Local Container Drayage Problem (LCDP) refers to the optimization of process planning and scheduling container trucks between a terminal customers offer door-to-door service in local area. The time required for (un)packing containers at customers’ sites are often relatively long uncertain due current work level unexpected deviations operational situations, which has significant influence on tra...

OptWastewater, an easy-to-use computer program developed for regional wastewater system planning, is presented in this article. The regional planning of wastewater systems requires both the drainage of the wastewater generated by the population centers of a region and the meeting of the quality standards defined for the river. OptWastewater is designed to be a decision-aid tool. It incorporates...

Chance-constrained programming is a relevant model for many concrete problems. However, it is known to be very hard to tackle directly. In this paper, the chance-constrained knapsack problem (CKP) is addressed. Relying on the recent advances in robust optimization, a tractable combinatorial algorithm is proposed to solve CKP. It always provides feasible solutions for CKP. Moreover, for two spec...

Robust design optimization is a modeling methodology, combined with a suite of computational tools, which is aimed to solve problems where some kind of uncertainty occurs in the data or in the model. This paper explores robust optimization complexity in the multiobjective case, describing a new approach by means of Polynomial Chaos expansions (PCE). The aim of this paper is to demonstrate that ...

The present paper presents the rst steps towards a theory to build Robust Nonlinear Predictive Control based on Fuzzy Models. The main idea behind this theory is to write the Predictive Control Problem as a Robust Optimization problem and apply Semideenite Programming to solve in a eecient way the optimization. The information about the plant behavior and its uncertainties will be provided by t...

Simulation-optimization aims to identify the setting of the input parameters (input variables, factors) of the simulated system that leads to the optimal system performance. In practice, however, some of these parameters cannot be perfectly controlled — due to measurement errors or other implementation issues, and the inherent uncertainty caused by fluctuations in the environment (e.g., demand)...

and Applied Analysis 3 independently by El-Ghaoui et al. 8, 9 propose to solve the following robust optimization problem

We denote, for all feasible x, by R(x) the associated robust problem when x is fixed, and we denote by R(x, d) the associated robust problem when x and d are fixed. In this note we assume that the deterministic problem doesn’t satisfied the full recourse property. We prove how to obtain a new MILP denoted (P̃ ) such that the robust associated problem has the same optimal solution as the initial ...

We show that a rst order problem can approximate solutions of a robust optimization problem when the uncertainty set is scaled, and explore further properties of this rst order problem.