Interactive multiobjective DEA target setting using lexicographic DDF

2020 ◽  
Vol 54 (6) ◽  
pp. 1703-1722 ◽  
Author(s):  
Narges Soltani ◽  
Sebastián Lozano
Keyword(s):  
Efficient Frontier ◽  
Directional Distance Function ◽  
Fixed Number ◽  
Optimization Approach ◽  
Target Setting ◽  
Interactive Process ◽  
Decision Making Unit ◽  
Setting Approach ◽  
Promising Area ◽  
Current Region

In this paper, a new interactive multiobjective target setting approach based on lexicographic directional distance function (DDF) method is proposed. Lexicographic DDF computes efficient targets along a specified directional vector. The interactive multiobjective optimization approach consists in several iteration cycles in each of which the Decision Making Unit (DMU) is presented a fixed number of efficient targets computed corresponding to different directional vectors. If the DMU finds one of them promising, the directional vectors tried in the next iteration are generated close to the promising one, thus focusing the exploration of the efficient frontier on the promising area. In any iteration the DMU may choose to finish the exploration of the current region and restart the process to probe a new region. The interactive process ends when the DMU finds its most preferred solution (MPS).

Nonradial Directional Performance Measurement with Undesirable Outputs: An Application to OECD and Non-OECD Countries

2015 ◽  
Vol 14 (03) ◽  
pp. 481-520 ◽  
Author(s):  
Po-Chi Chen ◽  
Ming-Miin Yu ◽  
Ching-Cheng Chang ◽  
Shih-Hsun Hsu ◽  
Shunsuke Managi
Keyword(s):  
Policy Making ◽  
Oecd Countries ◽  
Directional Distance Function ◽  
Undesirable Outputs ◽  
Efficiency Measure ◽  
Input Output ◽  
Decision Making Unit ◽  
Distance Model ◽  
Individual Input ◽  
Better Than

The objective of this paper is to provide a more comprehensive efficiency measure to estimate the performance of OECD and non-OECD countries. A Russell directional distance function that appropriately credits the decision-making unit not only for increase in desirable outputs but also for the decrease of undesirable outputs is derived from the proposed weighted Russell directional distance model. The method was applied to a panel of 116 countries from 1992 to 2010. This framework also decomposes the comprehensive efficiency measure into individual input/output components' inefficiency scores that are useful for policy making. The results reveal that the OECD countries perform better than the non-OECD countries in overall, goods, labor and capital efficiencies, but worse in bad and energy efficiencies.

A New MIP Approach on the Least Distance Problem in DEA

2020 ◽  
Vol 37 (06) ◽  
pp. 2050027
Author(s):  
Xu Wang ◽  
Kuan Lu ◽  
Jianming Shi ◽  
Takashi Hasuike
Keyword(s):  
State Of The Art ◽  
Efficient Frontier ◽  
Decision Makers ◽  
Mixed Integer ◽  
Data Envelopment ◽  
Output Level ◽  
Decision Making Unit ◽  
Dea Models ◽  
Distance Problem ◽  
User Friendly

In this paper, we deal with the least distance problem (LDP) in Data Envelopment Analysis (DEA), which is to find a closest efficient target over the whole efficient frontier. To this end, we define the efficient frontier by a linear complementarity system and propose a mixed integer programming (MIP) approach to solve the LDP. Our proposed MIP approach: (1) can solve the LDP based on [Formula: see text]-norm ([Formula: see text]) by using a state-of-the-art solver and obtain the closest efficient target over the whole efficient frontier instead of a subset of it; (2) can be applied for computing the least distance DEA models satisfying the monotonicity; (3) is more user-friendly, because it allows a decision maker to improve the efficiency of a decision making unit (DMU) by setting the affordable input/output level under his/her circumstance. Thus, the efficient target provided by our approach may be more appropriate from the perspective of the decision makers of DMUs.

scholarly journals Novel models for obtaining the closest weak and strong efficient projections in data envelopment analysis

2021 ◽  
Vol 39 (5) ◽  
pp. 9-24
Author(s):  
Javad Vakili ◽  
Hanieh Amirmoshiri ◽  
Mir Kamal Mirnia
Keyword(s):  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Efficient Frontier ◽  
Data Envelopment ◽  
Nonparametric Method ◽  
Target Setting ◽  
Production Possibility ◽  
Production Possibility Set ◽  
And Performance ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is a nonparametric method for measuring the relative efficiency and performance of Decision Making Units (DMUs). Traditionally, there are two issues regarding the DEA simultaneously i.e., the identification of a reference point on the efficient boundary of the production possibility set (PPS) and the use of some measures of distance from the unit under assessment to the efficient frontier. Due to its importance, in this paper, two alternative target setting models were developed to allow for lowefficient DMUs find the easiest way to improve its efficiency and reach to the efficient boundary. One seeks the closest weak efficient projection and the other suggests the most appropriate direction towards the strong efficient frontier surface. Both of these models provides the closest projection in one stage. Finally, a proposed problem is empirically checked by using a recent data related to 30 European airports.

scholarly journals Planar Thinned Arrays: Optimization and Subarray Based Adaptive Processing

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
P. Lombardo ◽  
R. Cardinali ◽  
M. Bucciarelli ◽  
D. Pastina ◽  
A. Farina
Keyword(s):  
Side Lobe ◽  
Fixed Number ◽  
Side Lobe Level ◽  
Optimization Approach ◽  
Optimized Design ◽  
Adaptive Processing ◽  
Active Elements ◽  
Single Antenna ◽  
Different Shapes ◽  
Thinned Array

A new approach is presented for the optimized design of a planar thinned array; the proposed strategy works with single antenna elements or with small sets of different subarray types, properly located on a planar surface. The optimization approach is based on the maximization of an objective function accounting for side lobe level and considering a fixed number of active elements/subarrays. The proposed technique is suitable for different shapes of the desired output array, allowing the achievement of the desired directivity properties on the corresponding antenna pattern. The use of subarrays with a limited number of different shapes is relevant for industrial production, which would benefit from reduced design and manufacturing costs. The resulting modularity allows scalable antenna designs for different applications. Moreover, subarrays can be arranged in a set of subapertures, each connected to an independent receiving channel. Therefore, adaptive processing techniques could be applied to cope with and mitigate clutter echoes and external electromagnetic interferences. The performance of adaptive techniques with subapertures taken from the optimized thinned array is evaluated against assigned clutter and jamming scenarios and compared to the performance achievable considering a subarray based filled array with the same number of active elements.

scholarly journals A New Robust Dynamic Data Envelopment Analysis Approach for Sustainable Supplier Evaluation

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Hava Nikfarjam ◽  
Mohsen Rostamy-Malkhalifeh ◽  
Abbasali Noura
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Robust Optimization ◽  
Supplier Selection ◽  
Optimization Approach ◽  
Data Envelopment ◽  
Environmental Criteria ◽  
Decision Making Unit ◽  
Modern Business ◽  
Dynamic Data Envelopment Analysis

Supplier selection is one of the intricate decisions of managers in modern business era. There are different methods and techniques for supplier selection. Data envelopment analysis (DEA) is a popular decision-making method that can be used for this purpose. In this paper, a new dynamic DEA approach is proposed which is capable of evaluating the suppliers in consecutive periods based on their inputs, outputs, and the relationships between the periods classified as desirable relationships, undesirable relationships, and free relationships with positive and negative natures. To this aim various social, economic, and environmental criteria are taken into account. A new method for constructing an ideal decision-making unit (DMU) is proposed in this paper which differs from the existing ones in the literature according to its capability of considering periods with unit efficiencies which do not necessarily belong to a unique DMU. Furthermore, the new ideal DMU has the required ability to rank the suppliers with the same efficiency ratio. In the concerned problem, the supplier that has unit efficiency in each period is selected to construct an ideal supplier. Since it is possible to have more than one supplier with unit efficiency in each period, the ideal supplier can be made with different scenarios with a given probability. To deal with such uncertain condition, a new robust dynamic DEA model is elaborated based on a scenario-based robust optimization approach. Computational results indicate that the proposed robust optimization approach can evaluate and rank the suppliers with unit efficiencies which could not be ranked previously. Furthermore, the proposed ideal DMU can be appropriately used as a benchmark for other DMUs to adjust the probable improvement plans.

DEA target setting approach within the cross efficiency framework

Omega ◽  
2020 ◽  
Vol 96 ◽  
pp. 102072 ◽  
Author(s):  
Lei Chen ◽  
Ying-Ming Wang
Keyword(s):  
Target Setting ◽  
Setting Approach ◽  
The Cross

scholarly journals Centralized Resource Allocation and Target Setting Based on Data Envelopment Analysis Model

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Tiantan Yang ◽  
Pingchun Wang ◽  
Feng Li
Keyword(s):  
Decision Making ◽  
Resource Allocation ◽  
Data Envelopment Analysis ◽  
Efficient Frontier ◽  
Total Output ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Analysis Model ◽  
Planning Period ◽  
Target Setting

This paper aims to develop a data envelopment analysis (DEA) based model for allocating input resources and deciding output targets in organizations with a centralized decision-making environment, for example, banks, police stations, and supermarket chains. The central decision-maker has an interest in maximizing the total output production and at the same time minimizing the total input consumption. Traditionally, all decision-making units (DMUs) can be easily projected to the efficient frontier, which is a mathematical feasibility; however, it does not guarantee the managerial feasibility during the planning period. In this paper, we will take potential limitations of input-output changes into account by building a difficulty coefficient matrix of modifying their production in the current production possibility set so that the solution guarantees managerial feasibilities. Three objectives, namely, maximizing aggregated outputs, minimizing the consumption of input resources, and minimizing the total difficulty coefficient, are proposed and incorporated into the formation of resource allocation and target setting scheme. Building on this, we combine DEA and multiobjective programming to solve the resource allocation and target setting problem. In the end, we apply our proposed approach to a real-world problem of sixteen chain hotels to illustrate the efficacy and usefulness of the proposed approach.

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