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.

1. 서 론
2. 자원 평준화 목적 함수
3. 자원 용량 감소 기반의 휴리스틱
4. 실험 및 분석
    4.1 HRCR 실행 절차
    4.2 HRCR과 SPEA2의 성능 비교
    4.3 HRCR해와 파레토 최적해 비교
5. 결 론
References

• Woo-Jin Jeong(한남대학교 산업공학과) | 정우진
• Sung-Chul Park(한남대학교 산업공학과) | 박성철
• Dong-Soon Yim(한남대학교 산업공학과) | 임동순 Corresponding Author