Pareto Front Particle Swarm Optimizer for Discrete Time-Cost Trade-Off Problem

2017 ◽  
Vol 31 (1) ◽  
pp. 04016040 ◽  
Author(s):  
Saman Aminbakhsh ◽  
Rifat Sonmez
Keyword(s):  
Discrete Time ◽  
Pareto Front ◽  
Particle Swarm ◽  
Time Cost ◽  
Particle Swarm Optimizer ◽  
Trade Off

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

scholarly journals A discrete-time approximation technique for the time-cost trade-off in PERT networks

2007 ◽  
Vol 41 (1) ◽  
pp. 61-81 ◽  
Author(s):  
Amir Azaron ◽  
Masatoshi Sakawa ◽  
Reza Tavakkoli-Moghaddam ◽  
Nima Safaei
Keyword(s):  
Discrete Time ◽  
Approximation Technique ◽  
Time Cost ◽  
Trade Off ◽  
Time Approximation ◽  
Discrete Time Approximation

scholarly journals Discrete time-cost tradeoff model for optimizing multi-mode construction project resource allocation

2015 ◽  
Author(s):  
Shuangshuang Nie ◽  
Jihong Gao
Keyword(s):  
Discrete Time ◽  
Pareto Front ◽  
Optimization Technique ◽  
Time Cost ◽  
Resource Constrained ◽  
Baseline Model ◽  
Resource Leveling ◽  
Project Scheduling Problem ◽  
Resource Cost ◽  
Multi Mode

The resource-constrained project scheduling problem has received broad attentions and was evolved into various sub-problems such as resource-constrained discrete time-cost tradeoff problem. The resource leveling problem was proposed to reduce the resource fluctuation and was always studied independently with RCPSP. This research proposed a new model which integrates the resource leveling problem and resource-constrained time-cost tradeoff problem. The evolutionary multi-objective optimization technique, strength Pareto evolutionary approach II (SPEA II) was applied to calculate the Pareto front of time and cost. The resource leveling measured by the metric, resource release/re-hiring, was converted to resource cost. The analysis of the time complexity of the model showed that the runtime of the algorithm was polynomial times of the number of activities. The results of case testing showed that the model was reasonably accurate in comparison with a proposed baseline model.

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