프로젝트 일정과 자원 평준화를 포함한 다목적 최적화 문제에서순차적 자원 감소에 기반한 파레토 집합의 생성
Generation of Pareto Sets based on Resource Reduction for Multi-Objective Problems Involving Project Scheduling and Resource Leveling
2
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
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.
Abstract
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.
참고문헌 (21)
- Ballestin, F., "Theoretical and practical fundamentals for multi-objective optimization in resourceconstrained project scheduling problems", Computers and Operations Research, 38(1), pp.51~62 (2011)
- Brucker, P., "Resource-constrained project scheduling :Notation, classification, models, and methods", European Journal of Operations Research, 112(1), pp.3~41 (1999)
- Deb, K., "A fast and elitist multi objective genetic algorithm : NSGA-", IEEE Transactions on Evolutionary Computing, 6(2), pp.182~197 (2002)
- Geiger, M. J., "Foundation of the pareto iterated local search metaheuristic", Proceedings of the 18th MCDM in Chania (2006)
- Geiger, M.J., "Randomized variable neighborhood search for multi-objective optimization", 4th EU, 2008, 34~42
- Gomes, H. C., "Multiobjective metaheuristic algorithms for the resource-constrained project scheduling problem with precedence relations", Computers and Operations Research, 44, pp.92~104 (2014)
- Harris, R., "Precedence and arrow networking techniques for construction" (1978), Wiley
- Hartmann, S., "Experimental evaluation of state-of-the art heuristics for the resource-constrained project scheduling problem", European Journal of Operational Research, 127(2), pp.394~407 (2000)
- Hu, J., "A multi-objective scheduling model for solving the resource-constrained project scheduling and resource leveling problem", ASCE International Conference on Computing in Civil Engineering, pp.49~56 (2012)
- Kolisch, R., "Experimental investigation of heuristics for resource-constrained project scheduling : An update", European Journal of Operational Research, 174(1), pp.23~37 (2006)
- Kolisch, R., "PSPLIB-A project scheduling library : OR software-ORSEP operations research software exchange program", European Journal of Operational Research, 96(1), pp.205~216 (1996)
- Kyriklidis, C., "Evolutionary computation for resource leveling optimization in project management", Integrated Computer Aided Engineering, 23(2), pp.173~184 (2016)
- Nebro, A.J., "SMPSO : A new PSO-based metaheuristic for multi-objective optimization", 2009IEEE Symposium on Computational Intelligence in Multi-Criteria Decision Making, pp.66~73 (2009)
- Neumann, K., "Resource leveling for projects with schedule-dependent time windows", European Journal of Operational Research, 117(3), pp.591~605 (1999)
- Ponz-Tienda, J. L., "The resource leveling problem with multiple resource using an adaptive genetic algorithm", Automation in Construction, 29, pp.161~172 (2013)
- Reynolds, A. P., "A multi-objective GRASP for partial classification", Soft Computing, 13, pp.227~243 (2009)
- Valls, V., "A population-based approach to the resource-constrained project scheduling problem", Annals of Operations Research, 131, pp.305~324 (2004)
- Valls, V., "Justification and RCPSP : a technique that pays", European Journal of Operational Research, 165(2), pp.375~386 (2005)
- 임동순, "확률적 자원제약 스케줄링 문제 해결을 위한 가변 이웃탐색 기반 동적 의사결정", 대한산업공학회지, 43(1), pp.1~11 (2017), 대한산업공학회[DOI]
- 임동순, "자원제약 프로젝트 스케쥴링 문제에 적용 가능한 부분 최적화 방법들의 성능 분석", 대한산업공학회지, 37(4), pp.408~414 (2011), 대한산업공학회
- Zitzler, E., "SPEA2 : improving the strength pareto evolutionary algorithm" (2001), Swiss Federal Institute of Technology