A new chance-constrained data envelopment analysis for selecting third-party reverse logistics providers in the existence of dual-role factors

2011 ◽  
Vol 38 (10) ◽  
pp. 12231-12236 ◽  
Author(s):  
Majid Azadi ◽  
Reza Farzipoor Saen
Keyword(s):  
Data Envelopment Analysis ◽  
Reverse Logistics ◽  
Dual Role ◽  
Third Party ◽  
Data Envelopment

A DECISION MODEL FOR SELECTING THIRD-PARTY REVERSE LOGISTICS PROVIDERS IN THE PRESENCE OF BOTH DUAL-ROLE FACTORS AND IMPRECISE DATA

2011 ◽  
Vol 28 (02) ◽  
pp. 239-254 ◽  
Author(s):  
REZA FARZIPOOR SAEN
Keyword(s):  
Data Envelopment Analysis ◽  
Reverse Logistics ◽  
Decision Model ◽  
Dual Role ◽  
Third Party ◽  
Data Envelopment ◽  
Imprecise Data ◽  
Numerical Example ◽  
Proposed Model

This paper introduces a model for dealing with selecting third-party reverse logistics (3PL) providers in the presence of both dual-role factors and imprecise data. The proposed model is based on data envelopment analysis (DEA). A numerical example demonstrates the application of the proposed method.

A New Look at Selecting Third-Party Reverse Logistics Providers

2012 ◽  
pp. 246-254
Author(s):  
Reza Farzipoor Saen
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Reverse Logistics ◽  
Third Party ◽  
Data Envelopment ◽  
Weight Restrictions ◽  
Use Of Data ◽  
Available Information ◽  
Nondiscretionary Factors ◽  
Decision Making Criteria

The use of Data Envelopment Analysis (DEA) in many fields is based on total flexibility of the weights. However, the problem of allowing total flexibility of the weights is that the values of the weights obtained by solving the unrestricted DEA program are often in contradiction to prior views or additional available information. Also, many applications of DEA assume complete discretionary of decision making criteria. However, they do not assume the conditions that some factors are nondiscretionary. To select the most efficient third-party reverse logistics (3PL) provider in the conditions that both weight restrictions and nondiscretionary factors are present, a methodology is introduced. A numerical example demonstrates the application of the proposed method.

A New Look at Selecting Third-Party Reverse Logistics Providers

2010 ◽  
Vol 3 (1) ◽  
pp. 58-67 ◽  
Author(s):  
Reza Farzipoor Saen
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Reverse Logistics ◽  
Third Party ◽  
Data Envelopment ◽  
Weight Restrictions ◽  
Numerical Example ◽  
Use Of Data ◽  
Available Information ◽  
Nondiscretionary Factors

The use of Data Envelopment Analysis (DEA) in many fields is based on total flexibility of the weights. However, the problem of allowing total flexibility of the weights is that the values of the weights obtained by solving the unrestricted DEA program are often in contradiction to prior views or additional available information. Also, many applications of DEA assume complete discretionary of decision making criteria. However, they do not assume the conditions that some factors are nondiscretionary. To select the most efficient third-party reverse logistics (3PL) provider in the conditions that both weight restrictions and nondiscretionary factors are present, a methodology is introduced. A numerical example demonstrates the application of the proposed method.

scholarly journals A new approach for considering a dual-role factor in data envelopment analysis

2012 ◽  
Vol 14 (2) ◽  
pp. 135 ◽  
Author(s):  
Abdollah Noorizadeh ◽  
Mahdi Mahdiloo ◽  
Reza Farzipoor Saen
Keyword(s):  
Data Envelopment Analysis ◽  
Dual Role ◽  
Data Envelopment ◽  
New Approach

scholarly journals Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse Logistics

Inge CUC ◽  
2018 ◽  
Vol 14 (2) ◽  
pp. 137-146
Author(s):  
César David Ardila Gamboa ◽  
Frank Alexander Ballesteros Riveros
Keyword(s):  
Data Envelopment Analysis ◽  
Reverse Logistics ◽  
Relative Performance ◽  
Data Envelopment ◽  
Supply Network ◽  
Distribution Centers ◽  
Frontier Analysis ◽  
Dea Model ◽  
Key Indicators ◽  
A Company

Introduction: Data Envelopment Analysis (DEA) is used to measure the relative performance of a series of distribution centers (DCs), using key indicators based on reverse logistics for a company that produces electric and electronic supplies in Colombia. Objective: The aim is to measure the relative performance of distribution centers based on Key Performance Indicators (KPI) from a supply network with reverse logistics. Methodology: A DEA model is applied through 5 steps: KPIs selection; Data collection for all 18 DCs in the network; Build and run the DEA model; Identify the DCs that will be the focus of improvement; Analyze the DCs that restrict or diminish the total performance of the system. Results− KPIs are defined, data is collected and KPI’s for each DCs are presented. The DEA model is run and the relative efficiencies for each DCs are determined. A frontier analysis is made and DCs that limit or reduce the performance of the system were analyzed to find options for improving the system. Conclusions: Reverse logistics, brings numerous advantages for companies. The analysis of the indicators allows logistics managers involved to make relevant decisions for higher performance. The DEA model identifies which DCs have a relative superior and inferior performance, making it easier to make informed decisions to change, increase or decrease resources, and activities or apply best practices that optimize the performance of the network.

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