A new multi-objective multi-mode model for solving preemptive time-cost-quality trade-off project scheduling problems

被引:113
作者
Tavana, Madjid [1 ,2 ]
Abtahi, Amir-Reza [3 ]
Khalili-Damghani, Kaveh [4 ]
机构
[1] La Salle Univ, Business Syst & Analyt Dept, Lindback Distinguished Chair Informat Syst & Deci, Philadelphia, PA 19141 USA
[2] Univ Paderborn, Fac Business Adm & Econ, Business Informat Syst Dept, D-33098 Paderborn, Germany
[3] Univ Econ Sci, Dept Knowledge Engn & Decis Sci, Tehran, Iran
[4] Islamic Azad Univ, South Tehran Branch, Dept Ind Engn, Tehran, Iran
关键词
Project scheduling problem; Discrete time-cost trade-off problem; Multi-objective evolutionary algorithm; Activity preemption; Generalized precedence relations; PARTICLE SWARM OPTIMIZATION; DISCRETE-TIME; GENETIC ALGORITHM; EPSILON-CONSTRAINT; MANAGEMENT; RESOURCES; NETWORK;
D O I
10.1016/j.eswa.2013.08.081
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Considering the trade-offs between conflicting objectives in project scheduling problems (PSPs) is a difficult task. We propose a new multi-objective multi-mode model for solving discrete time-cost-quality trade-off problems (DTCQTPs) with preemption and generalized precedence relations. The proposed model has three unique features: (1) preemption of activities (with some restrictions as a minimum time before the first interruption, a maximum number of interruptions for each activity, and a maximum time between interruption and restarting); (2) simultaneous optimization of conflicting objectives (i.e., time, cost, and quality); and (3) generalized precedence relations between activities. These assumptions are often consistent with real-life projects. A customized, dynamic, and self-adaptive version of a multi-objective evolutionary algorithm is proposed to solve the scheduling problem. The proposed multi-objective evolutionary algorithm is compared with an efficient multi-objective mathematical programming technique known as the efficient epsilon-constraint method. The comparison is based on a number of performance metrics commonly used in multi-objective optimization. The results show the relative dominance of the proposed multi-objective evolutionary algorithm over the epsilon-constraint method. (C) 2013 Elsevier Ltd. All rights reserved.
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页码:1830 / 1846
页数:17
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