Comparing an ACO algorithm with other heuristics for the single machine scheduling problem with sequence-dependent setup times

Gagné, Caroline; Price, Wilson L.; Gravel, Marc

doi:10.1057/palgrave.jors.2601390

Cited by 140 publications

(78 citation statements)

References 27 publications

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“…Examples include the applications to assignment problems (Costa and Hertz, 1997;Stützle and Hoos, 2000;Socha et al, 2003), scheduling problems (Merkle et al, 2002;Gagné et al, 2002;Blum and Sampels, 2004), and vehicle routing problems Reimann et al, 2004).…”

Section: Aco Applicationsmentioning

confidence: 99%

Ant colony optimization for continuous domains

Socha

Dorigo

2008

European Journal of Operational Research

1,186

681

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Section: Aco Applicationsmentioning

confidence: 99%

Ant colony optimization for continuous domains

Socha

Dorigo

2008

European Journal of Operational Research

1,186

681

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“…The only comparison work found on this topic is the work by Gagné et al presented in (Gagn et al, 2002). The results presented in Table 1 concern this work.…”

Section: Single Machine Problemmentioning

confidence: 73%

A Logistic Process Scheduling Problem: Genetic Algorithms or Ant Colony Optimization?

Silva

Sousa

Runkler

et al. 2005

IFAC Proceedings Volumes

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This paper compares the optimization of a logistic scheduling problem using two different optimization techniques; the genetic algorithms and the ant colony optimization. The comparison is preceded by a literature review that summarizes the available comparison results for different benchmark problems and tries to generalize the differences between the techniques. The simulation results for the logistic problem confirm the conclusions of the literature survey: both methods perform equally well, but in general the genetic algorithms are faster. However, the ant colonies give more information about the solution, which is advantage in some applications.

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“…After defining the cross section (jobs set from the first parent), the filling section (jobs set from the second parent) is completed using a transition rule adapted to the 1|s ij |ΣT j problem, similar to that used by the ant colony optimization (ACO) (Dorigo & Gambardella, 1997) and inspired by the work of Gagné et al (2002).…”

Section: The Integrative Hybrid Genetic Algorithmmentioning

confidence: 99%

“…This Branch and Bound approach solved some of these instances in more than 7 days. Because this problem is NP-hard, many researchers used a wide variety of metaheuristics to solve this problem, such as a genetic algorithm (Franca et al, 2001;Sioud et al, 2009), a memetic algorithm (Armentano & Mazzini, 2000;Franca et al, 2001;Rubin & Ragatz, 1995), a simulated annealing (Tan & Narasimhan, 1997), a GRASP (Gupta & Smith, 2006), an ant colonies optimization (ACO) (Gagné et al, 2002;Liao & Juan, 2007) and a Tabu/VNS (Gagné et al, 2005). Heuristics such as Random Start Pairwise Interchange (RSPI) (Rubin & Ragatz, 1995) and Apparent Tardiness Cost with Setups (ATCS) (Lee et al, 1997) have also been proposed for solving this problem.…”

Section: Introductionmentioning

confidence: 99%