Disasters inevitably trigger far-reaching consequences affecting all living things and the environment.  Therefore, top managers and decision-makers in disaster management seek comprehensive approaches to evaluate facilities and network preparedness in dealing with the response phase of predicted disaster scenarios in terms of number of casualties, costs, and unmet demands.  In this regard, pre...

Stochastic integer programming is more complicated than stochastic linear programming, as will be explained for the case of the two-stage stochastic programming model. A survey of the results accomplished in this recent field of research is given.

A dynamic (multi-stage) stochastic programming model for the weekly cost-optimal generation of electric power in a hydro-thermal generation system under uncertain demand (or load) is developed. The model involves a large number of mixed-integer (stochastic) decision variables and constraints linking time periods and operating power units. A stochastic Lagrangian relaxation scheme is designed by...

Multi-stage simulation and optimization models are effective for solving long-term financial planning problems. Prominent examples include: asset-liability management for pension plans, integrated risk management for insurance companies, and long-term planning for individuals. Several applications will be briefly mentioned. A multi-stage framework provides advantages over single-period myopic a...

We analyze the financial planning problems of young households whose main decisions are how to finance the purchase of a house (liabilities) and how to allocate investments in pension savings schemes (assets). The problems are solved using a multi–stage stochastic programming model where the uncertainty is described by a scenario tree generated from a vector auto-regressive process for equity r...

This paper presents a stochastic optimization model and efficient decomposition algorithm for multi-site capacity planning under the uncertainty of the TFT-LCD industry. The objective of the stochastic capacity planning is to determine a robust capacity allocation and expansion policy hedged against demand uncertainties because the demand forecasts faced by TFT-LCD manufacturers are usually ina...

Optimisation under uncertainty has always been a focal point within the Process Systems Engineering (PSE) research agenda. In particular, efficient manipulation of large amount data for uncertain parameters constitutes crucial condition effectively tackling stochastic programming problems. this context, work proposes new data-driven Mixed-Integer Linear Programming (MILP) model Distribution & M...

This paper presents an investigation on the computational complexity of stochastic optimization problems. We discuss a scenariobased model which captures the important classes of two-stage stochastic combinatorial optimization, two-stage stochastic linear programming, and two-stage stochastic integer linear programming. This model can also be used to handle chance constraints, which are used in...