A review of Goal Programming and its applications

This paper presents a review of the current literature on the branch of multi-criteria decision modelling known as Goal Programming (GP). The result of our indepth investigations of the two main GP methods, lexicographic and weighted GP together with their distinct application areas is reported. Some guidelines to the scope of GP as an application tool are given and methods of determining which problem areas are best suited to the different GP approaches are proposed. The correlation between the method of assigning weights and priorities and the standard of the results is also ascertained.     

Generalized Assignment Type Goal Programming Problem: Application to Nurse Scheduling

The notion of the Generalized Assignment Type Goal Programming Problem is introduced to consider the additional side constraints of an Assignment Type problem as goal functions. A short term Tabu Search method together with diversification strategies are used to deal with this model. The methods are tested on real-world Nurse Scheduling Problems.     

Goal Programming and Extensions

Journal of the Operational Research Society -     

Nonlinear data-bounded polynomial approximations and their applications in ENO methods

A class of high-order data-bounded polynomials on general meshes are derived and analyzed in the context of numerical solutions of hyperbolic equations. Such polynomials make it possible to circumvent the problem of Runge-type oscillations by adaptively varying the stencil and order used, but at the cost of only enforcing C ⁰ solution continuity at data points. It is shown that the use of these polynomials, based on extending the work of Berzins (SIAM Rev 1(4):624–627, 2007) to nonuniform meshes, provides a way to develop positivity preserving polynomial approximations of potentially high order for hyperbolic equations. The central idea is to use ENO (Essentially Non Oscillatory) type approximations but to enforce additional restrictions on how the polynomial order is increased. The question of how high a polynomial order should be used will be considered, with respect to typical numerical examples. The results show that this approach is successful but that it is necessary to provide sufficient resolution inside a front if high-order methods of this type are to be used, thus emphasizing the need to consider nonuniform meshes.     

Generalized polynomial approximation

We estimate the rate of covergence to functions in the spacesL ^p [0,1] and C[0,1] by polynomial of the form ∑ _λ α _λ x ^λ, where the λ′s are positive real numbers and 0.     

Goal Programming and Agricultural Planning

Goal Programming is similar in structure to linear programming, but offers a more flexible approach to planning problems by allowing a number of goals which are not necessarily compatible to be taken into account, simultaneously. The use of linear programming in farm planning is reviewed briefly. Consideration is given to published evidence of the goals of farmers, and ways in which these goals can be represented. A goal programming model of a 600 acre mixed farm is described and evaluated. Advantages and shortcomings of goal programming in relation to linear programming are considered. It is found that goal programming can be used as a means of generating a range of possible solutions to the planning problem.     

Interactive Sequential Goal Programming

This paper introduces a new solution method based on Goal Programming for Multiple Objective Decision Making (MODM) problems. The method, called Interactive Sequential Goal Programming (ISGP), combines and extends the attractive features of both Goal Programming and interactive solution approaches for MODM problems. ISGP is applicable to both linear and non-linear problems. It uses existing single objective optimization techniques and, hence, available computer codes utilizing these techniques can be adapted for use in ISGP. The non-dominance of the "best-compromise" solution is assured. The information required from the decision maker in each iteration is simple. The proposed method is illustrated by solving a nutrition problem.     

Goal Programming: the RPMS Network Approach

This paper presents an application of the Resource Planning and Management Systems (RPMS) network approach to the goal programming (GP) problem as an alternative to several other approaches used in the GP process. The RPMS approach to solving the GP problem is illustrated through an example. Sensitivity analysis is also discussed.     

可拓目标规划方法及其应用

本文基于可拓数学和物元分析理论，介绍了可拓集合、关联函数和可拓满意点等概念，提出了可拓优化的概念，建立了可拓目标规划模型，给出了可拓目标规划方法的算法．实例表明，可拓目标规划方法有一定的实用性．     

Goal Programming in Preference Decomposition

In choosing the best alternative with respect to multiple criteria, one approach is to estimate the criterion weights that influence the preferences of all the alternatives presented. When decision makers are able to make preference judgements based on the alternative as a whole, preference decomposition methods attempt to determine part-worths which represent the contribution of the criterion levels to the overall preference values. For each alternative, the sum of the part-worths estimates its overall preference value. In this work, we use linear goal programming to determine the part-worths of all the criterion levels. Simulation experiments are conducted to compare the performances of linear goal programming and ordinary least squares regression in preference decomposition and to examine the effectiveness of including constraints on the parameters. Our simulated results suggest that the linear goal programming model with constrained parameters has better predictive power.     

Generalized star configurations and the Tutte polynomial

From the generating matrix of a linear code, one can construct a sequence of generalized star configurations which are strongly connected to the generalized Hamming weights and the underlying matroid of the code. When the code is MDS, the matrix is generic and we obtain the usual star configurations. In our main result, we show that the degree of a generalized star configuration as a projective scheme is determined by the Tutte polynomial of the code. In the process, we obtain preliminary results on the primary decomposition of the defining ideals of these schemes. Additionally, we conjecture that these ideals have linear minimal free resolutions and prove partial results in this direction.     

Generalized theta functions and the Deuring polynomial

In this paper we present a calculation to study a relationship between the generalized theta function and the Deuring polynomial.     

Generalized Lagrangian duality for nonconvex polynomial programs with polynomial multipliers

In this paper, under the existence of a certificate of nonnegativity of the objective function over the given constraint set, we present saddle-point global optimality conditions and a generalized Lagrangian duality theorem for (not necessarily convex) polynomial optimization problems, where the Lagrange multipliers are polynomials. We show that the nonnegativity certificate together with the archimedean condition guarantees that the values of the Lasserre hierarchy of semidefinite programming (SDP) relaxations of the primal polynomial problem converge asymptotically to the common primal–dual value. We then show that the known regularity conditions that guarantee finite convergence of the Lasserre hierarchy also ensure that the nonnegativity certificate holds and the values of the SDP relaxations converge finitely to the common primal–dual value. Finally, we provide classes of nonconvex polynomial optimization problems for which the Slater condition guarantees the required nonnegativity certificate and the common primal–dual value with constant multipliers and the dual problems can be reformulated as semidefinite programs. These classes include some separable polynomial programs and quadratic optimization problems with quadratic constraints that admit certain hidden convexity. We also give several numerical examples that illustrate our results.     

Diverse Imprecise Goal Programming Model Formulations

The goal programming (GP) model is probably the best known in mathematical programming with multiple objectives. Available in various versions, GP is one of the most powerful multiple objective methods which has been applied in much varied fields. It has also been the target of many criticisms among which are those related to the difficulty of determining precisely the goal values as well as those concerning the decision-maker's near absence in this modelling process. In this paper, we will use the concept of indifference thresholds for modelling the imprecision related to the goal values. Many classical imprecise and fuzzy GP model formulations can be considered as a particular case of the proposed formulation.     

Comparison of Four Goal Programming Algorithms

A major limitation in the use of goal programming has been the lack of an efficient algorithm for model solution. Schniederjans and Kwak recently published a proposal for more efficient solution of goal programming models utilizing dual simplex procedures. A goal programming algorithm based upon that method has been coded, as well as a revised, simplex-based algorithm. These algorithms are compared in terms of accuracy and time requirements with algorithms previously presented by Lee and by Arthur and Ravindran. Solution times for a series of 12 goal programming models are presented. The dual simplex method appears to have superior computational times for models with a large proportion of positive deviational variables in the solution. The revised simplex algorithm appears more consistent in time and accuracy for general goal programming models.     

GDDP: Generalized Dual Dynamic Programming Theory

This document presents theoretical considerations about the solution of dynamic optimization problems integrating the Benders Theory, the Dynamic Programming approach and the concepts of Control Theory. The so called Generalized Dual Dynamic Programming Theory (GDDP) can be considered as an extension of two previous approaches known as Dual Dynamic Programming (DDP): The first is the work developed by Pereira and Pinto [3–5], which was revised by Velásquez and others [8,9]. The second is the work developed by Read and others [2,6,7].