An ant colony system for permutation flow-shop sequencing

Ying, Kuo‐Ching; Liao, Ching‐Jong

doi:10.1016/s0305-0548(03)00038-8

Cited by 166 publications

(48 citation statements)

References 21 publications

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“…Therefore, to make a fair comparison, the CPU times of ACS reported in Ref. 47 have been divided by the transformation factor three in Table 4. With regard to this matter, it is seen that in all of the different sizes the proposed algorithm is much faster than ACS.…”

Section: Performance Analysis Of the Algorithmmentioning

confidence: 99%

A hybrid algorithm to minimize makespan for the permutation flow shop scheduling problem

Ahmadizar¹,

Barzinpour²

2010

IJCIS

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This paper deals with the permutation flow shop scheduling problem. The objective is to minimize the maximum completion time, or makespan. To solve this problem which has been proved to be strongly NP-hard, a combination between an ant colony algorithm, a heuristic algorithm and a local search procedure is proposed and presented. The hybrid approach is to use artificial ants to construct solutions by applying a stochastic greedy rule based on the Gupta's heuristic and pheromone trails. A local search is then performed to improve the performance quality of constructed solutions. Once all ants have terminated their generations, the pheromone trails are modified according to a global updating rule. The proposed algorithm is applied to benchmark problems taken from the literature and compared with other metaheuristics. Computational experiments are given to demonstrate the superiority of the algorithm in the quality of solution and CPU time.

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Section: Performance Analysis Of the Algorithmmentioning

confidence: 99%

A hybrid algorithm to minimize makespan for the permutation flow shop scheduling problem

Ahmadizar¹,

Barzinpour²

2010

IJCIS

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“…ACO has been successfully applied to several combinatorial optimization problems, from the first travelling salesman problem applications (Dorigo et al, 1991), (Dorigo et al, 1996), to vehicle routing problems (Bullnheimer et al, 1999), (Reinmann et al, 2004), and to single machine and flow shop scheduling problems (den Besten et al, 2000), (Gagné et al, 2002) and (Ying & Liao, 2004). In this section we present a new hybrid ACO-SA approach to face the STWTSDS problem.…”

Section: The Hybrid Ant Colony Optimization Algorithmmentioning

confidence: 99%

Simulated Annealing as an Intensification Component in Hybrid Population-based Metaheuristics

Anghinolfi¹,

Paolucci²

2008

Simulated Annealing

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“…In the next chapter, we will explain this algorithm in details. Ying and Liao [19], In their paper, the problem of flow shop has been studied with Ant Colony System (ACS) method and this problem has been studied in multipurpose state by Yagmahan and Yenisey [20]. Also Khalouli et al [21] has solved hybrid flow shop by the use of ACO algorithm.…”

Section: Introductionmentioning

confidence: 99%

An ant colony system for solving fuzzy flow shop scheduling problem

Pour

Ashkezari²,

Andargoli³

2012

IJET

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During the past years, the flow shop has been regarded by many researchers and some extensive investigations have been done on this respect. Flow Shop includes n works performed on m machines in a same sequence. It is very difficult in the real world to determine the exact process time of an operation on a machine. Therefore, we consider in this article the process time as trapezoidal fuzzy numbers. Our purpose is that we obtain a sequence of works using such fuzzy numbers in order to minimize maximum fuzzy time of completion entire jobs or fuzzy makespan. We offered an optimization algorithm of Ant Colony System (ACS) to solve this problem. Finally, we present computational results for explanation and comparison with other articles in future.

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An ant colony system for permutation flow-shop sequencing

Cited by 166 publications

References 21 publications

A hybrid algorithm to minimize makespan for the permutation flow shop scheduling problem

A hybrid algorithm to minimize makespan for the permutation flow shop scheduling problem

Simulated Annealing as an Intensification Component in Hybrid Population-based Metaheuristics

An ant colony system for solving fuzzy flow shop scheduling problem

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