Markov-modulated processes have been used in stochastic inventory models with setup costs for modeling demand under the innuence of uncertain environmental factors, such as uctuat-ing economic and marketing conditions. The analyses of these models have been carried out in the literature only under the assumption that unsatissed demand is fully backlogged. We study a Markovian demand model in th...

In this paper, we study a dynamic fleet management problem with uncertain demands and customer chosen service levels. We first show that the problem can be transformed into a dynamic network with partially dependent random arc capacities, and then develop a structural decomposition approach which decomposes the network recourse problem into a series of tree recourse problems (TRPs). As each TRP...

In this paper, we develop a generalized network optimization model for the complex supply chain of human blood, which is a life-saving, perishable product. In particular, we consider a regionalized blood banking system consisting of collection sites, testing and processing facilities, storage facilities, distribution centers, as well as points of demand, which, typically, include hospitals. Our...

This paper considers a dyadic supply chain with uncertain supplier. Both retailer and supplier use continuous review policy. The supplier is randomly available for supplying the retailer. The retailer faces Poisson demands. In this system, lead time is a random variable consists of a constant transportation time plus a random delay occurs due to availability of stock at the supplier. This paper...

Percutaneous endoscopic gastrostomy (PEG) is an accepted technique for longterm enteral feeding. Its use has increased because of its simplicity and low complication rate. The demands for gastrostomy insertion have risen and now encompass indications where the long-term outcomes are uncertain. Dementia has become one of the commonest indications for gastrostomy feeding. This article reviews the...

Two formulations of the stochastic model predictive control (SMPC) problem for the control of large-scale drinking water networks are presented in this chapter. The first approach, named chance-constrained MPC, makes use of the assumption that the uncertain future water demands follows some known continuous probability distribution while at the same time certain risk (probability) for the state...

We propose an approach to the classical newsvendor problem and its extensions subject to uncertain demand that: (a) works directly with data, i.e., combines historical data and optimization in a single framework, (b) yields robust solutions and incorporates risk preferences using one scalar parameter, rather than utility functions, (c) allows for tractable formulations, specifically, linear pro...

In this paper, global optimisation and nonlinear simulation are used to search for the worst-case control demands which may be input by a pilot flying a modern high performance aircraft with a full authority flight control law. The flexibility of global optimisation is exploited to simultaneously search for worst-case pilot inputs and worst-case combinations of uncertain parameters in the nonli...