2010
DOI: 10.1007/s00170-010-2543-4
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Project time–cost trade-off scheduling: a hybrid optimization approach

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Cited by 35 publications
(19 citation statements)
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“…In other words, this technique reduces the stochastic network to a deterministic structure by using mean durations of activities instead of real distributions. -Path criticality indices and activity criticality indices [23,24]. These methods are the statistical techniques used for determining the degree of criticality of paths in stochastic projects.…”
Section: And Response Variable In Cost Functionmentioning
confidence: 99%
See 1 more Smart Citation
“…In other words, this technique reduces the stochastic network to a deterministic structure by using mean durations of activities instead of real distributions. -Path criticality indices and activity criticality indices [23,24]. These methods are the statistical techniques used for determining the degree of criticality of paths in stochastic projects.…”
Section: And Response Variable In Cost Functionmentioning
confidence: 99%
“…-Maximizing project completion probability in a predefined deadline using limited budget [10] -Minimizing direct cost to reach a predefined threshold of mean completion time [5] -Minimizing direct cost to reach a predefined threshold of project completion probability in a deadline [23,24] -Minimizing total cost including direct and indirect [7] -Minimizing mean of project completion time and minimizing variance of completion time and cost [19] -Minimizing mean of project completion time and minimizing mean of total project cost [18] The solution approaches developed for traditional TCTP include the following:…”
Section: And Response Variable In Cost Functionmentioning
confidence: 99%
“…For example, cost optimization of project schedules has been effectively carried out by genetic algorithms (Eshtehardian et al 2009), simulated annealing (He et al 2009), tabu search (Hazir et al 2011), neural networks (Adeli and Karim 1997), ant colony optimization (Kalhor et al 2011), particle swarm optimization (Yang 2007), differential evolution (Nearchou 2010), harmony search (Geem 2010) and hybrid methods, such as genetic algorithm and dynamic programming (Ezeldin and Soliman 2009), cutting plane method and Monte Carlo simulation (Mokhtari et al 2010), genetic algorithm and simulated annealing (Sonmez and Bettemir 2012) among others. Certainly, there are also various extensions of aforesaid techniques that can be found in the literature.…”
Section: Introductionmentioning
confidence: 99%
“…genetic algorithms [7÷12], simulated annealing [13,14], tabu search [14,15], neural networks [16], ant colony optimization [17÷20], particle swarm optimization [21], differential evolution [22], harmony search [23] mixedinteger linear programming [24÷28] and hybrid methods, such as genetic algorithm and simulated annealing [28], genetic algorithm and dynamic programming [29], cutting plane method and Monte Carlo simulation [30], etc.…”
Section: Introductionmentioning
confidence: 99%