scholarly journals Allocating fixed resources for DMUs with interval data

Author(s):  
Jiasen Sun ◽  
Meng Chen ◽  
Yelin FU ◽  
Hao Luo

Conventional DEA models tend to allocate the fixed resources to multiple decision-making units (DMUs) and treat the allocated resource as an extra input for every single DMU. However, the existing DEA resource allocation (DEA-RA) methods are applicable exclusively to the DMUs with exact values of inputs and outputs. A lack of precision for the input or output data of DMUs, such as the interval data, would cause a failure of the existing methods to allocate resources to DMUs. In order to resolve this problem, three DEA-RA models are proposed in this paper for different scenarios of decision-making. All of the proposed DEA-RA models are based on a set of common weights. Finally, the proposed models are empirically tested and validated through three examples. As revealed by the results, our proposed models are capable of providing a more fair and practical initial allocation scheme for decision makers.

Author(s):  
HAN-LIN LI ◽  
LI-CHING MA

Data Envelop Analysis (DEA) and Analytic Hierarchy Process (AHP) are widely used methods in ranking decision alternatives. However, current DEA models are difficult to discriminate decision-making units through articulating the decision makers' preferences. While AHP and Gower plot models have to specify complete pairwise preferences without providing assisting information. This study develops an iterative method of ranking decision alternatives by integrating DEA, AHP and Gower plot techniques. The developed method first utilizes a modified DEA model to narrow the ranges of a decision maker's preferences. Then, the tentative ranks of the decision alternatives, computed by embedding the decision maker's preferences, are depicted via Gower plots to illustrate the cardinal and ordinal inconsistencies of these preferences. The decision maker then adjusts the preferences iteratively until the inconsistencies are within the tolerance.


2015 ◽  
Vol 3 (6) ◽  
pp. 538-548 ◽  
Author(s):  
Jianping Fan ◽  
Weizhen Yue ◽  
Meiqin Wu

AbstractThe conventional data envelopment analysis (DEA) measures the relative efficiency of decision making units (DMUs) consuming multiple inputs to produce multiple outputs under the assumption that all the data are exact. In the real world, however, it is possible to obtain interval data rather than exact data because of various limitations, such as statistical errors and incomplete information, et al. To overcome those limitations, researchers have proposed kinds of approaches dealing with interval DEA, which either use traditional DEA models by transforming interval data into exact data or get an efficiency interval by using the bound of interval data. In contrast to the traditional approaches above, the paper deals with interval DEA by combining traditional DEA models with error propagation and entropy, uses idea of the modified cross efficiency to get the ultimate cross efficiency of DMUs in the form of error distribution and ranks DMUs using the calculated ultimate cross efficiency by directional distance index. At last we illustrate the feasibility and effectiveness of the proposed method by applying it to measure energy efficiency of regions in China considering environmental factors.


2011 ◽  
Vol 35 (3) ◽  
pp. 278 ◽  
Author(s):  
Abdolvahab Baghbanian ◽  
Ian Hughes ◽  
Freidoon A. Khavarpour

Objective. To explore dimensions and varieties of economic evaluations that healthcare decision-makers do or do not use. Design. Web-based survey. Setting and participants. A purposive sample of Australian healthcare decision-makers was recruited by direct invitation through email. All were invited to complete an online questionnaire derived from the EUROMET 2004 survey. Results. A total of 91 questionnaires were analysed. Almost all participants were involved in financial resource allocations. Most commonly, participants based their decisions on patient needs, effectiveness of interventions, cost of interventions or overall budgetary effect, and policy directives. Evidence from cost-effectiveness analysis was used by half of the participants. Timing, ethical issues and lack of knowledge about economic evaluation were the most significant barriers to the use of economic evaluations in resource allocation decisions. Most participants reported being moderately to very familiar with the cost-effectiveness analysis. There was a general impression that evidence from economic evaluations should play a larger role in the future. Conclusions. Evidence from health economic evaluations may provide valuable information in some decisions; however, at present, it is not central to many decisions. The study suggests that, for economic evaluation to be helpful in real-life policy decisions, it has to be placed into context – a context which is complex, political and often resistant to voluntary change. What is known about the topic? There are increasing calls for the use of evidence from formal economic evaluations to improve the quality of healthcare decision making; however, it is widely acknowledged that such evidence, as presently constituted, is underused in its influence on allocation decisions. What does this paper add? This study highlights that resource allocation decisions cannot be purely based on the use of technical, economic data or systematic evidence-based reviews. In order to exploit the full potential value of economic evaluations, researchers need to make better sense of decision contexts at specific times and places. What are the implications for practitioners? The study has the potential to expand researchers and policy-makers’ understanding of the limited use of economic evaluation in decision-making. It produces evidence that decision-making in Australia’s healthcare system is not or cannot be a fully rational bounded process. Economic evaluation is used in some contexts, where information is accurate, complete and available.


Author(s):  
JOSÉ E. BOSCÁ ◽  
VICENTE LIERN ◽  
RAMÓN SALA ◽  
AURELIO MARTÍNEZ

This paper presents a method for ranking a set of decision making units according to their level of efficiency and which takes into account uncertainty in the data. Efficiency is analysed using fuzzy DEA techniques and the ranking is based on the statistical analysis of cases that include representative situations. The method enables the removal of the sometimes unrealistic hypothesis of a perfect trade-off between increased inputs and outputs. This model is compared with other DEA models that work with imprecise or fuzzy data. As an illustration, we apply our ranking method to the evaluation of a group of Spanish seaports, as well as teams playing in the Spanish football league. We compare the results with other methods and we show that our method enables a total ranking of the seaports, and that the ranking of football teams is found to be more consistent with final league positions.


2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Azarnoosh Kafi ◽  
Behrouz Daneshian ◽  
Mohsen Rostamy-Malkhalifeh

Data Envelopment Analysis (DEA) is a well-known method that based on inputs and outputs calculates the efficiency of decision-making units (DMUs). Comparing the efficiency and ranking of DMUs in different time periods lets the decision makers to prevent any loss in the productivity of units and improve the production planning. Despite the merits of DEA models, they are not able to forecast the efficiency of future time periods with known input/output records of the DMUs. With this end in view, this study aims at proposing a forecasting algorithm with a 95% confidence interval to generate fuzzy data sets for future time periods. Moreover, managers’ opinions are inserted in the proposed forecasting model. Equipped with the forecasted data sets and with respect to the data sets from previous periods, this model can rightly forecast the efficiency of the future time periods. The proposed procedure also employs the simple geometric mean to discriminate between efficient units. Examples from a real case including 20 automobile firms show the applicability of the proposed algorithm.


Author(s):  
somayeh khezri ◽  
Akram Dehnokhalaji ◽  
Farhad Hosseinzadeh Lotfi

One of interesting subjects in Data Envelopment Analysis (DEA) is estimation of congestion of Decision Making Units (DMUs). Congestion is evidenced when decreases (increases) in some inputs re- sult in increases (decreases) in some outputs without worsening (im- proving) any other input/output. Most of the existing methods for measuring the congestion of DMUs utilize the traditional de nition of congestion and assume that inputs and outputs change with the same proportion. Therefore, the important question that arises is whether congestion will occur or not if the decision maker (DM) increases or de- creases the inputs dis-proportionally. This means that, the traditional de nition of congestion in DEA may be unable to measure the con- gestion of units with multiple inputs and outputs. This paper focuses on the directional congestion and proposes methods for recognizing the directional congestion using DEA models. To do this, we consider two di erent scenarios: (i) just the input direction is available. (ii) none of the input and output directions are available. For each scenario, we propose a method consists in systems of inequalities or linear pro- gramming problems for estimation of the directional congestion. The validity of the proposed methods are demonstrated utilizing two nu- merical examples.


2021 ◽  
Vol 46 (4) ◽  
pp. 339-360
Author(s):  
Mojtaba Ghiyasi ◽  
Akram Dehnokhalaji

Abstract In this paper, we consider the problem of allocating resources among Decision Making Units (DMUs). Regarding the concept of overall (cost) efficiency, we consider three different scenarios and formulate three Resource Allocation (RA) models correspondingly. In the first scenario, we assume that overall efficiency of each unit remains unchanged. The second scenario is related to the case where none of overall efficiency scores is deteriorated. We improve the overall efficiencies by a pre-determined percentage in the last scenario. We formulate Linear Programming problems to allocate resources in all scenarios. All three scenarios are illustrated through numerical and empirical examples.


2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Nafiseh Javaherian ◽  
Ali Hamzehee ◽  
Hossein Sayyadi Tooranloo

Data envelopment analysis (DEA) is a powerful tool for evaluating the efficiency of decision-making units for ranking and comparison purposes and to differentiate efficient and inefficient units. Classic DEA models are ill-suited for the problems where decision-making units consist of multiple stages with intermediate products and those where inputs and outputs are imprecise or nondeterministic, which is not uncommon in the real world. This paper presents a new DEA model for evaluating the efficiency of decision-making units with two-stage structures and triangular intuitionistic fuzzy data. The paper first introduces two-stage DEA models, then explains how these models can be modified with intuitionistic fuzzy coefficients, and finally describes how arithmetic operators for intuitionistic fuzzy numbers can be used for a conversion into crisp two-stage structures. In the end, the proposed method is used to solve an illustrative numerical example.


Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Marzieh Ghasemi ◽  
Mohammad Reza Mozaffari ◽  
Farhad Hosseinzadeh Lotfi ◽  
Mohsen Rostamy malkhalifeh ◽  
Mohammad Hasan Behzadi

One of the mathematical programming techniques is data envelopment analysis (DEA), which is used for evaluating the efficiency of a set of similar decision-making units (DMUs). Fixed resource allocation and target setting with the help of DEA is a subject that has gained much attention from researchers. A new model was proposed by determining a common set of weights (CSW). All DMUs were involved with the aim of achieving higher efficiency in every DMU after the procedure. The minimum resources and targets allocated to each DMU were commensurate to the efficiency of that DMU and the share of DMU in the input resources and the output productions. To examine the proposed method, other methods in the DEA literature were examined as well, and then, the efficiency of the method was demonstrated through a numerical example.


Sign in / Sign up

Export Citation Format

Share Document