Showing papers on "Goal programming published in 2002"

Dispersed generator placement using fuzzy-GA in distribution systems

Abstract: This paper presents a fuzzy-GA method to resolve dispersed generator placement for distribution systems. The problem formulation considers an objective to reduce power loss costs of distribution systems and the constraints with the number or size of dispersed generators and the deviation of the bus voltage. The main idea of solving fuzzy nonlinear goal programming is to transform the original objective function and constraints into the equivalent multi-objectives functions with fuzzy sets to evaluate their imprecise nature and solve the problem using the proposed genetic algorithm without any transformation for this nonlinear problem to a linear model or other methods. Moreover, this algorithm proposes a satisfying method to solve the constrained multiple objective problem. Analyzing the results and updating the expected value of each objective function allows the dispatcher to obtain the compromised or satisfied solution efficiently.

Reasoning with Goal Models

Abstract: Over the past decade, goal models have been used in Computer Science in order to represent software requirements, business objectives and design qualities. Such models extend traditional AI planning techniques for representing goals by allowing for partially defined and possibly inconsistent goals. This paper presents a formal framework for reasoning with such goal models. In particular, the paper proposes a qualitative and a numerical axiomatization for goal modeling primitives and introduces label propagation algorithms that are shown to be sound and complete with respect to their respective axiomatizations. In addition, the paper reports on preliminary experimental results on the propagation algorithms applied to a goal model for a US car manufacturer.

Farmer Decision‐Making with Conflicting Goals: A Recursive Strategic Programming Analysis

Abstract: This paper concerns the analysis of farmers' goal trade-offs using a series of representative dairying and beef/sheep farm models. The models employ an adaptive feedback structure and expectations model to track adjustment processes over a seven-year planning horizon, 1991/92 to 1997/98. Model solutions, under a conventional profit maximising objective function, and using a weighted goal programming formulation, under a series of empirically specified alternative goal orientations, are examined and compared. The paper identifies significant variation among farm families in terms of ability to attain key goals concerning farm profitability, family consumption, farm investment, growth and cash flow. The results quantify the trade-off between family consumption and farm investment/growth goals.

A unified approach for dual response surface optimization

Abstract: In the robust design of industrial products and processes, better quality can be achieved with optimization of dual response systems. To optimize a dual response system, a goal programming approach is proposed that is general enough to include som..

Aggregation of preferences in an environmental economics context: a goal-programming approach

Abstract: This paper has been devised with two different and at the same time complementary aims. First, to propose a methodology based upon goal programming that allows the aggregation of individual preferences provided by several social groups towards different criteria in a cardinal manner. The main feature of the procedure lies in the easy utility interpretation of the social consensus obtained. Second, to apply the proposed methodology to a case study on electricity planning in Spain within an environmental context, where several criteria of different nature and some social groups with different interests are involved. The social weights that have to be attached to the different criteria in a multi-objective programming model are obtained this way.

Toward the Formal Foundation of Ant Programming

Abstract: This paper develops the formal framework of ant programming with the goal of gaining a deeper understanding on ant colony optimization and, more in general, on the principles underlying the use of an iterated Monte Carlo approach for the multi-stage solution of combinatorial optimization problems. Ant programming searches for the optimal policy of a multi-stage decision problem to which the original combinatorial problem is reduced. In order to describe ant programming we adopt on the one hand concepts of optimal control, and on the other hand the ant metaphor suggested by ant colony optimization. In this context, a critical analysis is given of notions such as state, representation, and sequential decision process under incomplete information.

Modelling a nationwide crop planning problem using a multiple criteria decision making tool

Abstract: Multiple criteria decision making (MCDM) tools have been used in recent years to solve a wide variety of problems. In this paper we consider a nation-wide crop-planning problem and show how an MCDM tool can be used efficiently and effectively for these types of problems. A crop-planning problem is usually formulated as a single objective linear programming model. The objective is either the maximization of return from cultivated land or the minimization of cost of cultivation. This type of problem, however, normally involves more than one goal. We thus formulate a crop-planning problem as a goal program (an MCDM tool) and discuss the importance of three different goals for a case problem. We solve the goal program with a real world data set, and compare the solution with that of linear program. We argue that the goal program provides better insights to the problem and thus allows better decision support.

A conceptual framework for evaluation of information technology investments

Abstract: The decision to acquire a new information technology poses a number of serious evaluation and selection problems to technology managers, because the new system must not only meet current information requirements of the organisation, but also the needs for future expansion. Tangible and intangible benefits factors, as well as risks factors, must be identified and evaluated. The paper provides a review of ten major evaluation categories and available models, which fall under each category, showing their advantages and disadvantages in handling the above difficulties. This paper describes strategic implications involved in the selection decision, and the inherent difficulties in: (1) choosing or developing a model, (2) obtaining realistic inputs for the model, and (3) making tradeoffs among the conflicting factors. It proposes a conceptual framework to help the decision maker in choosing the most appropriate methodology in the evaluation process. It also offers a new model, called GAHP, for the evaluation problem combining integer goal linear programming and Analytic Hierarchy Process (AHP) in a single hybrid multiple objective multi-criteria model. A goal programming methodology, with zero-one integer variables and mixed integer constraints, is used to set goal target values against which information technology alternatives are evaluated and selected. AHP is used to structure the evaluation process providing pairwise comparison mechanisms to quantify subjective, nonmonetary, intangible benefits and risks factors, in deriving data for the model. A case illustration is provided showing how GAHP can be formulated and solved.

An expert system for multiobjective decision making: application of fuzzy linguistic preferences and goal programming

Abstract: This paper introduces an interactive approach for solving multiobjective decision making (MODM) problems based on linguistic preferences and architecture of a fuzzy expert system. The aim of this paper is to consider the decision maker's (DMs) preferences in determining the priorities and aspiration levels, in addition to analysis of conflict among the goals. The main concept presented in this paper is to convert the MODM problem into its equivalent goal programming (GP) problem by appropriately setting the priority and aspiration level for each objective. The conversion approach introduced is based on fuzzy linguistic preferences of DM, in addition to the concepts of conflict analysis among objective functions and fuzzy set theory.

An Integrated Approach to MCDA

Abstract: In this final chapter we return to focus on and explicitly to address the title and overarching theme of the book — An Integrated Approach to Multicriteria Decision Analysis. This discussion is directed at both theoreticians and practitioners in the MCDA community. We believe that integration, in many different forms, is essential to the growth and success of MCDA. Hence, let us begin the discussion by expanding on what we envisage by integration.

Introduction to Multi-Objective Forest Planning

Abstract: The purpose of forest planning is to support forestry decision-making by suggesting management alternatives, providing information about their consequences, and helping the decision maker to rank the alternatives. In multi-objective forest planning, forest plans are evaluated using various multiple criteria decision support methods and multi-objective optimisation algorithms. Multiple criteria comparison methods help to systematise subjective evaluations whereas multi; objective optimisation seeks the best plan among a huge number of alternatives using automated computer-based search methods. The ranking of alternatives depends on the preferences of the decision maker, both in multiple criteria comparison and in multi-objective optimisation. A careful analysis of preferences is an important step of any multi-objective planning case. The quantitative approach to decision-making suggests that a specific planning model be developed for every planning situation. This model is then solved, the result being a candidate plan that must pass various post-optimisation tests and analyses. There are several ways to prepare a multi-objective planning model, based on linear programming, goal programming, penalty functions or multi-attribute utility theory. The planning model may be solved using mathematical programming techniques or various heuristics. The use of heuristic optimisation has gained popularity in forest planning along the increasing importance of ecological forest management goals, which are often described with spatial variables. Examples of heuristics available to multi-objective forest planning are random ascent heuristics, simulated annealing, tabu search and genetic algorithm. Practical forest plans are produced in a computerised system, which includes subsystems for data management, simulation of stand development, planning model generation and optimisation, and subjective evaluation of alternative plans.

Analysis via goal programming of the minimum achievable stay in surgical waiting lists

Abstract: In this paper, a Goal Programming model is developed in order to study the possibility of decreasing the length of stay on the waiting list of a hospital that belongs to the Spanish Health Service. First, a problem is solved to determine the optimal planning for one year, so as to make the maximum waiting time decrease to six months (at present, some operations have a waiting list of more than a year). Afterwards, two other problems are solved in order to determine the impact that a further reduction of the waiting time (four months) would have on the requirements of extra resources for the hospital. The particular problem for the Trauma service is described in detail, but global results are shown and commented.

A Fuzzy Goal Programming Approach for Solving Bilevel Programming Problems

Abstract: This paper presents a fuzzy goal programming procedure for solving linear bilevel programming problems. The concept of tolerance membership functions for measuring the degree of satisfactions of the objectives of the decision makers at both the levels and the degree of optimality of vector of decision variables controlled by upper-level decision maker are defined first in the model formulation of the problem. Then a linear programming model by using distance function to minimize the group regret of degree of satisfactions of both the decision makers is developed. In the decision process, the linear programming model is transformed into an equivalent fuzzy goal programming model to achieve the highest degree (unity) of each of the defined membership function goals to the extent possible by minimizing their deviational variables and thereby obtaining the most satisfactory solution for both the decision makers. To demonstrate the approach, a numerical example is solved and compared the solution with the solutions of other two fuzzy programming approaches [11,12 ] studied previously.

Generalized goal programming: polynomial methods and applications

Abstract: In this paper we address a general Goal Programming problem with linear objectives, convex constraints, and an arbitrary componentwise nondecreasing norm to aggregate deviations with respect to targets. In particular, classical Linear Goal Programming problems, as well as several models in Location and Regression Analysis are modeled within this framework. In spite of its generality, this problem can be analyzed from a geometrical and a computational viewpoint, and a unified solution methodology can be given. Indeed, a dual is derived, enabling us to describe the set of optimal solutions geometrically. Moreover, Interior-Point methods are described which yield an $\varepsilon$-optimal solution in polynomial time.

Solving a multi-objective simulation model using a hybrid response surface method and lexicographical goal programming approach—a case study on integrated circuit ink-marking machines

Abstract: In an integrated circuit (IC) packaging plant, the ink-marking machine has a significantly higher throughput than the other processing machines. When periodic demand surges result in backlog orders or in lost customers, there is a need to increase system throughput. To resolve this problem, the purchase of a new machine often results in excess capacity in addition to added operation and acquisition costs. Therefore, the productivity improvement effort has priority over the machine purchase decision. This paper seeks to optimize both throughput and cycle time performance for IC ink-marking machines. While throughput increase is the primary objective, there is an acceptable cycle time limit for a feasible solution. It is a multi-objective problem. The proposed solution methodology constructed a simulation metamodel for the ink-marking operation by using a fractional factorial experimental design and regression analysis. It is then solved by a hybrid response surface method and lexicographical goal programming approach. Solution results illustrated a successful application.

The use of goal programming, regression analysis and data envelopment analysis for estimating efficient marginal costs of hospital services

Abstract: This paper presents a new composite methodology for estimating efficient marginal costs of outputs. The methodology is based on multi-criteria methods involving Data Envelopment Analysis (DEA), Goal Programming (GP) and Regression Analysis (RA) techniques. Firstly, DEA is used to find an empirical frontier onto which each observed dependent variable (cost) is projected. Then GP or RA is applied to the projected data set to yield the parameter estimates of a cost function. That is, firstly, the amounts of technical inefficiency are removed and then the cost function is estimated. The parameters of the cost function provide the efficient marginal costs of outputs. The filtering of the data can be made by means of DEA as it yields target values with frontier rather than average behaviour. The results clearly indicate the superiority of the combination of DEA and GP over the combination of DEA and RA. The methodology was applied to estimate the efficient marginal costs of hospital services (inpatient days, outpatient visits, ancillary services) of public general hospitals in Greece. Concluding, the analysis proves that the estimation of efficient marginal costs of outputs is in author's view particularly useful for hospitals, and can serve as a theoretical basis for determining the prices of different hospital services. Copyright © 2003 John Wiley & Sons, Ltd.

An interactive fuzzy satisficing method for multiobjective stochastic linear programming problems using chance constrained conditions

Abstract: Two major approaches to deal with randomness or ambiguity involved in mathematical programming problems have been developed. They are stochastic programming approaches and fuzzy programming approaches. In this paper, we focus on multiobjective linear programming problems with random variable coefficients in objective functions and/or constraints. Using chance constrained programming techniques, the stochastic programming problems are transformed into deterministic ones. As a fusion of stochastic approaches and fuzzy ones, after determining the fuzzy goals of the decision maker, interactive fuzzy satisficing methods to derive a satisficing solution for the decision maker by updating the reference membership levels is presented. Copyright © 2003 John Wiley & Sons, Ltd.

Multiple objective and goal programming : recent developments

Abstract: I Theory.- Measuring the Balance Space Sensitivity in Vector Optimization.- On a Certain Approach to Fuzzy Goal Programming.- A Multiobjective Linear Programming Algorithm Based on the Dempster-Shafer Composition Rule.- Data Envelopment Analysis by Multiobjective Linear Programming Methods.- A Fuzzy Borda Count in Multi-person Decision Making.- Multiple Criteria Choice Models for Quantitative and Qualitative Data.- On the Computational Effectiveness of Multiple Objective Metaheuristics.- On Considering Flexible Constraints of Different Importance in Goal Programming Problems.- Trade-offs - A Lost Dimension in Multiple Criteria Decision Making.- Multiple Objective Path Optimization for Time Dependent Objective Functions.- Multicriteria Decision Support in Bargaining a Problem of Players' Manipulations.- Some Concepts of Solution for a Game under Uncertainty.- Towards the Development of an Integrated Multi-objective Solution and Analysis System.- Dynamic Discrete Programming with Partially Ordered Criteria Set.- Dual Approach to Generalized Data Envelopment Analysis Based on Production Possibility.- II Applications.- Using Interactive Multiple Objective Methods to Determine the Budget Assignment to the Hospitals of a Sanitary System.- Fuzzy Multi-objective Approach to the Supply Chain Model.- Goal Programming Model for Airport Ground Support Equipment Parking.- Multicriteria Decision Aid in Inventory Management.- Solution Concepts in Multiple Criteria Linear Production Games.- Identyfying Important Attributes for the Siberian Forests Management Using Rough Sets Analysis.- On Optimization Model Formulation Using Neural Networks.- The Design of the Physical Distribution System with the Application of the Multiple Objective Mathematical Programming. Case Study.- Integer Goal Programming Applications Using Modelling Systems and Excel.- On Shaping Multi Criteria Marketing Strategy of a Pension Fund.- Multiple Criteria Company Benchmarking using the BIPOLAR Method.- Multiobjective Analysis of a Financial Plan in a Bank.- Inverse Stochastic Dominance and its Applications in Production Process Control.- Multi Criteria Multilevel Transshipment Problem and its Software Support.- On Ranking of Economic Educational Institutions in Poland.- Multicriterion Analysis Based on Marginal Conditional Stochastic Dominance in Financial Analysis.- Multicriteria Analysis Based on Stochastic and Probabilistic Dominance in Measuring Quality of Life.- Company Financial Multiple Attributive Evaluation under Vagueness Conditions.

A new integrated system for loading and scheduling in cellular manufacturing

Abstract: In this paper, a multiple objective optimization technique, based on simulation, and a taboo search algorithm are proposed for loading and scheduling Cellular Manufacturing systems (CM). Machine independent capability units known as Resource Elements (REs) are used to define the processing capabilities of machines and the processing requirements of parts. The loading problem is formally represented by a Goal Programming formulation with the objectives of system performance measures that are obtained by a simulation-based scheduling system. The problem is solved by a taboo search based algorithm. The proposed integrated system is implemented in C/C++ programming language and simulation. A practical case study is also explained and reported in detail.

An Interactive Goal Programming Procedure for Operational Recovery Problems

Abstract: We propose an interactive Goal Programming (GP) for operational recovery problems that are present in diverse areas of application. After defining the problem we discuss its relevance to scenarios taken from airline scheduling, supply chain management and call centers operations. We then construct the GP procedure for operational recovery decision making and illustrate the mechanics of the proposed procedure through two examples: an abstract example based on a minimum spanning tree problem and a more practical example of a production-inventory problem.

System and method for travel carrier contract management and optimization

Abstract: A method and system for air carrier contract management and optimization is disclosed. In particular, the present invention receives and tracks client travel data (105) and air carrier contract data (100), analyzes this data (110) and configures the data structure to be used in a goal programming algorithm to determine an optimum travel carrier solution (115).

Forest management optimisation models and habitat diversity: a goal programming approach

Abstract: This paper proposes an analytical framework, based upon Goal Programming, to incorporate an operational measure of habitat diversity into a forest management optimisation model. First of all, the trade-off curve between the proposed measure of habitat diversity and financial returns is determined. Then, the measure of habitat diversity is integrated in conjunction with other relevant criteria into a compact forest management model. The way in which both models work is explained with the help of a simple but illustrative case study.