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Abstract: Abstract DIKEDOC is a knowledge-based multicriteria methodology that is here proposed to organise dispersed knowledge about a complex problem when a decision process has not yet been activated, or is latent, and to generate an interaction space that produces new knowledge. An integrated use of logical and analytical tools is proposed, first for use at a technical level to organise any dispersed knowledge in a way that generates insights that can be communicated, and then in a participative context, to create an opportunity to interact, share personal points of view and experiences and to explore spaces of action, where such tools facilitate understanding, criticism and proposals. A pilot study was developed, by an interdisciplinary research team, in relation to the enhancement process of the “Ivrea, industrial city of the twentieth century” UNESCO site, which still needs to be activated after a long and complex decision process that led to the inclusion of the site in the World Heritage List. Several research activities and enhancement projects have been developed in the last few years, but a series of critical conditions have limited their implementation. A new perspective is now necessary to identify and control the uncertainties that have emerged, guide the incremental development of knowledge and foster relationships, decisions and policies. The paper presents DIKEDOC, a new knowledge organisation and problem description methodology, and the conducted pilot study, which led to the proposal of a constructive vision of decision aiding that logically and analytically “described” the space of action and its uncertainties. PubDate: 2022-05-21
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Abstract: Abstract In this study, we consider the problem of healthcare resource management and location planning problem during the early stages of a pandemic/epidemic under demand uncertainty. Our main ambition is to improve the preparedness level and response effectiveness of healthcare authorities in fighting pandemics/epidemics by implementing analytical techniques. Building on lessons from the Chinese experience in the COVID-19 outbreak, we first develop a deterministic multi-objective mixed integer linear program (MILP) which determines the location and size of new pandemic hospitals (strategic level planning), periodic regional health resource re-allocations (tactical level planning) and daily patient-hospital assignments (operational level planning). Taking the forecasted number of cases along a planning horizon as an input, the model minimizes the weighted sum of the number of rejected patients, total travel distance, and installation cost of hospitals subject to real-world constraints and organizational rules. Next, accounting for the uncertainty in the spread speed of the disease, we employ an across scenario robust (ASR) model and reformulate the robust counterpart of the deterministic MILP. The ASR attains relatively more realistic solutions by considering multiple scenarios simultaneously while ensuring a predefined threshold of relative regret for the individual scenarios. Finally, we demonstrate the performance of proposed models on the case of Wuhan, China. Taking the 51 days worth of confirmed COVID-19 case data as an input, we solve both deterministic and robust models and discuss the impact of all three level decisions to the quality and performance of healthcare services during the pandemic. Our case study results show that although it is a challenging task to make strategic level decisions based on uncertain forecasted data, an immediate action can considerably improve the response effectiveness of healthcare authorities. Another important observation is that, the installation times of pandemic hospitals have significant impact on the system performance in fighting with the shortage of beds and facilities. PubDate: 2022-05-21
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Abstract: Abstract Supply chains with either perishables or non-perishables have been well-studied as evidenced through extant published literature. Among these studies, very few consider supply chains with both perishable and non-perishable products. Since the early 2000s, RFID (Radio-Frequency IDentification) tags have been increasingly used in supply chains that deal with perishables as well as non-perishables. While there is a reasonably large amount of published literature on RFID use in supply chains, we are unaware of any that considers the dynamics of RFID-generated information in supply chains that simultaneously involve perishables substitutable by non-perishables in retail environments. We attempt to address this void. We consider the relative benefits of sensor-enabled RFID tag use in supply chains that simultaneously contain perishables substitutable by non-perishables. We also derive expressions for conditions on their dynamics through specific consideration of their pre-determined and actual expiry dates. We operationalize our analysis from the perspective of retailers and customers. PubDate: 2022-05-20
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Abstract: Abstract Mean-semivariance and minimum semivariance portfolios are a preferable alternative to mean-variance and minimum variance portfolios whenever the asset returns are not symmetrically distributed. However, similarly to other portfolios based on downside risk measures, they are particularly affected by parameter uncertainty because the estimates of the necessary inputs are less reliable than the estimates of the full covariance matrix. We address this problem by performing PCA using Minimum Average Partial on the downside correlation matrix in order to reduce the dimension of the problem and, with it, the estimation errors. We apply our strategy to various datasets and show that it greatly improves the performance of mean-semivariance optimization, largely closing the gap in out-of-sample performance with the strategies based on the covariance matrix. PubDate: 2022-05-20
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Abstract: Abstract In recent times, the literature has seen considerable growth in research at the intersection of digital innovation, data analytics, and supply chain resilience. While the number of studies on the topic has been burgeoning, due to the absence of a comprehensive literature review, it remains unclear what aspects of the subject have already been investigated and what are the avenues for impactful future research. Integrating bibliometric analysis with a systematic review approach, this paper offers the review of 262 articles at the nexus of innovative technologies, data analytics, and supply chain resiliency. The analysis uncovers the critical research clusters, the evolution of research over time, knowledge trajectories and methodological development in the area. Our thorough analysis enriches contemporary knowledge on the subject by consolidating the dispersed literature on the significance of innovative technologies, data analytics and supply chain resilience thereby recognizing major research clusters or domains and fruitful paths for future research. The review also helps improve practitioners’ awareness of the recent research on the topic by recapping key findings of a large amount of literature in one place. PubDate: 2022-05-19
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Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract In order to improve the development effect of small and medium-sized enterprises clusters in developing countries, this article combines big data visualization technology to study small and medium-sized enterprises in developing countries, and constructs an interactive data intelligent framework system for small and medium-sized enterprises based on big data visualization technology. Moreover, this paper improves the data mining algorithm based on the characteristics of the data of small and medium-sized enterprises in developing countries, and adopts a pattern integration scheme. In order to increase the scalability of the data source in the enterprise data space and reduce the requirements for the data source model, this paper constructs the overall system architecture and verifies the system's practical performance through experiments. From the perspective of experimental research, the interactive data visualization intelligent business framework system for small and medium-sized enterprises in developing countries constructed in this paper can effectively promote the interaction of small and medium-sized enterprises in developing countries, and can provide a reference for the development and decision-making of small and medium-sized enterprises. PubDate: 2022-05-17
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Abstract: Abstract The aim of the current study is to analyze the effects of red and yellow cards on the scoring rate in elite soccer. The sample was composed of 1826 matches in the top five European leagues. All events were structured in 5-min intervals and were analyzed by means of a Generalized Linear Mixed Model with Poisson distribution, considering the presence of correlated data, where the dependent variable is represented by scoring rate. Team strength and home advantage were considered implicitly by means of a transformation of the betting odds for each game. The model also took into account the goal difference and time evolution. Overall, we found that after a sending off, each team’s scoring rate changes significantly, damaging the penalised team and favouring its opponent. When the player who is sent off belongs to the Away team, the impact of a red card is more or less maintained over time intervals. The red card effect, on the other hand, tends to fade over time when the affected team is stronger. The relative difference in scoring rates is also affected by the goal difference and the difference in booked players, being slightly lower for the team going ahead if it has more booked players. Our approach allows estimating the expected cumulative soring rate through time for various red card scenarios. Particularly if a red card is given with 30 min of remaining time, the expected impact is 0.39 goals if the guilty player is on the visiting team and 0.50 if he plays for the home team. Coaches and analysts could use this information to establish objectives for players and teams in training and matches and to be prepared for these very different scenarios of numerical superiority or inferiority. PubDate: 2022-05-16
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Abstract: Abstract This research investigates the connectedness and the tail risk spillover between clean energy and oil firms, from January 2011 to October 2021. To this, we use the Tail-Event driven NETworks (TENET) risk model. This approach allows for a measurement of the dynamics of tail-risk spillover for each sector and firm. Hence, we can provide a detailed picture of the existing extreme relationships within these markets. We find that the total connection between the markets varies during the period analysed, showing how the uncertainty in oil price plays a critical role in the risk dynamics for oil companies. Also, we find that relationships between energy firms tend to be intrasectoral; that is, each sector receives (emits) risk from (to) itself. These results can have important practical implications for risk management and policymakers. PubDate: 2022-05-15
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Abstract: Abstract We discuss a widely used air traffic flow management formulation. We show that this formulation can lead to a solution where air delays are assigned to flights during their take-off which is prohibited in practice. Although air delay is more expensive than ground delay, the model may assign air delay to a few flights during their take-off to save more on not having as much ground delay. We present a modified formulation and verify its functionality in avoiding incorrect solutions. PubDate: 2022-05-14
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Abstract: Abstract The paper presents a new mixed-integer programming formulation for the maximally diverse grouping problem (MDGP) with attribute values. The MDGP is the problem of assigning items to groups such that all groups are as heterogeneous as possible. In the version with attribute values, the heterogeneity of groups is measured by the sum of pairwise absolute differences of the attribute values of the assigned items, i.e. by the Manhattan metric. The advantage of the version with attribute values is that the objective function can be reformulated such that it is linear instead of quadratic like in the standard MDGP formulation. We evaluate the new model formulation for the MDGP with attribute values in comparison with two different MDGP formulations from the literature. Our model formulation leads to substantially improved computation times and solves instances of realistic sizes (for example the assignment of students to seminars) with up to 70 items and three attributes, 50 items and five attributes, and 30 items and ten attributes to (near) optimality within half an hour. PubDate: 2022-05-12
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Abstract: Abstract Fantasy Sports allows individuals to assemble a virtual team to participate in free or paid tournaments and earn rewards. Selecting a good team forms a crucial decision in fantasy cricket. Existing team selection methods cater only to professional cricket and are not suited well to accommodate the differences between fantasy cricket and the on-field game. This paper proposes a two-step methodology for player assessment and team selection in fantasy cricket. Player assessment is carried out using recursive feature elimination in random forest, in which context relevant player metrics are considered and the selection of players is based on modified genetic algorithm. We illustrate the efficacy of the proposed method on Dream11, a popular fantasy sports application. The results show that the proposed method outshines the traditional team selection process in fantasy sports, which is based on hit and trial. Furthermore, we provide a typology to analyse the proposed algorithm along the dimensions of reward distribution and entry fee. PubDate: 2022-05-12
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Abstract: Abstract The Pearson’s \(X^2\) statistic and the likelihood ratio statistic \(G^2\) are most frequently used for testing independence or homogeneity, in two-way contingency table. These indexes are members of a continuous family of Power Divergence (PD) statistics, but they perform badly in studying the association between ordinal categorical variables. Taguchi’s and Nair’s statistics have been introduced in the literature as simple alternatives to Pearson’s index for contingency tables with ordered categorical variables. It’s possible to show, using a parameter, how to link Taguchi’s and Nair’s statistics obtaining a new class called Weighted Cumulative Chi-Squared (WCCS-type tests). Therefore, the main aim of this paper is to introduce a new divergence family based on cumulative frequencies called Weighted Cumulative Power Divergence. Moreover, an extension of Cumulative Correspondence Analysis based on WCCS and further properties are shown. PubDate: 2022-05-12
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Abstract: Abstract This paper surveys the increasing use of statistical approaches in non-parametric efficiency studies. Data Envelopment Analysis (DEA) and Free Disposable Hull (FDH) are recognized as standard non-parametric methods developed in the field of operations research. Kneip et al. (Econom Theory, 14:783–793, 1998) and Park et al. (Econom Theory, 16:855–877, 2000) develop statistical properties of the variable returns-to-scale (VRS) version of DEA estimators and FDH estimators, respectively. Simar & Wilson (Manag Sci 44, 49–61, 1998) show that conventional bootstrap methods cannot provide valid inference in the context of DEA or FDH estimators and introduce a smoothed bootstrap for use with DEA or FDH efficiency estimators. By doing so, they address the main drawback of non-parametric models as being deterministic and without a statistical interpretation. Since then, many articles have applied this innovative approach to examine efficiency and productivity in various fields while providing confidence interval estimates to gauge uncertainty. Despite this increasing research attention and significant theoretical and methodological developments in its first two decades, a specific and comprehensive bibliometric analysis of bootstrap DEA/FDH literature and subsequent statistical approaches is still missing. This paper thus, aims to provide an extensive overview of the key articles and their impact in the field. Specifically, in addition to some summary statistics such as citations, the most influential academic journals and authorship network analysis, we review the methodological developments as well as the pertinent software applications. PubDate: 2022-05-11
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Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The health care system is characterized by limited resources, including the physical facilities as well as skilled human resources. Due to the extensive fixed cost of medical facilities and the high specialization required by the medical staff, the problem of resource scarcity in a health care supply chain is much more acute than in other industries. In the pandemic of the Coronavirus, where medical services are the most important services in communities, and protective and preventive guidelines impose new restrictions on the system, the issue of resource allocation will be more complicated and significantly affect the efficiency of health care systems. In this paper, the problem of activating the operating rooms in hospitals, assigning active operating rooms to the COVID-19 and non-COVID-19 patients, assigning specialty teams to the operating rooms and assigning the elective and emergency patients to the specialty teams, and scheduling their operations is studied by considering the new constraints of protective and preventive guidelines of the Coronavirus. To address these issues, a mixed-integer mathematical programming model is proposed. Moreover, to consider the uncertainty in the surgery duration of elective and emergency patients, the stochastic robust optimization approach is utilized. The proposed model is applied for the planning of operating rooms in the cardiovascular department of a hospital in Iran, and the results highlight the role of proper management in supplying sufficient medical resources effectively to respond to patients and scheduled surgical team to overcome the pressure on hospital resources and medical staff results from pandemic conditions. PubDate: 2022-05-10
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Abstract: Abstract China’s government has always attached great importance to protecting women and children’s rights and has made a consistent effort to promote their all-round health. The on-going medical reforms in the country have resulted in a falling birth rate and rising demand for increasingly diversified, multi-level healthcare services. Maternal and child health (MCH) institutions have had to implement more extensive changes and deal with greater challenges in order to survive and develop. MCH organizations thus need to find ways to improve their competitiveness. This study integrates qualitative and quantitative business analytics methods by conducting empirical research using varied techniques, namely the Delphi method and analytic hierarchy process (AHP). A panel of experts were consulted in three rounds of surveys, and the results were processed with AHP in order to develop an evaluation indicator model of MCH institutions’ competitive advantages. Ten hospitals were selected to verify the model’s applicability. Sensitivity analysis confirmed that the model has good stability. The proposed model highlights that human resources have the greatest weight as a determinant of competitive advantage. The findings, therefore, concentrate on people as the core resource, suggesting that MCH institutions can acquire and maintain competitive advantages based on three dimensions: department leader development, improved scientific research capacity, and a more flexible hospital culture. PubDate: 2022-05-10
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Abstract: Abstract In this preface, we investigate the past, study the present, and look for the future of financial modeling, risk management of energy and environmental instruments, and derivatives based on articles selected in this special issue (SI). We also summarize the significant findings of those articles and identify the research trends. PubDate: 2022-05-10
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Abstract: Abstract To deal with the destructive environmental effects of organizations, a closed-loop supply chain (CLSP), which has a significant role in the circular economy, has been introduced as one of the effective environmentally friendly tools. For a cohesive CLSP, appropriate supplier selection is a requirement. For this purpose, a three-step research method is used in this research. We start by presenting a hierarchy structure of criteria classified into operational capabilities, system management capabilities, culture capabilities, technological capabilities, human capabilities, sustainable energy consumption, and circular waste management dimensions. Then, we evaluate the performance of suppliers with the best–worst method (BWM). Finally, to provide useful insights about the performance of suppliers and the criteria used, we applied the geometrical analysis for interactive aid (GAIA) plane on the results of the BWM. We evaluated the performance of five suppliers in the pharmaceutical industry by employing this hybrid methodology. On-time delivery was identified as the most important criterion. The visualisation with GAIA permits us to understand the outcomes better and to communicate them. PubDate: 2022-05-09
Hybrid journal (It can contain Open Access articles)
[2469 journals]
