A two-stage modeling and solution framework for multisite midterm planning under demand uncertainty

Research output: Contribution to journalArticle

133 Citations (Scopus)

Abstract

A two-stage, stochastic programming approach is proposed for incorporating demand uncertainty in multisite midterm supply-chain planning problems. In this bilevel decision-making framework, the production decisions are made `here-and-now' prior to the resolution of uncertainty, while the supply-chain decisions are postponed in a `wait-and-see' mode. The challenge associated with the expectation evaluation of the inner optimization problem is resolved by obtaining its closed-form solution using linear programming (LP) duality. At the expense of imposing the normality assumption for the stochastic product demands, the evaluation of the expected second-stage costs is achieved by analytical integration yielding an equivalent convex mixed-integer nonlinear problem (MINLP). Computational requirements for the proposed methodology are shown to be much smaller than those for Monte Carlo sampling. In addition, the cost savings achieved by modeling uncertainty at the planning stage are quantified on the basis of a rolling horizon simulation study.

Original languageEnglish (US)
Pages (from-to)3799-3813
Number of pages15
JournalIndustrial and Engineering Chemistry Research
Volume39
Issue number10
DOIs
StatePublished - Jan 1 2000

Fingerprint

Planning
Supply chains
Stochastic programming
Linear programming
Costs
Decision making
Sampling
Uncertainty

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering

Cite this

@article{283475f4cb414101aba9dd8180eadb23,
title = "A two-stage modeling and solution framework for multisite midterm planning under demand uncertainty",
abstract = "A two-stage, stochastic programming approach is proposed for incorporating demand uncertainty in multisite midterm supply-chain planning problems. In this bilevel decision-making framework, the production decisions are made `here-and-now' prior to the resolution of uncertainty, while the supply-chain decisions are postponed in a `wait-and-see' mode. The challenge associated with the expectation evaluation of the inner optimization problem is resolved by obtaining its closed-form solution using linear programming (LP) duality. At the expense of imposing the normality assumption for the stochastic product demands, the evaluation of the expected second-stage costs is achieved by analytical integration yielding an equivalent convex mixed-integer nonlinear problem (MINLP). Computational requirements for the proposed methodology are shown to be much smaller than those for Monte Carlo sampling. In addition, the cost savings achieved by modeling uncertainty at the planning stage are quantified on the basis of a rolling horizon simulation study.",
author = "A. Gupta and Maranas, {Costas D.}",
year = "2000",
month = "1",
day = "1",
doi = "10.1021/ie9909284",
language = "English (US)",
volume = "39",
pages = "3799--3813",
journal = "Industrial and Engineering Chemistry Research",
issn = "0888-5885",
publisher = "American Chemical Society",
number = "10",

}

A two-stage modeling and solution framework for multisite midterm planning under demand uncertainty. / Gupta, A.; Maranas, Costas D.

In: Industrial and Engineering Chemistry Research, Vol. 39, No. 10, 01.01.2000, p. 3799-3813.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A two-stage modeling and solution framework for multisite midterm planning under demand uncertainty

AU - Gupta, A.

AU - Maranas, Costas D.

PY - 2000/1/1

Y1 - 2000/1/1

N2 - A two-stage, stochastic programming approach is proposed for incorporating demand uncertainty in multisite midterm supply-chain planning problems. In this bilevel decision-making framework, the production decisions are made `here-and-now' prior to the resolution of uncertainty, while the supply-chain decisions are postponed in a `wait-and-see' mode. The challenge associated with the expectation evaluation of the inner optimization problem is resolved by obtaining its closed-form solution using linear programming (LP) duality. At the expense of imposing the normality assumption for the stochastic product demands, the evaluation of the expected second-stage costs is achieved by analytical integration yielding an equivalent convex mixed-integer nonlinear problem (MINLP). Computational requirements for the proposed methodology are shown to be much smaller than those for Monte Carlo sampling. In addition, the cost savings achieved by modeling uncertainty at the planning stage are quantified on the basis of a rolling horizon simulation study.

AB - A two-stage, stochastic programming approach is proposed for incorporating demand uncertainty in multisite midterm supply-chain planning problems. In this bilevel decision-making framework, the production decisions are made `here-and-now' prior to the resolution of uncertainty, while the supply-chain decisions are postponed in a `wait-and-see' mode. The challenge associated with the expectation evaluation of the inner optimization problem is resolved by obtaining its closed-form solution using linear programming (LP) duality. At the expense of imposing the normality assumption for the stochastic product demands, the evaluation of the expected second-stage costs is achieved by analytical integration yielding an equivalent convex mixed-integer nonlinear problem (MINLP). Computational requirements for the proposed methodology are shown to be much smaller than those for Monte Carlo sampling. In addition, the cost savings achieved by modeling uncertainty at the planning stage are quantified on the basis of a rolling horizon simulation study.

UR - http://www.scopus.com/inward/record.url?scp=0034306197&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034306197&partnerID=8YFLogxK

U2 - 10.1021/ie9909284

DO - 10.1021/ie9909284

M3 - Article

VL - 39

SP - 3799

EP - 3813

JO - Industrial and Engineering Chemistry Research

JF - Industrial and Engineering Chemistry Research

SN - 0888-5885

IS - 10

ER -