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

  • S.I.: Reliability Modeling with Applications Based on Big Data
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Abstract

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.

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Funding

This work was supported by the Ministry of Science and Technology, Taiwan (https://www.most.gov.tw/) under Grants MOST 106-2221-E-009-114- MY2 and MOST 108-2221-E-009-029 -.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, National Chiao Tung University, 1001 University Road, Hsinchu, Taiwan

    Sheng-I Chen & Delvinia Su

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  1. Sheng-I Chen
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  2. Delvinia Su
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Correspondence to Sheng-I Chen.

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Chen, SI., Su, D. A multi-stage stochastic programming model of lot-sizing and scheduling problems with machine eligibilities and sequence-dependent setups. Ann Oper Res 311, 35–50 (2022). https://doi.org/10.1007/s10479-019-03462-1

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  • DOI: https://doi.org/10.1007/s10479-019-03462-1

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