On Pareto-optimality in the cross-efficiency evaluation

Davtalab-Olyaie, Mostafa; Asgharian, Masoud

doi:10.1016/j.ejor.2020.05.040

Cited by 31 publications

(4 citation statements)

References 56 publications

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“…It should be emphasized that model (11) remains a nonlinear programming model even with the common weights profile. Therefore, based on the weight expression proposed by Davtalab-Olyaie and Asgharian [8], this paper reflects the preferred degree of DMUj relative to all supply chains by defining the portion of the total resources devoted to DMUj.…”

Section: ˆˆˆˆˆˆˆˆˆ0mentioning

confidence: 99%

“…Furthermore, Wu et al [28] proposed the Pareto optimality estimation model to determine whether the given set of cross-efficiency scores are Pareto-optimal solutions, and the Pareto improvement model is developed to make the cross-efficiency score of the evaluated DMU better off without making any DMU's cross-efficiency scores worse off. Furthermore, Davtalab-Olyaie and Asgharian [8] proposed a multi-objective programming model based on the self-prioritizing principle, and applied the weighted sum technique to develop a linear model, which can determine an optimal weights profile to calculate the Pareto-optimal cross-efficiency scores of all DMUs. And Sharafi et al…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, Chen et al [4] introduced the preference for all individual DMUs into proposing a new centralized resource allocation strategy based on the cross-efficiency iterative method. In addition, Davtalab-Olyaie and Asgharian [8] applied the Pareto-optimal cross-efficiency model to the R&D project selection, and the total budget was allocated among all projects based on the Pareto-optimal results, which can improve resource allocation and fund more projects. In the existing research, however, there are few studies on the development of the Pareto-optimal cross-efficiency model from the perspective of enterprises' self-interested principle to explore the optimal allocation strategy of common resources among two-stage structure systems, and the relationship between the cross-efficiency of the overall system and that of subsystems in the resource allocation environment also needs to be further studied.…”

Section: Introductionmentioning

confidence: 99%

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Resource allocation for supply chains based on Pareto-optimal two-stage cross-efficiency model

Zhao

Fang

2022

RAIRO-Oper. Res.

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The contradiction between limited resources and endless demand has a significant impact on social development. Therefore, resource allocation can make the best use of limited resources in economic activities. Taking the two-stage supply chain where the outputs from the upstream supplier are taken as the inputs for the downstream manufacturer as an example, this paper applies cross-efficiency model to comprehensively evaluate the efficiency scores of supply chains in the process of resource allocation, and explores the relationship between the cross-efficiency of supply chain and that of two enterprises within this supply chain. Furthermore, the self-interest of enterprises is taken as Pareto improvement principle to propose a Pareto-optimal two-stage cross-efficiency model that can be used for allocating optimally the limited resources among two-stage supply chains. And a set of common weights is determined to make all supply chains DEA efficient. Finally, the proposed model is shown to be feasible and effective through a practical application about 27 Iranian resin production companies.

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Section: ˆˆˆˆˆˆˆˆˆ0mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Resource allocation for supply chains based on Pareto-optimal two-stage cross-efficiency model

Zhao

Fang

2022

RAIRO-Oper. Res.

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“…Meanwhile, linear programming can show the data based on Consumers' Demand and Prices, which [11,12] research, as well as the Pareto optimization method to find a solution with an optimal goal.…”

Section: Introductionmentioning

confidence: 99%

Design Serious Game For Automatic Optimization Production Based on Blockchain

Putri

Hariadi

Fuad

2022

Preprint

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Indonesian agricultural production is unpredictable. The harvest quality causes this is not-optimal harvest quality. The researchers provide a solution by utilizing a severe game design model as a learning medium to make decisions for optimizing production. In comparison, Blockchain technology is for data security, Distributed ledgers and Smart Contracts. The transaction between producers and consumers are permanent and verified. For the literature study of optimization to maximize farmers' production, the researchers use Simplex Method as an objective function and the Pareto function as an optimization model for multiple problems by finding solutions for all purposes to improve farmers' production. Multi-Objective Optimization, Linear Programming, Blockchain, and Hierarchical State Finite Machine are needed to design a serious game. This study designed Hierarchical State Finite Machine Serious Game using Blockchain and Multi-Objective Optimization method as a learning medium to simulate farmers making decisions In producing agricultural quantities based on consumer demand, stock, price, season, and planting time. Blockchain is used to track the availability of farm stocks so that the supply of consumer needs is met.

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Supporting distributed energy resources with optimal placement and sizing of voltage regulators on the distribution system by an improved teaching‐learning‐based optimization algorithm

Taheri

Salles

Khan

2021

Int Trans Electr Energ Syst

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Summary The continuous increase of the distributed energy resources (DERs) penetration levels leads to voltage stability problems in the distribution system. One of the approaches for the mentioned emerging challenge is the proper placement of automatic voltage regulators (AVRs). This paper investigates the optimal placement and sizing of AVRs in a distribution network by presenting a new modification of the teaching‐learning‐based optimization (TLBO) algorithm. The objective functions consist of minimizing the distribution system voltage deviation, energy generation cost, and electrical losses. The modification improves the convergence velocity and accuracy of the TLBO algorithm using the combination of mutation technique and quasi‐opposition‐based‐learning concept. This paper compares the performance of the proposed algorithm with other famous evolutionary algorithms. The test distribution system contains installed DERs that work more efficiently after the placement of AVRs based on the mentioned objective functions by the proposed optimization algorithm. The simulation results display the best optimization algorithms for AVRs placement with a significant level of less than 0.10 (ie, probability‐value). The proposed multiobjective optimization algorithm's considerable merit is the accuracy and convergence velocity in solving this specific optimization problem.

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On Pareto-optimality in the cross-efficiency evaluation

Cited by 31 publications

References 56 publications

Resource allocation for supply chains based on Pareto-optimal two-stage cross-efficiency model

Resource allocation for supply chains based on Pareto-optimal two-stage cross-efficiency model

Design Serious Game For Automatic Optimization Production Based on Blockchain

Supporting distributed energy resources with optimal placement and sizing of voltage regulators on the distribution system by an improved teaching‐learning‐based optimization algorithm

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