the paper deals with data envelopment analysis (dea) and artificial neural network (ann). we believe that solving for the dea efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. in this paper, a new neural network model is used to estimate the inefficiency of dmus in large datasets.

A two-stage data envelopment analysis (DEA) model is created to provide valuable managerial insights when assessing the dual impacts of operating and business strategies for the Canadian life and health (L&H) insurance industry. This new model allows integration of the production performance and investment performance for the insurance companies and provides management overall performance evalu...

Sustainability in the supply chain means considering environmental, social, and economic practices. Conventional data envelopment analysis (DEA) models deal with desirable, discretionary, nonnegative data. However, there might be undesirable outputs, nondiscretionary factors, negative On other hand, some criteria can considered as outputs inputs. These factors are named dual-role criteria. The ...

A new method for enantioselective organocatalytic cyclopropanation is described. This study outlines the identification of a new class of iminium catalyst based on the concept of directed electrostatic activation (DEA). This novel organocatalytic mechanism exploits dual activation of ylide and enal substrates through a proposed electrostatic activation and stereodirected protocol. Formation of ...

Data envelopment analysis (DEA) is widely used in various practical problems as a general framework for efficiency-evaluation by containing the input-output data. With increasingly complex factors practice, portraying uncertainty necessary ensuring reasonableness of results. As probabilistic linguistic term set (PLTS) powerful tool depicting uncertain information comprehensively, we aim to prop...

traditional dea models deal with measurements of relative eciency ofdmus regarding multiple - inputs vs. multiple-outputs. one of the drawbacksof these model is the neglect of intermediate products or linkong activities. af-ter pointing out needs for inclusion of them to dea models. we propose hybridmodel that can deal with intermediate products formally using this model wecan evaluate divisio...

while the conventional dea based production plans aim to minimize all the inputs consumption and maximize all the outputs production, there are many real world production systems may also generate undesirable by-products. one methodological difficulty associated with the previous dea-based production planning models is how to incorporate undesirable factors in the planning models, while the sim...

Supplier selection is a multi-Criteria problem. This study proposes a hybrid model for supporting the suppliers’ selection and ranking. This research is a two-stage model designed to fully rank the suppliers where each supplier has multiple Inputs and Outputs. First, the supplier evaluation problem is formulated by Data Envelopment Analysis (DEA), since the regarded decision deals with uncertai...

This paper addresses comprehensive ranking systems determining an ordering of entities by aggregating quantitative data for multiple attributes. We propose a DEA-CP (Data Envelopment Analysis Compromise Programming) model for the comprehensive ranking, including preference voting (ranked voting) to rank candidates in terms of aggregate vote by rank for each candidate. Although the DEA-CP model ...