Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/18466

Title: Solving multi-objective job shop scheduling problems using artificial immune system shifting bottleneck approach
Authors: Gopinath, S.
Page, Tom
Arumugam, C.
Chandrasekaran, M.
Keywords: Job shop scheduling
Multi objectives
Artificial immune system shifting bottleneck
Issue Date: 2015
Publisher: © Trans Tech Publications
Citation: GOPINATH, S. ... et al, 2015. Solving multi-objective job shop scheduling problems using artificial immune System shifting bottleneck approach. Applied Mechanics and Materials, 766-767, pp. 1209 - 1213
Abstract: Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using reasonable resources in many cases. Scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered in this work. The Artificial Immune System Shifting Bottleneck Approach is used for finding optimal makespan, mean flow time, mean tardiness values of two benchmark problems. In this Artificial Immune System Shifting Bottleneck Approach (AISSB), initial sequences are generated with Artificial Immune System Algorithm (AIS) and Shifting Bottleneck Algorithm (SB) is used for finding final solutions. The results show that the AISSB Approach is effective algorithm that gives better results than literature results. The proposed AISSB Approach is an efficient problem-solving technique for multi objective job shop scheduling problem.
Description: This article is closed access.
Version: Published
DOI: 10.4028/www.scientific.net/AMM.766-767.1209
URI: https://dspace.lboro.ac.uk/2134/18466
Publisher Link: http://dx.doi.org/10.4028/www.scientific.net/AMM.766-767.1209
ISSN: 1660-9336
Appears in Collections:Closed Access (Design School)

Files associated with this item:

File Description SizeFormat
AMM.766-767.1209-1.pdfPublished version212.07 kBAdobe PDFView/Open


SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.