Vira Chankong

Does choice of multicriteria method matter? An experiment in water resources planning

Many multiple criteria decision making methods have been proposed and applied to water planning. Their purpose is to provide information on tradeoffs among objectives and to help users articulate value judgments in a systematic, coherent, and documentable manner. The wide variety of available techniques confuses potential users, causing inappropriate matching of methods with problems. Experiments in which water planners apply more than one multicriteria procedure to realistic problems can help dispel this confusion by testing method appropriateness, ease of use, and validity. We summarize one such experiment where U.S. Army Corps of Engineers personnel used several methods to screen urban water supply plans. The methods evaluated include goal programming, ELECTRE I, additive value functions, multiplicative utility functions, and three techniques for choosing weights (direct rating, indifference tradeoff, and the analytical hierarchy process). Among the conclusions we reach are the following. First, experienced planners generally prefer simpler, more transparent methods. Additive value functions are favored. Yet none of the methods are endorsed by a majority of the participants; many preferred to use no formal method at all. Second, there is strong evidence that rating, the most commonly applied weight selection method, is likely to lead to weights that fail to represent the trade-offs that users are willing to make among criteria. Finally, we show that decisions can be as or more sensitive to the method used as to which person applies it. Therefore, if who chooses is important, then so too is how a choice is made.

Analysis of the value for unit commitment of improved load forecasts

Load forecast errors can yield suboptimal unit commitment decisions. The economic cost of inaccurate forecasts is assessed by a combination of forecast simulation, unit commitment optimization, and economic dispatch modeling for several different generation/load systems. The forecast simulation preserves the error distributions and correlations actually experienced by users of a neural net-based forecasting system. Underforecasts result in purchases of expensive peaking or spot market power; overforecasts inflate start-up and fixed costs because too much capacity is committed. The value of improved accuracy is found to depend on load and generator characteristics; for the systems considered here, a reduction of 1% in mean absolute percentage error (MAPE) decreases variable generation costs by approximately 0.1%-0.3% when MAPE is in the range of 3%-5%. These values are broadly consistent with the results of a survey of 19 utilities, using estimates obtained by simpler methods. A conservative estimate is that a 1% reduction in forecasting error for a 10,000 MW utility can save up to

Paper: Kuhn-Tucker multipliers as trade-offs in multiobjective decision-making analysis

1.6 million annually.

Application of the carcinogenicity prediction and battery selection (CPBS) method to the Gene-Tox data base.

Useful relationships between the optimal Kuhn-Tucker multipliers and trade-offs in the multiobjective decision-making problems are developed based on the sensitivity interpretation of such multipliers. Practical and theoretical applications of these results are discussed. The results provide a convenient way for obtaining necessary (trade-off) information for continuing into the analyst-decision-maker interactive phase of the multiobjective decision-making process. This paper further extends the theoretical basis of the Surrogate Worth Trade-off (SWT) Method; a multiobjective optimization method which first appeared in the scientific literature in 1974.

The Interactive Surrogate Worth Trade-Off (ISWT) Method for Multiobjective Decision-Making

The carcinogenicity prediction and battery selection (CPBS) method (Chankong et al., 1985) utilizes the results of short-term tests to predict the carcinogenicity of chemicals and select batteries of tests that are capable of giving accurate predictions at reasonable costs. The CPBS method has been applied to the data compiled under the aegis of the Gene-Tox Program of the U.S. Environmental Protection Agency as a demonstration of the method on a typical data base. A number of batteries were selected by the methodology as having superior performance characteristics. The Bayesian predictions resulting from most of the selected 3-assay batteries were very good (greater than 90% of the carcinogens were correctly identified). It was also found that the 3-assay batteries of specified composition gave generally more accurate predictions than batteries of 4 or more assays of unspecified composition. A number of problems which may have affected our results have been identified: (1) the reliability of the sensitivities and specificities of the individual assays, (2) the prior probability that a chemical is a carcinogen was assumed to be 0.5, and (3) we have not (as yet) taken into account that some of the carcinogens are non-genotoxic and will produce false negative assays results. We are currently investigating approaches to take these factors into consideration. Our analysis also indicates that more testing of chemicals for carcinogenicity (especially probable non-carcinogens) is needed to further enhance the predictive capability of the CPBS method.

The impact of respiratory motion and treatment technique on stereotactic body radiation therapy for liver cancer

This paper presents an interactive and modified version of the Surrogate Worth Trade-off method for multiobjective decision-making which was developed by Haimes and Hall [1974], An attempt is made to develop an algorithm that is both theoretically appealing and yet intuitively simple to understand and implement particularly from the point of view of the decision-maker (DM). The method recognizes and emphasizes the importance of both the structural part, by insisting that only the Pareto optimal solutions need be considered as candidates for the final decision, and the nonstructural part, by providing a simple and effective procedure by which the DM and the analyst can interactively and systematically explore the Pareto optimal set while trying to maximize the DM’s unknown utility function. This results in a sequence of improving Pareto-optimal solutions which, under some rational and consistent choices on the part of the DM, converges to a solution having maximum DM utility. The DM-analyst dialogue as well as the tasks to be performed by both are kept simple. The method applies to both linear and nonlinear multiobjective decision-making problems. Other features in which the method has its strengths and weaknesses are discussed and demonstrated by means of a numerical example.

Recurrent neural networks for linear programming: analysis and design principles

Stereotactic body radiation therapy (SBRT), which delivers a much higher fractional dose than conventional treatment in only a few fractions, is an effective treatment for liver metastases. For patients who are treated under free-breathing conditions, however, respiration-induced tumor motion in the liver is a concern. Limited clinical information is available related to the impact of tumor motion and treatment technique on the dosimetric consequences. This study evaluated the dosimetric deviations between planned and delivered SBRT dose in the presence of tumor motion for three delivery techniques: three-dimensional conformal static beams (3DCRT), dynamic conformal arc (DARC), and intensity-modulated radiation therapy (IMRT). Five cases treated with SBRT for liver metastases were included in the study, with tumor motions ranging from 0.5 to 1.75 cm. For each case, three different treatment plans were developed using 3DCRT, DARC, and IMRT. The gantry/multileaf collimator (MLC) motion in the DARC plans and the MLC motion in the IMRT plans were synchronized to the patients respiratory motion. Retrospectively sorted four-dimensional computed tomography image sets were used to determine patient-organ motion and to calculate the dose delivered during each respiratory phase. Deformable registration, using thin-plate-spline models, was performed to encode the tumor motion and deformation and to register the dose-per-phase to the reference phase images. The different dose distributions resulting from the different delivery techniques and motion ranges were compared to assess the effect of organ motion on dose delivery. Voxel dose variations occurred mostly in the high gradient regions, typically between the target volume and normal tissues, with a maximum variation up to 20%. The greatest CTV variation of all the plans was seen in the IMRT technique with the largest motion range (D99: -8.9%, D95: -8.3%, and D90: -6.3%). The greatest variation for all 3DCRT plans was less than 2% for D95. Dose variations for DARC fell between the 3DCRT and IMRT techniques. The dose volume histogram variations for normal organs were negligible. Therefore, the IMRT technique may be a preferable treatment choice in cases where the target volume and critical organs are in close proximity, or when normal organ protection is a high priority, provided that motion effect for the target volume can be managed.

Design of cellular manufacturing systems with assembly considerations

Abstract This paper explores the potential role of recurrent neural networks for solving linear programs. The emphases of the paper are on analyzing the asymptotic properties of recurrent neural networks that are relevant to linear programming and on developing general principles for designing such neural networks. A class of recurrent neural networks with monotonically increasing penalty variables is presented for solving linear programming problems. The proposed recurrent neural networks are asymptotically stable and able to generate optimal solutions to linear programming problems. The asymptotic properties of the proposed recurrent neural networks for linear programming are analyzed theoretically, and the design principles for synthesizing the recurrent networks are discussed based on the results of analysis. Some illustrative examples are also presented to demonstrate the performance behavior and operational characteristics of the recurrent neural networks.

Decision models for management of cogeneration plants

This research proposes incorporating assembly aspects associated with a product into the design of Cellular manufacturing System (CMS). The literature on CMS design implicitly assumes that finished part is the end product by itself. In practice, often, manufacturers produce parts which are assembled into a finished product. The methodology employs a part-subassembly matrix derived from the product structure in addition to the part-machine matrix. A mathematical programming model is developed which determines an assignment of parts, machines and subassemblies to manufacturing cells. The proposed model employs a new similarity coefficient between part, machine and subassembly. The model resulted in a nonlinear program with 0-1 variables. A case study has been analyzed based on a published part-machine matrix and a randomly generated product structure. The analysis reveals that it may be required to forego some of the efficiencies of Group Technology (GT) in order to achieve integration of assembly operations with production of parts. From a practical stand point of view it is preferred to have a system design which has a mix of GT and integration efficiencies, compared to a design which outperforms on GT criteria and completely lacks integration of assembly operations with production of parts.

Transient Stability and Voltage Regulation in Multimachine Power Systems Vis-à-Vis STATCOM and Battery Energy Storage

This work focusses on the development of modelling tools for optimal energy management in an industrial/commercial setting having cogeneration plants. Both cases of gas and steam turbine-based cogeneration plants are taken into consideration. A scheduling tool is also built into this model to help in scheduling of multi-interval shiftable loads with variable amplitude load profiles. >