Pareto archived simulated annealing for single machine job shop scheduling with multiple objectives Hanoun, Samer, Nahavandi, Saeid and Kull, Hans 2011, Pareto archived simulated annealing for single machine job shop scheduling with multiple objectives, in ICCGI 2011 : Sixth International Multi-Conference on Computing in the Global Information Technology, [IARIA], [Luxembourg City, Luxembourg], pp. 99-104. Attached Files Name Description MIMEType Size Downloads hanoun-paretoarchived-2011.pdf Published version application/pdf 1.77MB 45 Title Pareto archived simulated annealing for single machine job shop scheduling with multiple objectives Author(s) Hanoun, Samer orcid.org/0000-0002-8697-1515 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Kull, Hans Conference name International Multi-Conference on Computing in the Global Information Technology (6th : 2011 : Luxembourg City, Luxembourg) Conference location Luxembourg City, Luxembourg Conference dates 19-24 June 2011 Title of proceedings ICCGI 2011 : Sixth International Multi-Conference on Computing in the Global Information Technology Editor(s) Paleologu, Constantin Mavromoustakis, Constandinos Minea, Marius Publication date 2011 Conference series International Multi-Conference on Computing in the Global Information Technology Start page 99 End page 104 Total pages 6 Publisher [IARIA] Place of publication [Luxembourg City, Luxembourg] Keyword(s) multi-criteria optimization simulated annealing metaheuristic procedures pareto optimal job shop scheduling Summary In this paper, the single machine job shop scheduling problem is studied with the objectives of minimizing the tardiness and the material cost of jobs. The simultaneous consideration of these objectives is the multi-criteria optimization problem under study. A metaheuristic procedure based on simulated annealing is proposed to find the approximate Pareto optimal (non-dominated) solutions. The two objectives are combined in one composite utility function based on the decision maker’s interest in having a schedule with weighted combination. In view of the unknown nature of the weights for the defined objectives, a priori approach is applied to search for the non-dominated set of solutions based on the Pareto dominance. The obtained solutions set is presented to the decision maker to choose the best solution according to his preferences. The performance of the algorithm is evaluated in terms of the number of non-dominated schedules generated and the proximity of the obtained non-dominated front to the true Pareto front. Results show that the produced solutions do not differ significantly from the optimal solutions. ISBN 9781612081397 Language eng Field of Research 010303 Optimisation Socio Economic Objective 970109 Expanding Knowledge in Engineering HERDC Research category E1 Full written paper - refereed Copyright notice ©2011, IARIA Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30042234 Document type: Conference Paper Collections: Institute for Intelligent Systems Research and Innovation (IISRI) Open Access Collection GTP Research Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Tue, 14 Feb 2012, 15:21:33 EST Thu, 15 Mar 2012, 12:32:46 EST Thu, 15 Mar 2012, 12:34:19 EST Fri, 30 Mar 2012, 11:55:04 EST Tue, 03 Apr 2012, 09:06:57 EST Tue, 03 Apr 2012, 09:08:22 EST Wed, 04 Apr 2012, 09:04:07 EST Thu, 15 May 2014, 09:51:31 EST Thu, 20 Aug 2015, 12:25:36 EST Tue, 05 Dec 2017, 12:28:55 EST Fri, 07 Sep 2018, 15:43:40 EST Sat, 16 Feb 2019, 01:31:28 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 679 Abstract Views, 49 File Downloads  -  Detailed Statistics Created: Tue, 14 Feb 2012, 15:21:33 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.