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Production lot sizing under setup and worker learning
Author(s) -
Karwan Kirk R.,
Mazzola Joseph B.,
Morey Richard C.
Publication year - 1988
Publication title -
naval research logistics (nrl)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.665
H-Index - 68
eISSN - 1520-6750
pISSN - 0894-069X
DOI - 10.1002/1520-6750(198804)35:2<159::aid-nav3220350202>3.0.co;2-1
Subject(s) - sizing , heuristic , time horizon , mathematical optimization , computer science , function (biology) , focus (optics) , learning curve , production (economics) , learning effect , horizon , artificial intelligence , mathematics , economics , microeconomics , art , visual arts , physics , geometry , evolutionary biology , optics , biology , operating system
Previous lot‐sizing models incorporating learning effects focus exclusively on worker learning. We extend these models to include the presence of setup learning, which occurs when setup costs exhibit a learning curve effect as a function of the number of lots produced. The joint worker/setup learning problem can be solved to optimality by dynamic programming. Computational experience indicates, however, that solution times are sensitive to certain problem parameters, such as the planning horizon and/or the presence of a lower bound on worker learning. We define a two‐phase EOQ‐based heuristic for the problem when total transmission of worker learning occurs. Numerical results show that the heuristic consistently generates solutions well within 1% of optimality.