Heuristics for the economic lot scheduling problem with returns. [In special section on problems and models of inventories selected papers of the fourteenth International symposium on inventories]
Ruud, Teunter, Tang, Ou and Kaparis, Konstantinos (2009) Heuristics for the economic lot scheduling problem with returns. [In special section on problems and models of inventories selected papers of the fourteenth International symposium on inventories]. International Journal of Production Economics, 118, (1), 323-330. (doi:10.1016/j.ijpe.2008.08.036).
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We study the multi-item economic lot scheduling problem (ELSP) with two sources of production: manufacturing of new items and remanufacturing of returned items. Manufacturing and remanufacturing operations are performed on the same production line. Tang and Teunter [2006. Economic lot scheduling problem with returns. Production and Operations Management 15 (4), 488–497.] recently presented a complex algorithm for this problem that determines the optimal solution within the class of policies with a common cycle time and a single (re)manufacturing lot for each item in each cycle. This algorithm is rather complex and time consuming, combining a large MIP formulation with a search procedure, and may therefore not always be practical. In this paper, we deal with this type of problems and propose simple heuristics that are very fast and can be applied in a spreadsheet package. A large numerical study shows that the heuristics provide close to optimal solutions.
|Digital Object Identifier (DOI):||doi:10.1016/j.ijpe.2008.08.036|
|Keywords:||elsp, returns, remanufacturing, reverse logistics|
|Subjects:||T Technology > TS Manufactures
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
H Social Sciences > HB Economic Theory
|Divisions :||University Structure - Pre August 2011 > School of Mathematics > Operational Research
|Accepted Date and Publication Date:||
|Date Deposited:||22 Sep 2009|
|Last Modified:||31 Mar 2016 12:54|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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