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Mathematics
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Data Envelopment Analysis for Decision Making
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Data Envelopment Analysis for Decision Making

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 2052

Special Issue Editors

Prof. Dr. Ming-Min Yu

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Guest Editor
Department of Transportation Science, National Taiwan Ocean University, Keelung, Taiwan
Interests: economics; operational research; transportation; logistics performance evaluation; data envelopment analysis
Prof. Dr. Bo Hsiao

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Guest Editor
Department of Information Management, Chang Jung Christian University, Tainan City, Taiwan
Interests: performance evaluation; data analysis; operational research; machine learning
Dr. Li-Hsueh Chen

E-Mail Website
Guest Editor
Department of Applied Economics, Fo Guang University, Yilan, Taiwan
Interests: performance evaluation; industrial economics; microeconomics

Special Issue Information

Dear colleagues,

Data Envelopment Analysis (DEA), initially developed by Charnes et al. (1978), is an optimization method of mathematical programming to generalize the Farrell (1957) single-input/single-output technical efficiency measure to the multiple-input/ multiple-output case by constructing a relative efficiency score as the ratio of a single virtual output to a single virtual input. Thus, DEA has become a new tool in operational research for measuring the technical efficiency of decision-making units.

Recently, DEA has been extended in both theoretical development and applications. We look forward to original analytical and empirical research using the DEA approaches with practical applications in various areas. Applications (including but not limited to) in finance, banking, healthcare, transportation, education, energy, environment, and more will be considered. New and improved models of DEA are encouraged. A Special Issue that compiles current research on the state of the art will benefit the DEA community.

Prof. Dr. Ming-Min Yu
Prof. Dr. Bo Hsiao
Dr. Li-Hsueh Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • performance evaluation
  • technology gaps
  • benchmarking
  • allocation
  • reallocation
  • mobility
  • efficiency
  • decision making
  • productivity
  • data reduction
  • productivity
  • optimization
  • efficiency measurement
  • data analysis
  • bootstrapping
  • regression analysis
  • competition
  • multiple activities
  • network
  • dynamics
  • malmquist index
  • returns to scale
  • problem solving
  • data envelopment
  • mathematical programming
  • numerical model
  • sensitivity analysis
  • total factor productivity

Published Papers (2 papers)

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Research

19 pages, 329 KiB  
Article
An Improved Inverse DEA for Assessing Economic Growth and Environmental Sustainability in OPEC Member Nations
by Kelvin K. Orisaremi, Felix T. S. Chan and Xiaowen Fu
Mathematics 2023, 11(23), 4861; https://doi.org/10.3390/math11234861 - 04 Dec 2023
Cited by 1 | Viewed by 788
Abstract
Economic growth is essential for nations endowed with natural resources as it reflects how well those resources are utilized in an efficient and sustainable way. For instance, OPEC member nations, which hold a large proportion of the world’s oil and gas reserves, may [...] Read more.
Economic growth is essential for nations endowed with natural resources as it reflects how well those resources are utilized in an efficient and sustainable way. For instance, OPEC member nations, which hold a large proportion of the world’s oil and gas reserves, may require a frequent evaluation of economic growth patterns to ensure that the natural resources are best used. For this purpose, this study proposes an inverse data envelopment analysis model for assessing the optimal increase in input resources required for economic growth among OPEC member nations. In this context, economic growth is reflected in the GDP per capita, taking into account possible environmental degradation. Such a model is applied to the selected OPEC member nations, which suggests that in terms of increasing the GDP per capita, only one member was able to achieve the best efficiency (i.e., reaching the efficiency frontier), resulting in a hierarchy or dominance within the sample countries. The analysis results further identify the economic growth potential for each member country. For the case of Indonesia, the analysis suggests that further economic growth may be achieved for Indonesia without additional input resources. This calls for diversification of the nation’s economy or investment in other input resources. In addition, the overall results indicated that each member nation could increase its GDP per capita while experiencing minimal environmental degradation. Our analysis not only benchmarks the growth efficiency of countries, but also identifies opportunities for more efficient and sustainable growth. Full article
(This article belongs to the Special Issue Data Envelopment Analysis for Decision Making)
25 pages, 448 KiB  
Article
Data Envelopment Analysis Approaches for Multiperiod Two-Level Production and Distribution Planning Problems
by Tomohiro Hayashida, Ichiro Nishizaki, Shinya Sekizaki and Junya Okabe
Mathematics 2023, 11(21), 4492; https://doi.org/10.3390/math11214492 - 30 Oct 2023
Viewed by 690
Abstract
This paper deals with two-level production and distribution planning problems in supply chain management where the leader is a distributor and the follower is a manufacturer. Assuming that the distributor can observe the input–output data in the production process, we formulated the data [...] Read more.
This paper deals with two-level production and distribution planning problems in supply chain management where the leader is a distributor and the follower is a manufacturer. Assuming that the distributor can observe the input–output data in the production process, we formulated the data envelopment analysis (DEA) production problem corresponding to the production planning problem of the manufacturer. This paper proposes a novel data envelopment analysis (DEA) approach to solve a challenging multiperiod two-level production and distribution planning problem in supply chain management. The innovative idea behind the proposed approach is to allow the distributor to observe the input–output data regarding the production activities of the manufacturer, even if the distributor cannot fully comprehend all parameters of the manufacturer’s production cost minimization problem. This approach addresses the challenge of uncertain demands by employing a two-stage model with simple recourse and considering the usage of the input–output data. The paper demonstrates the validity of the proposed DEA approaches through computational experiments and discusses the accuracy, reliability, and importance of the input–output data. The proposed approach provides a practical and effective solution to the multiperiod two-level production and distribution planning problem in supply chain management, and can help decision-makers improve the efficiency and effectiveness of their operations. The innovative idea of allowing the distributor to observe the input–output data about the production activities of the manufacturer is a significant contribution to the field of supply chain management and has the potential to advance the state of the art in this area. Full article
(This article belongs to the Special Issue Data Envelopment Analysis for Decision Making)
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