Journal of the Operational Research Society

Zervopoulos, Panagiotis D.; Triantis, Konstantinos; Sklavos, Sokratis; Kanas, Angelos

doi: 10.1080/01605682.2022.2053309pmid: N/A

Abstract In this study, we introduce an alternative Bayesian data envelopment analysis (DEA) approach yielding consistent efficiency estimators for convex sets. This approach draws on two distributional assumptions, a uniform likelihood and a beta prior. It is known from the literature that the combination of those two distributions leads to a reasonable estimator of the parameter. The prior of our approach is “non-informative” in a relative sense and does not depend on sample size. The consistency of the estimates is proven by formal statistical analysis, empirical analysis using real-world data, and computational analysis using simulated data. Our findings justify the appropriateness of our distributional assumptions and the validity of the presented bias-correction procedure. Our estimates are strongly correlated with the DEA-smoothed bootstrap estimates while presenting lower mean square error (MSE) and mean absolute error (MAE). Specifically, the correlation coefficients range between 0.898 and 0.952. The MSE of the alternative Bayesian DEA estimates gradually decreases while sample size increases. For samples of 500, 1,000 and 1,500 units, the MSE is as low as 6.4 10−4, 1.4 10−4 and 6 10−5, respectively. Also, an inverse relationship between the sample size and the length of the confidence intervals of the alternative Bayesian DEA estimates is present.

Crisci, Anna

doi: 10.1080/01605682.2022.2053310pmid: N/A

Abstract The aim of this paper is to describe and illustrate the application of generalized estimation equations and several diagnostic measures. The principal idea behind generalized estimating equations is to generalize and extend the usual likelihood score equation for a generalized linear model by including the covariance matrix of the clustered responses. The advantage of generalized estimating equations is that we do not need to specify the whole response distribution, only the mean structure and, with the aim to increase efficiency, the covariance structure consisting of a working correlation matrix along with the variance function defining the mean-variance relationship. The paper investigates, from a methodological point of view, various measures for the identification of the strength of association between a response variable and covariates including the coefficient of determination based on Wald Statistics, and the pseudo-coefficient of determination based on a quasi-likelihood method. Moreover, diagnostic measures for checking the adequacy of the generalized estimating equations method are considered and applied to a dataset to assess the impact of governance factors on environmental policy disclosure. The case study presents one of the most comprehensive applications of Generalized Estimating Equations regression diagnostics in the economics literature and is a novelty in the analysis of the Environmental Social and Governance disclosure determinants in the Non-Financial Industry.

Noro, Jorge; Dias, Luís C.

doi: 10.1080/01605682.2022.2056530pmid: N/A

Abstract This article presents a new bi-objective optimization model for project portfolio management. The model formulation selects which projects will be implemented and by whom. The objective functions seek to maximize the economic gains of the project portfolio selected and to maximize the skills development of the agents allocated to these projects, thus promoting the improvement of the team's performance over time. The constraints to the choice and allocation of projects take into account the workload of the agents and the way the distribution of work affects their employment commitment, considering the dimensions of Absorption, Dedication and Strength of the UWES (Utrecht Work Engagement Scale), in its reduced form. Experimental results are presented, for a scenario based on the experience of administration offices for the management of research and innovation projects at a higher education institution.

Lu, Wen-Min; Kuo, Kuo-Cheng; Tran, Thu Huong

doi: 10.1080/01605682.2022.2056531pmid: N/A

Abstract This study aims to investigate the effects of corporate social responsibility (CSR) on corporate performance in the global retail industry. We adopt the modified data envelopment analysis (DEA) game cross-efficiency approach in assessing the longitudinal efficiency of a sample of 414 listed retail firm-year observations given by Forbes 2000 between 2013 and 2018. The performance result indicates that the Americas retail industry is growing steadily and continues to dominate other regions. Not only the retail industry but all industries in Europe have severe environmental safety rules and management requirements in business operations, hence, Europe also scored high in both the overall environmental index in CSR and all their component indices. Additionally, panel regression revealed that the environmental dimension in CSR is significantly and directly correlated to firm performance. This study’s findings will provide practitioners and policymakers with managerial and strategic implications to enhance their efficiency by applying the CSR dimension in the retail industry.

Gul, Evren; Lim, Alvin; Xu, Jiefeng

doi: 10.1080/01605682.2022.2056532pmid: N/A

Abstract It is well-established that increased product visibility to shoppers leads to higher sales for retailers. In this study, we propose an optimization methodology which assigns product categories and subcategories to store locations and sublocations to maximize the overall visibility of products to shoppers. The methodology is hierarchically developed to meet strategic and tactical layout planning needs of brick-and-mortar retailers. Layouts in both levels of planning are optimized considering eligibility requirements and complete set of shopper paths, thus, they successfully capture the unique shopping behaviour of consumers in a store’s region. The resulting mathematical optimization problem is recognized as a special instance of the well-known Quadratic Assignment Problem, which is considered computationally as one of the hardest optimization problems. We adopt a linearization technique and demonstrate via a real-world numerical example that our linearized optimization models substantially improve the store layout, hence, can be used in practical applications as a vital decision support model for store layout planning.

Khatami, Mostafa; Salehipour, Amir

doi: 10.1080/01605682.2022.2056533pmid: N/A

Abstract The minimum covering location problem with distance constraints deals with locating a set of undesirable facilities on a geographical map, where there is a given minimum distance between any pair of located facilities. A covering radius is defined within which the population node is fully covered and beyond that it is not covered at all. This setting may not be applicable in practice because usually the coverage gradually decreases with an increase in the distance. Additionally, undesirable facilities may have a cooperative adverse impact on the nearby population. We introduce the gradual coverage to the problem that extends the classic definition of the coverage and is more suitable for modelling real-world applications. We name the problem the gradual minimum covering location problem with distance constraints (GMCLPDC). We propose a mixed-integer program for GMCLPDC where cooperation of facilities is also considered. We propose a threshold accepting heuristic as the solution method. We conduct computational experiments on instances with up to 10,000 nodes. The outcomes indicate that the heuristic delivers quality solutions and outperforms the solver Gurobi. We also show an application of our model in Sydney metropolitan area.

Wen, Yao; Hu, Junhua; An, Qingxian; Gong, Yeming

doi: 10.1080/01605682.2022.2056534pmid: N/A

Abstract As an effective strategy for improving resource utilization and increasing profits, resource sharing exists not only between independent systems but also between sub-systems in the same system. Many researchers study the external cooperation among independent systems through building Data Envelopment Analysis (DEA) games. However, they do not consider the internal resource sharing between sub-systems and think players possess the same risk attitudes for gain and loss when allocating potential gains, which may be inconsistent with practice. To fill these gaps and answer the question: “For the system containing multiple independent DMUs, how to measure and allocate the potential gains derived from the internal resource sharing”, we construct an internal resource-sharing DEA game based on the DEA approach and propose a novel payoff allocation method considering players’ bounded rationality. Our proposed game is super-additive, monotone, not necessarily convex, and has a non-empty core. By the proposed payoff method, we obtain a unique, efficient, stable, and fair payoff allocation. Finally, to validate our method, we conduct a numerical experiment with inland transportation systems and compare the novel payoff allocation method with the core, Shapley value, and nucleolus.

Chen, Lei; Wang, Ying-Ming

doi: 10.1080/01605682.2022.2056535pmid: N/A

Abstract Faced with a large number of the efficiency evaluation problems with non-homogeneous decision-making units (DMUs) in reality, the existing efficiency evaluation methods are always unfair in terms of evaluation perspective and inputs/outputs allocation. Therefore, this paper adopts the concept of network data envelopment analysis (DEA) to define the inclusion relationship of production structure between non-homogeneous DMUs. Sequentially, the DEA cross-efficiency model and its corresponding cross-evaluation strategy are constructed by combining the self-evaluation perspective with peer-evaluation perspective, and then the efficiency of these DMUs can be evaluated in a more objective and reasonable way. Actually, the cross-evaluation perspective can fully reflect the effect of non-homogeneous structure on efficiency. In addition, the inclusion relationship is extended to a mixed relationship, and the application scope of the new cross-efficiency method is thus further expended. Finally, two examples are provided to illustrate the effectiveness of the new method.

Anderson, Billie; Newman, Mark A.; Grim II, Philip A.; Hardin, J. Michael

doi: 10.1080/01605682.2022.2057819pmid: N/A

Abstract Credit scoring is the process of determining whether applicants should be granted a financial loan. When a financial institution decides to create a credit scoring model for all applicants, the institution only has the known good/bad loan outcomes for accepted applicants. This causes inherent bias in the model We address a gap in the reject inference literature by developing a methodology to simulate rejected applicants. A methodology to illustrate how to simulate rejected applicants must be developed so that the reject inference techniques can be studied and appropriate reject inference techniques can be selected. This study uses a peer-to-peer financial loan information from accepted and rejected financial loan applicants to perform Monte Carlo simulation of rejected applicants. Using simulated data, the researchers compare the performance of three widely used reject inference techniques.

Omrani, Hashem; Oveysi, Zeynab; Emrouznejad, Ali; Teplova, Tamara

doi: 10.1080/01605682.2022.2064783pmid: N/A

Abstract Conventional DEA performs like a “black box” and provides no information about sub-processes. In some cases, such as banks, providing services made up of interactive and interdependent processes. Also, in real-world applications, inputs could be shared among these sub-processes. Moreover, due to the characteristics of some variables, such as number of employees, only integer values could be assigned to them. Hence, to address these shortcomings, in this study, a mixed-integer network DEA (MI-NDEA) with shared inputs and undesirable outputs has been proposed to evaluate the efficiency of decision-making units. The proposed model considers integer values for some of the input variables. Also, it assumes that some inputs are shared among different stages of the production process. To illustrate the capability of the model, the efficiency of “Internet banking”, “profitability”, “production”, and “overall” performance of a set of bank branches have been evaluated and results are discussed. The results indicate that the mean of overall efficiency for all branches is high. However, some branches are not efficient enough in the “Production” stage or “Profitability” stage. To identify the source of inefficiency in such branches, projection values have been calculated and recommendations have been made for policy makers.