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Register a new userCharging-as-a-Service: On-demand battery delivery for light-duty electric vehicles for mobility service
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This study presents an innovative solution for powering electric vehicles, named Charging-as-a-Service (CaaS), that concerns the potential large-scale adoption of light-duty electric vehicles (LDEV) in the Mobility-as-a-Service (MaaS) industry. Analogous to the MaaS, the core idea of the CaaS is to dispatch service vehicles (SVs) that carry modular battery units (MBUs) to provide LDEVs for mobility service with on-demand battery delivery. The CaaS system is expected to tackle major bottlenecks of a large-scale LDEV adoption in the MaaS industry due to the lack of charging infrastructure and excess waiting and charging time. A hybrid agent-based simulation model (HABM) is developed to model the dynamics of the CaaS system with SV agents, and a trip-based stationary charging probability distribution is introduced to simulate the generation of charging demand for LDEVs. Two dispatching algorithms are further developed to support the optimal operation of the CaaS. The model is validated by assuming electrifying all 13,000 yellow taxis in New York City (NYC) that follow the same daily trip patterns. Multiple scenarios are analyzed under various SV fleet sizes and dispatching strategies. The results suggest that optimal deployment of 250 SVs may serve the LDEV fleet in NYC with an average waiting time of 5 minutes, save the travel distance at over 50 miles per minute, and gain considerable profits of up to $50 per minute. This study offers significant insights into the feasibility, service efficiency, and financial sustainability for deploying city-wide CaaS systems to power the electric MaaS industry.
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We consider the scheduling of multiple tasks with pre-determined deadlines under random processing cost. This problem is motivated by the potential of large scale adoption of plug-in (hybrid) electric vehicles (PHEVs) in the near future. The charging requests of PHEVs usually have deadline constraints, and the electricity cost associated with PHEV charging is usually random due to the uncertainty in both system load and renewable generation. We seek to properly schedule the battery charging of multiple PHEVs so as to minimize the overall cost, which is derived from the total charging cost and the penalty for not completing charging before requested deadlines. Through a dynamic programming formulation, we establish the Less Laxity and Longer remaining Processing time (LLLP) principle that improves any charging policy on a sample-path basis, when the non-completion penalty is a convex function of the additional time needed to fulfill the uncompleted request. Specifically, the LLLP principle states that priority should be given to vehicles that have less laxity and longer remaining processing times. Numerical results demonstrate that heuristic policies that violate the LLLP principle, for example, the earliest deadline first (EDF) policy, can result in significant performance loss.
Mobility-as-a-Service (MaaS) is emerging mobility trend driven by the concept of Everything-as-a-Service and enabled through mobile internet technologies. In the context of economic deregulation, a MaaS system consists of a typical two-sided market, where travelers and transportation service providers (TSPs) are two groups of agents interacting with each other through a MaaS platform. In this study, we propose a modeling and optimization framework for the regulation of two-sided MaaS markets. We consider a double-auction mechanism where travelers submit purchase-bids to accommodate their travel demand via MaaS, and TSPs submit sell-bids to supply mobility resources for the MaaS platform in exchange for payments. We cast this problem as a single-leader multi-follower game (SLMFG) where the leader is the MaaS regulator and two groups of follower problems represent the travelers and the TSPs. The MaaS regulator makes the operating decisions to maximize its profits. In response to the MaaS regulators decisions, travelers (resp. TSPs) decide the participation levels of joining the MaaS platform to minimize their travel costs (resp. maximize their profits). We formulate SLMFGs without and with network effects leading to mixed-integer linear bilevel programming and mixed-integer quadratic bilevel programming problems, respectively. We propose customized branch-and-bound algorithms based on strong duality reformulations to solve the SLMFGs. Extensive numerical experiments conducted on large scale simulation instances generated from realistic mobility data highlight that the performance of the proposed algorithms is significantly superior to a benchmarking approach and provide meaningful insights for the regulation of two-sided MaaS markets.
Tempelmeier (2007) considers the problem of computing replenishment cycle policy parameters under non-stationary stochastic demand and service level constraints. He analyses two possible service level measures: the minimum no stock-out probability per period ({alpha}-service level) and the so called fill rate, that is the fraction of demand satisfied immediately from stock on hand ({beta}-service level). For each of these possible measures, he presents a mixed integer programming (MIP) model to determine the optimal replenishment cycles and corresponding order-up-to levels minimizing the expected total setup and holding costs. His approach is essentially based on imposing service level dependent lower bounds on cycle order-up-to levels. In this note, we argue that Tempelmeiers strategy, in the {beta}-service level case, while being an interesting option for practitioners, does not comply with the standard definition of fill rate. By means of a simple numerical example we demonstrate that, as a consequence, his formulation might yield sub-optimal policies.
Software as a Service (SaaS) is well established as an effective model for the development, deployment and customization of software. As it continues to gain more momentum in the IT industry, many user experience challenges and issues are being reported by the experts and end users.
Understanding individual mobility behavior is critical for modeling urban transportation. It provides deeper insights on the generative mechanisms of human movements. Emerging data sources such as mobile phone call detail records, social media posts, GPS observations, and smart card transactions have been used before to reveal individual mobility behavior. In this paper, we report the spatio-temporal mobility behaviors using large-scale data collected from a ride-hailing service platform. Based on passenger-level travel information, we develop an algorithm to identify users visited places and the category of those places. To characterize temporal movement patterns, we reveal the differences in trip generation characteristics between commuting and non-commuting trips and the distribution of gap time between consecutive trips. To understand spatial mobility patterns, we observe the distribution of the number of visited places and their rank, the spatial distribution of residences and workplaces, and the distributions of travel distance and travel time. Our analysis highlights the differences in mobility patterns of the users of ride-hailing services, compared to the findings of existing mobility studies based on other data sources. It shows the potential of developing high-resolution individual-level mobility models that can predict the demand of emerging mobility services with high fidelity and accuracy.
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