Goal programming

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

Fuzzy and Multi-Level Decision Making: An Interactive Computational Approach

Abstract: 1. Introduction.- 1.1 Decision Making in Hierarchy Systems: Multi-ploy versus Interactive Decisions.- 1.2 Bi-Level and Multi-level Programming.- 1.3 Characteristics of Duo-ploy Systems.- 1.4 Characteristics of Duo-ploy Systems with Multi-followers.- 1.5 Fuzzy Interactive Decision Making.- 2. Linear Bi-level Programming.- 2.1 Linear Bi-level Programming.- 2.2 Extreme-point Search.- 2.2.1 kth-best Algorithm.- 2.2.2 Grid-search Algorithm.- 2.3 Transformation Approach.- 2.3.1 Mixed-integer Approach.- 2.3.2 Complementary-pivot algorithm.- 2.3.2.1 Parametric Complementary-pivot Algorithm.- 2.3.2.2 Sequential Linear Complementary Algorithm with Branch-and-bound.- 2.3.3 Branch-and-bound Algorithm.- 2.3.3.1 Algorithm of Bard and Moore.- 2.3.3.2 Algorithm of Hansen et al.- 2.3.4 Penalty-function Approach.- 2.4 Discussions.- 3. Other Multi-level Programming Algorithms.- 3.1 Linear Bi-level Distributed Programming.- 3.1.1 Mixed-integer Problem with Complementary Slackness.- 3.1.2 Penalty-function Approach.- 3.2 Linear Three-level Programming Problem.- 3.2.1 Hybrid Extreme-point Search Algorithm.- 3.2.2 Mixed-integer Problem with Complementary Slackness.- 3.2.3 Simplex-cutting-plane Algorithm.- 3.2.4 Penalty-function Approach.- 3.3 Non-linear Multi-level Programming.- 3.3.1 Sequential Linear-quadratic Complementary Algorithm with Branch-and-bound.- 3.3.2 Steepest-descent Approach.- 3.3.3 Evolutionary Approach: Genetic Algorithms.- 3.4 Discrete Bi-level Programming.- 3.5 Discussions.- 4. Possibility Theory and Knowledge Representation.- 4.1 Possibility Theory.- 4.1.1 Possibility Distribution.- 4.1.2 Possibility Measure.- 4.1.3 Possibility Measure Based on Fuzzy Set with Fuzzy Subset.- 4.1.4 Possibility versus Probability.- 4.2 Knowledge Representation.- 4.2.1 Linguistic Variable.- 4.2.2 The Syntactic Rule.- 4.2.3 The Semantic Rule.- 4.2.4 Test-score Semantics.- 5. Fuzzy Decision Making.- 5.1 Fuzzy Linear Programming.- 5.2 Multiple-objective Programming.- 5.2.1 Compromise Programming.- 5.2.2 Goal Programming.- 5.3 Fuzzy Approach to Multiple-objective Programming.- 5.4 Fuzzy Multiple-objective Programming with Fuzzy Parameters.- 5.5 Possibility Programming.- 5.5.1 Possibility Linear Programming.- 5.5.2 Multiple-objective Possibility Programming.- 6. Fuzzy Interactive Multi-level Decision Making.- 6.1 Fuzzy Bi-level Interactive Decision Making.- 6.2 Fuzzy Bi-level Interactive Decision Making with Multi-followers.- 6.3 Fuzzy Multi-level Interactive Decision Making.- 6.4 Fuzzy Multi-level Interactive Decision Making with Multi-followers.- 6.5 Discussions.- 7. Aggregation of Fuzzy Systems in Multi-level Decisions.- 7.1 Compensation in Bi-level Decisions.- 7.2Compensation in Multiple-level Problems.- 7.3 Bi-level Decentralized Problem with Equally Important Objectives.- 7.4 Bi-level Decentralized Problem with Unequally Important Objectives.- 7.5 Multiple-level Decentralized Problem.- 7.6 Fuzzy Multi-level Problem.- 7.7 Discussions.- 8. Possibilistic Minimum-cost Flow Problem.- 8.1 Minimum-cost Flow Problem.- 8.2 Possibility Approach to Minimum-cost Flow Problem.- 8.2.1 Capacity Constraint Modification.- 8.2.2 Possibility Programming.- 8.3 Possibility Approach to Multi-objective Minimum-cost Flow Problem.- 8.4 Possibility Approach to Multi-level Minimum-cost Flow Problem.- 8.5 Discussions.- References.

A lexicographical goal programming based decision support system for logistics of Humanitarian Aid

Abstract: Each year people affected by disasters, either natural or human-made, can be counted by millions When a major disaster strikes a country, local and international communities usually respond with an outpouring of assistance, which has to be efficiently managed in order to arrive where it is needed as soon as possible and under adverse conditions Despite its importance, not until recently has Humanitarian Logistics received much attention as a specific field, and there is a lack of specific tools In this work, a lexicographical goal programming model for distribution of goods to the affected population of a disaster in a developing country is presented, which sustains a decision support system currently in development

Constrained optimization using multiple objective programming

Abstract: In practical applications of mathematical programming it is frequently observed that the decision maker prefers apparently suboptimal solutions. A natural explanation for this phenomenon is that the applied mathematical model was not sufficiently realistic and did not fully represent all the decision makers criteria and constraints. Since multicriteria optimization approaches are specifically designed to incorporate such complex preference structures, they gain more and more importance in application areas as, for example, engineering design and capital budgeting. The aim of this paper is to analyze optimization problems both from a constrained programming and a multicriteria programming perspective. It is shown that both formulations share important properties, and that many classical solution approaches have correspondences in the respective models. The analysis naturally leads to a discussion of the applicability of some recent approximation techniques for multicriteria programming problems for the approximation of optimal solutions and of Lagrange multipliers in convex constrained programming. Convergence results are proven for convex and nonconvex problems.

The modular multivariable controller: I: Steady‐state properties

Abstract: The modular multivariable controller (MMC) represents a multivariable controller design methodology which is based on the solution of multiobjective optimization problems using the strategy of lexicographic goal programming; priority-driven, sequential satisfaction of objectives. This article formally introduces the concept of the MMC, analyzes its static characterstics, and proposes a specific methodology for the design of steady-state MMCs. It is shown that the framework of MMC can explicitly handle all types of control objectives (for example, equality or inequality specifications on controlled outputs), and constraints on manipulations. Its priority-driven, sequential satisfaction of control objectives leads to a modular, hierarchical structure of controllers with specific objectives. The modular character of MMC allows the explicit maintenance, tuning, and reconfiguration of multivariable control systems, while its hierarchical structure explicitly expresses engineering decisions and trade-offs. Its static design incorporates uncertainty in process gains and automatic reconfiguration to account for failure in sensors and/or actuators. The design of an MMC for a heavy oil fractionator is presented to illustrate the controller's character and the proposed methodology for the design of static MMCs.