An Approach for Strategic Supply Chain Planning under Uncertainty based on Stochastic 0-1 Programming

Authors:
A. Alonso-Ayuso;L. F. Escudero;A. Garí/n;M. T. Ortuñ/o;G. Pé/rez
Affiliations:
Dpto. de CC. Experimentales y Tecnologí/a, Universidad Rey Juan Carlos, Mó/stoles (Madrid), Spain/ e-mail: a.alonso@escet.urjc.es/;Centro de Investigació/n Operativa, Universidad Miguel Herná/ndez, Elche (Alicante), Spain/ e-mail: escudero@umh.es/;Dpto. de Economí/a Aplicada III, Universidad del Paí/s Vasco, Bilbao (Vizcaya), Spain/ e-mail: ag@alcib.bs.ehu.es/;Dpto. de Estadí/stica e Investigació/n Operativa I, Universidad Complutense de Madrid, Spain/ e-mail: mteresa@eucmax.sim.ucm.es/;Dpto. de Matemá/tica Aplicada, Estadí/stica e I.O., Universidad del Paí/s Vasco, Leioa (Vizcaya), Spain/ e-mail: gloria@picasso.lc.ehu.es
Venue:
Journal of Global Optimization
Year:
2003

Citing 0
Cited 21

The Million-Variable "March" for Stochastic Combinatorial Optimization

Journal of Global Optimization
Stochastic programming and scenario generation within a simulation framework: An information systems perspective

Decision Support Systems
On a stochastic sequencing and scheduling problem

Computers and Operations Research
A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor

Information Sciences: an International Journal
A comparative study of decomposition algorithms for stochastic combinatorial optimization

Computational Optimization and Applications
Computations with disjunctive cuts for two-stage stochastic mixed 0-1 integer programs

Journal of Global Optimization
A general algorithm for solving two-stage stochastic mixed 0-1 first-stage problems

Computers and Operations Research
Enhanced Cut Generation Methods for Decomposition-Based Branch and Cut for Two-Stage Stochastic Mixed-Integer Programs

INFORMS Journal on Computing
Fuzzy optimization for supply chain planning under supply, demand and process uncertainties

Fuzzy Sets and Systems
On BFC-MSMIP strategies for scenario cluster partitioning, and twin node family branching selection and bounding for multistage stochastic mixed integer programming

Computers and Operations Research
Evaluating optimization models to solve SALBP

ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Survey: Facility location dynamics: An overview of classifications and applications

Computers and Industrial Engineering
A branch-and-cluster coordination scheme for selecting prison facility sites under uncertainty

Computers and Operations Research
A profit-maximizing supply chain network design model with demand choice flexibility

Operations Research Letters
Scenario Cluster Decomposition of the Lagrangian dual in two-stage stochastic mixed 0-1 optimization

Computers and Operations Research
Supply Chain Optimization and Planning in Heracles General Cement Company

Interfaces
An algorithmic framework for solving large-scale multistage stochastic mixed 0-1 problems with nonsymmetric scenario trees. Part II: Parallelization

Computers and Operations Research
Fix-and-Relax-Coordination for a multi-period location-allocation problem under uncertainty

Computers and Operations Research
Risk-averse two-stage stochastic programs in furniture plants

OR Spectrum
Scenario grouping in a progressive hedging-based meta-heuristic for stochastic network design

Computers and Operations Research
Bi-objective supply chain planning in a fuzzy environment

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a two-stage stochastic 0-1 modeling and a related algorithmic approach for Supply Chain Management under uncertainty, whose goal consists of determining the production topology, plant sizing, product selection, product allocation among plants and vendor selection for raw materials. The objective is the maximization of the expected benefit given by the product net profit over the time horizon minus the investment depreciation and operations costs. The main uncertain parameters are the product net price and demand, the raw material supply cost and the production cost. The first stage is included by the strategic decisions. The second stage is included by the tactical decisions. A tight 0-1 model for the deterministic version is presented. A splitting variable mathematical representation via scenario is presented for the stochastic version of the model. A two-stage version of a Branch and Fix Coordination (BFC) algorithmic approach is proposed for stochastic 0-1 program solving, and some computational experience is reported for cases with dozens of thousands of constraints and continuous variables and hundreds of 0-1 variables.