A multi-stage stochastic programming model of lot-sizing and scheduling problems with machine eligibilities and sequence-dependent setups

We focus on the lot-sizing and scheduling problem with the additional considerations of machine eligibility, sequence-dependent setups, and uncertain demands. Multi-stage stochastic programming is proposed. We analyze the problem structure and suggest ways for modeling and solving large-scale stocha...

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Veröffentlicht in:Annals of operations research Jg. 311; H. 1; S. 35 - 50
Hauptverfasser: Chen, Sheng-I, Su, Delvinia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.04.2022
Springer
Springer Nature B.V
Schlagworte:
Business and Management
Combinatorics
Demand
Economic lot size
Eligibility
Job shops
Lot sizing
Methods
Operations research
Operations Research/Decision Theory
Production planning
S.I.: Reliability Modeling with Applications Based on Big Data
Scheduling
Scheduling (Management)
Stochastic models
Stochastic programming
Theory of Computation
Taiwan
Multi-stage stochastic programming
Machine eligibility
Lot-sizing and scheduling
Sequence-dependent setups
ISSN:0254-5330, 1572-9338
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Zusammenfassung:We focus on the lot-sizing and scheduling problem with the additional considerations of machine eligibility, sequence-dependent setups, and uncertain demands. Multi-stage stochastic programming is proposed. We analyze the problem structure and suggest ways for modeling and solving large-scale stochastic integer programs. The analysis compares deterministic and stochastic model solutions to assess demand variance effects under the circumstances of increasing, fluctuating, and decreasing demands. The result shows that the expected cost performance of the stochastic programming model outperforms that of the deterministic model, in particular, when the demand is highly uncertain in the circumstance of an upward market trend. Our study can apply to the wafer fab manufacturing and other industries that heavily restricted by machine eligibility and demand uncertainties.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-019-03462-1