An Ant Colony Algorithm for the Flowshop Scheduling Problem

Sadjadi, Seyed Jafar; Bouquard, Jean-Louis; Ziaee, Mohsen

doi:10.3923/jas.2008.3938.3944

Cited by 8 publications

(15 citation statements)

References 13 publications

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“…As for the population size, fewer ants have been used compared to Rajendran and Ziegler (2004; 40 ants) and compared to Sadjadi et al (2008; 1000 ants) in order to reduce the processing time. Consequently, the evaporation rate has been reduced compared to Sadjadi et al (ρ = 0.9) to reduce the effect of random search.…”

Section: Computation Experimentsmentioning

confidence: 99%

“…The stop criterion from Sadjadi et al (2008) is a fixed computation time. Instead we use a stability condition, corresponding to 3000 epochs with error reduction of at least one processing time unit.…”

Section: Computation Experimentsmentioning

confidence: 99%

“…Standard ACO by Bonabeau et al (1999) and disjunctive graph model inspired by Rossi and Dini (2007) are combined in this paper. It seems that the only ant colony algorithm applied to NPFS scheduling is by Sadjadi et al (2008), which provides the relative average performance on Taillard's benchmarks. Other NPFS benchmarks from Demirkol have been considered by Ying and Lin (2007) and by the authors (Rossi & Lanzetta, 2013 a , 2013 b ).…”

Section: Introductionmentioning

confidence: 99%

“…Sadjadi et al (2008) applied the standard ACO specifications from Bonabeau et al (1999), except for the diversification mechanism. The other main difference is on the selection of the initial population, which is determined by improving a permutation solution found by heuristics using local search.…”

Section: Introductionmentioning

confidence: 99%

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Nonpermutation flow line scheduling by ant colony optimization

Rossi

Lanzetta

2013

AIEDAM

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A flow line is a conventional manufacturing system where all jobs must be processed on all machines with the same operation sequence. Line buffers allow nonpermutation flowshop scheduling and job sequences to be changed on different machines. A mixed-integer linear programming model for nonpermutation flowshop scheduling and the buffer requirement along with manufacturing implication is proposed. Ant colony optimization based heuristic is evaluated against Taillard's (1993) well-known flowshop benchmark instances, with 20 to 500 jobs to be processed on 5 to 20 machines (stages). Computation experiments show that the proposed algorithm is incumbent to the state-of-the-art ant colony optimization for flowshop with higher job to machine ratios, using the makespan as the optimization criterion

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Section: Computation Experimentsmentioning

confidence: 99%

Section: Computation Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nonpermutation flow line scheduling by ant colony optimization

Rossi

Lanzetta

2013

AIEDAM

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“…Earlier works have adopted ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms to simulate the swarm behavior of ant colonies and flocks of birds, respectively. There are a few researches which implements the PSO and ACO for solving the MOPFSSP [25][26][27][28][29] subject to makespan, TFT and completion time variance. Recently, a lot many algorithms have been proposed by modeling the intelligent behaviors of real bee swarms in this regard.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Multi-Objective Artificial Bee Colony Algorithms and Chaotic-TOPSIS Method for Solving Flowshop Scheduling Problem and Decision Making

Panda

Dehuri

Jagadev

2020

IJCAI

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Retrieval of optimal solution(s) for a Permutation Flow-Shop Scheduling Problem (PFSSP) within a reasonable computational timeframe has been a challenge till yet. The problem includes optimization of various criteria like makespan, total flowtime, earliness, tardiness, etc for obtaining a set of Pareto solutions in the process of Multi-Objective Optimization (MOO). This paper remodels a Discrete Artificial Bee Colony Algorithm (DABC) from a single objective optimization method to a multiobjective optimization one to solve the PFSSP executed and explored through the alternative and combined use of two local search algorithms named as: Iterated Greedy Search Algorithm (IGRS) and Iterated Local Search Algorithm (ILS). The algorithm has been classified into three different scenarios raised with the analysis of time complexity measure of applied local search methods prioritized through the insertion and swap operation of neighborhood structures that intensifies the local optima in the search space. The results of the DABC algorithm are summarized with respect to Total Completion Time (TCT), Mean Weighted Tardiness (MWT), and Mean Weighted Earliness (MWE). Based on the time complexity measure of the obtained results a Multi-Objective Artificial Bee Colony Algorithm (MOABC) has been proposed by adopting the simplest local search method of all in order to reflect the enhanced version of previously remodeled DABC algorithm. Finally, we propose a Chaotic based Technique for Order of Preference by Similarity to Ideal Solution (Chaotic-TOPSIS) using a suitable chaotic map for criteria adaptation in order to enhance the decision accuracy in the multi-Criteria Decision Making (MCDM) domain. Povzetek: Članek se ukvarja z NP problemom večkriterijske optimizacije izdelave urnika z imenom Permutation Flow-Shop Scheduling Problem (PFSSP). Uvede Multi-Objective Artificial Bee Colony Algorithm (MOABC), tj. več-kriterijski algoritem z umetno čebeljo kolonijo in pokaže izboljšane rezultate.

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