A Hybrid Genetic Approach for Optimizing Integrated Resources Selection and Operation Sequences

This paper considers a prevalent problem existing in modern manufacturing system, which called integrated Resource Selection and Operation Sequences (iRS/OS) problem. Resource selection and operation sequences act an important key role in manufacturing systems, especially recently in Advanced Planning and Scheduling (APS) component of supply chain management, an optimal resource selection for the operations sequences can significantly reduce the execution time and simultaneously improve the flexibility of production plans. That is, the makespan for orders should be minimized and withal, workloads among machine tools should also be balanced in our iRS/OS model. To solve this multiple criteria model, a new multistage operation-based Genetic Algorithm (moGA) has been proposed to improve the efficiency by designing a chromosome containing two kinds of information, i.e., operation sequences and machine selection. In addition, a local search procedure which called left-shift hillclimber is combined within our proposed GA to improve the efficiency. Finally, the experimental results of several iRS/OS problems indicate that our proposed approach can obtain good solutions. Further more comparing with previous approach, GA perform better for finding Pareto solutions.