Showing papers on "Goal programming published in 1999"

Use of vehicle signature analysis and lexicographic optimization for vehicle reidentification on freeways

Abstract: The vehicle reidentification problem is the task of matching a vehicle detected at one location with the same vehicle detected at another location from a feasible set of candidate vehicles detected at the other location. This paper formulates and solves the vehicle reidentification problem as a lexicographic optimization problem. Lexicographic optimization is a preemptive multi-objective formulation, and this lexicographic optimization formulation combines lexicographic goal programming, classification, and Bayesian analysis techniques. The solution of the vehicle reidentification problem has the potential to yield reliable section measures such as travel times and densities, and enables the measurement of partial dynamic origin/destination demands. Implementation of this approach using conventional surveillance infrastructure permits the development of new algorithms for ATMIS (Advanced Transportation Management and Information Systems). Freeway inductive loop data from SR-24 in Lafayette, California, demonstrates that robust results can be obtained under different traffic flow conditions.

An application of visual interactive goal programming: a case in vendor selection decisions

Abstract: This article presents the vendor selection problem of the hydraulic pump division of a US original equipment manufacturing company. The division wants to identify appropriate vendors and allocate purchase orders among them while minimizing product acquisition costs and maximizing total product quality and delivery reliability. Visual interactive goal programming is used as a multi-criteria decision analysis tool. Interaction with decision makers during the model building and solution process, and implications are discussed. Copyright © 1999 John Wiley & Sons, Ltd.

Dependent-chance programming with fuzzy decisions

Abstract: Dependent-chance programming (DCP) is a new type of stochastic programming and has been extended to the area of fuzzy programming. This paper provides a spectrum of DCP and dependent-chance multiobjective programming (DCMOP) as well as dependent-chance goal programming (DCGP) models with fuzzy rather than crisp decisions. The terms of uncertain environment, event, chance function, and induced constraints are discussed in the case of fuzzy decisions. A technique of fuzzy simulation is also designed for computing chance functions. Finally, we present a fuzzy simulation-based genetic algorithm for solving these models and illustrate its effectiveness by some numerical examples.

Solving goal programming problems using multi-objective genetic algorithms

Abstract: Goal programming is a technique often used in engineering design activities primarily to find a compromised solution which will simultaneously satisfy a number of design goals. In solving goal programming problems, classical methods reduce the multiple goal-attainment problem into a single objective of minimizing a weighted sum of deviations from goals. In this paper, we pose the goal programming problem as a multi-objective optimization problem of minimizing deviations from individual goals. This procedure eliminates the need of having extra constraints needed with classical formulations and also eliminates the need of any user-defined weight factor for each goal. The proposed technique can also solve goal programming problems having a non-convex trade-off region, which are difficult to solve using classical methods. The efficacy of the proposed method is demonstrated by solving a number of test problems and by solving an engineering design problem. The results suggest that the proposed approach is a unique, effective, and practical tool for solving goal programming problems.

Information resource planning for a health-care system using an AHP-based goal programming method

Abstract: This paper presents a goal programming (GP) model which aids in allocating a health-care system's information resources pertinent to strategic planning. The model is developed based on the data obtained from a major health-care system in the United States. The overall objective is to design and evaluate a model for effective information resource planning in a health-care system. The proposed model: (1) utilizes a GP approach to reflect the multiple, conflicting goals of the health-care system; (2) employs a GP solution process to reflect multi-dimensional aspects of the resource allocation planning; and (3) allows for some degree of flexibility of decision-making with respect to resource allocation. The goals are decomposed and prioritized with respect to the corresponding criteria using the analytic hierarchy process (AHP). The model result is derived and discussed. This GP model facilitates decision-making planning process and managerial policy in health-care information resources planning and similar planning settings.

Goal programming approach to maintenance scheduling of generating units in large scale power systems

Abstract: This paper presents a goal programming methodology for solving maintenance scheduling of thermal generating units under economic and reliability criteria. The advantages of a multicriteria approach are demonstrated by comparing the effects that costs and reliability have on each other in power plants maintenance scheduling. The problem is formulated as a large scale mixed integer programming problem implemented in the mathematical programming language GAMS and solved using OSL. Weekly maintenance scheduling of the large scale Spanish power system for a year period illustrates the proposed methodology.

Distance-based consensus methods: a goal programming approach

Abstract: Several authors have proposed a social choice function based upon distance-consensus between different committee rankings. Under this framework, the total absolute disagreement between committees is minimised. The purpose of this paper is to formulate the underlying optimisation problem as a goal programming (GP) model. To do this, the following three GP formulations are proposed: (a) a linear weighting GP model, where consensus is established by the minimisation of the weighted aggregated disagreement, (b) a MINMAX GP model, where the consensus is defined as the minimisation of the maximum disagreement and (c) an extended GP model, which subsumes the two previous formulations as particular cases.

DEA Models via Goal Programming

Abstract: In this paper, we investigate the relationship between Data Envelopment Analysis (DEA) and Multiple Criteria Decision Making Theory. We re-examine DEA models from a goal programming perspective. It has been shown in this work that many known DEA models and new ones, can be derived via this approach.

Extensions of Pareto efficiency analysis to integer goal programming

Abstract: This paper focuses on the design, development and implementation of new Pareto efficiency detection and restoration techniques for integer goal programming. The design of the algorithms and their implementation issues within (an otherwise continuous) goal programming system are detailed. The differences between continuous and integer goal programming regarding Pareto efficiency detection and restoration analysis are described. The integer Pareto efficiency techniques have been applied to a selection of problems from different industrial contexts in order to assess their computational performance. Finally, Pareto restoration and detection techniques are applied to an integer goal programming problem to illustrate the methodology.

Comparison analysis of methods for deriving priorities in the analytic hierarchy process

Abstract: Presents a comparison study of some methods for prioritisation in the analytic hierarchy process. The main objective of this analysis is to compare and evaluate a fuzzy preference programming method with the most popular prioritisation techniques: the eigenvector method, the weighted least squares method, the logarithmic least squared method and the goal programming method. The analysis is based on three evaluation criteria: the total deviation, measuring the Euclidean distance between the ratios obtained by the derived weights and the decision-maker ratios; the minimum violations criterion, measuring the rank reversal properties of the methods; and the conformity, indicating the outliers with respect to the other methods. The evaluation procedure uses randomly generated inconsistent pairwise comparison matrices of different dimensions.

Solution of goal programming models using a basic taboo search algorithm

Abstract: Goal programming is a very powerful technique for solving multiple objective optimisation problems. It has been successfully applied to numerous diverse real life problems. In this paper a Taboo search based method is developed to solve preemptive goal programming problems. The method can easily be applied to any kind of preemptive goal programming problems.

Extended interval goal programming

Abstract: This paper focuses on possible problems associated with the use of penalty functions in Goal Programming (GP). In this sense we illustrate, with the help of numerical examples, how an assumption of separability amongst decision maker's preferences which underlies these approaches, can produce in the corresponding GP models extremely biased results towards certain goals. A new GP variant is proposed to overcome this type of problem.

Goal lattices for sensor management

Abstract: A new methodology for quantifying the relative contribution of specific sensor actions to a set of mission goals is presented. The mission goals are treated as a set, and an ordering relationship is applied to it leading to a partially ordered set which can be represented as a lattice. At each layer in the lattice, each goal's value is computed as the sum of the (higher) goals in which it is included and its value is apportioned among the (lower) goals which it includes. A system designer is forced to make a zero-sum apportionment of each goal's value among those goals which it includes. The net result of this methodology is a quantifiable measure of the contributing value of each real type of sensor action to the system of goals, leading to more effective allocation of resources. While applied here to sensor scheduling, the method has applications to other decision making processes as well.

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...

Combinatorial optimization of elk habitat effectiveness and timber harvest volume

Abstract: Forest resource planning processes in the western United States have been placing an increasing emphasis on wildlife and fish habitat goals. With this in mind, we developed a method that incorporates a Habitat Effectiveness Index (HEI) for Roosevelt elk (Cervus elaphus roosevelti) into the objective function of a mathematical forest planning model. In addition, a commodity production goal is proposed (maximum timber production), and the habitat and commodity production goals are allowed to act as goals in a multi-objective goal programming planning problem. A heuristic programming technique (tabu search) is used to develop feasible solutions to the resulting non-linear, integer programming problem. Using a hypothetical example, we illustrate results of five scenarios, where the emphasis of the achievement of one or both goals is altered. The main contribution of this approach is the ability to measure and evaluate the trade-offs among achieving a certain level of a complex wildlife goal and achieving commodity production goals. These trade-offs are measured using a flexible model, allowing planners to formulate non-linear spatial goals as objectives of a problem, rather than forcing them to rely on posterior evaluations of the suitability of management plans to goals such as elk HEI.

Dependent-chance integer programming applied to capital budgeting

Abstract: Abjtruct This paper attempts to model capital budgeting problems by a new technique of dependentchance integer programming as well as dependent-chance multiobjective programming and goal programming. Some examples are provided to illustrate the potential applications in the area of capital budgeting. A stochastic simulation based genetic algorithm is also designed to solve both chance constrained integer programming and dependent-chance integer programming models.

Optimizing inventory stockpiles and mine production: An application of separable and goal programming to phosphate mining using AMPL/Cplex

Abstract: In the Phosporia formation of SE Idaho, ore quality varies by depth, seam and distance along strike; phosphorus recovery is sensitive to these variations. Plant ore feed can be blended over time to minimize this fluctuation by using a combination of mine production and inventory stockpile scheduling. Mixed-integer and goal programming has been used to optimize short-term mine production, but the seasonal nature of mine haulage requires the use of stockpiles both at the mill and the mine. The contents of the stockpiles can also be optimized as part of the production scheduling problem, but introducing inventory control introduces a nonlinear term into the formulation. A solution using a piecewise linear formulation is modelled and solved using AMPL/Cp/ex, and solution strategies are discussed for similar large-scale problems.

Water supply planning under interdependence of actions: Theory and application

Abstract: An ongoing water supply planning problem in the Regional Municipality of Waterloo, Ontario, Canada, is studied to select the best water supply combination, within a multiple-objective framework, when actions are interdependent. The interdependencies in the problem are described and shown to be essential features. The problem is formulated as a multiple-criteria integer program with interdependent actions. Because of the large number of potential actions and the nonconvexity of the decision space, it is quite difficult to find nondominated subsets of actions. Instead, a modified goal programming technique is suggested to identify promising subsets. The appropriateness of this technique is explained, and the lessons learned in applying it to the Waterloo water supply planning problem are described.