DE ) . In MDE, more high quality individuals can be reserved to update the population. And the control parameter F in conventional DE was removed,which could simplify the DE. To verify the proposed MDE,162 randomly generated instances were conducted. The comparison results with DE,genetic algorithm ( GA ) ,simulated annealing ( SA ) , and largest processing time ( LPT) method show that the MDE is an efficient and competitive approach in solving UPMSP.展开更多
This paper considers the scheduling problem observed in chip sorting operation of LED manufacturing, where each lot (job) with release time have four operations to be processed on a set of processing stages without pr...This paper considers the scheduling problem observed in chip sorting operation of LED manufacturing, where each lot (job) with release time have four operations to be processed on a set of processing stages without pre-determined necessary route. Each stage has one and more identical sorting machines. The sorting machines scheduling problem can be treated as a four-stage multiprocessor open shop problem with dynamic job release, and the objective is minimizing the makespan in the paper. This problem is formulated into a mixed integer programming (MIP) model and empirically shows its computational intractability. Due to the computational intractability, a particle swarm optimization (PSO) algorithm is proposed. A series of computational experiments are conducted to evaluate the performance of the proposed PSO in comparison with exact solution on various small-size problem instances. The results show that the PSO algorithm could finds most optimal or better solutions in one second.展开更多
This paper discusses an efficient heuristic to minimize the makespan of scheduling n independent jobs on m unrelated parallel machines. The problem of scheduling the jobs on the unrelated parallel machines is combinat...This paper discusses an efficient heuristic to minimize the makespan of scheduling n independent jobs on m unrelated parallel machines. The problem of scheduling the jobs on the unrelated parallel machines is combinatorial in nature. Hence, the heuristic approach is inevitable for quicker solution. In this paper, a simple and efficient heuristic is designed to minimize the makespan of scheduling n independent jobs on m unrelated parallel machines. A mathematical model is also presented for this problem. A factorial experiment is used to compare the results of the proposed heuristic with that of a mathematical model by taking “Method” (Heuristic and Model) as the first factor and “Problem Size” (No. of machines X No. of Jobs: 2X5, 2X6, ……, 2X9, 3X5, 3X6, ……, 3X9, ……., 5X5, 5X6, …5X9) as the second factor. It is found that there is no significant difference between the results of the proposed heuristic and that of the mathematical model. Further, the mean percent error of the results obtained by the heuristic from the optimal results obtained by the model is 2.336 %. The heuristic gives optimal solution for 76.67 % of the problems.展开更多
A two-agent scheduling problem on parallel machines is considered in this paper. Our objective is to minimize the makespan for agent A, subject to an upper bound on the makespan for agent B. In this paper, we provide ...A two-agent scheduling problem on parallel machines is considered in this paper. Our objective is to minimize the makespan for agent A, subject to an upper bound on the makespan for agent B. In this paper, we provide a new approximation algorithm called CLPT. On the one hand, we compare the performance between the CLPT algorithm and the optimal solution and find that the solution obtained by the CLPT algorithm is very close to the optimal solution. On the other hand, we design different experimental frameworks to compare the CLPT algorithm and the A-LS algorithm for a comprehensive performance evaluation. A large number of numerical simulation results show that the CLPT algorithm outperformed the A-LS algorithm.展开更多
Cloud computing has rapidly evolved into a critical technology,seamlessly integrating into various aspects of daily life.As user demand for cloud services continues to surge,the need for efficient virtualization and r...Cloud computing has rapidly evolved into a critical technology,seamlessly integrating into various aspects of daily life.As user demand for cloud services continues to surge,the need for efficient virtualization and resource management becomes paramount.At the core of this efficiency lies task scheduling,a complex process that determines how tasks are allocated and executed across cloud resources.While extensive research has been conducted in the area of task scheduling,optimizing multiple objectives simultaneously remains a significant challenge due to the NP(Non-deterministic Polynomial)Complete nature of the problem.This study aims to address these challenges by providing a comprehensive review and experimental analysis of task scheduling approaches,with a particular focus on hybrid techniques that offer promising solutions.Utilizing the CloudSim simulation toolkit,we evaluated the performance of three hybrid algorithms:Estimation of Distribution Algorithm-Genetic Algorithm(EDA-GA),Hybrid Genetic Algorithm-Ant Colony Optimization(HGA-ACO),and Improved Discrete Particle Swarm Optimization(IDPSO).Our experimental results demonstrate that these hybrid methods significantly outperform traditional standalone algorithms in reducing Makespan,which is a critical measure of task completion time.Notably,the IDPSO algorithm exhibited superior performance,achieving a Makespan of just 0.64 milliseconds for a set of 150 tasks.These findings underscore the potential of hybrid algorithms to enhance task scheduling efficiency in cloud computing environments.This paper concludes with a discussion of the implications of our findings and offers recommendations for future research aimed at further improving task scheduling strategies,particularly in the context of increasingly complex and dynamic cloud environments.展开更多
The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because o...The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution framework.However,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale DPFSPs.As a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the mini-malmakespan.InMLIGA,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous solutions.In addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism.Meanwhile,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules.At last,a discrete adaptive learning rate is employed to enhance the stability of the memory and learningmechanisms.Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large extent.Furthermore,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs.This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling.展开更多
基金Foundation item： Supported by the National Natural Science Foundation of China（11201121, 11101383） Supported by the China Scholarship Counci1（201309895008）+1 种基金 Supported bythe 2013GGJS-079 Supported by the 2011B110008
文摘It is known that the problem of minimizing total weighted completion time on a series-batching machine is NP-hard. We consider a series-batching bicriteria scheduling problem of minimizing makespan and total weighted completion time with equal length job simultaneously. A batching machine can handle up to b jobs in a batch, where b is called the batch capacity of the machine. We study the unbounded model with b ≥ n, where n denotes the number of jobs. A dynamic programming algorithm is proposed to solve the unbounded model, which can find all Pareto optimal schedules in O(n3) time.
文摘This paper discusses design and comparison of Simulated Annealing Algorithm and Greedy Randomized Adaptive Search Procedure (GRASP) to minimize the makespan in scheduling n single operation independent jobs on m unrelated parallel machines. This problem of minimizing the makespan in single machine scheduling problem with uniform parallel machines is NP hard. Hence, heuristic development for such problem is highly inevitable. In this paper, two different Meta-heuristics to minimize the makespan of the assumed problem are designed and they are compared in terms of their solutions. In the first phase, the simulated annealing algorithm is presented and then GRASP (Greedy Randomized Adaptive Search procedure) is presented to minimize the makespan in the single machine scheduling problem with unrelated parallel machines. It is found that the simulated annealing algorithm performs better than GRASP.
文摘In the dynamic, complex and unbounded Grid systems, failures of Grid resources caused by malicious attacks and hardware failures are inevitable and have an adverse effect on the execution of tasks. To mitigate this problem, a makespan and reliability driven （MRD） sufferage scheduling algorithm is designed and implemented. Different from the traditional Grid scheduling algorithms, the algorithm addresses the makespan as well as reliability of tasks. The simulation experimental results show that the MRD sufferage scheduling algorithm can increase reliability of tasks and can trade off reliability against makespan of tasks by adjusting the weighting parameter in its cost function. So it can be applied to the complex Grid computing environment well.
文摘This paper proposes a heuristic algorithm, called list-based squeezing branch and bound algorithm, for solving a machine-fixed, machining-assembly flowshop scheduling problem to minimize makespan. The machine-fixed, machining-assembly flowshop consists of some parallel two-machine flow lines at a machining stage and one robot at an assembly stage. Since an optimal schedule for this problem is not always a permutation schedule, the proposed algorithm first finds a promising permutation schedule, and then searches better non-permutation schedules near the promising permutation schedule in an enumerative manner by elaborating a branching procedure in a branch and bound algorithm. The results of numerical experiments show that the proposed algorithm can efficiently provide an optimal or a near-optimal schedule with high accuracy such as mean relative error being less than 0.2% and the maximum relative error being at most 3%.
文摘The objective of the research discussed in this paper has been to find an optimal schedule on mixed mass production lines of two and three machines. Johnson's rule on flow shops is generalized to mixed mass production lines. Scheduling methods on three-machine lines are proposed for six special kinds of data of processing times of jobs. The scheduling method on two-machine lines is also proposed.
基金National Natural Science Foundation of China ( No. 60804052 )Chen Guang Project from Shanghai Municipal Education Commission and Shanghai Education Development Foundation,China ( No. 09CG39 )Project of Shanghai Municipal Education Commission,China ( No. 12YZ020)
文摘The problem of scheduling jobs on uniform parallel machines with makespan ( C max ) minimization objective was studied. Due to its non-deterministic polynomial-time ( N ) hard nature,it is difficult to achieve an optimal solution with traditional optimization methods. For this reason,a novel modified differential evolution ( MDE) was proposed to tackle the uniform parallel machine scheduling problem ( UPMSP ) ,taking out the mutation operation and introducing the roulette wheel selection strategy into conventional differential evolution ( DE ) . In MDE, more high quality individuals can be reserved to update the population. And the control parameter F in conventional DE was removed,which could simplify the DE. To verify the proposed MDE,162 randomly generated instances were conducted. The comparison results with DE,genetic algorithm ( GA ) ,simulated annealing ( SA ) , and largest processing time ( LPT) method show that the MDE is an efficient and competitive approach in solving UPMSP.
文摘This paper considers the scheduling problem observed in chip sorting operation of LED manufacturing, where each lot (job) with release time have four operations to be processed on a set of processing stages without pre-determined necessary route. Each stage has one and more identical sorting machines. The sorting machines scheduling problem can be treated as a four-stage multiprocessor open shop problem with dynamic job release, and the objective is minimizing the makespan in the paper. This problem is formulated into a mixed integer programming (MIP) model and empirically shows its computational intractability. Due to the computational intractability, a particle swarm optimization (PSO) algorithm is proposed. A series of computational experiments are conducted to evaluate the performance of the proposed PSO in comparison with exact solution on various small-size problem instances. The results show that the PSO algorithm could finds most optimal or better solutions in one second.
文摘This paper discusses an efficient heuristic to minimize the makespan of scheduling n independent jobs on m unrelated parallel machines. The problem of scheduling the jobs on the unrelated parallel machines is combinatorial in nature. Hence, the heuristic approach is inevitable for quicker solution. In this paper, a simple and efficient heuristic is designed to minimize the makespan of scheduling n independent jobs on m unrelated parallel machines. A mathematical model is also presented for this problem. A factorial experiment is used to compare the results of the proposed heuristic with that of a mathematical model by taking “Method” (Heuristic and Model) as the first factor and “Problem Size” (No. of machines X No. of Jobs: 2X5, 2X6, ……, 2X9, 3X5, 3X6, ……, 3X9, ……., 5X5, 5X6, …5X9) as the second factor. It is found that there is no significant difference between the results of the proposed heuristic and that of the mathematical model. Further, the mean percent error of the results obtained by the heuristic from the optimal results obtained by the model is 2.336 %. The heuristic gives optimal solution for 76.67 % of the problems.
文摘A two-agent scheduling problem on parallel machines is considered in this paper. Our objective is to minimize the makespan for agent A, subject to an upper bound on the makespan for agent B. In this paper, we provide a new approximation algorithm called CLPT. On the one hand, we compare the performance between the CLPT algorithm and the optimal solution and find that the solution obtained by the CLPT algorithm is very close to the optimal solution. On the other hand, we design different experimental frameworks to compare the CLPT algorithm and the A-LS algorithm for a comprehensive performance evaluation. A large number of numerical simulation results show that the CLPT algorithm outperformed the A-LS algorithm.
基金Acknowledgements： The authors are grateful for professors LIN Yi-xun and YUAN Jin-jiang for the helpful suggestions. The research in part was supported by NSFC （10671183）, NSFHN（082300410190）, The Nature Science Foundation of the Education Department of Henan （2008AI10004）, Science Foundation （07XJCO02） and Doctor Science Foundation of Henan University of Technology.
文摘Cloud computing has rapidly evolved into a critical technology,seamlessly integrating into various aspects of daily life.As user demand for cloud services continues to surge,the need for efficient virtualization and resource management becomes paramount.At the core of this efficiency lies task scheduling,a complex process that determines how tasks are allocated and executed across cloud resources.While extensive research has been conducted in the area of task scheduling,optimizing multiple objectives simultaneously remains a significant challenge due to the NP(Non-deterministic Polynomial)Complete nature of the problem.This study aims to address these challenges by providing a comprehensive review and experimental analysis of task scheduling approaches,with a particular focus on hybrid techniques that offer promising solutions.Utilizing the CloudSim simulation toolkit,we evaluated the performance of three hybrid algorithms:Estimation of Distribution Algorithm-Genetic Algorithm(EDA-GA),Hybrid Genetic Algorithm-Ant Colony Optimization(HGA-ACO),and Improved Discrete Particle Swarm Optimization(IDPSO).Our experimental results demonstrate that these hybrid methods significantly outperform traditional standalone algorithms in reducing Makespan,which is a critical measure of task completion time.Notably,the IDPSO algorithm exhibited superior performance,achieving a Makespan of just 0.64 milliseconds for a set of 150 tasks.These findings underscore the potential of hybrid algorithms to enhance task scheduling efficiency in cloud computing environments.This paper concludes with a discussion of the implications of our findings and offers recommendations for future research aimed at further improving task scheduling strategies,particularly in the context of increasingly complex and dynamic cloud environments.
基金supported in part by the National Key Research and Development Program of China under Grant No.2021YFF0901300in part by the National Natural Science Foundation of China under Grant Nos.62173076 and 72271048.
文摘The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution framework.However,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale DPFSPs.As a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the mini-malmakespan.InMLIGA,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous solutions.In addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism.Meanwhile,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules.At last,a discrete adaptive learning rate is employed to enhance the stability of the memory and learningmechanisms.Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large extent.Furthermore,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs.This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling.
