Undesirable Input in Production Process: A DEA-Based Approach

Maghboulı, Mahnaz; Yekta, Azam Pourhabib

doi:10.56578/josa010201

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…Then, for each efficient DMU optimal solution (v * , u * , u * 0 ) of the VRS model satisfies the AR constraints. This means that the optimal solution of the VRS model is also optimal in the VRS-AR model (14). Hence, the efficient units of the VRS model stay efficient in the VRS-AR model.…”

Section: Proofmentioning

confidence: 93%

“…. , r in the VRS-AR model (14) such that the set of efficient units of this model coincides with the set of efficient units of the VRS model.…”

Section: Algorithm For Synthetic Data Generationmentioning

confidence: 99%

“…After that, Z is projected radially onto the frontier of the VRS-AR model by solving problems (14) or (15). The projection of Z is the new artificial efficient unit H that is added to the PPS.…”

Section: Lemma 1 Algorithm 2 Converges In a Finite Number Of Steps An...mentioning

confidence: 99%

“…The DEA approach is widely used in various fields, including manufacturing systems, power industry, governance, finance, supply chain, transportation, etc. [2][3][4][5][6][7][8][9][10][11][12][13][14]. It is assumed that each DMU consumes multiple inputs to produce multiple outputs.…”

Section: Introductionmentioning

confidence: 99%

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Synthetic Data Generation for Data Envelopment Analysis

Lychev

2023

Data

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The paper is devoted to the problem of generating artificial datasets for data envelopment analysis (DEA), which can be used for testing DEA models and methods. In particular, the papers that applied DEA to big data often used synthetic data generation to obtain large-scale datasets because real datasets of large size, available in the public domain, are extremely rare. This paper proposes the algorithm which takes as input some real dataset and complements it by artificial efficient and inefficient units. The generation process extends the efficient part of the frontier by inserting artificial efficient units, keeping the original efficient frontier unchanged. For this purpose, the algorithm uses the assurance region method and consistently relaxes weight restrictions during the iterations. This approach produces synthetic datasets that are closer to real ones, compared to other algorithms that generate data from scratch. The proposed algorithm is applied to a pair of small real-life datasets. As a result, the datasets were expanded to 50K units. Computational experiments show that artificially generated DMUs preserve isotonicity and do not increase the collinearity of the original data as a whole.

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Section: Proofmentioning

confidence: 93%

“…. , r in the VRS-AR model (14) such that the set of efficient units of this model coincides with the set of efficient units of the VRS model.…”

Section: Algorithm For Synthetic Data Generationmentioning

confidence: 99%

Section: Lemma 1 Algorithm 2 Converges In a Finite Number Of Steps An...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Synthetic Data Generation for Data Envelopment Analysis

Lychev

2023

Data

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“…The utilization of the standard DEA approach did not provide satisfactory results. Namely, employing the standard DEA approach [34][35][36][37] resulted in an efficiency score of one for all nine distribution channels. Consequently, the CI-DEA approach was employed to overcome this limitation.…”

mentioning

confidence: 99%

Optimizing Cross-Dock Terminal Location Selection: A Multi-Step Approach Based on CI-DEA–IDOCRIW–MABAC for Enhanced Supply Chain Efficiency—A Case Study

Wang,

Wen,

Pajić

et al. 2024

Mathematics

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Thedistribution of products stands out as one of the pivotal activities for logistics companies in recent years, particularly in the aftermath of the COVID-19 pandemic and other geopolitical events. Intense competition compels companies to efficiently execute their logistical processes, with cross-docking emerging as a frequently applied solution. However, the location of cross-dock terminals in urban areas remains a problem insufficiently addressed in the literature, with a dearth of studies and models tackling this issue. This paper introduces a novel and innovative model for locating cross-dock terminals based on the CI-DEA–IDOCRIW–MABAC (Composite Indicators–Data Envelopment Analysis-Integrated Determination of Objective Criteria Weights–Multi-Attributive Border Approximation Area Comparison) methods. In the process of defining input indicators, the following three sources were utilized: relevant literature, practical insights from logistics experts, and the knowledge and experience of the authors. Eight inputs and three outputs were considered (the number of users in the observed channel; the area served by the channel; the average distance a vehicle travels in one delivery; the required number of vehicles; labor availability; competition; construction, and expansion possibilities; proximity to the main infrastructure and traffic facilities; the average number of deliveries; average delivered quantity; and service level). The model underwent testing in a case study analyzing nine distribution channels (areas within the observed urban zone). The results indicated that alternative A4 (in the southwest area) ranked the highest since it was the best-ranked in accordance with the most important criteria, suggesting that the terminal is best located in the southwest zone. The accuracy of the results was confirmed by company management. By developing a completely new model and addressing the identified gap in the literature, this paper provides unequivocal scientific contributions.

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