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

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