Goal programming

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

An optimal multivariate stratified sampling design using auxiliary information: an integer solution using goal programming approach

Abstract: In multivariate stratified random sampling, for practical purposes we need an allocation which is optimum in some sense for all characteristics because the individual optimum allocations usually differ widely unless the characteristics are highly correlated. Cochran (1977) suggested the use of characteristic wise average of individual optimum allocations as a compromise allocation for correlated characteristics. For uncorrelated ones, many authors have suggested various criteria to work out an optimum compromise allocation. For example a compromise criterion may be to minimize the total loss in efficiency of the estimate due to not using the individual optimum allocations. When auxiliary information is also available, it is customary to use it to increase the precision of the estimates. Moreover, for practical implementation of an allocation, we need integer values of the sample sizes. The present article addresses the problem of determining the integer optimum compromise allocation when the population means of various characteristics are of interest and auxiliary information is available for the separate and combined ratio and regression estimates. The problem is formulated as a multiobjective nonlinear programming problem and a solution procedure is developed using goal programming technique. The goal is to minimize the weighted sum of the increases in the variances due to not using the individual optimum allocations subject to budgetary and other constraints.

A Distributed Algorithm for Resource Allocation in OFDM Cognitive Radio Systems

Abstract: We study the problem of allocating subchannels, bits, and powers in a cognitive radio system, in which available system resources are highly dynamic. The modulation scheme employed is orthogonal frequency-division multiplexing (OFDM). In a resource-limited situation under which the nominal-rate requirements of users cannot be satisfied, it is desirable to provide fair degradation among users. In a situation with abundant resources, we may choose to maximize system throughput while ensuring that user nominal-rate requirements are met. The problem is formulated as a single objective nonlinear optimization problem using techniques from goal programming. A distributed resource allocation algorithm is proposed and shown to provide good fairness. In resource-abundant situations, the proposed distributed algorithm yields significantly better system throughput compared with the proportional-rate algorithm.

Neutrosophic Goal Programming

Abstract: In this chapter, the goal programming in neutrosophic environment is introduced. The degree of acceptance, indeterminacy and rejection of objectives is considered simultaneous. In the two proposed models to solve Neutrosophic Goal Programming Problem (NGPP), our goal is to minimize the sum of the deviation in the model (I), while in the model (II), the neutrosophic goal programming problem NGPP is transformed into the crisp programming model using truth membership, indeterminacy membership, and falsity membership functions. Finally, the industrial design problem is given to illustrate the efficiency of the proposed models. The obtained results of Model (I) and Model (II) are compared with other methods.

Multiobjective Optimization in Terms of Economics and Potential Environment Impact for Process Design and Analysis in a Chemical Process Simulator

Abstract: This paper is devoted to an application of the MOOP (multiobjective optimization programming) concept to the practical field of chemical engineering to take into account the trade-off between economics and pollution with appropriate analysis methods. Optimization of the process is performed along an infeasible path with the SQP (successive quadratic programming) algorithm. One of the objective functions, the global pollution index function, is based on potential environmental impact indexes calculated by using the hazard value (HV). The other is the cost−benefit function. To analyze the biobjective optimization system in terms of economics and potential environmental impact, the noninferior solution curve (Pareto curve) is formed using SWOF (summation of weighted objective functions), GP (goal programming), and PSI (parameter space investigation) methods within a chemical process simulator. We can find the ideal compromise solution set based on the Pareto curve. The multiobjective problem is then interpre...