A Review of DEA-based Resource and Cost Allocation models: Implications for services

Data envelopment analysis (DEA), by its design, was not intended for resource allocation but for measuring relative efficiency of decision-making units. Despite this, many researchers have successfully applied this modelling technique to a variety of resource and cost allocation decisions in order to improve operational efficiencies. This paper is a comprehensive review and classification of such articles. The papers were classified by industry and by DEA model-orientation. The findings of this paper show that existing models predominately apply DEA to mass service industries (e.g., banking), thus, revealing the opportunity for researchers to further develop DEA-based resource allocation modelling toward improving the operational efficiencies of other service industries (e.g., professional services). To guide researchers to this end, we offer a discussion of the use of DEA modelling when the service provider and the customer are both resources needing to be allocated, in other words, using DEA to model professional or co-created services

Quota Trading and Profitability: Theoretical Models and Applications to Danish Fisheries

Using Data Envelopment Analysis (DEA), we provide a framework to analyze the potential gains from quota trading. We compare the industry profit and structure before and after a free trade reallocation of production quotas. The effects of tradable production quotas depend on several technological and behavioral characteristics, including the ability to learn best practice (catch-up) and the ability to change the input and output composition (mix). To illustrate the usefulness of our approach, we analyze a dataset from the Danish fishery. We study the industry profit and structure under each of four sets of technological and behavioral characteristics.Data Envelopment Analysis (DEA), Individual Transferable Quotas (ITQ), reallocation, technical efficiency, allocative efficiency, fishery, Agribusiness, C61, L51, Q22, Q28,

Análise de Envoltória de Dados para alocação de recursos: uma proposta de algoritmo sequencial.

Este trabalho apresenta um modelo sequencial de atribuição de recursos em modelos DEA, inspirado no modelo de votação de Hondt, considerando-se que o excesso de recursos a ser distribuído tem soma constante. Caso fosse de interesse realocal os recursos já existentes, mantendo-se constante o total dos recursos (soma dos recursos constante) poderia ser usado o modelo DEA com Ganhos de Soma Zero - GSZ-DEA, orientado a inputs. O algoritmo sequencial de alocação de recursos em DEA proposto neste artigo é aplicado à distribuição de vagas docentes aos departamentos de ensino do Centro Tecnológico da UFF. O modelo considera o número de professores de cada departamento, o envolvimento com atividades de ensino e pesquisa e a existência de projetos de expansão aprovados

Deslocamento de DMUS pela fronteira de eficiência em modelos de Análise de Envoltória de Dados com ganhos de soma zero.

O trabalho apresenta uma extensão do modelo DEA com Ganhos de Soma Zero (DEA-GSZ) para os casos em que devido à redução de outputs (para que a soma seja constante), há a possibilidade ou a imposição de redução dos inputs utilizados. Nesses casos, não há o deslocamento da fronteira, mas sim o deslocamento das DMUs pela fronteira eficiente (ou pelas camadas de iso-eficiência). São apresentados os casos bidimensional e multidimensional. Para este, devido à complexidade dos algoritmos de determinação de faces do poliedro envolvente (fronteira DEA), é proposto o uso do modelo de suavização da fronteira, que representa a fronteira inteira por uma única equação polinomial

Optimal Capacity Utilization and Reallocation in a German Bank Branch Network: Exploring Some Strategic Scenarios

Quite a few studies have considered efficiency at the bank branch level by comparing mostly a single branch network, while an abundance of studies have focused on comparing banking institutions. However, to the best of our knowledge no study has ever assessed performance at the level of the branch bank network by looking for ways to reallocate resources such that overall performance improves. Here, we introduce the Johansen-Färe measure of plant capacity of the firm into a multi-output, frontier-based version of the short-run Johansen industry model. The first stage capacity model carefully checks for the impact of the convexity assumption on the estimated capacity utilization results. Policy scenarios considered for the short-run Johansen industry model vary in terms of their tolerance with respect to existing bank branch inefficiencies, the formulation of closure policies, the reallocation of labor in terms of integer units, etc. The application to a network of 142 bank branches of a German savings bank in the year 1998 measures their efficiency and capacity utilization and demonstrate that by this industry model approach one can improve the performance of the whole branch network.Bank Branch Network, Efficiency, Capacity, Reallocation

Undesirable Outputs’ Presence in Centralized Resource Allocation Model

Data envelopment analysis (DEA) is a common nonparametric technique to measure the relative efficiency scores of the individual homogenous decision making units (DMUs). One aspect of the DEA literature has recently been introduced as a centralized resource allocation (CRA) which aims at optimizing the combined resource consumption by all DMUs in an organization rather than considering the consumption individually through DMUs. Conventional DEA models and CRA model have been basically formulated on desirable inputs and outputs. The objective of this paper is to present new CRA models to assess the overall efficiency of a system consisting of DMUs by using directional distance function when DMUs produce desirable and undesirable outputs. This paper initially reviewed a couple of DEA approaches for measuring the efficiency scores of DMUs when some outputs are undesirable. Then, based upon these theoretical foundations, we develop the CRA model when undesirable outputs are considered in the evaluation. Finally, we apply a short numerical illustration to show how our proposed model can be applied

Análise de envoltória de dados com ganhos de soma zero na modelagem de outputs indesejáveis.

A avaliação de eficiência com modelos de Análise de Envoltória de Dados (DEA) de unidades que produzem outputs indesejáveis tem caracterizado uma particular linha de pesquisa em DEA. Situações em que existe um equilíbrio global dos outputs indesejáveis, embora comuns, não foram tratadas na literatura. Entretanto, os modelos DEA com Ganhos de Soma Zero (DEA-GSZ) recentemente desenvolvidos parecem adaptar-se particularmente a este tipo de problema, já que nestes modelos a soma das quantidades produzidas por todas as DMUs pode ser considerada como o limite máximo permitido em nível global. Este artigo traz a aplicação do modelo DEA-GSZ ao caso das emissões de dióxido de carbono, estudo que se insere no contexto do Protocolo de Kyoto

Optimal resource allocation: Convex quantile regression approach

Optimal allocation of resources across sub-units in the context of centralized decision-making systems such as bank branches or supermarket chains is a classical application of operations research and management science. In this paper, we develop quantile allocation models to examine how much the output and productivity could potentially increase if the resources were efficiently allocated between units. We increase robustness to random noise and heteroscedasticity by utilizing the local estimation of multiple production functions using convex quantile regression. The quantile allocation models then rely on the estimated shadow prices instead of detailed data of units and allow the entry and exit of units. Our empirical results on Finland's business sector reveal a large potential for productivity gains through better allocation, keeping the current technology and resources fixed

Multi-Plant Production and Transportation Planning Based on Data Envelopment Analysis

This paper proposes a methodology for developing a coordinated aggregate production plan for manufacturers producing multiple products at multiple plants simultaneously, in a centralized environment via data envelopment analysis (DEA). Based on demand forecast of the planning horizon, the central decision maker (DM) specifies the optimal combination of input resources required by the optimal output targets for each plant to keep the supply and demand in balance, and the accompanying transportation trips and volumes among distribution centers (DCs) or warehouse facilities. In this paper, we focus on an integrated production-transportation problem since production and transportation are two fundamental ingredients in the whole operation chain. We deal with multiple products manufactured in multiple plants.The proposed mixed integer DEA models minimize both production costs and transportation costs. The capacity constraint for each plant is enforced by using the production possibility set theory. Finally, we validate our models by a numerical example and sensitivity analysis