Combined simulation and network optimization analysis of a production/distribution system

Abstract

Simulation and optimization together form a powerful frame work for solving production-transportation problems with nonlinear costs and probabilistic demand. After a problem is linearized and transformed into a network flow equivalent, it is solved by combining a Monte Carlo simulation of demand with a network algorithm for finding the most cost-effective circulation flow. Specialized procedures identify solutions by determining plant-market pairs that are candidates for in creases or decreases of flow, depending on demand level. The result is a set of criticality indexes that measure the probability of production levels or transport links being required to operate the system. An example problem with nonlinear costs illustrates the approach.

Keywords

GASP industrial engineering Monte Carlc methods network models nonlinear models,optimization probabilistic models produc tion sensitivity analysis stochastic models

Get full access to this article

View all access options for this article.

References

Ford, L.R. JR Fulkerson, D.R. Flows in Networks Princeton Univ Press

Princeton, New Jersey

1962

D.R., Fulkerson An Out-of-Kilter Method for Minimal Cost Flow Problems" Journal of the Society of Industrial and Applied Mathematics vol 9 no 1 1961 pp 18-27

J.F., Sharp A Decomposition Algorithm for Solving the Multifacility Production-Transportation Problem with Nonlinear Production Costs" Econometrica vol 38 no 3 1970 pp 490-506