Discrete symbiotic organisms search method for solving large-scale time-cost trade-off problem in construction scheduling

被引:44
|
作者
Liu, Dian [1 ]
Li, Heng [2 ]
Wang, Hongwei [3 ]
Qi, Chao [3 ]
Rose, Timothy [4 ]
机构
[1] Shanghai Univ, SILC Business Sch, 20 Chengzhong Rd, Shanghai 201800, Peoples R China
[2] Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hung Hom, Hong Kong, Peoples R China
[3] Huazhong Univ Sci & Technol, Sch Management, Wuhan 430074, Peoples R China
[4] Queensland Univ Technol, Sci & Engn Fac, Sch Civil Engn & Built Environm, Brisbane, Qld, Australia
基金
中国国家自然科学基金;
关键词
Large-scale construction project; Deadline constraint; Time-cost trade-off; Discrete symbiotic organisms search; GENETIC ALGORITHM; OPTIMIZATION ALGORITHM;
D O I
10.1016/j.eswa.2020.113230
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Construction projects are becoming increasingly larger and more complex in terms of size and cost. An optimization tool is necessary for the construction management system to develop the desired construction schedule to save time and cost. However, only a few efforts have been made to deal with the time-cost trade-off problem (TCTP) in the large-scale construction projects, and the existing optimization methods are slightly limited by the trouble of parameter tuning. As TCTP is known to be an NP-hard problem, this paper aims to introduce a new variant of Symbiotic Organisms Search (SOS) algorithm that does not contain control parameters, called DSOS (Discrete Symbiotic Organisms Search) which generates the parasite organism using a heuristic rule based on the network levels. This enhancement helps to improve the exploration phase and avoid premature stagnation. Performances are evaluated on project instances with different numbers of activities varying from 180 to 6300, as well as nine newly generated project instances with 720 activities but different network structures. The obtained results show a good performance of DSOS in terms of robustness and deviation from optimum in comparison with other meta-heuristics and variants of DSOS without using the heuristic rule. The good performance implies that DSOS is sufficient to serve as an effective tool to generate an optimized construction schedule. (C) 2020 Published by Elsevier Ltd.
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页数:13
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