Determination of due dates in job shop scheduling by simulated annealing

Mamalis, AG; Malagardis, I

dc.contributor.author	Mamalis, AG	en
dc.contributor.author	Malagardis, I	en
dc.date.accessioned	2014-03-01T01:11:52Z
dc.date.available	2014-03-01T01:11:52Z
dc.date.issued	1996	en
dc.identifier.issn	0951-5240	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/11839
dc.subject	simulation	en
dc.subject	simulated annealing	en
dc.subject	manufacturing systems	en
dc.subject	production planning	en
dc.subject	group technology	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Manufacturing	en
dc.subject.classification	Operations Research & Management Science	en
dc.title	Determination of due dates in job shop scheduling by simulated annealing	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/0951-5240(95)00039-9	en
heal.identifier.secondary	http://dx.doi.org/10.1016/0951-5240(95)00039-9	en
heal.language	English	en
heal.publicationDate	1996	en
heal.abstract	The scheduling of n jobs to m machines in a job shop is considered. A predefined due date, a release time and the minimization of the maximal job's lateness is the objective assigned to each job. A search space consisting of triads (job, operation, machine) is formulated, and an iterrative improvement approach, the simulated annealing method, is then used to obtain feasible and global optimal solution. The simulated annealing method is applied to two alternative energy functions to model the maximum lateness. For calculation of the first energy function at each step, complete schedules are created and the lateness of each job is derived by abstracting the job's completion time from the corresponding due date. The second energy function is calculated on the basis of partial estimates often used by pairwise interchange techniques. The convergence of the algorithm in relation to the initial temperature, temperature iterrations and temperature cycles has been verified in various case studies. Specific characteristics of the scheduling, such as its dimensionality and the deviation of the total processing time from the due dates, were considered. Common characteristics derived were subsequently used for the definition of an efficient annealing schedule. Copyright (C) 1996 Published by Elsevier Science Ltd	en
heal.publisher	BUTTERWORTH-HEINEMANN LTD	en
heal.journalName	COMPUTER INTEGRATED MANUFACTURING SYSTEMS	en
dc.identifier.doi	10.1016/0951-5240(95)00039-9	en
dc.identifier.isi	ISI:A1996UV03100001	en
dc.identifier.volume	9	en
dc.identifier.issue	2	en
dc.identifier.spage	65	en
dc.identifier.epage	72	en