Efficient Resource Allocation Strategies Based on Nash Bargaining Solution with Linearized Constraints

Purpose Resource allocation has always been a critical problem with significant economic relevance. Many industries allocate the resources based on classical methods such as overall equipment effectiveness (OEE) and data envelopment analysis (DEA). The lack of OEE factors’ weight, how it is defined, analyzed, interpreted and compared in OEE and selection of unrealistic weights, self-appraisal and disability of complete ranking in DEA are challenges that are possible to occur. These defects may result in unfair allocation of the resources. This study aims to overcome the mentioned weaknesses. Design/methodology/approach In this paper, an approach using a set of various DEA models and Nash bargaining solution (NBS) is designed to solve the resource allocation problem based on OEE, among a set of comparable and uniform DMUs (decision-making units) in a fair way. Findings The results show that a unique Pareto optimal allocation solution is obtained by the proposed DEA–NBS model among the DMUs. This allocation is more acceptable for players, because the allocation results are commonly determined by all DMUs rather than a specific one. Furthermore, the rankings achieved by the utilized methods and TOPSIS (technique for order preference by similarity to ideal solution) are compared by Spearman’s rank correlation coefficient to validate the resource allocation plan. The findings indicate that the DEA–NBS method has the best correlation with the TOPSIS approach. Originality/value To the best of authors’ knowledge, no research has considered the use of DEA and NBS with OEE.

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Nash bargaining solution as negotiation concept for resource allocation problem: analysis of experimental data

Contributions to Game Theory and Management ◽

10.21638/11701/spbu31.2020.11 ◽

2020 ◽

Vol 13 ◽

pp. 207-217

Author(s):

Nikolay A. Korgin ◽

◽

Vsevolod O. Korepanov ◽

Keyword(s):

Resource Allocation ◽

Nash Bargaining Solution ◽

Negotiation Process ◽

Nash Bargaining ◽

Problem Analysis ◽

Bargaining Solution ◽

Proportional Allocation ◽

Best Response ◽

Behavior Models ◽

Analyze Data

Motivated by research works on Zeuthen-Hicks bargaining, which leads to the Nash bargaining solution (Vetschera, 2018), we analyze data obtained during experimental resource allocation gaming with Yang-Hajek's mechanism from the class of proportional allocation mechanisms. Games were designed in the form of negotiation to allow players to reach consensus. Behavior models based on best response, constant behavior, and Nash bargaining solution are defined. Analysis conducted over decisions made by participants shows that a significant share of all decisions leads to an increase of Nash bargaining value. It is even higher than the share of decisions that are in agreement with the best-response concept. Consensus-ended games show more but subtle attraction to Nash bargaining solution behavior. We discuss how these decisions correspond with other types of behavior actively exhibited by participants of this experiments — so-called constant behavior and with the end of negotiation process in games.

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Nash Bargaining Solution as Negotiation Concept for Resource Allocation Problem with Groves-Ledyard Mechanism

Contributions to Game Theory and Management ◽

10.21638/11701/spbu31.2021.16 ◽

2021 ◽

Vol 14 ◽

pp. 216-226

Author(s):

Nikolay A. Korgin ◽

◽

Vsevolod O. Korepanov ◽

Keyword(s):

Experimental Data ◽

Resource Allocation ◽

Nash Bargaining Solution ◽

Allocation Problem ◽

Nash Bargaining ◽

Bargaining Solution ◽

Resource Allocation Problem ◽

Proportional Allocation ◽

Best Response ◽

Behavior Models

Motivated by research works on Zeuthen-Hicks bargaining, which leads to the Nash bargaining solution, we analyze experimental data of resource allocation gaming with Groves-Ledyard mechanism. The games were designed in the form of negotiation to allow players to reach consensus. Behavior models based on best response, constant behavior, and Nash bargaining solution are defined. Analysis conducted over decisions made by participants shows that a significant share of all decisions leads to an increase of the Nash bargaining value. It is even higher than the share of decisions that are in agreement with the best-response concept. Consensusended games show light attraction to the Nash bargaining solution, it's less than we obtained in games with the mechanism of Yang-Hajek from another class of so-called proportional allocation mechanisms. We discuss differences of consensus-ended games from timeout-ended games, what decisions lead to the situations with the Nash bargaining value increasing and differences between balanced mechanism Groves-Ledyard and unbalanced mechanism Yang-Hajek.

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Resource Allocation in a Generalized Framework for Virtualized Heterogeneous Wireless Network

Mobile Information Systems ◽

10.1155/2016/8570568 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Bo Fan ◽

Hui Tian ◽

Yuan Zhang ◽

Xiao Yan

Keyword(s):

Resource Allocation ◽

Wireless Network ◽

Service Providers ◽

Nash Bargaining Solution ◽

Nash Bargaining ◽

Bargaining Solution ◽

Heterogeneous Wireless Network ◽

Network Access ◽

Medium Access ◽

Resource Allocation Algorithm

As a prevailing concept in 5G, virtualization provides efficient coordination among multiple radio access technologies (RATs) and enables multiple service providers (SPs) to share different RATs’ infrastructure. This paper proposes a generic framework for virtualizing heterogeneous wireless network with different RATs. A novel “VMAC” (virtualized medium access control) concept is introduced to converge different RAT protocols and perform inter-RAT resource allocation. To suit the proposed framework, a virtualization based resource allocation scheme is devised. We formulate the problem as a mixed combinatorial optimization, which jointly considers network access and rate allocation. First, to solve the network access problem, “adaptability ratio” is developed to model the fact that different RATs possess different adaptability to different services. And a Grey Relational Analysis (GRA) method is adopted to calculate the adaptability ratio. Second, services are modeled as players, bargaining for RAT resources in a Nash bargaining game. And a closed-form Nash bargaining solution (NBS) is derived. Combining adaptability ratio with NBS, a novel resource allocation algorithm is devised. Through simulation, the superiority and feasibility of the proposed algorithm are validated.

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