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
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
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.
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.
