Solving hybrid flow shop scheduling problems using bat algorithm

Marichelvam, M.K.; Prabaharan, T.; Yang, Xin; Mani, Geetha

doi:10.1504/ijleg.2013.054428

Cited by 28 publications

(15 citation statements)

References 33 publications

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“…According to OG, the average results of dominance rules is 1.49%, while that of simplification of subproblems is 1.77%. Because of applying the heuristic approach to calculate the upper bound, the solution with OG of less than 3% is very 13 close to optimal [10]. In term of CPU time, the LR with dominance rules can achieve the final solution in 32.8s, but the simplification of subproblems takes about 45.9s to solve the test problems in average.…”

Section: Results Analysis For Small Size Problemsmentioning

confidence: 99%

Lagrangian relaxation approach to minimize makespan for hybrid flow shop scheduling problem with unrelated parallel machines

Asadi-Gangraj

2017

Scientia Iranica

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This research addresses scheduling problem of n jobs on a Hybrid Flow Shop (HFS) with unrelated parallel machines in each stage. A monolithic mixed integer linear programming (MILP) model is presented to minimize the maximum completion time (makespan). As the research problem is shown to be strongly NP-hard, a Lagrangian relaxation (LR) algorithm is developed to handle the HFS scheduling problem. We design two approaches, simplification of subproblems and dominance rules, to solve the subproblems which are generated in each iteration. For evaluation purposes, numerical experiments with small and large size problems are randomly generated with up to 50 jobs and four stages. The experimental results show that the Lagrangian relaxation approaches outperform the MILP model with respect to CPU time. Furthermore, from the results, it can be conclude that the simplification of subproblems shows slightly better solutions in comparison with dominance rules to solve the subproblems.

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Section: Results Analysis For Small Size Problemsmentioning

confidence: 99%

Lagrangian relaxation approach to minimize makespan for hybrid flow shop scheduling problem with unrelated parallel machines

Asadi-Gangraj

2017

Scientia Iranica

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“…Liao et al (2012) added 10 new instances of slightly larger sizes to this benchmark. The same HFS problem was studied by Marichelvam et al (2013) who proposed a bat algorithm (BA). Wang et al (2013) designed an estimation of distribution algorithm (EDA).…”

Section: Problem Description and Literature Reviewmentioning

confidence: 99%

Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems

Alfaro-Fernández

Ruíz

Pagnozzi

et al. 2020

European Journal of Operational Research

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Industrial production scheduling problems are challenges that researchers have been trying to solve for decades. Many practical scheduling problems such as the hybrid flowshop are N P-hard. As a result, researchers resort to metaheuristics to obtain e ective and e cient solutions. The traditional design process of metaheuristics is mainly manual, often metaphor-based, biased by previous experience and prone to producing overly tailored methods that only work well on the tested problems and objectives. In this paper, we use an Automatic Algorithm Design (AAD) methodology to eliminate these limitations. AAD is capable of composing algorithms from components with minimal human intervention. We test the proposed AAD for three di erent optimization objectives in the hybrid flowshop. Comprehensive computational and statistical testing demonstrates that automatically designed algorithms outperform specifically tailored state-of-the-art methods for the tested objectives in most cases.

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“…Since this first implementation, the BA has been applied in a wide range of fields. Some of these fields are the continuous optimization, in which some additional works have been published apart from to the original one, ([Bora et al(2012) Bora, Coelho & Lebensztajn, Yang & Hossein Gandomi(2012)]), combinatorial optimization ( [Marichelvam et al(2013) Marichelvam, Prabaharan, Yang & Geetha]), image processing ( [Zhang & Wang(2012)]) and clustering problems ([Komarasamy & Wahi(2012)]).…”

Section: Related Workmentioning

confidence: 99%

An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems

Osaba

Yang

Díaz

et al. 2016

Engineering Applications of Artificial Intelligence

289

115

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Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric traveling salesman problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Student's ttest, the Holm's test, and the Friedman test. We have also compared the convergence behaviour shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases.

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Solving hybrid flow shop scheduling problems using bat algorithm

Cited by 28 publications

References 33 publications

Lagrangian relaxation approach to minimize makespan for hybrid flow shop scheduling problem with unrelated parallel machines

Lagrangian relaxation approach to minimize makespan for hybrid flow shop scheduling problem with unrelated parallel machines

Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems

An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems

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