Goal programming

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

Goal-programming methodology for integrating pavement and bridge programs

Abstract: A goal-programming methodology is presented for integrating the decisions involved in the development of annual pavement and bridge programs. The methodology involves four major steps: (1) identification of multiple objective functions with specific numerical goals; (2) assessment of the “importance” of each objective; (3) development of an assignment model that enables both project and network levels of integration; and (4) formulation of a goal program for optimal program development. Project-level integration is achieved by defining integrable highway units that constitute a bridge and adjacent pavement sections whose repair work can be implemented simultaneously. Network-level integration is achieved by defining objectives and constraints that link the decision variables corresponding to the entire population of pavement sections and bridges. The methodology has the advantage of capturing economies of scale and scope, and determines an optimal program that best achieves the goals. The results from the...

A goal programming approach for the joint optimization of energy consumption and response time in computational grids

Abstract: We study the multi-objective problem of mapping independent tasks onto a set of computational grid machines that simultaneously minimizes the energy consumption and response time (makespan) subject to the constraints of deadlines and architectural requirements. We propose an algorithm based on goal programming that effectively converges to the compromised Pareto optimal solution. Compared to other traditional multi-objective optimization techniques that require identification of the Pareto frontier, goal programming directly converges to the compromised solution. Such a property makes goal programming a very efficient multi-objective optimization technique. Moreover, simulation results show that the proposed technique achieves superior performance compared to the greedy and linear relaxation heuristics, and competitive performance relative to the optimal solution implemented in LINDO for small-scale problems.

A parametric generalized goal function for fuzzy decision making with unequally weighted objectives

Abstract: In multiobjective fuzzy decision making, averaging operators are commonly used as goal functions. It is assumed that such a goal function attains the maximum value for the optimal alternative. A parametric generalized goal function is given that unifies many of the commonly used averaging operators for equally weighted objectives. The parameters can be interpreted as an indication of the decision maker's optimism. Conditions are defined that a generalized weighted goal function should satisfy. A correct generalization of the goal function for unequally weighted objects is given. >

Preemptive goal programming using simulated annealing

Abstract: Goal programming is a commonly used technique for modelling and solving multiple objective optimization problems. It has been successfully applied to many diverse real-life problems in engineering design and optimization. One of the first attempts is made in this article to solve preemptive goal programming (PGP) problems by using a simulated annealing (SA) algorithm. The developed algorithm can be applied to non-linear, linear, integer and combinatorial goal programs. However, the main concentration is on non-linear programs, mainly due to the difficulty in solving these programs with the classical approaches. Several test problems are solved in order to test the suitability of SA in solving preemptive goal programs. It is observed that the SA algorithm is a suitable candidate to solve goal programs. The method can easily be applied to any kind of PGP problem.