TY - JOUR
T1 - A hybrid of genetic algorithm and evidential reasoning for optimal design of project scheduling
T2 - A systematic negotiation framework for multiple decision-makers
AU - Monghasemi, Shahryar
AU - Nikoo, Mohammad Reza
AU - Fasaee, Mohammad Ali Khaksar
AU - Adamowski, Jan
PY - 2017/3/1
Y1 - 2017/3/1
N2 - 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.
AB - 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.
KW - Discrete optimization
KW - Evidential reasoning
KW - Fallback bargaining
KW - Genetic algorithm
KW - Multi-criteria decision-making
KW - Project scheduling
UR - http://www.scopus.com/inward/record.url?scp=85010842245&partnerID=8YFLogxK
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U2 - 10.1142/S0219622017500079
DO - 10.1142/S0219622017500079
M3 - Article
AN - SCOPUS:85010842245
SN - 0219-6220
VL - 16
SP - 389
EP - 420
JO - International Journal of Information Technology and Decision Making
JF - International Journal of Information Technology and Decision Making
IS - 2
ER -