A Hybrid of Genetic Algorithm and Evidential Reasoning for Optimal Design of Project Scheduling: A Systematic Negotiation Framework for Multiple Decision-Makers

Shahryar Monghasemi, Mohammad Reza Nikoo (), Mohammad Ali Khaksar Fasaee () and Jan Adamowski ()
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Shahryar Monghasemi: Department of Civil Engineering, Eastern Mediterranean University, Famagusta, North Cyprus, via Mersin 10 Turkey
Mohammad Reza Nikoo: #x2020;Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran
Mohammad Ali Khaksar Fasaee: #x2020;Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran
Jan Adamowski: #x2021;Department of Bioresource Engineering, Faculty of Agricultural and Environmental Sciences, McGill University, Canada

International Journal of Information Technology & Decision Making (IJITDM), 2017, vol. 16, issue 02, 389-420

Abstract: Traditional project scheduling methods inherently assume that the decision makers (DMs) are a unique entity whose acts are based on group rationality. However, in practice, DMs’ reliance on individual rationality and the wish to optimize their own objectives skew negotiations towards their preferred solutions. This makes conventional project scheduling solutions unrealistic. Here, a new two-step method is proposed that seeks to increase the overall efficiency of project schedules without violating individual rationality criteria, to find scheduling solutions that are acceptable to all DMs. First, a genetic algorithm is combined with evidential reasoning (ER) to obtain near optimal project schedule alternatives with respect to the priorities of each DM, separately. Second, the fallback bargaining method is used to help the DMs reach a consensus on an alternative with the highest group satisfaction. The proposed model is tested on a benchmark project scheduling problem with over 3.6 billion possible project scheduling alternatives. The results show that the model helps DMs when appointing their preferences using a well-organized procedure to provide a transparent view of each project schedule performance solution. Furthermore, the model is able to absorb the maximum support from the DMs, not necessarily a unique entity, by collecting all the self-optimizing DMs’ preferences and fairly allocating the benefits.

Keywords: Discrete optimization; evidential reasoning; fallback bargaining; project scheduling; genetic algorithm; multi-criteria decision-making (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219622017500079

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