E lectric vehicles (EVs) have been proposed as a key technology to help cut down the massive greenhouse gas emissions from the transportation sector. Unfortunately, because of the limited capacity of batteries, typical EVs can only travel for about 100 miles on a single charge and require hours to be recharged. The industry has proposed a novel solution centered around the use of "swapping stations," at which depleted batteries can be exchanged for recharged ones in the middle of long trips. The possible success of this solution hinges on the ability of the charging service provider to deploy a cost-effective infrastructure network, given only limited information regarding adoption rates. We develop robust optimization models that aid the planning process for deploying battery-swapping infrastructure. Using these models, we study the potential impacts of battery standardization and technology advancements on the optimal infrastructure deployment strategy.

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Key to the mass adoption of electric vehicles (EVs) is the establishment of successful business models based on sound understanding of consumer behavior in adopting this new technology. In this paper, we study the impact of two major barriers to mass adoption of EVs: (i) range anxiety, the concern that the driving range of EVs may be insufficient to meet the driving needs, and (ii) resale anxiety, the concern that used values of EVs may deteriorate quickly. Using a stylized model calibrated to a data set based on the San Francisco Bay Area, we show that although both types of consumer anxieties typically harm the firm’s profit, they often improve consumer surplus. In addition, we show that a business model that requires consumers to lease the EV batteries (rather than purchase them) may lead to a greater level of adoption and emission savings when the level of resale anxiety is high. Further, a business model that offers EV range improvement through enhanced charging infrastructure typically yields greater adoption and consumer surplus, but lowers the firm’s profit, compared with one that offers enlarged batteries. Overall, we find that the combinations of battery owning/leasing with enhanced charging service, referred to as the (O, E) and (L, E) models in our paper, typically yield the best balance among the objectives of EV adoption, emission savings, profitability, and consumer surplus, when the degree of resale anxiety is low and high, respectively.

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Emerging collaborative consumption business models have shown promise in terms of both generating business opportunities and enhancing the efficient use of resources. In the transportation domain, car-sharing models are being adopted on a mass scale in major metropolitan areas worldwide. This mode of servicized mobility bridges the resource efficiency of public transit and the flexibility of personal transportation. Beyond the significant potential to reduce car ownership, car sharing shows promise in supporting the adoption of fuel-efficient vehicles, such as electric vehicles (EVs), because of these vehicles' special cost structure with high purchase but low operating costs. Recently, key players in the car-sharing business, such as Autolib', car2go, and DriveNow, have begun to employ EVs in an operations model that accommodates one-way trips. On the one hand (and particularly in free-floating car sharing), the one-way model results in significant improvements in coverage of travel needs and therefore in adoption potential compared with the conventional round-trip-only model (advocated by Zipcar, for example). On the other hand, this model poses tremendous planning and operational challenges. In this work, we study the planning problem faced by service providers in designing a geographical service region in which to operate the service. This decision entails trade-offs between maximizing customer catchment by covering travel needs and controlling fleet operation costs. We develop a mathematical programming model that incorporates details of both customer adoption behavior and fleet management (including EV repositioning and charging) under imbalanced travel patterns. To address inherent planning uncertainty with regard to adoption patterns, we employ a distributionally robust optimization framework that informs robust decisions to overcome possible ambiguity (or lacking) of data. Mathematically, the problem can be approximated by a mixed integer second-order cone program, which is computationally tractable with practical scale data. Applying this approach to the case of car2go's service with real operations data, we address a number of planning questions and suggest that there is potential for the future development of this service.

I n this paper, we develop distribution-free models that solve the appointment sequencing and scheduling problem by assuming only moments information of job durations. We show that our min-max appointment scheduling models, which minimize the worst-case expected waiting and overtime costs out of all probability distributions with the given marginal moments, can be exactly formulated as tractable conic programs. These formulations are obtained by exploiting hidden convexity of the problem. In the special case where only the first two marginal moments are given, the problem can be reformulated as a second-order cone program. Based on the structural properties of this formulation, under a mild condition, we derive the optimal time allowances in closed form and prove that it is optimal to sequence jobs in increasing order of job duration variance. We also prove similar results regarding the optimal time allowances and sequence for the case where only means and supports of job durations are known.

T he 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.