Based on genetic algorithm for Job Shop scheduling problem
基于遗传算法的Job Shop调度问题研究

This study addressed a job scheduling and resource allocation problem with distinct release dates and due dates to minimize total tardiness in parallel work centers with a multi-processor environment. To solve the problem, this study also proposed a hybrid genetic algorithm (HGA) with release and due dates based decomposition heuristic. Experimental results show that the percentage deviations between the HGA and Lingo are smaller than 15%, and the HGA has smaller variance than the GA. This study proposed a decision-supporting model, which integrated simulation, genetic algorithms and decision support tools, for solving the JSRA problem by practical perspective.