Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives

Gao, Kaizhou; Suganthan, Ponnuthurai Nagaratnam; Pan, Quan-Ke; Chua, Tay Jin; Cai, Tian Xiang; Chong, Chin Soon

doi:10.1007/s10845-014-0869-8

Cited by 137 publications

(51 citation statements)

References 32 publications

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“…MCP is highly untractable and its decision version is among the first 21 NP-complete problems presented in Karp's seminal paper on computational complexity [23] . Even its approximation with in a constant factor are NP-Hard [24][25][26][27][28]. On the one hand, effective exact methods have been developed mainly based on the general branch and bound framework.…”

Section: Overview Of Harmony Search Applicationsmentioning

confidence: 99%

State-of-the-Art Review on Applications of Harmony Search Meta Heuristic Algorithm

Assad¹

2019

IJCET

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Harmony Search (HS) a meta heuristic algorithm inspired by music improvisation process in which the musician searches for the best harmony and continues to polish the harmony in order to improve its aesthetics. The HS algorithm was introduced in the year 2001 and has found applications in diverse fields.This manuscript reviews state-of-the-art applications of Harmony Search algorithm. As evidenced by a number of studies, this algorithm features several innovative aspects in its operational procedure that foster its utilization in diverse fields such as engineering, construction, telecommunications, robotics, health and energy

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Section: Overview Of Harmony Search Applicationsmentioning

confidence: 99%

State-of-the-Art Review on Applications of Harmony Search Meta Heuristic Algorithm

Assad¹

2019

IJCET

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“…When the intelligent optimization algorithm is utilized to solve the optimization problem that must make multiple decisions simultaneously, a feasible solution is usually divided into multiple stages, each of which gives a corresponding decision that it represents. The classical studies that utilized this solution representation can be found in Refs [34][35][36][37]. In this paper, this solution representation is used in the SGA, the algorithm framework of SGA in Ref.…”

Section: Comparison Algorithms and Parameter Settingmentioning

confidence: 99%

Parallel machine scheduling with dynamic resource allocation via a master–slave genetic algorithm

Tian

et al. 2018

IEEJ Transactions Elec Engng

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Scheduling problems with resource allocation have become of increasing interest in the academic and industrial areas in recent years. This study investigates parallel machine scheduling with dynamic resource allocation under the objective of minimizing the makespan, where the processing of jobs needs an additional resource that can be assigned and reassigned among all jobs, and the processing time of jobs is a function of the amount of allocated resources. Four decisions must be made in a schedule, namely job assignment, job sequence, resource allocation and jobs' starting time. In order to solve it efficiently, a master-slave genetic algorithm is proposed to determine the decisions of job assignment, job sequence and resource allocation, and a greedy heuristic rule is designed to determine the jobs' starting time. To examine the performance of the proposed algorithm, simulation experiments are carried out on a set of instances, and similar existing algorithms and the standard genetic algorithm are chosen as for comparison. The experimental results show that the proposed algorithm is a promising optimizer in solving the investigated problem.

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“…[67]. 2016 Discrete harmony search algorithm Minimising the makespan of machine and the mean of earliness and tardiness [22].…”

Section: Electre-based Multi-objective Gamentioning

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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Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives

Cited by 137 publications

References 32 publications

State-of-the-Art Review on Applications of Harmony Search Meta Heuristic Algorithm

State-of-the-Art Review on Applications of Harmony Search Meta Heuristic Algorithm

Parallel machine scheduling with dynamic resource allocation via a master–slave genetic algorithm

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

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