Five classes of zero-one programming models for discrete facility location problems are compared to counterpart models for the selection of conservation reserves. The basic problem of siting facilities to cover demand...
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Five classes of zero-one programming models for discrete facility location problems are compared to counterpart models for the selection of conservation reserves. The basic problem of siting facilities to cover demand for services is analogous to the problem of selecting reserves to support species diversity. The classes of models include the set covering and maximal covering models, as well as models for backup and redundant coverage. Issues of reliability and uncertainty are addressed by chance constrained covering models and maximal expected covering models. Exact and heuristic solution approaches are discussed. multi-objective and economic issues are considered.
This paper discusses the "inverse" data envelopment analysis (DEA) problem with preference cone constraints. An inverse DEA model can be used for a decision making unit (DMU) to estimate its input/output lev...
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This paper discusses the "inverse" data envelopment analysis (DEA) problem with preference cone constraints. An inverse DEA model can be used for a decision making unit (DMU) to estimate its input/output levels when some or an of its input/output entities are revised, given its current DEA efficiency level. The extension of introducing additional preference cones to the previously developed inverse DEA model allows the decision makers to incorporate their preferences or important policies over inputs/outputs into the production analysis and resource allocation process. We provide the properties of the inverse DEA problem through a discussion of its related multi-objective and weighted sum single-objectiveprogramming problems. Numerical examples are presented to illustrate the application procedure of our extended inverse DEA model. In particular, we demonstrate how to apply the model to the case of a local home electrical appliance group company for its resource reallocation decisions. (C) 2002 Elsevier Science B.V. All rights reserved.
Several successful applications of optimal control theory (OCT) based on the Pontryagin's minimum principle have been recorded in literature. These applications were focused on optimizing the operating policy of m...
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Several successful applications of optimal control theory (OCT) based on the Pontryagin's minimum principle have been recorded in literature. These applications were focused on optimizing the operating policy of multi-reservoir systems. In this study, the performance of OCT algorithm in designing multi-reservoir system is investigated. Three deterministic optimization models based on the OCT were developed to design the best storage strategies in a multi-reservoir system to supply water. multi-objective programming methods were implemented in the three models in order to consider the two non-commensurate objectives of minimizing cost and water deficit. The applications of these models to a multi-reservoir system were compared to an existing dynamic programming model. The result of this study showed that in all cases, the developed OCT models presented sub-optimal solution in designing multi-reservoir systems. (C) 2002 Elsevier Science Ltd. All rights reserved.
Duality theory is applied to measure the sensitivity of a multi-objective programming problem. Since the dual problem does not always measure primal sensitivity, the paper states necessary and sufficient conditions (h...
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We consider a model for data envelopment analysis with infinitely many decisionmaking units. The determination of the relative efficiency of a given decision-making unit amounts to the solution of a semi-infinite opti...
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We consider a model for data envelopment analysis with infinitely many decisionmaking units. The determination of the relative efficiency of a given decision-making unit amounts to the solution of a semi-infinite optimization problem. We show that a decision-making unit of maximal relative efficiency exists and that it is 100% efficient. Moreover, this decision-making unit can be found by calculating a zero of the semiinfinite constraint function. For the latter task we propose a hi-level algorithm. We apply this algorithm to a problem from chemical engineering and present numerical results.
Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitnes...
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Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.
Maximum entropy algorithm for approximating multi-objective smoothless semi-infinite programming is presented. The convergence of the approximating algorithm is obtained in general from the convergence of the series o...
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Maximum entropy algorithm for approximating multi-objective smoothless semi-infinite programming is presented. The convergence of the approximating algorithm is obtained in general from the convergence of the series of entropy functions (such as variational convergence).
Subsidized housing policy in the United States must increasingly account for a greater use of tenant-based housing subsidies in place of traditional high-rise public housing. This paper focuses on design of policies t...
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In this paper, a computationally multi-objective programming approach and a Leontief inter-industry model are used to investigate the impact of mitigating CO2 emissions on Taiwan's economy. The estimated result sh...
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In this paper, a computationally multi-objective programming approach and a Leontief inter-industry model are used to investigate the impact of mitigating CO2 emissions on Taiwan's economy. The estimated result shows that Taiwan's GDP will drop 34% off the targeted GDP growth rate for the year 2000 and Taiwan's economy will be seriously weakened if annual CO2 emissions are stabilized at the 1990 level. When Taiwan maintains CO2 emissions at 128% of the 1990 level, then Taiwan's economy will be able to show a 5.37% average annual growth rate up to year 2000 a 157% CO2 emission level would mean a 5.92% annual GDP growth rate;and a 213% CO2 emission level for a 6.85% annual GDP growth rate. In addition, policy implications are presented in order to provide policy makers in economic planning. (C) 2001 Elsevier Science B.V. All rights reserved.
A genetic algorithm was used to calibrate the RUNOFF component of the EPA storm-water management model, SWMM. A multi-objective function was developed which attached user-specified weights to error terms for estimates...
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A genetic algorithm was used to calibrate the RUNOFF component of the EPA storm-water management model, SWMM. A multi-objective function was developed which attached user-specified weights to error terms for estimates of peak flow rate, runoff volume, and time of peak. The genetic algorithm proved to be a valuable tool for isolating the neighborhood of the optimal parameter set. A conventional calibration scheme was used whereby the model was first fitted to a low intensity storm which produced runoff from impervious al eas only After parameters for the impervious cover were found, a larger storm was used to determine the variables for pervious land use. The calibrated model was used to simulate Two additional storms with good accuracy.
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