A new approach for generating efficient solutions within the goal programming model

This paper deals with the issue of efficiency in the Goal Programming (GP) model. A general approach for the determination of an efficient solution of GP is presented. An efficiency test for the GP solution is developed; moreover, when this solution is not efficient, an efficient solution that dominates it is determine

Supply chain management through the stochastic goal programming model

Supply chain (SC) design problems are often characterized with uncertainty related to the decision-making parameters. The stochastic goal programming (SGP) was one of the aggregating procedures proposed to solve the SC problems. However, the SGP does not integrate explicitly the Manager's preferences. The aim of this paper is to utilize the chance constrained programming and the satisfaction function concept to formulate strategic and tactical decisions within the SC while demand, supply and total cost are random variables.Scopu

Fuzzy goal programming model for classification problems

The aim of this paper is to propose a new approach, based on fuzzy goal programming, for classification problems where the cut-off value c corresponding to the discriminant axe is considered as imprecise. The fuzziness was handled through different membership functions. The proposed model will be illustrated through two and multi-groups classification problems.Scopu

Mathematical Programming Approaches to Classification Problems

Discriminant Analysis (DA) is widely applied in many fields. Some recent researches raise the fact that standard DA assumptions, such as a normal distribution of data and equality of the variance-covariance matrices, are not always satisfied. A Mathematical Programming approach (MP) has been frequently used in DA and can be considered a valuable alternative to the classical models of DA. The MP approach provides more flexibility for the process of analysis. The aim of this paper is to address a comparative study in which we analyze the performance of three statistical and some MP methods using linear and nonlinear discriminant functions in two-group classification problems. New classification procedures will be adapted to context of nonlinear discriminant functions. Different applications are used to compare these methods including the Support Vector Machines- (SVMs-) based approach. The findings of this study will be useful in assisting decision-makers to choose the most appropriate model for their decision-making situation

Fuzzy chance-constrained goal programming model for multi-attribute financial portfolio selection

The aim of this paper is to propose a fuzzy chance constrained goal programming model for solving a multi-attribute financial portfolio selection problem under two types of uncertainty namely randomness and fuzziness. The chance-constrained goals are considered as random variables. The obtained portfolio through this model is the portfolio of the best compromise where the financial decision-maker was asked to make tradeoffs among conflicting and incommensurable attributes such as the expected return, risk and the earning price ratio. The proposed model has been applied to the Tunisian stock exchange market for the period July 2003 to December 2007.Scopu

Managing sustainable development through goal programming model and satisfaction functions

Managing the sustainable development path of a nation requires the aggregation of incommensurable and conflicting objectives related to economic, environmental and social dimensions. Their aggregation requires some tradeoffs from stakeholders with different priorities and preferences. The aim of this paper is to develop a multiple objectives decision aid model where stakeholders preferences are explicitly integrated within a group decision-making process based on consensus and tradeoffs. This model is based on the concept of the satisfaction function, where stakeholders preferences are explicitly taken into consideration. The developed model is illustrated through an example related to the Canadian 2030 agenda for sustainable development.Scopu

A Fuzzy Goal Programming Model for Venture Capital Investment Decision Making

The Venture Capital decision making process involves several conflicting and imprecise criteria. The decision to invest is a difficult one with serious adverse selection risk and surrounded with uncertainty. The aim of this paper is to propose a cardinality constrained Fuzzy Goal Programming (FGP) model to deal with such a complex scenario. A FGP model does not require any assumptions on the probability distribution, better fitting with the characteristics of the Venture Capital market. The developed model is illustrated through a numerical example which uses data taken from an Italian venture capital fun

Goal programming for financial portfolio management: a state-of-the-art review

Over the last decades, the Goal Programming (GP) model has been applied to financial portfolio management and/or selection problem in decision-making contexts where several conflicting and incommensurable objectives are simultaneously aggregated. The aim of this paper is to identify the research trends and publication outlets for the application of GP model to portfolio management. We point out an increasing interest and affirmation of more sophisticated models. We present a characterization of the existing GP variants and provide historical data and statistical analysis

Parameter estimation through the weighted goal programming model

Many models in economics, management and finance can be described in terms of nonlinear dynamical systems which usually depend on some unknown parameters. To conduct a long-run behaviour analysis of these models it is of paramount importance to establish efficient and accurate parameter estimation techniques. Today many sophisticated nonlinear model estimation, selection and testing approaches are available and reliable. However, when the nonlinear dynamical systems take the form of differential equations, many of them fail and it is required to use more advanced techniques. The aim of this paper is to present a weighted goal programming formulation for estimating the unknown parameters of dynamical models described in terms of differential equations. The method is illustrated through two different applications to population dynamics (Malthus model) and innovation diffusion (Bass model)