Interactive multiobjective optimization methods have provided promising results in the literature but still their implementations are rare. Here we introduce a core structure of interactive methods to enable their convenient implementation. We also demonstrate how this core structure can be applied when implementing an interactive method using a modeling environment. Many modeling environments ...

In this paper some high-performance mixed integer non-linear programming (MINLP) solvers in GAMS (General Algebraic Modeling System) are evaluated. AlphaECP, Baron, Dicopt and SBB are the MINLP (mixed integer non-linear programming) solvers in focus. A large test set with 250 problems is used and special attention is given to a new GAMS solver, GAMS/AlphaECP, which has recently been included in...

Evolutionary programming is a stochastic optimization procedure that can be applied to difficult combinatorial problems. Experiments are conducted with three standard optimal control problems (linear-quadratic, harvest, and push-cart). The results are compared to those obtained with genetic algorithms and the General Algebraic Modeling System (GAMS), a numerical optimization software package. T...

This document describes an implementation of Lagrangian Relaxation using GAMS.

To date, therapeutic method for in vivo gene delivery has not been established on bone engineering though its potential usefulness has been suggested. For clinical applications, an effective condition should be developed to transfer the genes in vivo without any transfection reagents or virus vectors. In this study, to facilitate the clinical setting of this strategy, particularly aimed at atro...

This document shows how to model two-stage stochastic linear programming problems in a GAMS environment. We will demonstrate using a small example, how GAMS can be used to formulate and solve this model as a large LP or using specialized stochastic solvers such as OSL-SE and DECIS. Finally a tailored implementation of the Benders Decomposition algorithm written in GAMS is used to solve the model.

The goal of this chapter is to describe the capabilities and the usage of SLP–IOR, our interactive model management system for stochastic linear programming (SLP). The main features of SLP–IOR are the following: the system is intended to support the entire life cycle of a model, including model formulation, analysis of the model instance, solving it, and analyzing the solution. A main design ch...