A Two-Stage Resource-Constrained Project Scheduling Model with Proactive and Reactive Strategies Under Uncertainty
The aim of this paper is to develop a two-stage model to obtain a proactive and reactive schedule in resource-constrained project scheduling problems (RCPSP) under uncertainty. In the proactive phase, the highest cumulative instability weight scheduling o
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A Two-Stage Resource-Constrained Project Scheduling Model with Proactive and Reactive Strategies Under Uncertainty Lu Chen and Zhe Zhang
Abstract The aim of this paper is to develop a two-stage model to obtain a proactive and reactive schedule in resource-constrained project scheduling problems (RCPSP) under uncertainty. In the proactive phase, the highest cumulative instability weight scheduling of resource buffering is selected to optimize the initial schedule, which is arrange the activities in decreasing order by the sum weights of the activity and its successors. For the reactive schedule, the tabu search is employed to ensure the scheduling process execution. Actually, in practice, the uncertain resource availabilities are inevitable in RCPSP. In this situation, the uncertain factors are considered as the fuzzy random variables in this paper, and some properties of fuzzy random variables are discussed. Subsequently, the Particle Swarm Optimization (PSO) algorithm that solve the two-stage model of RCPSP with proactive and reactive strategies under uncertainty is developed. Finally, by testing the example, the effectiveness of the proposed model and approach is validated by the computation results. Keywords Proactive and reactive scheduling · Two-stage · RCPSP · Fuzzy random variable
120.1 Introduction As one of the most representative problems in the project management field, resourceconstrained project scheduling problems (RCPSP) is a classical NP-hard problem and most researchers assume that the generation of a baseline schedule is in a static and determined environment with complete crisp information, meanwhile the project can be executed smoothly and not disrupted if the initial schedule is determined. In L. Chen · Z. Zhang (B) School Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, People’s Republic of China e-mail: [email protected]
J. Xu et al. (eds.), Proceedings of the Eighth International Conference on Management Science and Engineering Management, Advances in Intelligent Systems and Computing 281, DOI: 10.1007/978-3-642-55122-2_120, © Springer-Verlag Berlin Heidelberg 2014
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fact, however, these assumptions will be hardly satisfied. The baseline schedule is formulated in a dynamic and uncertain environment such as machine breakdown, rush order, and so on. No matter how much the initial schedule will be protected from the effect of possible disruptions, the occurrence can not be completely eliminated. To avoid these situations, many researchers start to pay more attention on proactive schedule and reactive schedule, such as Nikulin [9], Lambrechts et al. [6], Honkomp et al. [4] , and so on. From these articles, some based knowledge can be concluded about proactive schedule and reactive schedule. Proactive schedule is an off-line scheduling process [11] and is based on the construction of a baseline schedule, which will guide schedule execution by determining each activity its planned starting time. The objective is to gen
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