Cost/risk balanced management of scarce resources using stochastic programming

• Published in: European journal of operational research Vol. 216; no. 1; pp. 214 - 224
• Main Authors: Gaivoronski, Alexei, Sechi, Giovanni M., Zuddas, Paola
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 2012; Elsevier; Elsevier Sequoia S.A
• Subjects: Applied sciences; Cost control; Demand; Exact sciences and technology; Marketing; Mathematical analysis; Mathematical models; Mathematical programming; Operational research and scientific management; Operational research. Management science; Optimization; Optimization algorithms; OR in natural resources; Pollution; Programming; Renewable resources; Risk; Risk management; Risk theory. Actuarial science; Risk/performance tradeoff; Stochastic programming; Studies; Water resources management; Water treatment and pollution; OR in natural resources; Water resources management; Risk management; Risk/performance tradeoff; Stochastic programming; Natural resources; Script; Costs; Refeeding; Worst case method; Water resource management; Water distribution; Risk analysis; Modeling; Optimization; Renewable resources; Uncertain system; Replenishment; Deterministic model; Resource management; Water resources
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2011.06.040

► We develop multiperiod stochastic programming model for management of scarce resources under uncertainty. ► The model balances the costs of delivery of scarce resource against the risk of not meeting demand. ► Target barycentric resource delivery is followed by the reoptimization for the worst scenarios. ► Real water resource management system is considered as the reference example. We consider the situation when a scarce renewable resource should be periodically distributed between different users by a Resource Management Authority (RMA). The replenishment of this resource as well as users demand is subject to considerable uncertainty. We develop cost optimization and risk management models that can assist the RMA in its decision about striking the balance between the level of target delivery to the users and the level of risk that this delivery will not be met. These models are based on utilization and further development of the general methodology of stochastic programming for scenario optimization, taking into account appropriate risk management approaches. By a scenario optimization model we obtain a target barycentric value with respect to selected decision variables. A successive reoptimization of deterministic model for the worst case scenarios allows the reduction of the risk of negative consequences derived from unmet resources demand. Our reference case study is the distribution of scarce water resources. We show results of some numerical experiments in real physical systems.