关键词: 随机车间作业调度问题 ；遗传算法；生产管理；生产控制

Abstract:

A job-shop scheduling approach was studied where the processing time was treated as stochastic variables.The individual with highest frequency was
supposed through all generations,and presented the best
solution in terms of the fitness function value.Based on this hypothesis,an improved GA to stochastic job-shop scheduling problems(SJSSP) was proposed.A decoding algorithm considering the capacity space of machines was proposed to obtain active-scheduling solutions.Crossover/mutation gene recombining algorithms were introduced to ensure the new individual to meet precedence constraints.The Roulette strategy according to fitness function values was adopted to control the evolution direction of the generation. In addition,an instance shows that the algorithm possesses great validity.

Key words: stochastic job-shop scheduling problem;genetic algorithm;production management;production control