Compromise Programming Method Research Articles

Evaluation of waste minimization alternatives under uncertainty: a multiobjective optimization approach

A general methodology is proposed for implementing waste minimization programs. This paper focuses primarily in the evaluation of source reduction alternatives using a two criteria approach. The first criterion considers the economic performance of the process that includes all the waste related costs within an environmental accounting framework. Whereas the second criteria evaluates the environmental impact of the alternative taking into account a release factor, as well as toxicological data for each chemical present in the process stream. Realizing the uncertainty present in the evaluation of potential source reduction projects, the evaluation phase is accomplished using a multiple objective optimization approach by combining the compromise programming method and the stochastic annealing algorithm using the process simulator ASPEN PLUS. As an example, the methyl chloride process is evaluated using the proposed methodology.

Quality Utility—A Compromise Programming Approach to Robust Design

In robust design, associated with each quality characteristic, the design objective often involves multiple aspects such as “bringing the mean of performance on target” and “minimizing the variations.” Current ways of handling these multiple aspects using either the Taguchi’s signal-to-noise ratio or the weighted-sum method are not adequate. In this paper, we solve bi-objective robust design problems from a utility perspective by following upon the recent developments on relating utility function optimization to a Compromise Programming (CP) method. A robust design procedure is developed to allow a designer to express his/her preference structure of multiple aspects of robust design. The CP approach, i.e., the Tchebycheff method, is then used to determine the robust design solution which is guaranteed to belong to the set of efficient solutions (Pareto points). The quality utility at the candidate solution is represented by means of a quadratic function in a certain sense equivalent to the weighted Tchebycheff metric. The obtained utility function can be used to explore the set of efficient solutions in a neighborhood of the candidate solution. The iterative nature of our proposed procedure will assist decision making in quality engineering and the applications of robust design.

Stability analysis of grey compromise programming and its application to watershed land-use planning

Multiple conflicting objectives characterize the current water resources management systems. In particular, the conflict between environmental protection of reservoir water quality and economic development by different uses of land within the reservoir watershed is a problem that receives wide attention in many developing countries. The optimal balance between carrying capacity of watershed land-use and assimilative capacity of reservoir water quality remains unclear. However, the inherent uncertainties of both environmental and economic goals and parameter values may result in additional difficulties in public decision making. This paper presents a new methodology the grey compromise programming ( GCP ) method in which the parameter uncertainties in the optimization process are specifically considered as interval numbers and the related stability theory is proved as well. The case study is drawn from the optimal planning of land-use programme in the Tweng-Wen reservoir watershed in Taiwan.

Goal programming, compromise programming and reference point method formulations: linkages and utility interpretations

Goal programming, compromise programming and reference point method formulations: linkages and utility interpretations

Goal programming, compromise programming and reference point method formulations: linkages and utility interpretations

This paper looks at connections between the multi-criteria techniques of goal programming, compromise programming, and the reference point method. The utility function structure of each method is examined and interpretation of the techniques in terms of pareto efficiency and the equilibrated nature of the solution is given. Means of ensuring pareto efficiency whilst maintaining as much as possible the equilibrated character are given for a certain class of model. Methods for ensuring conceptual correctness and computational accuracy by reducing reference point models to equivalent goal programmes are given. The findings are illustrated by means of an example.

Goal Programming, Compromise Programming and Reference Point Method Formulations: Linkages and Utility Interpretations

Goal Programming, Compromise Programming and Reference Point Method Formulations: Linkages and Utility Interpretations

Some Difficulties in Applying Interactive Compromise Programming Illustrated by a Recent Method and a Case Study

The paper presents some of the difficulties in applying interactive, and interactive compromise, programming methods to real-life problems. These include the problem of dominated solutions when multiple optimal solutions exist to single objective function optimization, cycling, and the depiction of the analyst as a silent spectator in the decision making process. By taking as an example a recently proposed interactive compromise programming method, these difficulties are illustrated and necessary modifications are proposed to overcome these difficulties. These are further elucidated by a case study.

Some Difficulties in Applying Interactive Compromise Programming Illustrated by a Recent Method and a Case Study

The paper presents some of the difficulties in applying interactive, and interactive compromise, programming methods to real-life problems. These include the problem of dominated solutions when multiple optimal solutions exist to single objective function optimization, cycling, and the depiction of the analyst as a silent spectator in the decision making process. By taking as an example a recently proposed interactive compromise programming method, these difficulties are illustrated and necessary modifications are proposed to overcome these difficulties. These are further elucidated by a case study.

Dynamic compromise programming with application to water reservoir management

The dynamic compromise programming method is developed to perform multi-criteria optimization of dynamic systems. Compromise control minimizes the measure of distance, providing the closest solution to the ideal one. As the measure of ‘closeness’ the L p-metric is used. The developed algorithm is presented with an application to water reservoir management. The multi-purpose water reservoir control is considered as a multi-criteria optimization problem. Each purpose is represented by its own criterion, such as minimum deficits in water supply, maximum irrigation benefits and maximum benefits of hydropower generation. The hydrological input is known (given as recorded time series). The dynamic multi-criteria problem is formulated in discrete form and it is solved by a dynamic compromise programming algorithm.

An Interactive Approach to the Multicriteria Decision Making

The main purpose of this paper is to introduce a solution method for multicriteria problems: The method offers a practical solution by combining judgement with an automatic optimization technique in decision making. This is realised by using the basic ideas behind the method of compromise programming. The "nearly equal" and the "closest" sets of optimal weights of objective functions are established by using the maximum entropy (MEP) and minimum squared deviation principle (MSDP) respectively. The solut1ons in terms of deviation measure from the ideal point are presented to the decisionmaker (OM), and he is only asked to specify the amount by which specific alternatives should exceed the "optimal" alternatives. Such interactive methods allow a re-adjustment of optimal weights, displacement of the ideal point and new pre-decision conflict formation, and leads to a decision in a finite number of iterations. A numerical example is included to illustrate the proposed technique.

An Interactive Entropy Based Method for Solving the Multi-criteria Problems

The main purpose of this paper is to introduce a solution method for multi-criteria linear programming (MCLP) problems. The method offers a practical solution to MCLP problems by combining judgements with an automatic optimization technique in decision making. This is realised by using the ideas underlying the method of compromise programming and the maximum entropy principle (MEP) in an iterative way. The "nearly equal" optimal weights of objective functions in an MCLP problem are established by using the MEP. The solutions in terms of deviation measurement from an ideal point are presented to the decisionmaker, and he has only to specify the amount by which specific alternatives should exceed the “optimal” ones. In general, a nearly-equal weights procedure may incorporate additional constraints for eliciting weights. Such interactive method for solving MCLP allows re-adjustment of optimal weights, displacement of the ideal point and new pre-decision conflict formation, and leads to a decision in a finite number of iterations. A numerical example is included to illustrate the proposed technique.

Interactive Compromise Programming

The purpose of this paper is to introduce a solution method for multiple objective linear programming (MOLP) problems. The method, called interactive compromise programming (ICP), offers a practical solution to MOLP problems by combining judgement with an automatic optimization technique in decision-making. This is realised by using the method of compromise programming and the method of a two-person zero-sum game in an iterative way. The method is illustrated by a numerical example.