To make a satisfactory decision regarding project scheduling, a trade-off between the resource-related cost and project duration must be considered. A beneficial method for decision makers is to provide a number of alternative schedules of diverse project duration with minimum resource cost. In view of optimization, the alternative schedules are Pareto sets under multi-objective of project duration and resource cost. Assuming that resource cost is closely related to resource leveling, a heuristic algorithm for resource capacity reduction (HRCR) is developed in this study in order to generate the Pareto sets efficiently. The heuristic is based on the fact that resource leveling can be improved by systematically reducing the resource capacity. Once the reduced resource capacity is given, a schedule with minimum project duration can be obtained by solving a resource-constrained project scheduling problem. In HRCR, VNS (Variable Neighborhood Search) is implemented to solve the resource-constrained project scheduling problem. Extensive experiments to evaluate the HRCR performance are accomplished with standard benchmarking data sets, PSPLIB. Considering 5 resource leveling objective functions, it is shown that HRCR outperforms well-known multi-objective optimization algorithm, SPEA2 (Strength Pareto Evolutionary Algorithm-2), in generating dominant Pareto sets. The number of approximate Pareto optimal also can be extended by modifying weight parameter to reduce resource capacity in HRCR.

The efficiency of the purchasing and procurement logistics is important in automotive industry. The rationalization of production system is directly impact on productivity and quality. For this reason importance of logistics is high. Despite we are continuously making effort, our country are still below the level than developed country on logistics efficiency. Rising labor costs is an important factor in increasing logistics costs. So workforce reduction in logistics department is a large part. We deal with A-company inbound logistics, especially procurement logistics in automotive logistics as research object. So in this study we do research on work load balance about workers. We do research on 1,475 kinds of components in procurement process. We applied work load balance algorithm on chassis, final, sequence, trim warehouses workers. According to number of workers and average M/H, algorithm is applied in two ways. After applied work load balance algorithm we reduced numbers of workers from 28 to 20 and improved worker load balance rate from 47.1% to 93.7%

웨이퍼를 위한 생산 라인이 중설되었을 경우 현재 가지고 있는 라인을 기반으로 하여 시간과 생산량을 조사한 수 5M라인과 50M 라인의 문제점을 파악하고 각 공정별 대기시간과 재고를 파악하여 최적의 생산라인을 만들 수 있게 한다. Simulation은 ARENA를 사용하였으며 시간과 생산 수량의 Data는 기존의 생산라인에서 조사하였다. 새로운 생산 라인을 증설할 때 가장 큰 문제점인 재고와 라인 평준화 부분에 대한 자료가 Simulation을 통하여 나타나져 문제 분석이 효율적으로 이루어 질수 있으나 통계적인 예측을 하기에는 시간이 너무 짧아 기계의 평균적인 작업 시간과 이동시간을 가지고 작품을 만들었으며, 사실과 근사한 값을 가지도록 노력을 하였다. 그 결과, 가동률연과 WIP부분에서 문제점이 발생을 하는 것을 찾을 수 있었으며 이결과를 바탕으로 실제 생산라인을 설계함에 있어 도움을 줄 수 있다. Solar Wafer 생산라인은 표준화가 이루어져 있지 않으며 자동화 또한 이루어져 있지 않아 많은 생산을 하는데 어려움이 있으며 공정의 최적화를 찾기 힘든 부분에 대한 생산라인의 문제점을 파악하여 라인의 평준화에 도움을 줄 수 있으며 생산되기 전 월별 주별 생산량을 미리 파악하여 생산관리에 도움을 줄 것이라 기대된다.