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

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.

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.

Developing a novel data envelopment analysis model to determine prospective benchmarks of green supply chain in the presence of dual-role factor

2015 ◽  
Vol 22 (4) ◽  
pp. 711-730 ◽  
Author(s):  
Amir Shabani ◽  
Reza Farzipoor Saen
Keyword(s):  
Supply Chain ◽  
Data Envelopment Analysis ◽  
Critical Path ◽  
Dual Role ◽  
Green Supply Chain ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Content Type ◽  
Proposed Model ◽  
Inputs And Outputs

Purpose – The purpose of this paper is to develop a model based on data envelopment analysis (DEA) and program evaluation and review technique/critical path method (PERT/CPM) for determining prospective benchmarks. Design/methodology/approach – The idea of determining prospective benchmark is needed for developing a model for future planning where inputs and outputs of systems are influenced by external factors such as economic conditions, demographic changes, and other socio-economic factors. In this paper, the PERT/CPM method estimates prospective inputs and outputs. On the other hand, in particular systems some measures play the role of both input and output. Such factors in DEA literature are called dual-role factors. This paper integrates PERT/CPM technique and the DEA. Findings – The results of the proposed model depict that a present benchmark may not be a benchmark in future. A numerical example validates the proposed model. Originality/value – This paper, for the first time, applies the PERT/CPM technique to incorporate the ideas for identifying prospective benchmarks. Moreover, the proposed model is an alternative solution for classifying inputs and outputs in DEA. Also, the proposed model is utilized in benchmarking green supply chain management.

Modeling Centralized Resources Allocation and Target Setting in Imprecise Data Envelopment Analysis

2015 ◽  
Vol 14 (06) ◽  
pp. 1189-1213 ◽  
Author(s):  
Adel Hatami-Marbini ◽  
Zahra Ghelej Beigi ◽  
Hirofumi Fukuyama ◽  
Kobra Gholami
Keyword(s):  
Resource Allocation ◽  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Imprecise Data ◽  
Target Setting ◽  
Programming Methodology ◽  
Proposed Model ◽  
Common Set Of Weights ◽  
Interval Efficiency ◽  
Inputs And Outputs

Data Envelopment Analysis (DEA) is a nonparametric mathematical programming methodology for performance measurement of organizational units that can be utilized normatively and proactively in resource allocation and target setting. While previous studies along this line have commonly utilized exact (crisp) data, the prospective and proactive use of DEA in the activity planning frequently involves uncertainty or impreciseness as to the feasible ranges for resources to be allocated and output targets to be established. The current paper proposes an imprecise DEA-based linear programming method with interval inputs and outputs by addressing the gap of missing the imprecise data settings. For this aim, we present common set of weights models to obtain the interval efficiency of Decision-Making Units (DMUs) with interval inputs and outputs. We then propose DEA-based models to allocate imprecise resources and setting imprecise targets to DMUs such that the interval efficiency of all the DMUs improves or at least remains. The proposed model provides reasonable managerial objectives with respect to the efficiency of the subordinate units when the centralized planner implements resource allocation and target setting. We exemplify the applicability and efficacy of the proposed method using a numerical example in the frame of two distinct scenarios.

scholarly journals Dual-role factors for imprecise data envelopment analysis

Omega ◽  
2018 ◽  
Vol 77 ◽  
pp. 15-31 ◽  
Author(s):  
Mehdi Toloo ◽  
Esmaeil Keshavarz ◽  
Adel Hatami-Marbini
Keyword(s):  
Data Envelopment Analysis ◽  
Dual Role ◽  
Data Envelopment ◽  
Imprecise Data ◽  
Imprecise Data Envelopment Analysis

A cross evaluation-based measure of super efficiency in DEA with interval data

Kybernetes ◽  
2016 ◽  
Vol 45 (4) ◽  
pp. 666-679 ◽  
Author(s):  
Qian Yu ◽  
Fujun Hou
Keyword(s):  
Data Envelopment Analysis ◽  
Practical Method ◽  
Interval Data ◽  
Data Envelopment ◽  
Imprecise Data ◽  
Content Type ◽  
Super Efficiency ◽  
Dea Model ◽  
Model Based ◽  
Proposed Model

Purpose – The traditional data envelopment analysis (DEA) model as a non-parametric technique can measure the relative efficiencies of a decision-making units (DMUs) set with exact values of inputs and outputs, but it cannot handle the imprecise data. The purpose of this paper is to establish a super efficiency interval data envelopment analysis (IDEA) model, an IDEA model based on cross-evaluation and a cross evaluation-based measure of super efficiency IDEA model. And the authors apply the proposed approach to data on the 29 public secondary schools in Greece, and further demonstrate the feasibility of the proposed approach. Design/methodology/approach – In this paper, based on the IDEA model, the authors propose an improved version of establishing a super efficiency IDEA model, an IDEA model based on cross-evaluation, and then present a cross evaluation-based measure of super efficiency IDEA model by combining the super efficiency method with cross-evaluation. The proposed model cannot only discriminate the performance of efficient DMUs from inefficient ones, but also can distinguish between the efficient DMUs. By using the proposed approach, the overall performance of all DMUs with interval data can be fully ranked. Findings – A numerical example is presented to illustrate the application of the proposed methodology. The result shows that the proposed approach is an effective and practical method to measure the efficiency of the DMUs with imprecise data. Practical implications – The proposed model can avoid the fact that the original DEA model can only distinguish the performance of efficient DMUs from inefficient ones, but cannot discriminate between the efficient DMUs. Originality/value – This paper introduces the effective method to obtain the complete rank of all DMUs with interval data.

Measuring Performance of a Three-Stage Network Structure Using Data Envelopment Analysis and Nash Bargaining Game: A Supply Chain Application

2018 ◽  
Vol 17 (05) ◽  
pp. 1429-1467 ◽  
Author(s):  
Mohammad Amirkhan ◽  
Hosein Didehkhani ◽  
Kaveh Khalili-Damghani ◽  
Ashkan Hafezalkotob
Keyword(s):  
Supply Chain ◽  
Data Envelopment Analysis ◽  
Nash Bargaining ◽  
Data Envelopment ◽  
Second Stage ◽  
Proposed Model ◽  
Nash Bargaining Game ◽  
Dea Models ◽  
Overall Efficiency

The issue of efficiency analysis of network and multi-stage systems, as one of the most interesting fields in data envelopment analysis (DEA), has attracted much attention in recent years. A pure serial three-stage (PSTS) process is a specific kind of network in which all the outputs of the first stage are used as the only inputs in the second stage and in addition, all the outputs of the second stage are applied as the only inputs in the third stage. In this paper, a new three-stage DEA model is developed using the concept of three-player Nash bargaining game for PSTS processes. In this model, all of the stages cooperate together to improve the overall efficiency of main decision-making unit (DMU). In contrast to the centralized DEA models, the proposed model of this study provides a unique and fair decomposition of the overall efficiency among all three stages and eliminates probable confusion of centralized models for decomposing the overall efficiency score. Some theoretical aspects of proposed model, including convexity and compactness of feasible region, are discussed. Since the proposed bargaining model is a nonlinear mathematical programming, a heuristic linearization approach is also provided. A numerical example and a real-life case study in supply chain are provided to check the efficacy and applicability of the proposed model. The results of proposed model on both numerical example and real case study are compared with those of existing centralized DEA models in the literature. The comparison reveals the efficacy and suitability of proposed model while the pitfalls of centralized DEA model are also resolved. A comprehensive sensitivity analysis is also conducted on the breakdown point associated with each stage.

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