A Capacitated Lot Sizing Problem With Stochastic Setup Times and Overtime
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Date
2019
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Open Access Color
Green Open Access
Yes
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Publicly Funded
No
Abstract
In this paper, we study a Capacitated Lot Sizing Problem with Stochastic Setup Times and Overtime (CLSPSSTO). We describe a mathematical model that considers both regular costs (including production, setup and inventory holding costs) and expected overtime costs (related to the excess usage of capacity). The CLSP-SSTO is formulated as a two-stage stochastic programming problem. A procedure is proposed to exactly compute the expected overtime for a given setup and production plan when the setup times follow a Gamma distribution. A sample average approximation procedure is applied to obtain upper bounds and a statistical lower bound. This is then used to benchmark the performance of two additional heuristics. A first heuristic is based on changing the capacity in the deterministic counterpart, while the second heuristic artificially modifies the setup time. We conduct our computational experiments on well-known problem instances and provide comprehensive analyses to evaluate the performance of each heuristic. (C) 2018 Elsevier B.V. All rights reserved.
Description
Duygu Taş (MEF Author)
ORCID
Keywords
Sample average approximation, Stochastic setup times, Heuristics, Production, Lot sizing, Heuristics; Lot sizing; Production; Sample average approximation; Stochastic setup times; Computer Science (all); Modeling and Simulation; Management Science and Operations Research; Information Systems and Management
Fields of Science
0209 industrial biotechnology, 0211 other engineering and technologies, 02 engineering and technology
Citation
Taş, D., Gendreau, M., Jabali, O., & Jans, R. (January 01, 2019). A capacitated lot sizing problem with stochastic setup times and overtime. European Journal of Operational Research, 273, 1, 146-159.
WoS Q
Q1
Scopus Q
Q1

OpenCitations Citation Count
18
Source
European Journal Of Operational Research
Volume
273
Issue
1
Start Page
146
End Page
159
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Scopus : 22
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Mendeley Readers : 51
SCOPUS™ Citations
23
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Web of Science™ Citations
19
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Page Views
179
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Downloads
40
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