Multistage Stochastic Optimization for Production-Inventory Planning with Intermittent Renewable Energy

Mehdi Golari, Neng Fan, Tongdan Jin

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

A growing number of companies install wind and solar generators in their energy-intensive facilities to attain low-carbon manufacturing operations. However, there is a lack of methodological studies on operating large manufacturing facilities with intermittent power. This study presents a multi-period, production-inventory planning model in a multi-plant manufacturing system powered with onsite and grid renewable energy. Our goal is to determine the production quantity, the stock level, and the renewable energy supply in each period such that the aggregate production cost (including energy) is minimized. We tackle this complex decision problem in three steps. First, we present a deterministic planning model to attain the desired green energy penetration level. Next, the deterministic model is extended to a multistage stochastic optimization model taking into account the uncertainties of renewables. Finally, we develop an efficient modified Benders decomposition algorithm to search for the optimal production schedule using a scenario tree. Numerical experiments are carried out to verify and validate the model integrity, and the potential of realizing high-level renewables penetration in large manufacturing system is discussed and justified.

Original languageEnglish (US)
Pages (from-to)409-425
Number of pages17
JournalProduction and Operations Management
Volume26
Issue number3
DOIs
StatePublished - Mar 1 2017

Keywords

  • Benders decomposition
  • green energy coefficient
  • onsite renewable generation
  • production planning

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Management of Technology and Innovation

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