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