Abstract:Este artigo considera uma extensão de um problema hierárquico de localização de máxima cobertura, na qual as coberturas para os dois níveis de serviço oferecidos são maximizadas independentemente. Apresentamos o modelo matemático e buscamos obter uma aproximação para a fronteira eficiente do problema bi-objetivo correspondente. O modelo bi-objetivo é resolvido através do método das ponderações, utilizando-se uma heurística lagrangeana. Os resultados mostram que as soluções geradas pela mesma fornecem uma aprox… Show more
“…Step 3: Validation of solutions Fisher & Rushton [15] suggests to apply analytical techniques in order to validate the localizations proposed in the multiple-objective models. The Data Envelopment Analysis (DEA) is a frequent methodology (see [10]- [12], [1]) and it is used in order to validate the relative efficiency of the units, generally called DMUs (Decision Making Units) (see reference [9]).…”
In this article, we will try to find an efficient boundary approximation for the bi-objective location problem with coherent coverage for two levels of hierarchy (CCLP). We present the mathematical formulation of the model used. Supported efficient solutions and unsupported efficient solutions are obtained by solving the bi-objective combinatorial problem through the weights method using a Lagrangean heuristic. Subsequently, the results are validated through the DEA analysis with the GEM index (Global efficiency measurement).
“…Step 3: Validation of solutions Fisher & Rushton [15] suggests to apply analytical techniques in order to validate the localizations proposed in the multiple-objective models. The Data Envelopment Analysis (DEA) is a frequent methodology (see [10]- [12], [1]) and it is used in order to validate the relative efficiency of the units, generally called DMUs (Decision Making Units) (see reference [9]).…”
In this article, we will try to find an efficient boundary approximation for the bi-objective location problem with coherent coverage for two levels of hierarchy (CCLP). We present the mathematical formulation of the model used. Supported efficient solutions and unsupported efficient solutions are obtained by solving the bi-objective combinatorial problem through the weights method using a Lagrangean heuristic. Subsequently, the results are validated through the DEA analysis with the GEM index (Global efficiency measurement).
“…It implies that every (Espejo and Galvao [2004] citing Bitran [1977]). It implies that the efficient solutions will map efficient DMUs in a DEA problem, once they belong to the convex hull, and therefore can not be outside the efficient frontier.…”
Section: Evaluating Pareto Efficiency In Logistic Networkmentioning
confidence: 99%
“…For multi-objective combinatorial problems, Ehrgott [2000] and Ehrgott and Gandibleux [2000] present a detailed review. A survey on multi-objective meta-heuristics is available in Jones et al [2002], Hansen [1998] and Espejo and Galvao [2004].…”
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AbstractThe objective in this paper is to shed light into the design of logistic networks balancing profit and the environment. More specifically we intend to i) determine the main factors influencing environmental performance and costs in logistic networks ii) present a comprehensive framework and mathematical formulation, based on multiobjective programming, integrating all relevant variables in order to explore efficient logistic network configurations iii) present the expected computational results of such formulation and iv) introduce a technique to evaluate the efficiency of existing logistic networks.The European Pulp and Paper Industry will be used to illustrate our findings.
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