Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms
Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms
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DOI：
10.1016/s0377-2217(99)00094-6
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发表时间：
2000-01
期刊：
影响因子：
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通讯作者：
M. Sakawa;R. Kubota
中科院分区：
文献类型：
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作者：
M. Sakawa;R. Kubota
本文针对实际问题中数据的不精确性或模糊性，提出了具有模糊加工时间和模糊工期的作业车间调度问题。在模糊工期和模糊完工时间一致性指标的基础上，将多目标模糊作业车间调度问题描述为既最大化最小一致性指标又最大化平均一致性指标和最小化最大模糊完工时间的三目标问题。在引入决策者模糊目标的线性隶属函数后，采用Bellman和Zadeh的模糊决策。在基于甘特图的遗传算法中引入了个体间相似性的概念，提出了一种适合于求解公式化问题的遗传算法。作为算例，分别考虑了具有模糊工期和模糊加工时间的6×6和10×10个三目标作业车间调度问题，并与模拟退火法进行了比较，验证了该方法的可行性和有效性。
In this paper, by considering the imprecise or fuzzy nature of the data in real-world problems, job shop scheduling with fuzzy processing time and fuzzy duedate is introduced. On the basis of the agreement index of fuzzy duedate and fuzzy completion time, multiobjective fuzzy job shop scheduling problems are formulated as three-objective ones which not only maximize the minimum agreement index but also maximize the average agreement index and minimize the maximum fuzzy completion time. Having elicited the linear membership functions for the fuzzy goals of the decision maker, we adopt the fuzzy decision of Bellman and Zadeh. By incorporating the concept of similarity among individuals into the genetic algorithms using the Gannt chart, a genetic algorithm which is suitable for solving the formulated problems are proposed. As illustrative numerical examples, both 6×6 and 10×10 three-objective job shop scheduling problems with fuzzy duedate and fuzzy processing time are considered, and the feasibility and effectiveness of the proposed method are demonstrated by comparing with the simulated annealing method.