An Adaptive Multi-Strategy Artificial Bee Colony Algorithm for Integrated Process Planning and Scheduling

Abstract

Traditionally, process planning and scheduling were performed sequentially, where scheduling depended on the result of process planning. Considering their complementarity, the two functions are more tightly integrated to improve the performance of job shop flexible manufacturing environment. This study proposes an adaptive multi-strategy artificial bee colony (AMSABC) algorithm to solve integrated process planning and scheduling (IPPS) problem. In AMSABC, two search strategies with different characteristics are introduced into employed bees and onlooker bees to take on the responsibility of both exploration and exploitation. The selection probability of each search strategy is dynamically adjusted according to previous experiences. To further improve the exploitation performance of the approach, a problem-specific multi-objective local search has been embedded in the proposed algorithm. Furthermore, AMSABC algorithm presents a unique solution representation where a food source is represented by three discrete vectors, and a well-designed decoding scheme is developed. Next, the corresponding neighborhood structure is adopted that it can directly generate feasible solutions in the search space. The proposed algorithm is tested on the well-known benchmark instances and compared with the state-of-the-art algorithms. Through detail analysis of experimental results, AMSABC algorithm is more beneficial in the quality and efficiency of solution.