Operations Research Perspectives最新文献

We analyze an integrated inventory supply chain and seek the optimal production lot, optimal production rate, and optimal (integer) number of shipments per production lot. An increasing need for higher operational efficiency, as well as growing competition among multiple products for a limited storage capacity, is driving retailers to require more frequent shipping. This imposes pressure on suppliers to share the shipping cost with retailers. The sharing ratio of the shipment cost has not previously been considered within the context of an integrated supply chain. Therefore, we contribute to the literature by investigating this entirely new parameter, assuming that the shipment cost is shared between a manufacturer and a retailer. We also consider a distributed supply chain in which each party optimizes its own cost. We analyze the problem of finding the optimal sharing ratio of the shipment cost for such a supply chain and show that there exists a specific choice of shipment cost-sharing ratio (set by the manufacturer) that results in total costs similar to those obtained in the integrated inventory model. We develop deterministic models that provides basic insights into the investigated problem. Through mathematical analysis of a nested-designs model, we provide intermediate results (which are of interest in their own right) as well as optimal analytical solutions. We show, through numerical examples, that in the scenario where each party optimizes its own cost, the manufacturer's shipment cost is a central control variable in the sense that it affects the costs of both parties.

Despite a lot of research about predictive maintenance for onshore and offshore windmill farms, nearly no investigation has been performed to obtain the optimal sequence in which windmills are to be served in a predefined time frame. The higher fuel costs and the increasing time pressure on maintenance jobs urge the need for optimisation, so offshore windmills can be serviced at minimal costs and within a limited time frame. To minimise distance travelled, fuel consumption and average tardiness of all maintenance tasks to be carried out, a multi-objective, non-dominated sorting island model of genetic algorithms is used.

The following novel contributions are realised: (i) A multi-objective island model is used, where on each island a different genetic algorithm is used to minimise a separate cost function per island. (ii) A set of non-dominated maintenance sequences, shown as a Pareto plane, are computed and (iii) these optimal solutions can be used by the planner to select the route to be followed by the CTV when travelling from windmill to windmill during a maintenance sequence.

Tests on two of the islands have resulted in a relative improvement of around 65 to 70% on fuel consumption and distance in relation to a random sequence, while the third island has generated a relative gain of 69% in average weighed tardiness. The three islands combined have resulted in a set of Pareto optimal sequences for offshore windmill maintenance.

This article considers current capacity issues in health care and the development of quantitative techniques to facilitate a high-level strategic assessment of hospital activity within a region. In providing that assessment, a variety of decision problems are foreseen, and we test the notion that it is useful to provide decision support for those. To achieve that support, several optimization models are developed and tested. In theory the presented models may help health care planners organise hospital resources and activity better, to treat more patients. The first model that we propose identifies a maximal caseload that meets the patient type proportions specified in a regional case mix imposed by a planner, executive or manager. The second model identifies how spatially distributed demand can best be met amongst the different hospitals, such that travel distance and unmet demand are minimised. The third model identifies how individual hospitals can jointly achieve their goals with the help of outsourcing. Each of the models has been implemented and tested on some demonstrative examples of a smaller nature, before a larger study is presented. Our case study demonstrates that appropriate data can be collected, and the proposed decision models can provide a rational appraisal of regional capacity and utilization.

The analytic network process (ANP) is a well-known multi-criteria decision method that uses pairwise comparison matrices to assess the influence among elements and clusters. This method requires the participation of experts who need to answer a large number of questions. A recent paper proposes using Decision-Making Trial and Evaluation (DEMATEL) scales in ANP to assess influences and suggests the possibility of grouping all elements into a single cluster. This rise the following questions that this paper seek to answer: if no comparison matrices are used in ANP, how similar are the results, whether clusters are used or not, to the original results with ANP using pairwise matrices? Why should or should not one or several groups be used in ANP? How much does the result change when considering multiple groups versus a single group? Does the variation of questions compensate for the variation of the results? How should the evaluation of influences and the use of the scale be approached depending on whether there are one or several groups? For this purpose, published cases solved with ANP have been reviewed and solved without comparison matrices, with the original clustering and with a single cluster, using four different models for each case study. The results show that clustering does influence the results. It should also be noted that the use of clustering helps to identify the elements of the decision problem. Additionally, this work includes the compilation of 17 cases matrices which can be used in further studies

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.