Corporate environmental and resources management has become more strategically oriented when many pollution charges, environmental taxes, and resources conservation fees have been gradually imposing to the industry. T...
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Corporate environmental and resources management has become more strategically oriented when many pollution charges, environmental taxes, and resources conservation fees have been gradually imposing to the industry. The need for explicit consideration and incorporation of varying environmental costs within production-planning program is becoming critical to corporate management. This paper attempts to assess an optimal production-planning program in response to varying environmental costs in an uncertain environment. The optimal production strategy concerning numerous screening of possible production alternatives of dyeing cloth in a textile-dyeing firm in terms of market demand, resources availability, and impact of environmental costs is treated as an integral part of the multi-criteria decision-making framework based on the grey compromise programming approach. It covers not only the regular part of production costs and the direct income from product sales but also the emission/effluent charges and water resource fees reflecting part of the goals for internalization of external cost in a sustainable society. In particular, all the crucial variables in the model are addressed by interval expressions, the same as they are frequently applied in the grey systems theory, in support of a vital uncertainty assessment, which is much better suited for this particular study than other approaches. Research results demonstrate the applicability and significance of such an approach based on a case study. Industry looking for the competitive advantage of environmental management must be aware of the potential benefits from such an integrated production -planning program once the trend of increasing pollution charges, environmental taxes, and resources conservation fees remains. (C) 2003 Elsevier B.V. All rights reserved.
作者：
Lokman, BanuUniv Portsmouth
Portsmouth Business Sch Richmond BldgPortland St Portsmouth PO1 3DE Hants England
In this paper, we develop two algorithms to optimize a linear function over the nondominated set of multiobjective integer programs. The algorithms iteratively generate nondominated points and converge to the optimal ...
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In this paper, we develop two algorithms to optimize a linear function over the nondominated set of multiobjective integer programs. The algorithms iteratively generate nondominated points and converge to the optimal solution reducing the feasible set. The first algorithm proposes improvements to an existing algorithm employing a decomposition and search procedure in finding a new point. Differently, the second algorithm maximizes one of the criteria throughout the algorithm and generates new points by setting bounds on the linear function value. The decomposition and search procedure in the algorithms is accompanied by problem-specific mechanisms in order to explore the objective function space efficiently. The algorithms are designed to produce solutions that meet a prespecified accuracy level. We conduct experiments on multiobjective combinatorial optimization problems and show that the algorithms work well.
In this paper, we establish the Holder continuity of solution mappings to parametric vector quasiequilibrium problems in metric spaces under the case that solution mappings are set-valued. Our main assumptions are wea...
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In this paper, we establish the Holder continuity of solution mappings to parametric vector quasiequilibrium problems in metric spaces under the case that solution mappings are set-valued. Our main assumptions are weaker than those in the literature, and the results extend and improve the recent ones. Furthermore, as an application of Holder continuity, we derive upper bounds for the distance between an approximate solution and a solution set of a vector quasiequilibrium problem with fixed parameters. (C) 2010 Elsevier B.V. All rights reserved.
Since Narasimhan's first application of Fuzzy Set Theory to Goal programming (GP) in 1980, much research into fuzzy GP has been carried out. In fuzzy GP studies, although several researchers remained loyal to usin...
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Since Narasimhan's first application of Fuzzy Set Theory to Goal programming (GP) in 1980, much research into fuzzy GP has been carried out. In fuzzy GP studies, although several researchers remained loyal to using the traditional GP representation, the majority have followed the fuzzy programming approach. Among fuzzy-programming-based studies, only the studies of Tiwari et al. and Lin seem applicable when the objectives have relative importance. However, both have disadvantages. Because the former model uses the add operator, some of the objectives may not be preferred at the optimal solution even they have heavy weights. The latter model is based on the max-min approach, which does not guarantee a non-dominated solution. In this study, a weighted Fuzzy GP model is presented to overcome these disadvantages, along with the shortcomings of other existing models. The model is modified from Lai and Hwang's augmented max-min model, which is guaranteed to reach a non-dominated solution. The superiority of the model over the existing approaches is demonstrated using numerical examples chosen from the literature.
Artificial Neural Networks (ANNs) are well known for their credible ability to capture non-linear trends in scientific data. However, the heuristic nature of estimation of parameters associated with ANNs has prevented...
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Artificial Neural Networks (ANNs) are well known for their credible ability to capture non-linear trends in scientific data. However, the heuristic nature of estimation of parameters associated with ANNs has prevented their evolution into efficient surrogate models. Further, the dearth of optimal training size estimation algorithms for the data greedy ANNs resulted in their overfitting. Therefore, through this work, we aim to contribute a novel ANN building algorithm called TRANSFORM aimed at simultaneous and optimal estimation of ANN architecture, training size and transfer function. TRANSFORM is integrated with three standalone Sobol sampling based training size determination algorithms which incorporate the concepts of hypercube sampling and optimal space filling. TRANSFORM was used to construct ANN surrogates for a highly non-linear industrially validated continuous casting model from steel plant. Multiobjective optimization of casting model to ensure maximum productivity, maximum energy saving and minimum operational cost was performed by ANN assisted Non-dominated Sorting Genetic Algorithms (NSGA-II). The surrogate assisted optimization was found to be 13 times faster than conventional optimization, leading to its online implementation. Simple operator's rules were deciphered from the optimal solutions using Pareto front characterization and K-means clustering for optimal functioning of casting plant. Comprehensive studies on (a) computational time comparisons between proposed training size estimation algorithms and (b) predictability comparisons between constructed ANNs and state of art statistical models, Kriging Interpolators adds to the other highlights of this work. TRANSFORM takes physics based model as the only input and provides parsimonious ANNs as outputs, making it generic across all scientific domains. (C) 2017 Elsevier B.V. All rights reserved.
The user equilibrium in traffic assignment problem is to choose the minimum-cost path between every origin-destination pair and through this process, those utilized paths will have equal costs. In other words, giving ...
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The user equilibrium in traffic assignment problem is to choose the minimum-cost path between every origin-destination pair and through this process, those utilized paths will have equal costs. In other words, giving cost and demand function for transportation between every origin-destination pair, the solution of the problem is to provide the minimum cost of which the flow is generated. In this study, we consider this problem when the N-A incidence matrix for transportation is fuzzy,, in the sense that, which arcs are chosen into the desired path for traveling is uncertain. Therefore, we apply the method and concept of the theory of variational inequality with fuzzy convex cone to establish a user equilibrium pattern. Finally, the proposed method is demonstrated with a numerical example. (C) 1999 Published by Elsevier Science B.V. All rights reserved.
This paper develops a bi-level multi-objective model for road pricing optimization considering land use and transportation effects. The upper-level problem determines a cordon-based road pricing scheme, while the lowe...
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This paper develops a bi-level multi-objective model for road pricing optimization considering land use and transportation effects. The upper-level problem determines a cordon-based road pricing scheme, while the lower-level problem models the interaction between land use and transportation. To facilitate decision-making in a scenario characterized by a hierarchical ordering of objectives, a novel alpha-conditional lexicographic optimization method is established, which uses an alpha value to capture the decision-maker's perceived acceptability of the trade-off between different objectives with respect to the hierarchical objective ordering. The properties associated with this approach are derived, and an algorithm to find the alpha-conditional lexicographic dominance solutions is developed. To solve the model, a revised genetic algorithm is further developed to illustrate how the proposed alpha-conditional lexicographic optimization method can be embedded into existing heuristic or metaheuristic methods. A case study using data from Jiangyin, China, demonstrates the significance of considering land use effects when evaluating road pricing scenarios. The results reveal the trade-off between transportation and various land use objectives and the variation of such a trade-off among different types of traffic analysis zones. It is demonstrated that the proposed alpha-conditional lexicographic approach can improve most of the land use objective values while ensuring that the total travel time is constrained within an acceptable range, enabling a balance between various land use and transportation objectives. (C) 2021 Elsevier B.V. All rights reserved.
The paper focuses on investors whose strength of interest in sustainability issues (such as environmental, social, and governance) causes ESG to become a third criterion alongside risk and return in portfolio selectio...
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The paper focuses on investors whose strength of interest in sustainability issues (such as environmental, social, and governance) causes ESG to become a third criterion alongside risk and return in portfolio selection. This causes the efficient frontier to become an efficient surface. This means that an investor's optimal portfolio is no longer the point of most preferred risk/return tradeoff on the mean-variance (M-V) efficient frontier, but is the point of most preferred risk/return/ESG tradeoff on the investor's M-V-ESG efficient surface. However, to find such a point requires non-trivial ESG integration which is the name given to the process of integrating ESG into the portfolio construction process after screening. With the third objective transporting the problem into 3D-space, it is difficult to search the efficient surface in any kind of comprehensive fashion using M-V based or other bi-criterion techniques as this is akin to a 2 -dimensional being trying to view a 3-dimensional object. To remedy the situation, the paper proposes a tri-criterion approach that computes efficient surfaces and special non-contour curves (called NC-efficient fronts in the paper) that are stretched across the efficient surface so as to dragnet it for the points of best ESG integration within it. Using the methodology and data from the S&P500, the paper conducts computational tests on problems with up to 500 securities and under different constraint conditions so as to know what to expect from the new approach over a range of situations.(c) 2022 Elsevier B.V. All rights reserved.
In this paper, we study a method for finding robust solutions to multiobjective optimization problems under uncertainty. We follow the set-based minmax approach for handling the uncertainties which leads to a certain ...
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In this paper, we study a method for finding robust solutions to multiobjective optimization problems under uncertainty. We follow the set-based minmax approach for handling the uncertainties which leads to a certain set optimization problem with the strict upper type set relation. We introduce, under some assumptions, a reformulation using instead the strict lower type set relation without sacrificing the compactness property of the image sets. This allows to apply vectorization results to characterize the optimal solutions of these set optimization problems as optimal solutions of a multiobjective optimization problem. We end up with multiobjective semi-infinite problems which can then be studied with classical techniques from the literature.
Interactive multiobjective optimization methods have provided promising results in the literature but still their implementations are rare. Here we introduce a core structure of interactive methods to enable their con...
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Interactive multiobjective optimization methods have provided promising results in the literature but still their implementations are rare. Here we introduce a core structure of interactive methods to enable their convenient implementation. We also demonstrate how this core structure can be applied when implementing an interactive method using a modeling environment. Many modeling environments contain tools for single objective optimization but not for interactive multiobjective optimization. Furthermore, as a concrete example, we present GAMS-NIMBUS Tool which is an implementation of the classification-based NIMBUS method for the GAMS modeling environment. So far, interactive methods have not been available in the GAMS environment, but with the GAMS-NIMBUS Tool we open up the possibility of solving multiobjective optimization problems modeled in the GAMS modeling environment. Finally, we give some examples of the benefits of applying an interactive method by using the GAMS-NIMBUS Tool for solving multiobjective optimization problems modeled in the GAMS environment.
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