Credibility-based chance-constrained multimode resource-constrained project scheduling problem under fuzzy uncertainty

This paper investigates a multimode resource-constrained project scheduling problem under hybrid uncertain environment. With the activity durations are fuzzy variables, the resource requests change with the durations. The goal is to minimize the project duration and the cost associated with the requ...

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Vydáno v:Computers & industrial engineering Ročník 171; s. 108402
Hlavní autoři: Liu, Huiran, Fang, Zhiming, Li, Renjie
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.09.2022
Témata:
Cost
Heuristic algorithm
Multimode project scheduling
Resource constraint
Uncertainty
Uncertainty
Heuristic algorithm
Cost
Resource constraint
Multimode project scheduling
ISSN:0360-8352, 1879-0550
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Shrnutí:This paper investigates a multimode resource-constrained project scheduling problem under hybrid uncertain environment. With the activity durations are fuzzy variables, the resource requests change with the durations. The goal is to minimize the project duration and the cost associated with the required resources. Considering the stochastic duration and resource demand, a chance constrained programming model based on credibility is established. In order to solve the model, the chance constraints and fuzziness are treated by introducing the credibility distribution function and its inverse function. And, a hybrid algorithm combining particle swarm optimization (PSO) and genetic algorithm (GA) is proposed. It has shown fine performance in solving problems with 30, 60, and 120 activities. We also analyze the sensitivity of confidence, fuzziness, number of activities, number of modes, and resource unit cost. Meanwhile, fuzzy value-at-risk is used as a risk measure, and a model based on it is constructed. These numerical results show that the developed algorithm can obtain the near optimal solution of large-scale projects in a reasonable calculation time. •Resource requests vary with the stochastic duration.•A mixed integer programming model with chance constraints is constructed.•Uncertainties in model are dealt with through credibility distribution measure.•A hybrid meta-heuristic algorithm is developed to solve the problem.•A fuzzy-Value-at-risk-based model is constructed.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2022.108402