Research on Genetic Algorithm for Job Shop Scheduling Problem
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Through comprehensive research and practical implementation, we have successfully developed the core functionality of genetic algorithms for job shop scheduling, achieving significant optimization results. Our work implements key genetic operations including chromosome encoding using permutation-based representations, tournament selection mechanisms, customized crossover operators (such as POX and JBX), and mutation operations tailored for scheduling constraints. The fitness evaluation incorporates makespan minimization and resource utilization metrics. This research has gained widespread recognition as a classical approach in the field, providing not only functional algorithm implementation but also valuable references and guidance for future research and applications. We take pride in contributing to this domain and look forward to further exploration and development of advanced scheduling optimization techniques.
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