The transportation network design problem (NDP) with multiple objectives and demand uncertainty was originally formulated as a spectrum of stochastic multi-objective programming models in a bi-level programming framework. Solving these stochastic multi-objective NDP (SMONDP) models directly requires generating a family of optimal solutions known as the Pareto-optimal set. For practical implemen...

This paper will investigate the optimum portfolio for an investor, taking into account 5 criteria. The mean variance model of portfolio optimization that was introduced by Markowitz includes two objective functions; these two criteria, risk and return do not encompass all of the information about investment; information like annual dividends, S&P star ranking and return in later years which is ...

Genetic Programming (GP) is a method to evolve computer programs. And the reason we would want to try this is because, as anyone who’s done even half a programming course would know, computer programming is hard. Automatic programming has been the goal of computer scientists for a number of decades. Scientists would like to be able to give the computer a problem and ask the computer to build a ...

Goal programming (GP) is an important class of multi-criteria decision models widely used to analyze and solve applied problems involving conflicting objectives. Originally introduced in the 1950s by Charnes et al. (1955) the popularity and applications of GP has increased immensely due to the mathematical simplicity and modeling elegance. Over the recent decades algorithmic developments and co...

Intelligent robot navigation can be achieved using a control system comprised of a collection of special-purpose motion routines, or behaviors. An approach to behavior coordination in multi-behavior systems is described with emphasis on evolution of fuzzy coordination rules using the genetic programming (GP) paradigm. Both conventional GP and steady-state GP are applied to evolve a fuzzy-behavi...

There have been signi®cant advances in the theory of goal programming (GP) in recent years, particularly in the area of intelligent modelling and solution analysis. The intention of this paper is to provide an overview of these developments, to detail and assess the current state-of-the-art in the subject, and to highlight areas which seem promising for future research. Modelling techniques suc...

A very useful multi-objective technique is goal programming. There are many methodologies of goal programming such as weighted goal programming, min-max goal programming, and lexicographic goal programming. In this paper, weighted goal programming is reformulated as goal programming with logarithmic deviation variables. Here, a comparison of the proposed method and goal programming with weighte...

this paper presents a fuzzy goal programming (fgp) methodology for solving bi-level quadratic programming (blqp) problems. in the fgp model formulation, firstly the objectives are transformed into fuzzy goals (membership functions) by means of assigning an aspiration level to each of them, and suitable membership function is defined for each objectives, and also the membership functions for vec...

We propose an interactive Goal Programming (GP) for operational recovery problems that are present in diverse areas of application. After defining the problem we discuss its relevance to scenarios taken from airline scheduling, supply chain management and call centers operations. We then construct the GP procedure for operational recovery decision making and illustrate the mechanics of the prop...