This paper addresses a discontinuous multi-activity tour scheduling problem under demand uncertainty and when employees have identical skills. The problem is formulated as a two-stage stochastic programming model, where first-stage decisions correspond to the assignment of employees to weekly tours, while second-stage decisions are related to the allocation of work activities and breaks to dail...

This chapter reviews multi-stage financial planning models, with a focus on practical approaches for optimizing investors’ performance over time. We discuss two major frameworks for constructing financial planning models: 1) policy rule simulation and optimization; and 2) multi-stage stochastic programming. We advocate an integrated approach, in which a stylized stochastic program helps the inv...

This paper addresses a general class of capacity planning problems under uncertainty, which arises, for example, in semiconductor tool purchase planning. Using a scenario tree to model the evolution of the uncertainties, we develop a multi-stage stochastic integer programming formulation for the problem. In contrast to earlier twostage approaches, the multi-stage model allows for revision of th...

In this paper we discuss time consistency of multi-stage risk averse stochastic programming problems. We approach the concept of time consistency from an optimization point of view. That is, at each state of the system optimality of a decision policy should not involve states which cannot happen in the future. We also discuss a relation of this concept of time consistency to deriving dynamic pr...

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...

The paper considers modeling of risk-averse preferences in (multi-stage) stochastic programming problems using risk measures. We utilize the axiomatic foundation of coherent risk measures and deviation measures in order to develop simple representations for these measures that facilitate their incorporation into stochastic programs. It is demonstrated that the developed representations allow fo...

In this paper, we propose an extended relocation model for warehouses configuration in a supply chain network, in which uncertainty is associated to operational costs, production capacity and demands whereas, existing researches in this area are often restricted to deterministic environments. In real cases, we usually deal with stochastic parameters and this point justifies why the relocation m...

To solve the multi-stage linear programming problem, one may use a deterministic or a stochastic approach. The drawbacks of the two techniques are well known: the deterministic approach is unrealistic under uncertainty and the stochastic approach suffers from scenario explosion. We introduce a new scheme, whose objective is to overcome both drawbacks. The focus of this new scheme is on events i...