Seminar za primenjenu matematiku, 3. oktobar 2011. (vanredni sastanak)

Vanredni sastanak Seminara za primenjenu matematiku održaće se u ponedeljak, 3. oktobra 2011. u sali 301f, MI SANU sa početkom u 12 časova.

Predavač: Laureano F. Escudero Dept. of Statistics and Operations Research U. Rey Juan Carlos, Madrid, Spain

Naslov predavanja: RISK MINIMIZATION STRATEGIES FOR SUPPLY CHAIN MANAGEMENT UNDER UNCERTAINTY

Abstract: Supply Chain Management (SCM) is concerned with determining supply, production and stock levels in raw materials, subassemblies at different levels of the given Bills of Materials, end products and information exchange through a set of factories, depots and dealer centres of a given production and service network to meet fluctuating demand requirements. Key aspects of the problem are the supply chain topology, time, uncertainty and cost. Uncertainty is due to the inherent stochasticity in some parameters for dynamic (multiperiod) planning problems, mainly, product demand and price, raw material supply costs and production costs. We treat it via scenario analysis.
Strategic planning for supply chains, basically, consists of deciding on the production topology, plant sizing, product selection, product allocation among plants and vendor selection for crucial raw materials. The objective is the maximization of the expected global product net profit over the time horizon and, simultaneously, minimizing the weighted risk of having scenarios of the uncertain parameters with the  related net profit below given sets of thresholds. Tactical supply chain planning consists of deciding on the utilization of the available resources included by vendors, factories, depots and dealer centres along the time horizon, such that given targets are met at a minimum cost. It assumes that the supply chain topology is given. Both planning activities should be considered together.
In this talk we present a set of averse risk measures as alternatives to the maximization of the (risk neutral) expected net profit along a given time horizon over a set of representative scenarios in SCM, where the uncertainty is represented in a multiperiod nonsymmetric scenario tree. The main risk averse control strategies analyzed are: min-max regret, VaR strategy, Conditional VaR, mean-risk immunization, stochastic dominance constraints, and the new one, a mixture of VaR \& stochastic dominance. Most of these measures require from the modeller a threshold for the net profit to be achieved for each scenario and a failure's probability for not reaching the threshold or, at least, a weight for that probability to show the importance of each threshold. We shall also present a risk averse multistage mixed 0-1 stochastic optimization model for SCM by considering both the strategic and tactical decisions.



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