To solve the NP-complete no-wait flowshop problems, objective increment properties are analyzed and proved for fundamental operations of heuristics. With these properties, whether a new generated schedule is better or...To solve the NP-complete no-wait flowshop problems, objective increment properties are analyzed and proved for fundamental operations of heuristics. With these properties, whether a new generated schedule is better or worse than the original one is only evaluated by objective increments, instead of completely calculating objective values as the traditional algorithms do, so that the computational time can be considerably reduced. An objective increment-based hybrid genetic algorithm （IGA） is proposed by integrating the genetic algorithm （GA） with an improved various neighborhood search （VNS）as a local search. An initial solution generation heuristic（ISG） is constructed to generate one individual of the initial population. An expectation value-based selection mechanism and a crossover operator are introduced to the mating process. The IGA is compared with the traditional GA and two best-so-far algorithms for the considered problem on 110 benchmark instances. An experimental results show that the IGA outperforms the others in effectiveness although with a little more time consumption.展开更多
In order to solve the no-wait flowshop scheduling problem to minimize the maximum lateness,three job-block-based neighborhoods are proposed,among which the block exchange neighborhood have a size of O（n4）while the b...In order to solve the no-wait flowshop scheduling problem to minimize the maximum lateness,three job-block-based neighborhoods are proposed,among which the block exchange neighborhood have a size of O（n4）while the block swap and the simplified block exchange neighborhoods have a size of O（n3）.With larger sizes than the existing neighborhoods,the proposed neighborhoods can enhance the solution quality of local search algorithms.Speedup properties for the neighborhoods are developed,which can evaluate a neighbor in constant time and explore the neighborhoods in time proportional to their proposed sizes. Unlike the dominance-rule-based speedup method,the proposed speedups are applicable to any machine number.Three neighborhoods and the union of block swap and the simplified block exchange neighborhoods are compared in the tabu search.Computational results on benchmark instances show that three tabu search algorithms with O（n3）neighborhoods outperform the existing algorithms and the tabu search algorithm with the union has the best performance among all the tested algorithms.展开更多
In many practical flowshop production environments, there is no intermediate storage space available to keep partially completed jobs between any two machines. The workflow has to be continuous, implying that the no-w...In many practical flowshop production environments, there is no intermediate storage space available to keep partially completed jobs between any two machines. The workflow has to be continuous, implying that the no-wait conditions must be abided, which is typical in steel and plastic production. We discuss the three-machine no-wait flowshop scheduling problem where the setup times are considered as separated from processing times and sequence independent. The scheduling goal is to minimize the total flowtime. An optimal property and two heuristic algorithms for this problem are proposed. Evaluated over a large number of problems, the proposed heuristics are found that they can yield good solutions effectively with low computational complexity, and have more obvious advantage for the large size problem compared with the existing one.展开更多
The m-machine no-wait flowshop scheduling problem is addressed where setup times are treated as separate from processing times. The objective is to minimize total tardiness. Different dispatching rules have been inves...The m-machine no-wait flowshop scheduling problem is addressed where setup times are treated as separate from processing times. The objective is to minimize total tardiness. Different dispatching rules have been investigated and three were found to be superior. Two heuristics, a simulated annealing (SA) and a genetic algorithm (GA), have been proposed by using the best performing dispatching rule as the initial solution for SA, and the three superior dispatching rules as part of the initial population for GA. Moreover, improved versions of SA and GA are proposed using an insertion algorithm. Extensive computational experiments reveal that the improved versions of SA and GA perform about 95% better than SA and GA. The improved version of GA outperforms the improved version of SA by about 3.5%.展开更多
No-wait flowshop scheduling problems with the objective to minimize the total flow time is an important se-quencing problem in the field of developing production plans and has a wide engineering background. Genetic al...No-wait flowshop scheduling problems with the objective to minimize the total flow time is an important se-quencing problem in the field of developing production plans and has a wide engineering background. Genetic algo-rithm (GA) has the capability of global convergence and has been proven effective to solve NP-hard combinatorial op-timization problems,while simple heuristics have the advantage of fast local convergence and can be easily imple-mented. In order to avoid the defect of slow convergence or premature,a heuristic genetic algorithm is proposed by in-corporating the simple heuristics and local search into the traditional genetic algorithm. In this hybridized algorithm,the structural information of no-wait flowshops and high-effective heuristics are incorporated to design a new method for generating initial generation and a new crossover operator. The computational results show the developed heuristic ge-netic algorithm is efficient and the quality of its solution has advantage over the best known algorithm. It is suitable for solving the large scale practical problems and lays a foundation for the application of meta-heuristic algorithms in in-dustrial production.展开更多
The NP-hard no-wait flow shop scheduling problems with makespan and total flowtime minimization are considered. Objective increment properties of the problems are analyzed. A non-dominated classification method is int...The NP-hard no-wait flow shop scheduling problems with makespan and total flowtime minimization are considered. Objective increment properties of the problems are analyzed. A non-dominated classification method is introduced to class population individuals into Pareto fronts to improve searching efficiency. Besides investigating the crowding distance and the elitist solution strategy, two effective bi-criteria local search procedures based on objective increments are presented to improve searching effectiveness. Based on the properties and methods, a hybrid evolutionary algorithm is proposed for the considered problems and compared with the best existing algorithms. Experimental results show that the proposed algorithm is effective with high efficiency.展开更多
基金The National Natural Science Foundation of China(No.60504029,60672092)the National High Technology Research and Development Program of China(863Program)(No.2008AA04Z103)
文摘To solve the NP-complete no-wait flowshop problems, objective increment properties are analyzed and proved for fundamental operations of heuristics. With these properties, whether a new generated schedule is better or worse than the original one is only evaluated by objective increments, instead of completely calculating objective values as the traditional algorithms do, so that the computational time can be considerably reduced. An objective increment-based hybrid genetic algorithm （IGA） is proposed by integrating the genetic algorithm （GA） with an improved various neighborhood search （VNS）as a local search. An initial solution generation heuristic（ISG） is constructed to generate one individual of the initial population. An expectation value-based selection mechanism and a crossover operator are introduced to the mating process. The IGA is compared with the traditional GA and two best-so-far algorithms for the considered problem on 110 benchmark instances. An experimental results show that the IGA outperforms the others in effectiveness although with a little more time consumption.
基金The National Natural Science Foundation of China(No.60672092,60504029,60873236)the National High Technology Researchand Development Program of China(863 Program)(No.2008AA04Z103)
文摘In order to solve the no-wait flowshop scheduling problem to minimize the maximum lateness,three job-block-based neighborhoods are proposed,among which the block exchange neighborhood have a size of O（n4）while the block swap and the simplified block exchange neighborhoods have a size of O（n3）.With larger sizes than the existing neighborhoods,the proposed neighborhoods can enhance the solution quality of local search algorithms.Speedup properties for the neighborhoods are developed,which can evaluate a neighbor in constant time and explore the neighborhoods in time proportional to their proposed sizes. Unlike the dominance-rule-based speedup method,the proposed speedups are applicable to any machine number.Three neighborhoods and the union of block swap and the simplified block exchange neighborhoods are compared in the tabu search.Computational results on benchmark instances show that three tabu search algorithms with O（n3）neighborhoods outperform the existing algorithms and the tabu search algorithm with the union has the best performance among all the tested algorithms.
文摘In many practical flowshop production environments, there is no intermediate storage space available to keep partially completed jobs between any two machines. The workflow has to be continuous, implying that the no-wait conditions must be abided, which is typical in steel and plastic production. We discuss the three-machine no-wait flowshop scheduling problem where the setup times are considered as separated from processing times and sequence independent. The scheduling goal is to minimize the total flowtime. An optimal property and two heuristic algorithms for this problem are proposed. Evaluated over a large number of problems, the proposed heuristics are found that they can yield good solutions effectively with low computational complexity, and have more obvious advantage for the large size problem compared with the existing one.
文摘The m-machine no-wait flowshop scheduling problem is addressed where setup times are treated as separate from processing times. The objective is to minimize total tardiness. Different dispatching rules have been investigated and three were found to be superior. Two heuristics, a simulated annealing (SA) and a genetic algorithm (GA), have been proposed by using the best performing dispatching rule as the initial solution for SA, and the three superior dispatching rules as part of the initial population for GA. Moreover, improved versions of SA and GA are proposed using an insertion algorithm. Extensive computational experiments reveal that the improved versions of SA and GA perform about 95% better than SA and GA. The improved version of GA outperforms the improved version of SA by about 3.5%.
基金Project 60304016 supported by the National Natural Science Foundation of China
文摘No-wait flowshop scheduling problems with the objective to minimize the total flow time is an important se-quencing problem in the field of developing production plans and has a wide engineering background. Genetic algo-rithm (GA) has the capability of global convergence and has been proven effective to solve NP-hard combinatorial op-timization problems,while simple heuristics have the advantage of fast local convergence and can be easily imple-mented. In order to avoid the defect of slow convergence or premature,a heuristic genetic algorithm is proposed by in-corporating the simple heuristics and local search into the traditional genetic algorithm. In this hybridized algorithm,the structural information of no-wait flowshops and high-effective heuristics are incorporated to design a new method for generating initial generation and a new crossover operator. The computational results show the developed heuristic ge-netic algorithm is efficient and the quality of its solution has advantage over the best known algorithm. It is suitable for solving the large scale practical problems and lays a foundation for the application of meta-heuristic algorithms in in-dustrial production.
基金The National Natural Science Foundation of China(No.60504029,60672092)the National High Technology Research and Development Program of China(863Program)(No.2008AA04Z103)
文摘The NP-hard no-wait flow shop scheduling problems with makespan and total flowtime minimization are considered. Objective increment properties of the problems are analyzed. A non-dominated classification method is introduced to class population individuals into Pareto fronts to improve searching efficiency. Besides investigating the crowding distance and the elitist solution strategy, two effective bi-criteria local search procedures based on objective increments are presented to improve searching effectiveness. Based on the properties and methods, a hybrid evolutionary algorithm is proposed for the considered problems and compared with the best existing algorithms. Experimental results show that the proposed algorithm is effective with high efficiency.
