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

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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.

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