• 한국지능시스템학회논문지
• /
• 제24권2호
• /
• pp.218-224
• /
• 2014

The logic-based scheduling language RCPSV may be used to model resource-constrained project scheduling problems with variants for minimizing the project completion time. A diagram-based, nonredundant enumeration algorithm for the RCPSV-problem is proposed and the correctness of the algorithm is proved.

• 국제학술발표논문집
• /
• The 2th International Conference on Construction Engineering and Project Management
• /
• pp.739-748
• /
• 2007

The objective of this research study is to develop the permutation-based genetic algorithm for solving the resource-constrained project scheduling problem in construction engineering by incorporating elitism into genetic algorithm. A key aspect of the algorithm was the development of the elitist roulette selection operator to preserve the best individual solution for the next generation so the improved solution can be obtained. Another notable characteristic is the application of the parallel schedule generation scheme to generate a feasible solution to the problem. Case studies with a standard test problem were presented to demonstrate the performance and accuracy of the algorithm. The computational results indicate that the proposed algorithm produces reasonably good solutions for the resource-constrained project scheduling problem.

• 대한산업공학회지
• /
• 제43권1호
• /
• pp.1-11
• /
• 2017

Stochastic resource-constrained project scheduling problem is an extension of resource-constrained project scheduling problem such that activity duration has stochastic nature. In real situation where activity duration is not known until the activity is finished, open-loop based static policies such as activity-based policy and priority-based policy will not well cope with duration variability. Then, a dynamic policy based on closed-loop decision making will be regarded as an alternative toward achievement of minimal makespan. In this study, a dynamic policy designed to select activities to start at each decision time point is illustrated. The performance of static and dynamic policies based on variable neighborhood search is evaluated under the discrete-event simulation environment. Experiments with J120 sets in PSPLIB and several probability distributions of activity duration show that the dynamic policy is superior to static policies. Even when the variability is high, the dynamic policy provides stable and good solutions.

• 한국경영과학회:학술대회논문집
• /
• 대한산업공학회/한국경영과학회 1994년도 춘계공동학술대회논문집; 창원대학교; 08월 09일 Apr. 1994
• /
• pp.410-420
• /
• 1994

In many project-oriented production systems, e.g., shipyards or large-scale steel products manufacturing, resource loading by an activity is flexible, and the activity duration is a function of resource allocation. For example, if one doubles the size of the crew assigned to perform an activity, it may be feasible to complete the activity in half the duration. Such flexibility has been modeled by Weglarz [131 and by Leachman, Dincerler, and Kim [7] in extended formulations of the resource-constrained project scheduling problem. This paper presents a new algorithmic approach to the problem that combines the ideas proposed by the aforementioned authors. The method we propose involves a two-step approach: (1) solve the resource-constrained scheduling problem using a heuristic, and (2) using this schedule as an initial feasible solution, find improved resource allocations by solving a linear programming model. We provide computational results indicating the superiority of this approach to previous methodology for the resource-constrained scheduling problem. Extensions to the model to admit overlap relationships of the activities also are presented.

• 한국컴퓨터정보학회논문지
• /
• 제3권4호
• /
• pp.154-163
• /
• 1998

In this paper a heuristic procedure for a resource constrained project scheduling problem with multiple crashable modes is presented. A similar heuristic procedure by Ahn and Erengue was recently introduced, but the procedure assumes the resource capacities constant over the project life. The Procedure of this paper is able to deal with the resource capacities varying over the project life. The computational results with a set of 110 test problems demonstrate the efficacy of the heuristic procedure.

• 한국경영과학회지
• /
• 제19권3호
• /
• pp.139-149
• /
• 1994

In many project-oriented production systems, e. g., shipyards or large-scale steel products manufacturing, resource loading by an activity is flexible, and the activity duration is a function of resource allocation. For example, if one doubles the size of the crew assigned to perform an activity, it may be feasible to complete the activity in half the duration. Such flexibility has been modeled by Weglarz [13] and by Leachman, Dincerler, and Kim [7[ in extended formulations of the resource-constrained poject scheduling problem. This paper presents a new algorithmic approach to the problem that combines the ideas proposed by the aforementioned authors. The method we propose involves a two-step approach : (1) solve the resource-constrained scheduling problem using a heuristic, and (2) using this schedule as an initial feasible solution, find improved resource allocations by solving a linear programming model. We provide computational results indicating the superiority of this approach to previous methodology for the resource-constrained scheduling problem. Extensions to the model to admit overlap relationship of the activities also are presented.

• 한국콘텐츠학회논문지
• /
• 제11권6호
• /
• pp.59-66
• /
• 2011

자원 제약이 있는 프로젝트 스케줄링 문제는 자원의 양은 제한되어 있고 작업들 간에 선행조건이 있는 일정계획 문제로서 NP-hard 문제 중에 하나로 알려져 있다. 이러한 문제는 결정론적인 방법을 사용해서는 주어진 시간 내에 최적해를 구하기 어렵기 때문에 근사 최적해를 빠른 시간에 구할 수 있는 휴리스틱 방법을 이용한다. 본 논문에서는 자원 제약이 있는 프로젝트 스케줄링 문제를 효율적으로 해결할 수 있는 유전알고리즘을 소개한다. 제안한 유전알고리즘은 스키마 이론을 적용한 교차 연산자와 실세계 토너먼트 선택 전략을 이용하였다. 표준 문제에 실험한 결과는 제안한 알고리즘이 기존의 알고리즘 보다 우수함을 보여주었다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제12권1호
• /
• pp.20-28
• /
• 2012

We define a language $\mathcal{RS}$, a subclass of the scheduling language $\mathcal{RS}V$ (resource constrained project scheduling with variant processes). $\mathcal{RS}$ involves the determination of the starting times for ground activities of a project satisfying precedence and resource constraints, in order to minimize the total project duration. In $\mathcal{RS}$ ground activities and two structural symbols (operators) 'seq' and 'pll' are used to construct activity-terms representing scheduling problems. We consider three different variants for formalizing the $\mathcal{RS}$-scheduling problem, the optimizing variant, the number variant and the decision variant. Using the decision variant we show that the problem $\mathcal{RS}$ is $\mathcal{NP}$-complete. Further we show that the optimizing variant (or number variant) of the $\mathcal{RS}$-problem is computable in polynomial time iff the decision variant is computable in polynomial time.

• 국제학술발표논문집
• /
• The 9th International Conference on Construction Engineering and Project Management
• /
• pp.196-203
• /
• 2022

The resource constrained scheduling problem (RCSP) constitutes one of the most challenging problems in Project Management, as it combines multiple parameters, contradicting objectives (project completion within certain deadlines, resource allocation within resource availability margins and with reduced fluctuations), strict constraints (precedence constraints between activities), while its complexity grows with the increase in the number of activities being executed. Due to the large solution space size, this work investigates the application of Genetic Algorithms to approximate the optimal resource alolocation and obtain optimal trade-offs between different project goals. This analysis uses the cost of exceeding the daily resource availability, the cost from the day-by-day resource movement in and out of the site and the cost for using resources day-by-day, to form the objective cost function. The model is applied in different case studies: 1 project consisting of 10 activities, 4 repetitive projects consisting of 40 activities in total and 16 repetitive projects consisting of 160 activities in total, in order to evaluate the effectiveness of the algorithm in different-size solution spaces and under alternative optimization criteria by examining the quality of the solution and the required computational time. The case studies 2 & 3 have been developed by building upon the recurrence of the unit/sub-project (10 activities), meaning that the initial problem is multiplied four and sixteen times respectively. The evaluation results indicate that the proposed model can efficiently provide reliable solutions with respect to the individual goals assigned in every case study regardless of the project scale.

• 산업경영시스템학회지
• /
• 제43권2호
• /
• pp.79-86
• /
• 2020

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.