| We consider a strategic supply chain planning problem formulated as a two-stage
Stochastic Integer Programming (SIP) model. The strategic decisions include site
locations, choices of production, packing and distribution lines, and the capacity
increment or decrement policies. The SIP model provides a practical representation
of real world discrete resource allocation problems in the presence of future uncertainties
which arise due to changes in the business and economic environment. Such
models that consider the future scenarios (along with their respective probabilities)
not only identify optimal plans for each scenario, but also determine a hedged
strategy for all the scenarios. We,
(1) exploit the natural decomposable structure of the SIP problem through Benders’
decomposition,
(2) approximate the probability distribution of the random variables using the
Generalised Lambda distribution, and
(3) through simulations, calculate the performance statistics and the risk measures
for the two models, namely the expected-value and the here-and-now.
Key words: Supply Chain planning, Stochastic integer Programming, Benders’
decomposition, Generalised Lambda distribution, simulation, Genetic algorithm |
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