Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0098-1354(01)00677-9
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dc.titlePlanning production on a single processor with sequence-dependent setups. Part 2: Campaign sequencing and scheduling
dc.contributor.authorOh, H.-C.
dc.contributor.authorKarimi, I.A.
dc.date.accessioned2014-10-09T09:58:44Z
dc.date.available2014-10-09T09:58:44Z
dc.date.issued2001-08-15
dc.identifier.citationOh, H.-C., Karimi, I.A. (2001-08-15). Planning production on a single processor with sequence-dependent setups. Part 2: Campaign sequencing and scheduling. Computers and Chemical Engineering 25 (7-8) : 1031-1043. ScholarBank@NUS Repository. https://doi.org/10.1016/S0098-1354(01)00677-9
dc.identifier.issn00981354
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/92240
dc.description.abstractIn the preceding paper (Oh, H., Karimi, I., 2001. Planning production on a single processor with sequence-dependent setups - 1. Determination of campaigns, Comput. Chem. Eng., the preceding paper in this issue), we presented a methodology for determining the optimal campaign numbers for producing multiple products on a single processor with sequence-dependent setups and a fixed planning horizon. In this paper, we address the sequencing of these given product campaigns to obtain a detailed schedule of operation. Decomposing the problem into a sequencing subproblem and a scheduling subproblem, we develop efficient heuristic algorithms for both subproblems. For the former combinatorial subproblem, we propose an efficient tabu search based on a simple dummy search objective, while for the latter continuous subproblem, we present a novel linear programming approximation. Extensive computational evaluation on randomly generated problems shows that the combined algorithm is quite efficient and well suited for large-scale industrial problems. Moreover, it gives solutions consistently within 7% (on an average) of a lower bound and its performance does not deteriorate with high sequence-dependency or high machine utilization. Thus, the methodology of this two-part paper represents a significant improvement over existing methods for this problem. © 2001 Elsevier Science Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S0098-1354(01)00677-9
dc.sourceScopus
dc.subjectProduction planning
dc.subjectScheduling
dc.subjectSequencing
dc.subjectTabu search
dc.typeArticle
dc.contributor.departmentCHEMICAL & ENVIRONMENTAL ENGINEERING
dc.description.doi10.1016/S0098-1354(01)00677-9
dc.description.sourcetitleComputers and Chemical Engineering
dc.description.volume25
dc.description.issue7-8
dc.description.page1031-1043
dc.description.codenCCEND
dc.identifier.isiut000170696100008
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