Topic
Goal programming
About: Goal programming is a research topic. Over the lifetime, 4330 publications have been published within this topic receiving 117758 citations.
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TL;DR: In this paper, the use of a goal programming model to resolve a trucking terminal site location problem is presented, by allowing consideration of quantifiable personal preferences of the individuals who provide and use the truck terminal's services.
Abstract: This paper illustrates the use of a goal programming model to resolve a trucking terminal site location problem This is accomplished by allowing consideration of quantifiable personal preferences of the individuals who provide and use the truck terminal's services
31 citations
TL;DR: This study reviews published studies with human subjects where some of the techniques capable of reflecting nonlinear trade-offs of value were applied and support of nonlinear utility and enhancing decision-maker learning are considered.
Abstract: Multiple objective programming provides a means of aiding decision makers facing complex decisions where trade-offs among conflicting objectives must be reconciled. Interactive multiobjective programming provides a means for decision makers to learn what these trade-offs involve, while the mathematical program generates solutions that seek improvement of the implied utility of the decision maker. A variety of multiobjective programming techniques have been presented in the multicriteria decision-making literature. This study reviews published studies with human subjects where some of these techniques were applied. While all of the techniques have the ability to support decision makers under conditions of multiple objectives, a number of features in applying these systems have been tested by these studies. A general evolution of techniques is traced, starting with methods relying upon linear combinations of value, to more recent methods capable of reflecting nonlinear trade-offs of value. Support of nonlinear utility and enhancing decision-maker learning are considered.
31 citations
TL;DR: This work analyzes the international portfolio optimization problem by introducing the higher moments of the main financial index returns, and solves different optimization problems: skewness maximization, kurtosis minimization, Polynomial Goal Programming (PGP), and finally, truncated utility maximization.
Abstract: We analyze the international portfolio optimization problem by introducing the higher moments of the main financial index returns. We take especially account of their skewness and kurtosis. We introduce various decision criteria, based on these moments. In this framework, we solve different optimization problems: skewness maximization, kurtosis minimization, Polynomial Goal Programming (PGP), and finally, truncated utility maximization. For all of these objective functions, we determine, analyze and compare the optimal solutions, especially their degree of diversification. We illustrate our results on monthly returns of eighteen major international stock market indexes, for the period January 1988 through December 2007.
31 citations
TL;DR: A sequential goal programming approach is considered for not only well-defined flight trajectory problems but also ill-defined problems that have no feasible solutions satisfying all design requirements due to strict boundary conditions or tight path constraints.
Abstract: A sequential goal programming approach is considered for not only well-defined flight trajectory problems but also ill-defined problems that have no feasible solutions satisfying all design requirements due to strict boundary conditions or tight path constraints. By using a time integration algorithm, trajectory optimization problems are transformed into numerical optimization problems that seek optimal control variables at discrete time points to minimize an objective function and satisfy various design constraints. By defining the target goal values of both the constraints and the objective functions and by prioritizing each goal according to its significance, the GP formulation modifies ill-defined problems as multiobjective design problems. Additionally, a fuzzy decision making method is applied for those goals that are prioritized, not precisely, but in a fuzzy manner. Numerical applications for simple ascent trajectory problems show that this method can efficiently find the trajectories when various kinds of design requirements are imposed for the ill-defined problem.
31 citations
TL;DR: By means of introducing decision maker input into the goal programming model in order to produce more satisfactory solutions, both formal interactive methods and informal trial-and-error approaches are discussed.
Abstract: This paper discusses means of introducing decision maker input into the goal programming model in order to produce more satisfactory solutions. Both formal interactive methods and informal trial-and-error approaches are discussed. The design criteria for the choice of the initial test solution and the stopping criteria for the final solution are detailed. The area of presentation of results to, and elicitation of preferences from, the decision maker is dealt with. Practical suggestions for means of parameter alteration to produce alternative solutions are given. Finally, the integration of the above issues into an integrated framework is discussed. © 1997 John Wiley & Sons, Ltd.
31 citations