Discrete time–cost–environment trade-off problem for large-scale construction systems with multiple modes under fuzzy uncertainty and its application to Jinping-II Hydroelectric Project

2012 ◽  
Vol 30 (8) ◽  
pp. 950-966 ◽  
Author(s):  
Jiuping Xu ◽  
Huan Zheng ◽  
Ziqiang Zeng ◽  
Shiyong Wu ◽  
Manbin Shen
Keyword(s):  
Discrete Time ◽  
Large Scale ◽  
Scale Construction ◽  
Time Cost ◽  
Trade Off ◽  
Multiple Modes ◽  
Fuzzy Uncertainty ◽  
Hydroelectric Project

scholarly journals Utility of Genetic Algorithms for Solving Large-Scale Construction Time-Cost Trade-Off Problems

2018 ◽  
Vol 32 (1) ◽  
pp. 04017072 ◽  
Author(s):  
Duzgun Agdas ◽  
David J. Warne ◽  
Jorge Osio-Norgaard ◽  
Forrest J. Masters
Keyword(s):  
Genetic Algorithms ◽  
Large Scale ◽  
Scale Construction ◽  
Time Cost ◽  
Construction Time ◽  
Trade Off

scholarly journals Bi-Objective Scheduling Optimization for Discrete Time/Cost Trade-Off in Projects

2018 ◽  
Vol 10 (8) ◽  
pp. 2802 ◽  
Author(s):  
Hongbo Li ◽  
Zhe Xu ◽  
Wenchao Wei
Keyword(s):  
Discrete Time ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Critical Path ◽  
Large Set ◽  
Time Cost ◽  
Trade Off ◽  
Project Duration ◽  
Computational Performance ◽  
Trade Offs

In sustainable project management, time and cost are two critical factors affecting the success of a project. Time/cost trade-offs in projects accelerate the execution of some activities by increasing the amount of non-renewable resources committed to them and therefore shorten the project duration. The discrete time/cost trade-off problem (DTCTP) has been extensively studied during the past 20 years. However, due to its complexity, the DTCTP—especially the DTCTP curve problem (DTCTP-C)—has only been solved for relatively small instances. To the best of our knowledge, there is no computational performance analysis for solving the DTCTP-C on large project instances with up to 500 activities. This paper aims to fill this gap. We present two bi-objective heuristic algorithms for the DTCTP-C where both project duration and cost are minimized. The objective is to obtain a good appropriate efficient set for the large-scale instances. The first algorithm is based on the non-dominated sorting genetic algorithm II (NSGA-II) and uses a specially designed critical path-based crossover operator. The second algorithm is a steepest descent heuristic which generates efficient solutions by iteratively solving the DTCTP with different deadlines. Computational experiments are conducted to validate the proposed algorithms on a large set of randomly generated problem instances.

scholarly journals New Computational Results on the Discrete Time/Cost Trade-Off Problem in Project Networks

1998 ◽  
Vol 49 (11) ◽  
pp. 1153 ◽  
Author(s):  
E. Demeulemeester ◽  
B. De Reyck ◽  
B. Foubert ◽  
W. Herroelen ◽  
M. Vanhoucke
Keyword(s):  
Discrete Time ◽  
Computational Results ◽  
Time Cost ◽  
Trade Off ◽  
Project Networks

The discrete time/cost trade-off problem: extensions and heuristic procedures

2007 ◽  
Vol 10 (4-5) ◽  
pp. 311-326 ◽  
Author(s):  
Mario Vanhoucke ◽  
Dieter Debels
Keyword(s):  
Discrete Time ◽  
Time Cost ◽  
Trade Off ◽  
Heuristic Procedures
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