An Effective Multi-objective EDA for Robust Resource Constrained Project Scheduling with Uncertain Durations

Hao, Xinchang; Lin, Lin; Gen, Mitsuo

doi:10.1016/j.procs.2014.09.056

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…In this regard, Abbasi et al (2006) presented a RCPSP model with two objectives, where the project implementation time was minimized in the first objective function and maximization of floating time for increase of scheduling robustness were considered as the second objective function. Similar to Abbasi et al (2006), Hao et al (2014) presented a project scheduling problem with two objectives of minimizing the project completion time and maximizing the scheduling robustness, with the difference that Hao et al (2014) considered the scheduling robustness criteria as a weighted equation with regard to the number of successor activities. Khemakhem and Chtourou (2013) reviewed some criteria for scheduling robustness presented in the literature and also proposed some new criteria.…”

Section: Other Objective Functionsmentioning

confidence: 99%

Resource-constrained project scheduling problem: review of past and recent developments

Habibi¹,

Barzinpour²,

Sadjadi³

2018

10.5267/j.jpm

105

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The project scheduling problem is both practically and theoretically of paramount importance. From the practical perspective, improvement of project scheduling as a critical part of project management process can lead to successful project completion and significantly decrease of the relevant costs. From the theoretical perspective, project scheduling is regarded as one of the interesting optimization issues, which has attracted the attention of many researchers in the area of operations research. Therefore, the project scheduling issue has been significantly evaluated over time and has been developed from various aspects. In this research, the topics related to Resource-Constrained Project Scheduling Problem (RCPSP) are reviewed, recent developments in this field are evaluated, and the results are presented for future studies. In this regard, first, the standard problem of RCPSP is expressed and related developments are presented from four aspects of resources, characteristics of activities, type of objective functions, and availability level of information. Following that, details about 216 articles conducted on RCPSP during 1980-2017 are expressed. At the end, in line with the statistics obtained from the evaluation of previous articles, suggestions are made for the future studies in order to help the development of new issues in this area.

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Section: Other Objective Functionsmentioning

confidence: 99%

Resource-constrained project scheduling problem: review of past and recent developments

Habibi¹,

Barzinpour²,

Sadjadi³

2018

10.5267/j.jpm

105

View full text Add to dashboard Cite

show abstract

“…Based on decomposition strategy, Deirmenci [14] et al employed precise algorithm to minimize the completion time in discrete-time/cost tradeoff of largescale JSPs. et al [17] created a robust scheduling mechanism, constructed an optimization model for the minimal time and maximal robustness, and solved the model with heuristic algorithm. With the aid of the GA, Qi et al [18] optimized the production robustness under uncertain time, and effectively overcame the random differences caused by uncertain time.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Multi-objective Job-shop Scheduling under Uncertain Environment

Wang¹,

Zeng²

2019

JESA

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Job-shop production often faces many uncertainties arising from the interaction between various resources. The schedule must be robust enough to ensure smooth production under the uncertain environment. In this paper, the multi-objective job-shop scheduling problem (JSP) is optimized based on the tradeoff between time, cost and robustness. Firstly, a multi-objective optimization model was constructed, and the tradeoffs between three objectives, namely, time, cost and robustness, were discussed in details. After that, a genetic algorithm (GA) coupling non-dominated ranking was designed to solve the multi-objective JSP. Finally, the proposed model was verified using an example of JSP and the objective tradeoffs were validated through sensitivity analysis. The research findings shed important new light on multi-objective JSP under various constraints.

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“…For instance, advanced methodologies permit to determine the most likely critical path within a 1 3 probabilistic project network (Soroush 1994). Other models have been developed to propose solutions to the project scheduling problems with uncertain durations: based on sensitivity analyses (Samikoglu et al 1998), Markov chain-based models (Hao et al 2014), fuzzy logic (Shi and Blomquist 2012); (Masmoudi and Haït 2013), stochastic models, and associated heuristics (Bruni et al 2011). The Critical Path Method is a mathematical analysis which identifies the sequence of activities that add up to the longest overall duration.…”

Section: Gaps In Criticality Analysis Of Project Elementsmentioning

confidence: 99%