Approximation Methods for the Uncapacitated Dynamic Lot Size Problem

Gabriel R. Bitran, Thomas L. Magnanti and Horacio H. Yanasse
Additional contact information
Gabriel R. Bitran: Sloan School of Management, MIT, Cambridge, Massachusetts 02139
Thomas L. Magnanti: Sloan School of Management, MIT, Cambridge, Massachusetts 02139
Horacio H. Yanasse: Instituto de Pesquisas Espaciais, Sao Paulo, Brizil

Management Science, 1984, vol. 30, issue 9, 1121-1140

Abstract: We provide worst case error bounds for several approximation methods (heuristics, product aggregation, and partitioning of the planning horizon) for the uncapacitated dynamic lot size problem. We propose two managerially oriented heuristics and show that they have a relative wont case error bound equal to two, and develop similar analyses for methods known as the least cost per unit heuristic, the part period balancing heuristic, and an economic order quantity heuristic (expressed in terms of a time supply of demand). We also show how errors introduced by partitioning of the planning horizon in multi-product multi-facility problems are bounded by product set-up costs, and how errors introduced by product aggregation are bounded by set-up costs, holding costs, and demands. The latter results suggest methods for product aggregation that minimize the worst case error bounds.

Keywords: inventory/production:; lot; sizing (search for similar items in EconPapers)
Date: 1984
References: Add references at CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.30.9.1121 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:30:y:1984:i:9:p:1121-1140

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().