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This paper considers two pertinent research inquiries: ‘Can an AI-based predictive framework be utilised for the optimisation of solar energy management?’ and ‘What are the ways in which the AI-based predictive framework can be integrated within the Smart Grid infrastructure to improve grid reliability and efficiency?’ The study deploys a Deep Learning model based on Long Short-Term Memory techniques, leading to refined accuracy in solar electricity generation forecasts. Such an AI-supported methodology aids power grid operators in comprehensive planning, thereby ensuring a robust electricity supply. The effectiveness of this framework is tested using performance metrics such as MAE, RMSE, nMAE, nRMSE, and R2. A persistent model is utilised as a reference for comparison. Despite a slight decrease in predictive precision with the expansion of the forecast horizon, the proposed AI-based framework consistently surpasses the persistent model, particularly for horizons beyond two hours. Therefore, this research underscores the potential of AI-based prediction in fostering efficient solar energy management and enhancing Smart Grid reliability and efficiency.

Accurate travel time estimation is paramount for providing transit users with reliable schedules and dependable real-time information. This work is the first to utilize roadside urban imagery to aid transit agencies and practitioners in improving travel time prediction. We propose and evaluate an end-to-end framework integrating traditional transit data sources with a roadside camera for automated image data acquisition, labeling, and model training to predict transit travel times across a segment of interest. First, we show how the General Transit Feed Specification real-time data can be utilized as an efficient activation mechanism for a roadside camera unit monitoring a segment of interest. Second, automated vehicle location data is utilized to generate ground truth labels for the acquired images based on the observed transit travel time percentiles across the camera-monitored segment during the time of image acquisition. Finally, the generated labeled image dataset is used to train and thoroughly evaluate a Vision Transformer (ViT) model to predict a discrete transit travel time range (band). The results of this exploratory study illustrate that the ViT model is able to learn image features and contents that best help it deduce the expected travel time range with an average validation accuracy ranging between 80 and 85%. We assess the interpretability of the ViT model’s predictions and showcase how this discrete travel time band prediction can subsequently improve continuous transit travel time estimation. The workflow and results presented in this study provide an end-to-end, scalable, automated, and highly efficient approach for integrating traditional transit data sources and roadside imagery to improve the estimation of transit travel duration. This work also demonstrates the added value of incorporating real-time information from computer-vision sources, which are becoming increasingly accessible and can have major implications for improving transit operati

Aviation industry is extremely vulnerable to economic turbulence or a pandemic. Over the past decade, a number of well-known European airline brands have gone into bankruptcy, with consequences such as leaving thousands of passengers stranded abroad. The present research assesses the predictive power of the updated Altman Z-score model (1983 and 2017) using data on European airline bankruptcies over the period 2009–2020. The results indicate that the Z″-score (2017) for private non-manufacturing companies shows satisfactory predictive power when applied on European aviation industry. In this study, we analyze in depth the performance of the updated Altman Z′- and Z″-scores (1983, 2017), connecting airline's financial and non-financial information, and provide a comprehensive interpretation of Z-score predictive power. We aim to contribute to the literature by offering a unique and novel perspective on the Altman model's accuracy for the European air transport industry.

In an age where e-commerce can provide a huge variety of different products online, customers still face the issue of the last mile challenge. The purpose of this paper is to find out if the last-mile delivery of products is efficient and explore the possible improvement to this service. This research shows that home delivery is the preferred method of last-mile delivery. A significant gap was disclosed between the available options for delivery on e-commerce websites and consumer preferences of last-mile delivery. Time of delivery and accuracy are the greatest barriers in the delivery chain. The research also shows that there is a lack of home delivery services provided in the market. The research is limited by the usage of non-probability sampling and equal distribution of respondents of all ages. The research identifies a clear gap between customers’ demands in the last mile and the firms’ offerings. © 2021 Informa UK Limited, trading as Taylor & Francis Group

Transportation planning and mobility management are key for the transition towards sustainable cities. Research on tourists' transport modal choices at destination does not tend to analyse urban areas and instead sticks to one mode of transport. This study empirically testes a theoretical framework on tourists' transport modal choices for city destinations, and identifies the determinants that prompt tourists to choose among the full range of transport options. Based on a survey answered by tourists who visited Barcelona, multinomial logit regressions indicate that the transport mode used to reach the city, the characteristics of both the trip and the tourists, and the intensity of the visit within the destination are highly correlated with the mode selected. Compared to public transport users, those using tourist buses look for rapid access to attractions, those cycling are more interested in visiting the city in a flexible manner and those walking, using taxis and private vehicles have less interest in visiting attractions. This study suggests that the strategy to adopt to foster the use of sustainable modes of transport among visitors should also be considered in connection with targeting their profile.

Numerous studies suggest that a relationship exists between certain personality variables and entrepreneurial orientation (EO). The aim of this study is to conduct a predictive study of personality variables that may influence entrepreneurial orientation. To do so, we use the following indicators: big five personality, hardiness, self-esteem, self-efficacy, impulsivity, pro-activeness, curiosity, internal locus control, optimism and pessimism. Participants in the study were 883 workers selected through non-probabilistic sampling and the data collected were processed with the SPSS 25.0 program. Our results determine the predictive capacity of personality variables, curiosity, and optimism in relation to EO. We conclude that OE can be predicted through the variables openness to experience, proactivity, curiosity-I, optimism and internal locus of control that explain greater variance when predicting entrepreneurial behaviour. This study contributes to wider knowledge of entrepreneurial orientation and the empowerment of variables that influence it.

We conduct an international analysis of the cross-sectional risk premiums of uncertainty risk factors in addition to traditional risk factors. We consider international stock markets in five regions separately. We measure uncertainty by the local and US economic policy uncertainty indices. Economic policy uncertainty risk has negative risk premiums. This implies that investors get lower returns for assets with high uncertainty betas. We further analyze a nonlinear relationship between excess returns and uncertainty risk by adding the downside economic policy uncertainty risk factor which captures high levels of uncertainty, similar to downside market risk. The downside uncertainty risk factor has negative risk premiums.

We derive new prox-functions on the simplex from additive random utility models of discrete choice. They are convex conjugates of the corresponding surplus functions. In particular, we explicitly derive the convexity parameter of discrete choice prox-functions associated with generalized extreme value models, and specifically with generalized nested logit models. Incorporated into subgradient schemes, discrete choice prox-functions lead to a probabilistic interpretations of the iteration steps. As illustration, we discuss an economic application of discrete choice prox-functions in consumer theory. The dual averaging scheme from convex programming adjusts demand within a consumption cycle.

Dynamic capabilities such as flexibility are considered influential in achieving superior performance, especially under uncertain circumstances. Among others, postponement is a well-established concept in operations and supply chain management (OSCM) and has been regarded as a key concept in managing supply and demand while increasing flexibility. This study investigates the effect of postponement on logistics flexibility, and that of the latter on retail firm performance. In addition, the moderating roles of logistics integration and demand uncertainty on these relationships are investigated. The study utilizes a quantitative survey and draws on a sample of 261 retailers in Sweden. Logistics flexibility proves to have a mediating role in the postponement–performance relationship. Furthermore, we provide support for the direct effect that postponement can have on logistics flexibility, and the subsequent effect of logistics flexibility on retail firm performance. We contribute by simultaneously studying postponement and logistics flexibility in the retailing context. We find conditional support for the moderating roles of logistics integration and demand uncertainty. The results show that for medium levels of uncertainty, the positive relationship between postponement and logistics flexibility, as well as logistics flexibility and firm performance, are intensified. From a practical standpoint, the findings underline that in the presence of high or low demand uncertainty, applying postponement may not always be beneficial in achieving logistics flexibility, and subsequently better performance. Moreover, if retailers prioritize logistics integration, they should not always expect superior performance gains from the flexibility benefits of postponement.

This contribution has three objectives. First, it seeks to justify the use of the economic criterion, “Provision of health care in accordance with the preferences of current and potential patients” for guiding decisions concerning the adoption of costly innovation in health. Next, it proposes the measurement of these preferences in the guise of willingness to pay (WTP) values through Discrete Choice Experiments (DCEs). Third, it purports to examine two popular arguments against accepting lay persons´ preferences, viz. that they are unwilling or unable to express preferences with regard to health and health care, and that their preferences are unstable, depending on the current state of health. Both of these arguments are refuted by the findings of four DCEs designed to measure WTP for attributes of health insurance and of the treatment of diabetes, respectively [Zweifel in J Regul Econ 29(3): 319–332, 2006; MacNeil Vrooman and Zweifel in Eur J Health Econ 12(1): 87–95, 2011; Sennhauser and Zweifel in: Jakovlijevic M (ed.), Health Economics and Policy Challenges in Global Emerging Markets. NOVA Publishers, Hauppauge NY, 2016].

We propose to allocate the cost of a minimum cost spanning tree by defining a claims problem and using claims rules, then providing easy and intuitive ways to distribute this cost. Depending on the starting point that we consider, we define two models. On the one hand, the benefit-sharing model considers individuals' costs to the source as the starting point, and then the benefit of building the efficient tree is shared by the agents. On the other hand, the costs-sharing model starts from the individuals' minimum connection costs (the cheapest connection they can use), and the additional cost, if any, is then allocated. As we prove, both approaches provide the same family of allocations for every minimum cost spanning tree problem. These models can be understood as a central planner who decides the best way to connect the agents (the efficient tree) and also establishes the amount each agent has to pay. In so doing, the central planner takes into account the maximum and minimum amount they should pay and some equity criteria given by a particular (claims) rule. We analyze some properties of this family of cost allocations, specially focusing in coalitional stability (core selection), a central concern in the literature on cost allocation.

We compare the performance of various advanced forecasting techniques, namely artificial neural networks, k-nearest neighbors, logistic regression, Naive Bayes, random forest classifier, support vector machine, and extreme gradient boosting classifier to predict stock price movements based on past prices. We apply these methods with the high frequency data of 27 blue-chip stocks traded in the Istanbul Stock Exchange. Our findings reveal that among the selected methodologies, random forest and support vector machine are able to capture both future price directions and percentage changes at a satisfactory level. Moreover, consistent ranking of the methodologies across different time frequencies and train/test set partitions prove the robustness of our empirical findings.

Since the start of COVID-19 pandemic, public transport has been signalled as a potential contagion hot-spot, leading to a generalised decrease in its use. However, public transport use is still being used and little is known about how the perception of loyal users is configured in contexts of influenza-like viruses such as SARS-CoV-2. The configuration of the perception of safety acquires a critical importance in urban contexts where the public transport system is used both by tourists and local users. The presence of strangers or higher crowding levels could impact the perception of safety among residents and their consequent travel behaviour. In the present study, we explored how the presence of tourists influences the configuration of the perception safety related to the transmission of COVID-19 on public transport of daily users. We used data from an ad-hoc survey conducted at the main bus stations and stops of the Tarragona Urban Area (Catalonia, Spain) between August and September 2020. This area includes the Costa Daurada coastal destination. The 2020 summer holiday season was characterised for the relaxation of mobility restrictions and the start of the second wave of COVID-19. Results show how the presence of tourists in buses negatively influenced the perception of safety of local users. However, this influence can be mostly explained to their prior perception of risk of contagion. These findings will be useful for policymakers and public transport managers in both the latter stages of the COVID-19 pandemic and future virus-related epidemics to maintain public transport ridership.© 2022 The Authors.

© 2017 Springer Science+Business Media, LLC This paper considers the problem of finding suitable sites for wind farms in a region of Catalonia (Spain). The evaluation criteria are structured into a hierarchy that identifies several intermediate sub-goals dealing with different points of view. Therefore, the recent ELECTRE-III-H hierarchical multi-criteria analysis method is proposed as a good solution to help decision-makers. This method establishes an order among the set of possible sites for the wind farms for each sub-goal. ELECTRE-III-H aggregates these orders into an overall order using different parameters. The procedure is based on the construction and exploitation of a pairwise outranking relation, following the principles of concordance (i.e. majority rule) and discordance (i.e. respect for the minority opinions). This paper makes two main contributions. First, it contributes to the ELECTRE-III-H method by studying its mathematical properties for the construction of outranking relations. Second, the case study is solved and its results show that we can effectively represent and manage the overall influence of the various criteria on the global result at different levels of the hierarchy. The paper compares different scenarios with strict, normal, and optimistic preference, indifference and veto thresholds. Results show that the best site differs for technical, economic, environmental, and social intermediate criteria. Therefore, the best overall solution changes depending on the preference and veto thresholds fixed at the intermediate level of the hierarchy.

High-quality product support is vital for production, because utility can only be generated by working equipment and system downtime can cause serious consequences. Thus, the suppliers are often required to provide efficient product maintenance and repair services for customers. Component obsolescence is a challenging factor for spare parts management which affects the overall system performance. In this paper, we build a game-theoretic model to study joint contracting on product order quantity and after-sales service in a B2B supply chain, where spare part availability is affected by obsolescence risk, and the after-sales service is operated under the warranty and transaction-based contracts (W&T) and the performance-based contracts (PBC). Our model provides advice for practitioners on how to choose a service contract type: Given a low (high) cost of lifetime-buy of parts, W&T can lead to a higher (lower) supply chain profit than PBC when the warranty period is sufficiently long. Furthermore, we find that PBC can result in higher product availability; however, this may be achieved by overinvestment in spare parts when the supplier is penalised on the buyer's lost revenue, and in such a setting the buyer may order fewer products, which leads to lower profits for the supplier.

Many durable products are now servicized with outcome-based contracts under which customers pay for uptime. Outcome-based contracting poses challenges to product support management, because the customers’ losses due to system outages result in penalties for the OEM. Moreover, in many cases product failures are caused by defective subsystems or components that are manufactured by the OEM’s suppliers. Not involving suppliers in product support leads to a lack of incentives for product reliability improvement and thus higher failure rates, which increases product downtime risk. In this paper, we study how the OEM should incentivize its suppliers to improve product reliability under outcome-based contracts. We find that the more recent repair cost sharing contracts perform better than the traditional price-only contracts. However, in the presence of outcome-based contracting, sharing repair cost results in underinvestment in product reliability and overinvestment in repair capacity, which leads to lower system uptime. We then propose a downtime penalty sharing contract which can optimally incentivize the upstream suppliers to improve product reliability, regardless of the repair capacity structure or product separability into constituent parts.

Manufacturing data and information are produced and used during the lifecycle of product development. Product lifecycle management (PLM) systems provide a suitable platform for managing them. For appropriate management of manufacturing data, it needs to be identified, classified, and stored based on the structure of PLM systems. In this paper, the results of an industrial manufacturing data collection study are interpreted, and their relation to the main structures in PLM systems is specified. Subsequently, a new information model for assigning this data and information to the PLM data model is presented. The main contribution of this information model is the definition of property and change objects and integrating them with the structure of PLM systems; changes and revisions of those data are formally defined and hence traceable., Online publication date: Fri, 08-Oct-2021The full text of this article is only available to individual subscribers or to users at subscribing institutions., KDDS

Manufacturing data and information are produced and used during the lifecycle of product development. Product lifecycle management (PLM) systems provide a suitable platform for managing them. For appropriate management of manufacturing data, it needs to be identified, classified, and stored based on the structure of PLM systems. In this paper, the results of an industrial manufacturing data collection study are interpreted, and their relation to the main structures in PLM systems is specified. Subsequently, a new information model for assigning this data and information to the PLM data model is presented. The main contribution of this information model is the definition of property and change objects and integrating them with the structure of PLM systems; changes and revisions of those data are formally defined and hence traceable., Online publication date: Fri, 08-Oct-2021The full text of this article is only available to individual subscribers or to users at subscribing institutions., KDDS

It is known that an eco-efficiency strategy, which saves resources in the production process, may be offset by a rebound effect; it may even backfire. Less known are the exact conditions under which eco-efficiency rebounds or backfires. This article fills the gap by providing an analytical model of the rebound and backfire effects. We propose an optimal control framework of dynamic pricing and eco-efficiency investment, for which eco-efficiency reduces the unit production cost and boosts the demand of environmentally concerned consumers. Results, which hold with a general demand formulation, examine the analytic conditions for the rebound and backfire effects. They also highlight the possibility of a reverse rebound effect. Such results pave the way to sounder sustainability strategies.

We study the joint determination of optimal investment and optimal depollution in a spatiotemporal framework where pollution is transboundary. Pollution is controlled at a global level. The regulator internalizes that: (i) production generates pollution, which is bad for the wellbeing of population, and that (ii) pollution flows across space driven by a diffusion process. We solve analytically for the optimal investment and depollution spatiotemporal paths and characterize the optimal long-term spatial distribution when relevant. We finally explore numerically the variety of optimal spatial distributions obtained using a core/periphery model where the core differs from the periphery either in terms of input productivity, depollution efficiency or self-cleaning capacity of nature. We also compare the distributions with and without diffusion. Key aspects in the optimal policy of the regulator are the role of aversion to inequality, notably leading to smoothing consumption across locations, and the control of diffusive pollution adding another smoothing engine

Policymakers during COVID-19 operate in uncharted territory and must make tough decisions. Operational Research – the ubiquitous ‘science of better’ – plays a vital role in supporting this decision-making process. To that end, using data from the USA, India, UK, Germany, and Singapore up to mid-April 2020, we provide predictive analytics tools for forecasting and planning during a pandemic. We forecast COVID-19 growth rates with statistical, epidemiological, machine- and deep-learning models, and a new hybrid forecasting method based on nearest neighbors and clustering. We further model and forecast the excess demand for products and services during the pandemic using auxiliary data (google trends) and simulating governmental decisions (lockdown). Our empirical results can immediately help policymakers and planners make better decisions during the ongoing and future pandemics.

In this paper, we study the iteration complexity of cubic regularization of Newton method for solving composite minimization problems with uniformly convex objective. We introduce the notion of second-order condition number of a certain degree and justify the linear rate of convergence in a nondegenerate case for the method with an adaptive estimate of the regularization parameter. The algorithm automatically achieves the best possible global complexity bound among different problem classes of uniformly convex objective functions with Hölder continuous Hessian of the smooth part of the objective. As a byproduct of our developments, we justify an intuitively plausible result that the global iteration complexity of the Newton method is always better than that of the gradient method on the class of strongly convex functions with uniformly bounded second derivative.

Manufacturing data and information are produced and used during the lifecycle of product development. Product lifecycle management (PLM) systems provide a suitable platform for managing them. For appropriate management of manufacturing data, it needs to be identified, classified, and stored based on the structure of PLM systems. In this paper, the results of an industrial manufacturing data collection study are interpreted, and their relation to the main structures in PLM systems is specified. Subsequently, a new information model for assigning this data and information to the PLM data model is presented. The main contribution of this information model is the definition of property and change objects and integrating them with the structure of PLM systems; changes and revisions of those data are formally defined and hence traceable., Online publication date: Fri, 08-Oct-2021The full text of this article is only available to individual subscribers or to users at subscribing institutions., KDDS

Manufacturing data and information are produced and used during the lifecycle of product development. Product lifecycle management (PLM) systems provide a suitable platform for managing them. For appropriate management of manufacturing data, it needs to be identified, classified, and stored based on the structure of PLM systems. In this paper, the results of an industrial manufacturing data collection study are interpreted, and their relation to the main structures in PLM systems is specified. Subsequently, a new information model for assigning this data and information to the PLM data model is presented. The main contribution of this information model is the definition of property and change objects and integrating them with the structure of PLM systems; changes and revisions of those data are formally defined and hence traceable., Online publication date: Fri, 08-Oct-2021The full text of this article is only available to individual subscribers or to users at subscribing institutions., KDDS

Manufacturing data and information are produced and used during the lifecycle of product development. Product lifecycle management (PLM) systems provide a suitable platform for managing them. For appropriate management of manufacturing data, it needs to be identified, classified, and stored based on the structure of PLM systems. In this paper, the results of an industrial manufacturing data collection study are interpreted, and their relation to the main structures in PLM systems is specified. Subsequently, a new information model for assigning this data and information to the PLM data model is presented. The main contribution of this information model is the definition of property and change objects and integrating them with the structure of PLM systems; changes and revisions of those data are formally defined and hence traceable., Online publication date: Fri, 08-Oct-2021The full text of this article is only available to individual subscribers or to users at subscribing institutions., KDDS

At the moment, urban mobility research and governmental initiatives are mostly focused on motor-related issues, e.g. the problems of congestion and pollution. And yet, we cannot disregard the most vulnerable elements in the urban landscape: pedestrians, exposed to higher risks than other road users. Indeed, safe, accessible, and sustainable transport systems in cities are a core target of the UN?s 2030 Agenda. Thus, there is an opportunity to apply advanced computational tools to the problem of traffic safety, in regards especially to pedestrians, who have been often overlooked in the past. This paper combines public data sources, large-scale street imagery and computer vision techniques to approach pedestrian and vehicle safety with an automated, relatively simple, and universally-applicable data-processing scheme. The steps involved in this pipeline include the adaptation and training of a Residual Convolutional Neural Network to determine a hazard index for each given urban scene, as well as an interpretability analysis based on image segmentation and class activation mapping on those same images. Combined, the outcome of this computational approach is a fine-grained map of hazard levels across a city, and an heuristic to identify interventions that might simultaneously improve pedestrian and vehicle safety. The proposed framework should be taken as a complement to the work of urban planners and public authorities.

The field of 'women in entrepreneurship' is growing attention from public policy and academia. However, the literature has been dominated by models and frameworks that were developed mostly in developed economies with a set of particular characteristics that do not match the Latin American reality. This conceptual paper identifies five contextual conditions to consider while analysing women's entrepreneurship in Latin America compared to the Northern Hemisphere: 1) high rates of women's entrepreneurship; 2) high rates of informality; 3) traditional culture and self-expression; 4) low rates of innovation-based ventures; 5) developing ecosystems. We discuss how these contextual conditions interact with the COVID-19 pandemic and economic crisis. Finally, both a research agenda and a public agenda are suggested. This article calls for a Latin American research perspective on women entrepreneurs and suggests that promoting women into sustainable entrepreneurship contributes to regional development, impacts economic activity, improves income distribution, reduces poverty and fosters gender equality.

Based on a systematic review of SCF literature and the information processing theory, this paper builds an integrated conceptual framework to illustrate how SCF providers deal with uncertainties, develop capabilities and further achieve whole financial supply chain (SC) integration. It shows that uncertainties faced by SCF providers (including SCF task characteristics, SCF task environment and SCF task independence) delineate the information processing requirements. To meet these requirements, SCF providers could improve information processing capacity by enabling some mechanisms (i.e., organisation structure design, coordination and control, and information technology). The fit between SCF providers’ information processing requirements and capacity could further improve SCF capability, which would help to achieve integration of the whole financial SC. Building on this framework, seven future research directions are provided.

We study diffusion on a multiplex network where the contact dynamics between the nodes is governed by a random process and where the waiting-time distribution differs for edges from different layers. We study the impact on a random walk of the competition that naturally emerges between the edges of the different layers. In opposition to previous studies, which have imposed a priori inter-layer competition, the competition is here induced by the heterogeneity of the activity on the different layers. We first study the precedence relation between different edges and by extension between different layers, and show that it determines biased paths for the walker. We also discuss the emergence of cyclic, rock–paper–scissors effects on random walks, when the precedence between layers is non-transitive. Finally, we numerically show the slowing-down effect due to the competition on a multiplex network with heterogeneous layers activity as the walker is likely to be trapped for a longer time either on a single layer, or on an oriented cycle.

A single price for the European gas market has been the ultimate goal for European countries. Deregulation of the gas market started in Europe in the late 1990s and three European packages for the creation of a single market for natural gas and electricity have already been issued. The aim of this paper is twofold: to verify whether natural gas prices are converging into a single price in Europe and to identify the reference trading hub for the European market. We study the evolution of natural gas prices during the last decade (2007–2017) at various trading hubs with the goal of identifying their level of integration. We examine the integration by testing for the presence of a common stochastic trend among the prices reported at the hubs. In order to detect the reference hub we test for a lead lag relationship between each pair of trading hubs. Our results show a high level of integration among the European trading hubs with the Dutch hub, TTF, playing the role of the reference trading hub.

Nonparametric data envelopment analysis and free-disposal hull estimators are frequently used to estimate cost, revenue and profit efficiency as well as the corresponding allocative efficiencies. Papers in the literature often report sample means of such estimates along with sample standard deviations, inviting readers to make inference about means of these efficiencies using classical methods based on the standard Lindeberg–Feller central limit theorem (CLT). A number of papers explicitly make inference using the classical methods. However, the statistical properties of these estimators are (until now) unknown. This paper establishes rates of convergence and existence of limiting distributions for the various estimators. These properties are needed in order to make inference about individual producers using subsampling methods. In addition, properties of the first two moments of the estimators are derived, and these results are subsequently used to establish new CLTs for the estimators, providing formal justification for inference-making. The results reveal that the classical CLTs and methods do not provide valid inference when FDH estimators are used, and provide valid inference when DEA estimators only in a few restrictive, special cases.

The concept of cost efficiency has become tremendously popular in data envelopment analysis (DEA) as it serves to assess a decision-making unit (DMU) in terms of producing minimum-cost outputs. A large variety of precise and imprecise models have been put forward to measure cost efficiency for the DMUs which have a role in constructing the production possibility set; yet, there’s not an extensive literature on the cost efficiency (CE) measurement for sample DMUs (SDMUs). In an effort to remedy the shortcomings of current models, herein is introduced a generalized cost efficiency model that is capable of operating in a fuzzy environment-involving different types of fuzzy numbers-while preserving the Farrell’s decomposition of cost efficiency. Moreover, to the best of our knowledge, the present paper is the first to measure cost efficiency by using vectors. Ultimately, a useful example is provided to confirm the applicability of the proposed methods.

We introduce the noncooperative fixed charge transportation problem (NFCTP), which is a game-theoretic extension of the fixed charge transportation problem. In the NFCTP, competing players solve coupled fixed charge transportation problems simultaneously. Three versions of the NFCTP are discussed and compared which differ in the treatment of shared social costs. This may be used from central authorities in order to find a socially balanced framework which is illustrated in a numerical study. Using techniques from generalized Nash equilibrium problems with mixed-integer variables we show the existence of Nash equilibria for these models and examine their structural properties. Since there is no unique equilibrium for the NFCTP, we also discuss how to solve the Nash selection problem and, finally, propose numerical methods for the computation of Nash equilibria which are based on mixed-integer programming.

Bulk-service multi-server queues with heterogeneous server capacity and thresholds are commonly seen in several situations such as passenger transport or package delivery services. In this paper, we develop a novel decomposition-based solution approach for such queues using arguments from renewal theory. We then obtain the distribution of the waiting time measure for multi-type server systems. We also obtain other useful performance measures such as utilization, expected throughput time, and expected queue lengths.

© 2020, Maria-Victòria Sánchez-Rebull, Ramon Ferrer-Rullan, Ana-Beatriz Hernández-Lara and Angels Niñerola. Purpose: Cash flow deficit situations and working capital control are major challenges for many companies, especially those whose suppliers and clients have strong bargaining power. This study aims to describe the application of the Six Sigma methodology for solving these problems in a large German food can manufacturing company. Design/methodology/approach: This paper follows the qualitative methodology of case study research. During different define, measure, analyse, improve and control process phases, the problem and critical aspects are identified to improve the quality of the payment process and improvements are suggested and implemented. Findings: The results provide evidence of how Six Sigma can be useful in administrative–financial processes that are carried out within a company. This result is particularly interesting because it is about processes that have not applied Six Sigma methodology. For the company studied, this methodology has balanced its cash flow and this meant large amounts of savings, especially in bank interest to avoid having to ask for bank credits. Originality/value: This case can be extrapolated to other companies, regardless of the company size, that present similar symptoms of cash deficit, especially if their bargaining power with suppliers and customers is low.

© 2020 The Authors This paper analyses the impact that the lockdown decreed by the Spanish Government to combat the spread of COVID-19 has had on traffic accidents in Tarragona province (Spain). During the studied period of the lockdown (March 16 - April 26 2020) the number of accidents per day fell by 74,3% in coparison with those in February 14-20 (reference week) and 76% in respect to the equivalent period in 2018-2019. This reduction of accidents has been higher than the decrease of mobility during the same reference period (62.9%). This suggests a multiplicative positive effect of traffic reduction on roads safety. Our findings provide new evidences of the disruptive effect of the COVID-19 pandemic on transportation and of how it could be used as a catalyst to promote more sustainable and secure transport systems.

© 2020 Elsevier Ltd Natural catastrophes with their widespread damage can overwhelm the financial systems of large communities. Catastrophe insurance is a well-understood financial risk transfer mechanism, aiming to provide resilience in the face of adversity. However, catastrophe insurance has generally a low penetration, mainly due to its high cost or to distrust of the product in providing a fast financial recovery. Parametric insurance is a form of derivative insurance that pays quickly and transparently based on a few measurable features of the event, offering a promising avenue to increase catastrophe insurance coverage. In the context of seismic risk, parametric policies may use location and magnitude of an earthquake to determine whether a payment should be made. In this paper we follow a design typology referred to as ‘cat-in-a-box’, where magnitude thresholds are defined over a set of cuboids that partition Earth's crust. The main challenge in the design of these tools consists in finding the optimal magnitude thresholds for a large set of cubes that maximize efficiency for the insured, subjected to a budgetary constraint. Additional geometric constraints aim to reduce the volatility of payments under uncertainty. The parametric design problem is a combinatorial problem, which is NP-hard and large scale. In this paper we propose a fast heuristic and a biased-randomized algorithm to solve large-sized problems in reasonably low computing times. Experimental results illustrate the computational limits and solution quality associated with the proposed approaches.

Conditional efficiency measures are needed when the production process does not depend only on the inputs and outputs, but may be influenced by external factors and/or environmental variables (Z). They are estimated by means of a nonparametric estimator of the conditional distribution function of the inputs and outputs, conditionally on values of Z. For doing this, smoothing procedures and smoothing parameters, the bandwidths, are involved. So far, Least Squares Cross Validation (LSCV) methods have been used, which have been proven to provide bandwidths with optimal rates for estimating conditional distributions. In efficiency analysis, the main interest is in the estimation of the conditional efficiency score, which typically depends on the boundary of the support of the distribution and not on the full conditional distribution. In this paper, we show indeed that the rate for the bandwidths which is optimal for estimating conditional distributions, may not be optimal for the estimation of the efficiency scores. We propose hence a new approach based on the bootstrap which overcomes these difficulties. We analyze and compare, through Monte Carlo simulations, the performances of LSCV techniques with our bootstrap approach in finite samples. As expected, our bootstrap approach shows generally better performances and is more robust to the various Monte Carlo scenarios analyzed. We provide in an Appendix the Matlab code performing our experiments.

Recently, the number of machine learning based water demand forecasting solutions has been significantly increasing. Different case studies have already reported practical results proving that accurate forecasts may support optimization of operations in Water Distribution Networks (WDN). However, tuning the hyper-parameters of machine leaning algorithms is still an open problem.This paper proposes a parallel global optimization model to optimize the hyperparameters of Support Vector Machine (SVM) regression trained to provide accurate water demand forecasts in the short-time horizon (i.e. 24 h). Every SVM has the first 6 hourly water consumptions as input features and a specific hourly water demand as target to be predicted, among the remaining 18. The Mean Average Percentage Error (MAPE), computed on leave-one-out validation, is the black-box objective function optimized.Moreover, a preliminary time-series clustering has been applied in order to evaluate if this can improve the accuracy of the forecasting mechanism. Time-series clustering implies that the overall number of SVMs, whose hyperparameters are optimized through parallel global optimization, increases, with a SVM trained for each cluster identified and for each hourly water demand to be predicted, making even more critical a quick tuning of the hyperparameters.Results on the urban water demand data in Milan prove that forecasting error is significantly low and that preliminary clustering allows for further reducing error while also improving computational performances

This paper addresses the traveling salesman problem with drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years reflecting the recent trends in a new delivery method among logistics companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic population management and adaptive diversity control based on a split algorithm, problem-tailored crossover and local search operators, a new restore method to advance the convergence and an adaptive penalization mechanism to dynamically balance the search between feasible/infeasible solutions. The computational results show that the proposed algorithm outperforms two existing methods in terms of solution quality and improves many best known solutions found in the literature. Moreover, various analyses on the impacts of crossover choice and heuristic components have been conducted to investigate their sensitivity to the performance of our method.

While the interest in both collaborative logistics and location-routing has grown considerably, horizontal cooperation in location-routing problems remains fairly unattended. This article studies several variants of the location-routing problem using a cooperative game-theoretic framework. The authors derive characteristics in terms of subadditivity, convexity, and non-emptiness of the core. Moreover, for some of the game variants, it is shown that for facility opening costs substantially larger than the costs associated with routing, the core is always nonempty. The theoretical results are supported by numerical experiments aimed at illustrating the properties and deriving insights. Among others, it is observed that, while in general it is not possible to guarantee core allocations, in a huge majority of cases the core is nonempty.

Recently, the number of machine learning based water demand forecasting solutions has been significantly increasing. Different case studies have already reported practical results proving that accurate forecasts may support optimization of operations in Water Distribution Networks (WDN). However, tuning the hyper-parameters of machine leaning algorithms is still an open problem.This paper proposes a parallel global optimization model to optimize the hyperparameters of Support Vector Machine (SVM) regression trained to provide accurate water demand forecasts in the short-time horizon (i.e. 24 h). Every SVM has the first 6 hourly water consumptions as input features and a specific hourly water demand as target to be predicted, among the remaining 18. The Mean Average Percentage Error (MAPE), computed on leave-one-out validation, is the black-box objective function optimized.Moreover, a preliminary time-series clustering has been applied in order to evaluate if this can improve the accuracy of the forecasting mechanism. Time-series clustering implies that the overall number of SVMs, whose hyperparameters are optimized through parallel global optimization, increases, with a SVM trained for each cluster identified and for each hourly water demand to be predicted, making even more critical a quick tuning of the hyperparameters.Results on the urban water demand data in Milan prove that forecasting error is significantly low and that preliminary clustering allows for further reducing error while also improving computational performances