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A genetic algorithm for operation sequencing in CAPP using edge selection based encoding strategy

Research paper by Yuliang Su, Xuening Chu, Dongping Chen, Xiwu Sun

Indexed on: 12 Jun '15Published on: 12 Jun '15Published in: Journal of Intelligent Manufacturing



Abstract

Operation sequencing in CAPP aims at determining the optimal order of machining operations with minimal machining cost and satisfying all the precedence constraints. The genetic algorithm (GA) is widely used to solve precedence constrained operation sequencing problem (PCOSP) due to its efficiency and parallel processing capability. How to guarantee the precedence constraints is always a hot research topic and there are mainly two classes of methods. The first ones use additional adjustment approaches to repair the infeasible solutions that break precedence constraints. It is unreliable and low efficient. The second ones avoid infeasible solutions in initialization through some encoding approaches such as topological storing based encoding approach, but the premature convergence problem may occur facing some complicated PCOSPs. To solve these problems, an edge selection strategy based GA is proposed. The edge selection based strategy could produce feasible solutions in initialization, and assures that every feasible solution will be generated with acceptable probability so as to improve GA’s converging efficiency. Then the precedence constraints are kept by order crossover. Modified mutation operator is designed to optimize the selection of machine tool, tool access direction and cutting tool for each operation. The experiments illustrate that the proposed algorithm is effective and efficient.