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 technique, whose objective is to overcome both drawbacks. The focus of this new technique is on ev...

In this study, a new stochastic model is proposed to deal with a multi-product, multi-period, multi-stage, multi-site production and transportation supply chain planning problem under demand uncertainty. A two-stage stochastic linear programming approach is used to maximize the expected profit. Decisions such as the production amount, the inventory level of finished and semi-finished product, t...

In this study, a two-stage fuzzy robust integer programming (TFRIP) method has been developed for planning environmental management systems under uncertainty. This approach integrates techniques of robust programming and two-stage stochastic programming within a mixed integer linear programming framework. It can facilitate dynamic analysis of capacity-expansion planning for waste management fac...

Condition-Based Maintenance (CBM) optimization using Proportional Hazards Model (PHM) is a kind of maintenance optimization problem in which inspections of a system relevant to its failure rate depending on the age and value of covariates are performed in time intervals. The general approach for constructing a CBM based on PHM for a system is to minimize a long run average cost per unit of time...

Risk averse stochastic optimization is investigated in the context of elastic shape optimization, allowing for microstructures in the admissible shapes. In particular, a two-stage model for shape optimization under stochastic loading with risk averse cost functionals is combined with a two-scale approach for the simulation of microstructured materials. The microstructure is composed of an elast...

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 ...

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 paper addresses the class of nonlinear mixed integer stochastic programming problems. In particular, we consider two-stage problems with nonlinearities both in the objective function and constraints, pure integer first stage and mixed integer second stage variables. We exploit the specific problem structure to develop a global optimization algorithm. The basic idea is to decompose the orig...

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...