A knowledge system proposal with weighted goal programming approach for multi-criteria dea model and an application

In today's competitive environment, enterprises should use their resources correctly; they should continuously improve themselves and work efficiently. It is important to evaluate the performances of the units under the same conditions in enterprises according to each other, to see the current situations and to determine appropriate improvements in necessary points. One of the commonly used approaches to performance evaluation is Data Envelopment Analysis. Many approaches have been developed for the Data Envelopment Analysis model, and Goal programming using in multi-objective decision making solutions approaches is one of them. Goal Programming gives decision-makers the opportunity to evaluate many objectives together in the decision-making process. In this study, classical Data Envelopment Analysis and weighted goal programming approach for multi-criteria data envelopment analysis model was applied in the evaluation process of the projects worked in an automotive supplier industry. A knowledge system has also been proposed in order to evaluate the effectiveness of the projects periodically and to include new projects or conditions into the evaluation.

MODELO MATEMÁTICO DE OTIMIZAÇÃO PARA A SELEÇÃO DE AGENTES INDUTORES DE MATURAÇÃO APLICADOS NA CULTURA DA CANA-DE-AÇÚCAR

Considerando a relevância do setor sucroenergético para a economia brasileira, bem como a necessidade de se produzir metodologias de apoio à tomada de decisões para um segmento econômico tão relevante, a presente pesquisa teve por objetivo propor um modelo matemático de otimização voltado para o planejamento de gastos a serem realizados na aquisição de agentes indutores de maturação, com vistas ao atendimento da demanda total por sacarose de empreendimentos agrícolas produtores de cana-de-açúcar, e, ainda, a escolha das melhores alternativas em relação à identificação, compra e aplicação desse tipo de agente químico. Utilizando técnicas de programação linear inteira e o método de Programação por Metas Ponderada, ou ainda, Weighted Goal Programming, em três cenários compostos por dezoito, trinta e sessenta talhões, o modelo proposto nesta investigação científica foi implementado e validado computacionalmente, produzindo um conjunto de informações gerenciais que foi além das variáveis relacionadas à produtividade e demanda de sacarose e aos valores orçamentários. Ou seja, foi possível identificar também quanto comprar de cada tipo de maturador, quanto seria gasto por tipo de maturador, e, ainda, em que talhão cada maturador deveria ser aplicado para que se atingisse a máxima produtividade de sacarose mediante a interação entre o respectivo agente químico indutor e determinada variedade de cana-de-açúcar.

Optimizing agricultural production with socioeconomic considerations: A case study from Palestine

Agriculture is not only the main source of income to most Palestinian families; it is also the link to connect them to their valuable land and water resources. Farmers seek assistance from agronomists and decision makers to cultivate the proper products. In this study, the best selection of agricultural crops is addressed in the multiple-objective context. The study deals with three conflicting objective functions: net benefit, agricultural production, and labor employment. Four-stage procedure is adopted combining multiple-objective optimization, simple valuation methods, cluster analysis, and multiple criteria decision making (MCDM) methods. Pareto optimal curves are used to evaluate the marginal prices of both land area and labor day. The theories of utility and benefit cost are applied to rank the non-dominant alternatives. Two MCDM methods, namely weighted goal programming and step methods, are employed in the evaluation. The above methodology is applied to the case study of Qalqilya District in which irrigated agriculture under semi-arid conditions prevails. The results show that Pareto optimal is a powerful tool to determine the marginal price of non-monetary commodities. It is also found that the average annual net benefit, agricultural production, and labor employment for the cultivated area are $941,423, 3,288 tons, and 14,671 days, respectively, in the best compromise plan. The inclusion of socioeconomic considerations in decision making on agricultural systems is crucial for their sustainable development.

Optimizing agricultural production with socioeconomic considerations: A case study from Palestine

Agriculture is not only the main source of income to most Palestinian families; it is also the link to connect them to their valuable land and water resources. Farmers seek assistance from agronomists and decision makers to cultivate the proper products. In this study, the best selection of agricultural crops is addressed in the multiple-objective context. The study deals with three conflicting objective functions: net benefit, agricultural production, and labor employment. Four-stage procedure is adopted combining multiple-objective optimization, simple valuation methods, cluster analysis, and multiple criteria decision making (MCDM) methods. Pareto optimal curves are used to evaluate the marginal prices of both land area and labor day. The theories of utility and benefit cost are applied to rank the non-dominant alternatives. Two MCDM methods, namely weighted goal programming and step methods, are employed in the evaluation. The above methodology is applied to the case study of Qalqilya District in which irrigated agriculture under semi-arid conditions prevails. The results show that Pareto optimal is a powerful tool to determine the marginal price of non-monetary commodities. It is also found that the average annual net benefit, agricultural production, and labor employment for the cultivated area are $941,423, 3,288 tons, and 14,671 days, respectively, in the best compromise plan. The inclusion of socioeconomic considerations in decision making on agricultural systems is crucial for their sustainable development.

A Hybrid Multi-Criteria Methodology for Solving the Sustainable Dispatch Problem

Wind energy is becoming an increasingly substantial component of many nations’ energy portfolios. The intermittent nature of wind energy is traded off in a multi-objective sense against its environmental benefits when compared to conventional thermal energy sources. This gives rise to the multi-criteria sustainable dispatch problem considered in this paper. A relevant multi-objective model is formulated considering both environmental and economic criteria as well as ensuring adequate production levels. The techniques of weighted goal programming (WGP) and the progressive bounded constraint method (PBC) are combined in a novel manner in order to overcome computational challenges associated with the sinusoidal nature of the model. This allows the generation of a representative set of Pareto efficient solutions. The proposed methodology is demonstrated on a test set of relevant examples, and conclusions are drawn from both methodological and application perspectives. The results provide a quantification of the economic and environmental benefits of added wind power to a solely thermal system. However, a trade-off between the levels of economic versus environmental benefits gained is also demonstrated.

Evaluation and Selection of Software Vulnerabilities

Information systems are an integral part of every organization’s critical resource and are an important responsibility of all organizations in this digital world to defend them from attacks. Misuse of critical systems of data raises serious difficulties to organizations which involve loss of productivity, profits, company loss, credibility loss and often, legal issues. This paper develops a framework to analyze the optimal selection of vulnerabilities having maximum severity and can be resolved in minimum time. In this study, an integrated framework involving Analytical Hierarchy process (AHP) and Entropy method is used to calculate the subjective and objective weights of software vulnerabilities. Severity of vulnerabilities is then computed by taking into consideration the combined weights calculated from AHP and Entropy method. To select the most critical vulnerability, a bi-objective programming problem is used with the objective of weight maximization and time minimization. The weighted goal programming approach is used to achieve a compromise between conflicting objectives and to achieve a satisfactory solution to the bi-objective problem. The findings are tested in the Delhi-National Capital Region by a software firm.

A Fuzzy Goal Programming Approach for Solving Multi-Objective Supply Chain Network Problems with Pareto-Distributed Random Variables

Uncertainty is unavoidable and addressing the same is inevitable. That everything is available at our doorstep is due to a well-managed modern global supply chain, which takes place despite its efficiency and effectiveness being threatened by various sources of uncertainty originating from the demand side, supply side, manufacturing process, and planning and control systems. This paper addresses the demand- and supply-rooted uncertainty. In order to cope with uncertainty within the constrained multi-objective supply chain network, this paper develops a fuzzy goal programming methodology, with solution procedures. The probabilistic fuzzy goal multi-objective supply chain network (PFG-MOSCN) problem is thus formulated and then solved by three different approaches, namely, simple additive goal programming approach, weighted goal programming approach, and pre-emptive goal programming approach, to obtain the optimal solution. The proposed work links fuzziness in transportation cost and delivery time with randomness in demand and supply parameters. The results may prove to be important for operational managers in manufacturing units, interested in optimizing transportation costs and delivery time, and implicitly, in optimizing profits. A numerical example is provided to illustrate the proposed model.