Goal programming

About: Goal programming is a research topic. Over the lifetime, 4330 publications have been published within this topic receiving 117758 citations.

Simulation optimization: multi-response simulation optimization using stochastic genetic search within a goal programming framework

Abstract: This study presents a new approach to solve multi-response simulation optimization problems. This approach integrates a simulation model with a genetic algorithm heuristic and a goal programming model. The genetic algorithm technique offers a very flexible and reliable tool able to search for a solution within a global context. This method was modified to perform the search considering the mean and the variance of the responses. In this way, the search is performed stochastically, and not deterministically like most of the approaches reported in the literature. The goal programming model integrated with the genetic algorithm and the stochastic search present a new approach able to lead a search towards a multi-objective solution.

An integrated multiobjective portfolio management system

Abstract: The literature on portfolio management is rather abundant, with many valuable suggestions for improving the activity of large or small companies involved in the investment business. Few propositions, if any, are concerned with a full integrated system for helping portfolio managers in the considerable tasks of collecting information, making predictions about the market and about specific securities behaviours, on the one hand and, on the other hand, of compromising multiple conflictual objectives pursued by the investment committee, and controlling the complete activity of the service in charge of the sequence of tasks. The authors present the first global computerized system for managing, in an integrated way, the three phases involved in all portfolio management: the information phase; the decision phase; and the control phase (including the evaluation of performances and effectiveness of the system). The core of the system is the junction of the two main processing models. The single decision model (SDM) makes use of the Bayesian approach to process the information and to combine the forecasts supplied by internal analysts to the messages issued from external correspondents. The outputs of the SDM are eight criteria for evaluating each security scrutinized by this company. The simultaneous management model (SMM) is fed by these outputs and by the decisional parameters; it revises the current portfolio on the basis of a multiple criteria evaluation. Efficient compromises are obtained here by goal programming. In this paper, the control phase is not detailed, while an experiment of the system in a small company is reported.

Pareto set approximation by the method of adjustable weights and successive lexicographic goal programming

Abstract: A nonlinear multiobjective optimization problem is considered. Two methods are proposed to generate solutions with an approximately uniform distribution in a Pareto set. The first method is supposed to find the solutions as minimizers of weighted sums of objective functions where the weights are properly selected using a branch and bound type algorithm. The second method is based on lexicographic goal programming. The proposed methods are compared with several metaheuristic methods using two and three-criteria tests and applied problems.