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Accessibility of Electric Vehicle (EV) charging stations is an important factor for adoption of EV, which is an effective green technology for reducing carbon emissions. Recognizing this, many governments are contemplating ideas for achieving EV adoption targets, such as constructing extra EV charging stations directly or offering subsidies to entice automakers to construct more EV charging stations. To achieve these targets, governments need to coordinate with automakers to ensure that the total number of charging stations is planned optimally. We study this coordination problem by considering the interactions among the government, automakers, and consumers, our equilibrium analysis yields three major results. First, both the government and the automaker should build extra EV charging stations when their construction costs are independent. Simultaneously, the government should offer a per-station subsidy to the automaker only when the adoption target and the construction cost are both high. However, when the construction costs are dependent, the government should delegate the construction to the automaker by offering a per-station subsidy. Second, when the government considers consumer purchase subsidy as an extra lever, we find that the purchase subsidy for consumers is more cost-effective than offering a per-station subsidy to the automaker. Third, the structure of the optimal government policy remains the same regardless of whether the government's goal is to improve EV adoption or consumer welfare. Our results can serve as guidelines for governments when contemplating coordination with automakers for the construction of EV charging stations to improve EV adoption as well as consumer welfare further.

In this study, we propose an integrated model of two‐sided stochastic matching platforms to understand the design and operations of free‐float shared‐mobility systems. In particular, we address the joint design of incentives (via “crowd‐sourcing”) and spatial capacity allocations (enabled by “geo‐fencing”). From the platform's perspective, we formulate stylized models based on strategic double‐ended queues. We optimize the design and operations of such systems in a case study using a data set from a leading free‐float bicycle‐sharing system, and solve it via mixed‐integer second‐order conic programs (SOCPs). Both stylized results and computational studies generate insights about fundamental trade‐offs and triangular relationships among operational costs, capacity utilization rates and service levels. Interestingly, we identify the role of spatial capacity (parking) management to fine‐tune the market thickness (transient service availability) in such a two‐sided marketplace. We show that a “capacity‐dependent approximation” can be very close to optimality, and outperforms policies ignoring capacity management. We also demonstrate that this framework can be operationalized in multiple directions, which generates insights concerning matching efficiency, performance comparison between crowd‐sourcing and repositioning, strategic server behaviors and network externalities. Our insights guide the platform and the policy‐maker to embrace “crowd‐sourcing” and “geo‐fencing” technologies for shared‐mobility systems.

Data center networks provide the physical infrastructure that hosts Internet‐related services and cloud computing. Designing data center networks properly is imperative for Internet‐related service and cloud computing providers to gain competitive edges through cost efficiency and service quality. In this study, we formulate a mathematical programming model to address the data center network design problem, in which the objective is to minimize total operating cost and the service delay penalty by optimizing data center location, footprint allocation, and resource provisioning decisions, while incorporating essential features, such as latency, power, multiple resources, configuration limits, and interdependent footprints. We employ a queueing model to approximate the service latency and provide tractable reformulations. To enhance computational efficiency for large‐scale problems, we further develop Lagrangian relaxation methods and generate strengthening cuts by exploiting the structural properties of the problem. Our numerical studies demonstrate that the proposed model, which jointly optimizes location, allocation, and resource provisioning, can achieve significant cost reductions and improvements in service quality compared with a hierarchical approach that optimizes these decisions sequentially. Moreover, our proposed solution methods outperform state‐of‐the‐art commercial software in terms of computational efficiency. Based on real‐world datasets, the proposed model selects data centers that have been chosen by major cloud computing infrastructure providers. We also draw managerial insights that can be used as design guidelines in practice.

Three experiments investigated whether and why sharing experiences of social exclusion or social acceptance with others strengthens social bonds. Participants experienced either social exclusion or social acceptance alongside another co-participant who either also experienced the same outcome, or experienced a different outcome, as them. Multilevel modeling results showed that participant dyads who shared the experience of social exclusion or social acceptance felt closer to each other than those who experienced different outcomes, and that perceived similarity mediated the effect of shared experiences on social bonds. Interestingly, participants felt closer to one another after having shared social acceptance, more so than when they have shared social exclusion. Implications of the present findings are interpreted in light of theories of social exclusion, shared experiences, and social bonding.

Threat appeals have been widely utilized in numerous types of public service announcements (PSAs), and previous research has focused on the impact of the inherent messages in these announcements. By examining the research on the effects of framing PSAs in terms of the threat of the message to oneself or others, we proposed a clear conceptualization of "threat-target framing." The first two studies addressed the direct effects of threat-target framing and found that other-oriented threat appeals can evoke more guilt than can self-oriented threat appeals. Moreover, self-oriented threat appeals can evoke more fear and immediately direct recipients' attention to the smoker than can other-oriented threat appeals. Study 3 reported that a contextual factor-relationship norms-was introduced as a potential moderating factor. Results showed that relationship norms had the potential to moderate the effect of threat-target framing on recipients' fear response, but not the effect on recipients' guilt and coping response. In sum, the results highlighted the importance of message framing of advertising copies and the placement context. Our findings may be useful in understanding the antecedents of the persuasiveness of PSAs.

Most pricing and revenue management models have at their core an optimization problem; one needs to determine the optimal price or quantity to maximize a profit or revenue function. To ensure tractability, conditions that assure the objective function has a unique solution are enormously helpful. So far, several technical assumptions have been proposed for the continuous case, but comparatively little attention has been given to the discrete counterpart despite its prevalence in practice. Thus, this study aims to develop new technical assumptions, built upon relevant economic concepts, to guarantee the tractability of revenue management models in discrete settings. In particular, we present two sufficient conditions for the revenue function to be concave, in terms of quantity or price and propose a condition for the revenue function to be unimodal, called discrete increasing generalized failure rate (IGFR). Our definition has an appropriate economic interpretation and offers comparable properties to those of the continuous version. Finally, we show the discrete IGFR property holds for several discrete distributions.

In this paper, we develop an iterative piecewise linear approximation approach with a novel initialization method to solve natural gas pipeline transmission problems with the nonuniform network elevation. Previous approaches, such as energy minimization methods, cannot be applied to solve problems with the nonuniform network elevation because they exclude pressure range constraints, and thus provide solutions far from optimum. We propose a new initialization model that considers pressure range constraints and improves the optimality of the solutions and the computational efficiency. Furthermore, we extend the energy minimization methods and provide the necessary conditions under which the extended methods operate in networks with the nonuniform elevation. We test the performances of the methods with previously reported pipeline networks from the literature, with the open data set GasLib, and with our industrial collaborator. The initialization approach is shown to be more efficient than the method with fixed initial breakpoints. The newly proposed initialization approach generates solutions with a higher accuracy than the extended energy minimization methods, especially in large‐size networks. The proposed method has been applied to natural gas transmission planning by the China National Petroleum Corporation and has brought a direct profit increase of 330 million U.S. dollars in 2015‐2017.

Background The identification of V-raf murine sarcoma viral oncogene homolog B1 (BRAF)V600E mutations has been recommended in patients with Langerhans cell histiocytosis (LCH) with difficult diagnosis and failure of first-line treatment. The reported frequencies of BRAFV600E mutations vary in Chinese patients with LCH. Methods We conducted a retrospective analysis of LCH patients with a definitive pathological diagnosis who were hospitalized between 2013 and 2017. The BRAFV600E mutations were detected with the human BRAFV600E amplification refractory mutation system-PCR (ARMS-PCR) kit from the collected tissue samples. Results This study consisted of 46 male (68.7%) and 21 female (31.3%) patients, with a mean age of 29.1 years (range, 2-76 years). Most were adults (45/67.2%) with the multisysytem-LCH (MS-LCH) disease subtype (49/61.3%). The overall frequency of BRAFV600E mutations was 22.4% (15 of 67 patients), confirmed by PCR analysis. These mutations were not closely correlated with age (nonadults vs. adults = 5/22.7% vs. 10/22.2%, P = 0.54), gender (female vs. male = 9/19.6% vs. 6/28.6%, P = 0.61), LCH classification type (single system: MS-risk organ+ : MS-risk organ- = 3/16.7%: 12:28.6%: 0, P = 0.19) or prognosis (cured: improved/stable: exacerbated: died = 4/44.4%: 19.2%: 20%: 0, P = 0.37). There were 33 patients (49.2%) with lung involvement, and 12 patients (36.3%) underwent lung biopsies; after screening, four patients were diagnosed with solitary pulmonary LCH, all of whom were negative for BRAFV600E mutations. Conclusion The BRAFV600E mutation rate in patients with LCH was lower than those reported in other studies. In addition, BRAFV600E mutations might not be correlated with age, gender, LCH classification type or prognosis for Chinese cases.

When product recalls happen, firms not only have to incur additional logistics costs but also suffer from a damaged reputation. Besides, they may also supply disruption risk, under which a supplier could fail to deliver the quantity ordered at all. In this study, we discuss how to mitigate supply disruption and recall risks in the perspective of (outbound) product distribution strategy in joint with (inbound) sourcing decision. Specifically, we compare the dedicated and uniform distribution strategies as follows: in the dedicated strategy, the firm divides its market into several regions, and ships products from different suppliers based on the regions; whereas, in the uniform strategy, the firm serves the entire market by randomly shipping the products from several suppliers without distinguishing their sources. Through the comparison of both strategies, we show that the role of recall risk is different from that of supply disruption on the sourcing and distribution decision. In particular, single sourcing is optimal when both recall risk and disruption probability are low; dual sourcing with the dedicated strategy is optimal when the disruption probability is moderate and the recall risk is high; dual sourcing with the uniform strategy is optimal when the disruption probability is high. We also find that the positive correlation in recalls improves (undermines) the performance of the uniform (dedicated) strategy. In contrast, the positive correlation in supply disruptions only undermines the performance of the uniform strategy.

It is well‐known that adding a little flexibility to the right place is an effective strategy to improve the performance of operations in the face of demand uncertainties, to ensure high level of capacity utilization. However, given that system disruptions are ubiquitous, the legacy flexibility designs may perform poorly under disruptions to supply or capacity installations. In this study, we focus on the design of reliable and sparse flexibility structures that consistently meet a reasonable performance criterion under disruptions to both demand and supply. Specifically, we propose a class of structures termed as extended probabilistic expanders, based on the conjecture that the expansion property, rather than the global connectivity, is critical to good performance of the structures. We prove that for a system with n retailers, essentially only O(n) supply routes between suppliers and retailers are necessary to ensure good performance under disruption. In addition, we present an efficient randomized algorithm to construct extended probabilistic expanders, and demonstrate that the construction yields very good structure with the least number of edges asymptotically. We also investigate an extension to systems with structural constraints. Numerical results demonstrate that our design has not only a wide range of applications, but also better performance than a variety of known structures.

The Triple‐A supply chain (Lee 2004) has become one of the most influential concepts for practitioners and researchers in supply chain management. It stipulates that supply chains should strive to improve along the dimensions of agility, adaptability, and alignment, as opposed to focusing exclusively on cost and efficiency improvements. While various strategies have since been adopted across different industries to foster the Triple‐A, the digitalization movement poses both new challenges and opportunities for developing Triple‐A supply chains. In this article, we shall discuss an emerging mode of supply chain innovation, known as the consumer‐to‐manufacturer (C2M) model, and how it enables new possibilities for achieving Triple‐A “digital” supply chains. C2M establishes digital links between end consumers and upstream manufacturers and product designers, and provides a variety of tactics to shorten the information flow process of the supply chain. Our discussion focuses on the implementation of C2M at JD.com, a leading online retailer in China.

Objective To study the changes of white matter tracts in precentral gyrus and postcentral gyrus during normal brain aging process by analyzing fractional anisotropy (FA) values obtained from diffusion tensor imaging (DTI) technology. Methods Magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) were conducted on 120 healthy right‐handed subjects. The subjects were separated into four age groups, namely Young Male/Female (45 years old). Each subject undertakes routine MRI and DTI scans. Left/right precentral and left/right postcentral gyrus are automatically detected on the image. The area for region of interest (ROI) is set to be 18 ± 2 mm2. Results For each age group, the FA values of white matter in precentral gyrus and postcentral gyrus are statistically different (p, The FA values of precentral gyrus and postcentral gyrus are statistically different across genders, age groups, and hemispheres. Additionally, the FA values of both precentral gyrus and postcentral gyrus decrease over time, which is a strong indication of aging.

We review the operations research/management science literature on data‐driven methods in retail operations. This line of work has grown rapidly in recent years, thanks to the availability of high‐quality data, improvements in computing hardware, and parallel developments in machine learning methodologies. We survey state‐of‐the‐art studies in three core aspects of retail operations—assortment optimization, order fulfillment, and inventory management. We then conclude the paper by pointing out some interesting future research possibilities for our community.

We are entering an era of great expectations towards our cities. The vision of “smart city” has been pursued worldwide to transform urban habitats into superior efficiency, quality, and sustainability. This phenomenon prompts us to ponder what role the scholars in operations management (OM) can assume. In this essay, we express our initial thoughts on expanding OM to the smart‐city scope. We review smart‐city initiatives of governments, industry, national laboratories and academia. We argue that the smart‐city movement will transition from the tech‐oriented stage to the decision‐oriented stage. Hence, a smart city can be perceived as a system scope within which planning and operational decisions are orchestrated at the urban scale, reflective of multi‐dimensional needs, and adaptive to massive data and innovation. The benefits of studying smart‐city OM are manifold and significant: contributing to deeper understanding of smart cities by providing advanced analytical frameworks, pushing OM knowledge boundaries (such as data‐driven decision making), and empowering the OM community to deliver much broader impacts than before. We discuss several research opportunities to embody these thoughts, in the interconnected contexts of smart buildings, smart grid, smart mobility and new retail. These opportunities arise from the increasing integration of systems and business models at the urban scale.

In this study, we propose a stylized Cournot competition model under asymmetric market information and study the formation of producers’ informational coalitions. We offer guidance towards how the information service operations can improve the producers’ welfare. Our results shed interesting light on farmer producer organizations (FPOs) in their efforts to link farmers in developing economies by integrating market information. To be specific, by focusing on Nash equilibria wherein producers make coalition†joining decisions, we find that no information†sharing coalition will be formed when the public information provision is either too low or too high, or when the private information provision is high. When the private information provisions are endogenous, a coalition can be formed between two producers when the public information provision is high, while a fair allocation of payoff can be achieved by the dominant group architecture, but not efficiency in terms of aggregate payoff. The main theme is that the government or NGO should provide right amount of market information to the right target, and too much informational intervention leads to more inefficient production and unstable organization.

We tested the hypothesis that a sense of responsibility drives group representatives' decisions to be more risk averse compared with decisions made by individuals. The hypothesis was supported when the monetary considerations (i.e., payoff inequality and the magnitude effect) were controlled for in the potential gain domain as well as in the potential loss domain. Evidence showed that this is because the group representatives were concerned about how they would view themselves (e.g., guilt and self-blame) and also how they would be viewed by others (i.e., to avoid being blamed and looked down upon by others). This study provided new insights into understanding group representatives' decision making under risk.