Dispatching and routing of emergency vehicles in disaster mitigation using data fusion

The aim of this paper is to develop a robust methodology for the dispatching and routing of emergency vehicles (EVs) in a post-disaster environment with the support of data fusion. In this work, we consider an earthquake scenario with a large number of casualties needing medical attention. Given an influx of information (regarding casualties, road, traffic conditions, etc.), data are fused to provide estimates of the entities under consideration. We use this information to dispatch and route EVs to casualty pickup locations, followed by delivery to appropriate hospitals. Key factors here include patient priorities, clustering criteria, and distance. Similarly, factors affecting the dispatching of EVs from patient locations to hospitals include waiting time at hospital emergency rooms, hospital capacity, and distance. Routes must be generated for EVs by accounting for real-world road networks, existing road damage, congestion, and related issues. We develop a dispatching and routing simulation model, and utilize a case study to evaluate the performance of our proposed methodology.     

Data center supply chain configuration design: A two-stage decision approach

Data centers are special-purpose facilities that enable customers to perform cloud based real-time online transactions and rigorous computing operations. Service levels of data center facilities are characterized by response time between query and action, which to a large extent depends on data center location and data travel distance. Another aspect of service level is resource up-time availability, which is determined by data center configuration. Data center location and configuration decisions are, therefore, of great significance to ensure uninterrupted operations in customers of manufacturing and service industries relying on cloud-based computing resources. In this study, following a grid-based location approach, we present two mixed integer linear programming models for capacitated single-source data center location-allocation problems. The first model provides optimal locations, capacities and configurations of data centers, and allocation of demands to open facilities when there is no existing facilities in the region. Our second model considers the decision problem of meeting new demand when the existing demand is met by the already opened facilities. We term these newly arrived demand as replication demand, which results either from emergence of new users of existing customers at distant locations in the future, or as a means of increasing data resilience by creating data replication as a backup. To solve the decision problem for meeting primary and replication demand optimally, we propose a two-stage decision algorithm. The algorithm provides optimal locations, capacities and configurations for new data centers, capacity addition decisions to the existing facilities and subsequent allocation of demands. Both models and solution algorithm are implemented using AMPL programming language and solved with CPLEX solver. The models are found to be scalable and capable to provide high quality solutions in reasonable time.     

The Effect of Exchange Rate Changes on Geographic Segment Earnings of U.S.-Based Multinationals

This study examines the ability of exchange rate changes to aid in the explanation and prediction of the geographic segment earnings of U.S.-based multinational corporations. In order to evaluate the potential usefulness of geographic segment earnings disclosures, it is important to understand the effect that currency changes should be expected to have on these earnings. Two types of exchange rate effects are examined. These include the mechanical translation effects of an exchange rate change, as well as the operating effects. A sample of geographic segment earnings disclosures is developed for two geographic locations (Canada and Europe) and six industries in which adequate country-industry specific subsamples can be identified. Because the factors that impact exchange rate exposure tend to depend on country, industry, or both, provision is made for country, industry, and country-industry specific sensitivity to currency changes. Regressions and nonparametric analyses are performed to determine whether exchange rate changes can help explain the geographic segment earnings within these samples. Results indicate that the accounting-based translation effect that is typically modeled as the impact of exchange rate changes on earnings does not adequately capture the effect of exchange rate changes on segment earnings. Operating effects of past and current exchange rate changes do help explain earnings for geographic segments located in Europe and in four industries. Prediction models which are country or country-industry specific are able to outperform a random-walk prediction of geographic segment earnings in some circumstances.     

Knowledge Spillovers and Alliance Formation

Our study examines the conditions under which firms enter into strategic alliances subsequent to knowledge spillovers. We propose that spillovers serve as signals of knowledge dependence and potential complementarity, encouraging alliance formation to enable better learning and limit appropriation. We posit that the likelihood of a knowledge alliance subsequent to a spillover is contingent on the specialization of each of the firms in the knowledge involved in the spillover. We also hypothesize that the effects of such specialization on knowledge alliance formation are moderated by technological ties and geographic distance between the dyad involved in the spillover. Our results demonstrate significance for the effects of specialization, supporting learning and appropriation motivations. Technological ties strengthen while geographic distance weakens the relationship between specialization and alliance formation.     

Design of financial incentives and payment schemes in healthcare systems: A review

In this paper, we review the existing studies on the design of financial incentives and payment mechanisms for healthcare service providers, which have been published in the literature of operations research & management science. We classify these studies in two ways: first, by the care provider focused on: physicians or hospitals; and next, by categorizing payment/incentive schemes as retrospective or prospective models under each care provider group. The problem setting, including the geographic background and research methodology, are briefly discussed for each paper, as are the findings with regard to accessibility, quality, efficiency, and the facilitation of integration and collaboration, if reported. We also provide a detailed discussion of potential research problems that would offer promising avenues for future study.     

Empirical Tests of the Pollution Haven Hypothesis When Environmental Regulation is Endogenous

The pollution haven hypothesis (PHH) posits that production within polluting industries will shift to locations with lax environmental regulation. While straightforward, the existing empirical literature is inconclusive owing to two shortcomings. First, unobserved heterogeneity and measurement error are typically ignored due to the lack of a credible, traditional instrumental variable for regulation. Second, geographic spillovers have not been adequately incorporated into tests of the PHH. We overcome these issues utilizing two novel identification strategies within a model incorporating spillovers. Using US state‐level data, own environmental regulation negatively impacts inbound foreign direct investment. Moreover, endogeneity is both statistically and economically relevant. Copyright © 2015 John Wiley & Sons, Ltd.     

Information criteria for impulse response function matching estimation of DSGE models

We propose new information criteria for impulse response function matching estimators (IRFMEs). These estimators yield sampling distributions of the structural parameters of dynamic stochastic general equilibrium (DSGE) models by minimizing the distance between sample and theoretical impulse responses. First, we propose an information criterion to select only the responses that produce consistent estimates of the true but unknown structural parameters: the Valid Impulse Response Selection Criterion (VIRSC). The criterion is especially useful for mis-specified models. Second, we propose a criterion to select the impulse responses that are most informative about DSGE model parameters: the Relevant Impulse Response Selection Criterion (RIRSC). These criteria can be used in combination to select the subset of valid impulse response functions with minimal dimension that yields asymptotically efficient estimators. The criteria are general enough to apply to impulse responses estimated by VARs, local projections, and simulation methods. We show that the use of our criteria significantly affects estimates and inference about key parameters of two well-known new Keynesian DSGE models. Monte Carlo evidence indicates that the criteria yield gains in terms of finite sample bias as well as offering tests statistics whose behavior is better approximated by the first order asymptotic theory. Thus, our criteria improve existing methods used to implement IRFMEs.     

The directional profit efficiency measure: on why profit inefficiency is either technical or allocative

The directional distance function encompasses Shephard’s input and output distance functions and also allows nonradial projections of the assessed firm onto the frontier of the technology in a preassigned direction. However, the criteria underlying the choice of its associated directional vector are numerous. When market prices are observed and firms have a profit maximizing behavior, it seems natural to choose as the directional vector that projecting inefficient firms towards profit maximizing benchmarks. Based on that choice of directional vector, we introduce the directional profit efficiency measure and show that, in this general setting, profit inefficiency can be categorized as either technical, for firms situated within the interior of the technology, or allocative, for firms lying on the frontier. We implement and illustrate the analytical model by way of Data Envelopment Analysis techniques, and introduce the necessary optimization programs for profit inefficiency measurement.     

Effective pollution control policy for China

China began enforcing a system of pollution levies in 1982. However, senior environmental officials expressed doubt that this system was improving the environment and, in 1996, they began to place greater reliance on mill closure as the penalty for poor environmental performance. Since then, managers have found means of subverting many of the intended mill closures, and this causes us to return to the question of the abatement efficiency and effectiveness of the levies. This paper uses production evidence from 34 papermills in two representative provinces to examine the abatement efficiency and effectiveness of the levies. The paper industry is an important industry for this question because it is the largest polluter of China’s rural environment. We use a distance function to determine individual output-based and revenue-based shadow prices for each mill during the years that the levies were the main environmental incentive. The output-based shadow prices for pollutants display no recognizable trends over time and they are very different for firms in different locations. The revenue-based shadow prices are widely variable between mills and locations as well. These findings indicate that the marginal opportunity costs of abatement were also widely divergent and that there was no trend toward improved abatement efficiency. The way to correct this is to improve the performance of the market—not to reject the market altogether as the more recent reliance on mill closures does. This observation suggests that a system of tradable permits would be an improvement on relatively less successful administrative measures such as forced mill closures.     

Location,Proximity, and M&A Transactions

In this paper, we examine how the geographic location of firms affects acquisition decisions and value creation for acquirers in takeover transactions. We find that firms located in an urban area are more likely to receive a takeover bid and complete a takeover transaction as a target than firms located in rural areas, and takeover deals involving an urban target are associated with higher acquirer announcement returns, after controlling for the proximity between the target and the acquirer. In addition, a target's urban location significantly attenuates the negative effect of a long distance between the target and the acquirer on acquirer returns, a fact that is documented in the existing literature. Our findings reveal a previously underexplored force—firm location—that can affect takeover transactions, in addition to proximity. Our paper suggests that a firm's location plays an important role in facilitating the dissemination of soft information and enhancing information‐based synergies.     

Economic development and growth in Colombia: An empirical analysis with super-efficiency DEA and panel data models

In this paper, we analyse economic development and growth through traditional measures (gross domestic product and human development index) and Data Envelopment Analysis (DEA) in Colombian departments over the period 1993–2007. We use a DEA model to measure and rank economic development and growth from different approaches such as poverty, equality and security. The results show considerable variation in efficiency scores across departments. A second-stage panel data analysis with fixed effects reveals that higher levels of economic activity, quality life, employment and security are associated with a higher efficiency score based on the standards of living, poverty, equality and security. All findings of this analysis should demonstrate that economic development and growth could be achieved most effectively through a decrease in poverty, an increase in equality, a reduction in violence, and improved security. This indicates the need to generate effective policies that guarantee the achievement of these elements in the interest of all members of society.     

The influence of task- and location-specific complexity on the control and coordination costs in global outsourcing relationships

Several reputable industry sources have recognized that many organizations fail to realize the financial benefits sought with outsourcing. Further, prior research has found that outsourcing organizations struggle to estimate accurately the so called “hidden costs” associated with managing these inter-organizational relationships. This is especially true of complex, globally distributed outsourced services. In this study, we use dyadic data on 102 outsourcing relationships to investigate how dimensions of task- and location-specific complexity influence the degree of control and coordination costs incurred by the customer organization. Results from our hierarchical regression analysis demonstrate that the scale of the service and the geographic distance between the customer and provider locations are associated with higher levels of both control and coordination costs. Task breadth and geographic dispersion are significantly associated with increased control costs, but not coordination costs. Counter to our expectations, control costs decrease with the degree of service customization, whereas both control and coordination costs are negatively related to the average cultural distance between provider and customer organizations. These findings contribute unique empirical evidence to the outsourcing, offshoring, and international service operations literature.     

A minimum expected response model: Formulation, heuristic solution, and application

Responding to true emergencies in the shortest possible time saves lives, prevents permanent injuries and reduces suffering. Most covering models consider an emergency cover if an ambulance is available within a given time or distance threshold. From a modeling perspective, shorter or longer responses within this threshold are all tallied as covered; conversely, the emergencies immediately outside the threshold are considered uncovered. However, if the shorter responses are given more weight along with the volume of such incidents, while still meeting system-wide coverage requirements, both customers and providers can benefit from reduced response times. We formulate a model to determine the locations for a given set of ambulances to minimize the system-wide expected response distances while meeting coverage requirements. We solve the model with a heuristic search algorithm and present computational and comparative statistics using data from an existing Emergency Medical Services agency.     

Information Frictions in Uncertain Regulatory Environments: Evidence from U.S. Commercial Banks

Information frictions between firms and regulators are typically seen as a means by which firms evade enforcement. In contrast, we argue that information frictions between firms and regulators can reduce the efficiency of firms’ compliance efforts when the interpretation of regulatory standards is uncertain. We exploit plausibly exogenous variation in distance between firms and their regulators to demonstrate this for a panel of community banks in the US. We find that banks located at greater distance from regulatory field offices face significantly higher administrative costs, at a rate of 20% of administrative costs per hour of travel time. These differences do not come with reduced compliance, are not driven by endogenous regulator choice, and are stable over time. Further, the costs borne by distant firms are negatively related to the scale of the jurisdiction in which they operate, suggesting that information spillovers between firms limit uncertainty about regulatory expectations.     

Emergence of innovation networks from R&D cooperation with endogenous absorptive capacity

This paper extends the existing literature on strategic R&D alliances by presenting a model of innovation networks with endogenous absorptive capacity. The networks emerge as a result of dynamic cooperation between firms occupying different locations in the knowledge space. Partner selection is driven by absorptive capacity which is itself influenced by cognitive distance and R&D investment allocation. Under different knowledge regimes, we examine the structure of networks that emerge and how firms perform within such networks. We find networks that exhibit small world properties which are generally robust to changes in the knowledge regime. Certain network strategies such as occupying brokerage positions or maximising accessibility to potential partners pay off, especially in ‘young’ industries with limited involuntary but abundant voluntary spillovers. This particular result is driven by endogenous absorptive capacity.     

The political economy of organized crime: providing protection when the state does not

Organized crime emerges out of the power vaccuum that is created by the absence of state enforcement, and which can have many sources: geographic, social, and ethnic distance, prohibition, or simply collapse of state institutions. Mafias and gangs are hierarchically organized and can be thought of as providing primitive state functions, with economic costs that are typically much higher than those associated with modern governance. Though organized crime cannot be completely eradicated, its control is necessary, since it can easily corrupt existing institutions of governance. Some thoughts on what can be done to control organized crime are offered.     

A goal programming model for facility location planning

The paper presents a goal programming model for facility location planning. Often the location-decision is coupled with multiple objectives, at times conflicting among themselves. Specifically the model considers the four major objectives: (i) necessary locations, (ii) maximum number of locations, (iii) capacity restrictions and (iv) transportation cost/walking distance minimization, simultaneously and proposes optimal locations in conjunction with the existing facilities in the region. The model developed has been illustrated with an example considering the location of community storage facilities in a specified region.     

Planning for equal transit-based accessibility of healthcare facilities: A case study of Shenzhen,China

Recent location-allocation studies have made considerable progress in optimizing the equality of facility accessibility but are focused on automobile transport to facilities. In cities, however, the transit-based accessibility of essential services is crucial for social equality and sustainable development. In this study, we develop a modified transit-based maximal accessibility equality (MAE) model for optimizing the equality of the transit-based accessibility of healthcare facilities. In this model, equality is quantified as the weighted mean absolute deviation (WMAD) of accessibility across locations. Two scenarios are set up to reallocate resources or allocate newly added resources. The results reveal that the equality of transit-based healthcare accessibility can be significantly improved in both scenarios. A dispersed planning strategy for facilities is suggested to achieve equal accessibility. However, the transit-based optimization results significantly differ from the car-based optimization results, with more supply allocated to facilities close to transit corridors. This finding implies that the traditional car-based MAE model might generate unequal healthcare accessibility for transit-dependent populations and thus lead to biased recommendations for healthcare planning. Furthermore, it shows that traditional car-based optimization may engender a misallocation of healthcare supply, exacerbating the inequality in healthcare accessibility. The necessity of incorporating public transit into public facility planning is highlighted. The improved MAE model can be applied in cities where the supply of public services is relatively adequate and public transit plays an important role in daily mobility.     

On the Determinants of School District Efficiency: Competition and Monitoring

A number of researchers have asserted that inefficiency in the U.S. school system arises from a lack of incentives for public schools to behave efficiently. This paper uses a Shephard input distance function to model educational production, and a switching-regressions estimation to explore the relationship between school district efficiency and two existing incentive mechanisms—competition and voter monitoring. We find evidence that ease of monitoring enhances both technical and allocative efficiency of urban school districts, and that increased competition reduces allocative inefficiency in communities above a competitive threshold. We find no evidence that competition is related to technical inefficiency.     

Neighborhood effect on stock price comovement

We investigate how the geographic distance between firms’ headquarters affects their stock price comovement. Our results show that a firm's stock return has stronger comovement with the returns of nearby firms than with those of distant firms. Being in the same state and/or in the same industry strengthens the return comovement, but does not substitute for the negative effect of geographic distance on price comovement. Firms of similar share price and size also show stronger return comovement, but these factors do not mitigate the negative distance impact. Consistent with investor home bias and neighborhood effect literature, our results suggest that investors’ preference for local stocks and their interactions lead to correlated trading in local stocks and therefore stronger local price comovement.