본 연구는 통합공정일정계획(Integrated Process Planning and Scheduling; IPPS)의 최적화를 위한 계산 효율성이 높은 탐욕적 휴리스틱과 유전알고리즘(Genetic Algorithm; GA)을 결합한 하이브리드형 유전 알고리즘을 제안한다. IPPS는 기존의 공정계획과 일정계획을 동시에 풀고자 하는 NP-Hard 문제이다. 특히, 본 연구에서 다루는 IPPS는 tool related constraints가 포함된 것으로서 전통적인 GA는 수행도중 infeasible schedule을 빈번히 발생시킨다. 제안하는 방법의 아이디어는 전체적인 schedule의 구조에 영향을 미치는 operation의 sequence와 machine의 결정은 GA의 procedure를 따르고, 목적함수의 부분계산이 가능한 tool과 Tool Access Direction(TAD)는 greedy heuristics을 통하여 infeasibility를 해소하자는 것이다. 이를 통하여 계산시간의 급격한 증가 없이 또는 기존에 비해 계산시간을 감소시키면서 좋은 품질의 해를 구할 수 있다. 본 연구에서 제안하는 알고리즘은 benchmark problems을 이용하여 성능을 평가한다.

This paper proposes an improved standard genetic algorithm (GA) of making a near optimal schedule for integrated process planning and scheduling problem (IPPS) considering tool flexibility and tool related constraints. Process planning involves the selection of operations and the allocation of resources. Scheduling, meanwhile, determines the sequence order in which operations are executed on each machine. Due to the high degree of complexity, traditionally, a sequential approach has been preferred, which determines process planning firstly and then performs scheduling independently based on the results. The two sub-problems, however, are complicatedly interrelated to each other, so the IPPS tend to solve the two problems simultaneously. Although many studies for IPPS have been conducted in the past, tool flexibility and capacity constraints are rarely considered. Various meta-heuristics, especially GA, have been applied for IPPS, but the performance is yet satisfactory. To improve solution quality against computation time in GA, we adopted three methods. First, we used a random circular queue during generation of an initial population. It can provide sufficient diversity of individuals at the beginning of GA. Second, we adopted an inferior selection to choose the parents for the crossover and mutation operations. It helps to maintain exploitation capability throughout the evolution process. Third, we employed a modification of the hybrid scheduling algorithm to decode the chromosome of the individual into a schedule, which can generate an active and non-delay schedule. The experimental results show that our proposed algorithm is superior to the current best evolutionary algorithms at most benchmark problems.