Operations Research Perspectives最新文献

Orthogonal arrays are of great importance in mathematical sciences. This paper analyses a certain practical advantage of quasi-difference matrices over difference matrices to obtain orthogonal arrays with given parameters. We also study the existence of quasi-difference matrices over cyclic groups originating orthogonal arrays with $t=2$ and $\lambda =1$, proving their existence for some parameters sets. Moreover, we present an Integer Programming model to find such quasi-difference matrices and also a Bimodal Local Search algorithm to obtain them. We provide a conjecture related to the distributions of differences along rows and columns of arbitrary square matrices with entries in a cyclic group in positions outside the main diagonal which shows an intriguing symmetry, and we prove it when the matrix is a quasi-difference matrix.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal).

This article has been retracted at the request of Editor.

The Editor of Operations Research Perspectives has retracted the publication based on evidence that the authors have plagiarized parts from another paper. The original publication is entitled ‘Mathematical Modeling of Epidemic Diseases; A Case Study of the COVID-19 Coronavirus’ (Reza Sameni, ArXiV, https://doi.org/10.48550/arXiv.2003.11371). The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.

Item and set orderings help with data management. Depending on the context, it is just as important to order a list of items (customers from different provinces, companies from different sectors, players from different teams) as it is to order a list of sets of these items (provinces, sectors, teams). It is evident that the order that is chosen for the items is not independent of the order that is chosen for the sets. It is possible that several set orders are sensible for the same item order and vice versa, that several item orders are sensible for the same set order. In this work, we propose a bilevel model to calculate an adequate order of items when an order of sets is available and another bilevel model to calculate an adequate order of sets when an order of items is available. In addition, it is shown how to reduce both bilevel models to single level models. Two illustrative computational studies are presented, the first with collected on 25 tennis players and ATP statistics and the second with Biomedical data. Both examples illustrate the good behavior of the models and the interest of their application in a real case scenario

Traditional retailers (bricks-and-mortar) have been continuously increasing online sales. However, not all retail companies were able to respond to the increasing sales with the same efficiency level as their competitors. This paper aims to propose a dynamic model – incorporating principles of Optimal Control Theory (OCT) into a Data Envelopment Analysis (DEA) model - for measuring the performance of retailing companies’ cost efficiency. It also aims to contribute through the application by investigating the impact of the pandemic on companies from the most prominent developing market in Latin America, Brazil. Twenty-one companies publicly traded in the São Paulo Stock Exchanges (B3) between the third quarter of 2018 (3Q2018) and the third quarter of 2020 (3Q2020) were investigated. Also, six measures - initial inventory cost (IIC), final inventory cost (FIC), net operating income (NOI), cost of goods sold (COGS), cost of the purchased product (CPP), and plant, property, and equipment (PPE) – were considered. In this way, the findings have implications for researchers and practitioners. Practitioners can discover which competitor(s) is (are) adopting the best practices at each operational aspect (e.g., inventory cost). Additionally, the proposed method can be replicated in other markets (developing or not) and for other categories of retailing companies (e.g., small- and middle-sized). Further research directions are presented, and their implications are discussed.

Cryostorage of human embryos produced during the course of in vitro fertilization (IVF) treatment is an important issue for hospitals, governments and individuals who are facing fertility challenges. Embryo cryostorage is giving rise to increasing economic, ethical and legal concerns due to the increasing holding and operational costs of storage, the rise in the number of unused embryos, and the absence of economic incentives for hospitals to provide free cryostorage services. These issues may reduce the availability of storage services and encourage individuals to seek embryo donations from abroad with attendant medical risks and the possibility of illegal transactions. Given that both public and private healthcare institutions are increasingly motivated by economic factors, increasing the economic incentive to offer cryostorage has the potential to increase the provision of storage facilities. This paper proposes a nonlinear programming model to enable a hospital or other service provider to determine the optimal price it should charge for storage. The suggested pricing policy comprises three components; however, our analysis shows that an optimal solution can only include a maximum of two of these components. Finally, the paper introduces a numerical example as well as a real-data comparison among several providers to demonstrate the applicability and value of the proposed model.

The presented paper considers the pricing and lot-sizing decisions for a manufacturer who produces and sells a single product in different selling channels i.e physical stock, website, mobile, etc. The objective is to find the production plan and prices of each channel to maximize the total profit defined from difference between the revenues and the productions, holding and setups costs. The consumers’ demand in each channel is represented by attraction demand models which include the multinomial logit ($MNL$), multiplicative competitive interaction ($MCI$) and linear demand models. The addressed problem is formulated as a non-convex mixed-integer nonlinear program ($MINLP$). Based on properties of attraction functions, an efficient reformulation which transforms the initial non-convex problem into a convex one is presented. Therefore, an optimization approach based on the outer approximation algorithm is presented to solve the problem. Numerical tests based on large benchmark of real inspired instances show the efficiency of the proposed approach to solve the addressed problem compared to the initial non-convex model.

Recently, a large portion of the world's population has experienced an unprecedented devastating effect of the COVID-19 pandemic. At the time of its outbreak, not much was known about this disease and therefore, quarantine and social distancing were the only ways suggested to prevent its spread among humans. Although the current situation is much better than before however, strict social distancing norms as well as frequent long-lasting lockdowns with stringent guidelines and actions to control the spread in the early days have affected the physical and psychological health of the people. Consequently, this study was carried out to attain the following major objectives: (i) to identify the potential psychological problems/factors that might have been caused due to COVID-19 led social distancing and lockdowns, and (ii) to determine the ranks of the identified psychological factors to reflect their degree of criticality. The first objective was achieved by gathering information about the potential psychological factors from the experts. Data, in terms of linguistic variables, was collected from the experts and analyzed using two fuzzy-based multi-criteria decision-making (MCDM) methods i.e. Fuzzy Best Worst Method (F-BWM) and Fuzzy TOPSIS (F-TOPSIS) which led to the accomplishment of the second objective. The results of this study revealed that anxiety, stress, panic attacks, frustration, and insomnia were the top five critical psychological factors that might have affected people due to this pandemic. Consistency of the results was ensured by comparing the obtained ranks with the ranks found using the Fuzzy WSM and Fuzzy MABAC methods. In addition, the robustness of the results was ascertained by conducting the sensitivity analysis. Based on the findings of the study, the identified factors were categorized into most, average, and least critical psychological factors. This research might help the relevant authorities to understand the extent of the seriousness of the various psychological factors caused by this pandemic, so that an effective strategy may be developed for better management, control, and safety.

In the context of some multi-criteria decision-making methods, such as the Analytic Hierarchy Process, an expert is required to compare entities, e.g. alternatives and criteria. However, often, for various reasons, the expert cannot provide judgments on all pairs of entities. For these cases, several completion methods have been proposed in the literature to estimate the missing values of pairwise comparison matrices. In this paper, we study the similarity of eleven completion methods on the basis of numerical simulations and hierarchical clustering. We perform simulations for matrices of different orders considering various numbers of missing comparisons. Finally, the results suggest the existence of a cluster of five extremely similar methods, and a method significantly dissimilar from all the others.