Existing compartmental models in epidemiology are limited terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address core limitation by presenting a multi-stage stochastic programming model, which integrates uncertain progression and infectious outbreak. The proposed program involves various scenarios optimizes distrib...

A large number of problems in production planning and scheduling, location, transportation, finance, and engineering design require that decisions be made in the presence of uncertainty. Uncertainty, for instance, governs the prices of fuels, the availability of electricity, and the demand for chemicals. A key difficulty in optimization under uncertainty is in dealing with an uncertainty space ...

How to make decisions while the future is full of uncertainties is a major problem shared virtually by every human being including housewives, firm managers, as well as politicians. In this paper we introduce a mathematical programming resolution to the problem, namely multi-stage stochastic programming. An advantage of that approach is obviously that we will be working with a precise, tangible...

We introduce a new approach to distribution fitting, called Decision on Beliefs (DOB). The objective is to identify the probability distribution function (PDF) of a random variable X with the greatest possible confidence. It is known that f X is a member of = { , , }. 1 m S f L f To reach this goal and select X f from this set, we utilize stochastic dynamic programming and formulate this proble...

Mathematical programming has been applied to various problems. For many actual problems, the assumption that the parameters involved are deterministic known data is often unjustified. In such cases, these data contain uncertainty and are thus represented as random variables, since they represent information about the future. Decision-making under uncertainty involves potential risk. Stochastic ...

Placement of sensors in water distribution networks helps timely detection of contamination and reduces risk to the population. Identifying the optimal locations of these sensors is important from an economic perspective and has been previously attempted using the theory of optimization. This work extends that formulation by considering uncertainty in the network and describes a stochastic prog...