Solving a multiobjective job shop scheduling problem using Pareto Archived Cuckoo Search

Hanoun, Samer; Nahavandi, Saeid; Creighton, Doug; Kull, Hans

doi:10.1109/etfa.2012.6489617

Cited by 21 publications

(15 citation statements)

References 13 publications

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“…GA [15], simulated annealing (SA) [7], [6], and Cuckoo Search [5] are examples of meta-heuristic methods. In [15], the problem of time-tabling in a manufacturing system was examined.…”

Section: Introductionmentioning

confidence: 99%

“…Minimizing job tardiness and material cost were the objectives in the experimental studies. The Pareto-based Cuckoo Search model was adopted in another recent work [5] In the literature, most investigations pertaining to job-shop scheduling aim to solve single objective problems [13], [9]. However, it is more practical to study multi-objective optimisation problems (MOPs) in real-world situations.…”

Section: Introductionmentioning

confidence: 99%

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A multi-objective evolutionary algorithm-based decision support system: A case study on job-shop scheduling in manufacturing

Tan

Hanoun

Lim

2015

2015 Annual IEEE Systems Conference (SysCon) Proceedings

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In this paper, an evolutionary algorithm is used for developing a decision support tool to undertake multiobjective job-shop scheduling problems. A modified micro genetic algorithm (MmGA) is adopted to provide optimal solutions according to the Pareto optimality principle in solving multiobjective optimisation problems. MmGA operates with a very small population size to explore a wide search space of function evaluations and to improve the convergence score towards the true Pareto optimal front. To evaluate the effectiveness of the MmGA-based decision support tool, a multi-objective job-shop scheduling problem with actual information from a manufacturing company is deployed. The statistical bootstrap method is used to evaluate the experimental results, and compared with those from the enumeration method. The outcome indicates that the decision support tool is able to achieve those optimal solutions as generated by the enumeration method. In addition, the proposed decision support tool has advantage of achieving the results within a fraction of the time.

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“…GA [15], simulated annealing (SA) [7], [6], and Cuckoo Search [5] are examples of meta-heuristic methods. In [15], the problem of time-tabling in a manufacturing system was examined.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multi-objective evolutionary algorithm-based decision support system: A case study on job-shop scheduling in manufacturing

Tan

Hanoun

Lim

2015

2015 Annual IEEE Systems Conference (SysCon) Proceedings

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“…In the past few decades, evolutionary-based and nature-inspired multiobjective optimization techniques have drawn considerable attention for scheduling problems [2,3,4,5,6,7]. In this paper, we introduce a recently new heuristics called state transition algorithm [8,9,10,11] as the basic search engine for the multiobjective optimization.…”

Section: Introductionmentioning

confidence: 99%

A Multiobjective State Transition Algorithm for Single Machine Scheduling

Zhou

Hanoun

Gao

et al. 2014

Springer Proceedings in Mathematics &Amp; Statistics

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Abstract. In this paper, a discrete state transition algorithm is introduced to solve a multiobjective single machine job shop scheduling problem. In the proposed approach, a non-dominated sort technique is used to select the best from a candidate state set, and a Pareto archived strategy is adopted to keep all the non-dominated solutions. Compared with the enumeration and other heuristics, experimental results have demonstrated the effectiveness of the multiobjective state transition algorithm.

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“…Two objectives were considered, i.e., minimising material costs and tardiness. The same job-shop scheduling problem was evaluated using a Pareto-based Cuckoo search model in [28]. In a recent study [64], the GA was evaluated against a branch-and-bound method in a single objective optimisation problem.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the GA [52], examples of recent and classical meta-heuristics methods used to solve optimisation problems include Cuckoo search [28] and simulated annealing [29], [27], respectively. In cellular manufacturing systems, the problem of time-tabling in a flowline was investigated in [52].…”

Section: Introductionmentioning

confidence: 99%

Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

et al. 2017

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Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) Application of an Evolutionary Algorithm-based Ensemble Model to Job-Shop SchedulingChoo Jun TAN, Siew Chin NEOH, Chee Peng LIM, Samer HANOUN, Wai Peng WONG, Chu Kong LOO, Li ZHANG, Saeid NAHAVANDI Abstract In this paper, a novel evolutionary algorithm is applied to tackle job-shop scheduling tasks in manufacturing environments. Specifically, a modified micro genetic algorithm (MmGA) is used as the building block to formulate an ensemble model to undertake multi-objective optimisation problems in job-shop scheduling. The MmGA ensemble is able to approximate the optimal solution under the Pareto optimality principle. To evaluate the effectiveness of the MmGA ensemble, a case study based on real requirements is conducted. The results positively indicate the effectiveness of the MmGA ensemble in undertaking job-shop scheduling problems.

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Solving a multiobjective job shop scheduling problem using Pareto Archived Cuckoo Search

Cited by 21 publications

References 13 publications

A multi-objective evolutionary algorithm-based decision support system: A case study on job-shop scheduling in manufacturing

A multi-objective evolutionary algorithm-based decision support system: A case study on job-shop scheduling in manufacturing

A Multiobjective State Transition Algorithm for Single Machine Scheduling

Application of an evolutionary algorithm-based ensemble model to job-shop scheduling

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