Inconvenience Cost Research Articles

"Fast but not so furious": A condensed step-up dosing schedule of teclistamab for relapsed/refractory multiple myeloma.

Teclistamab is a B-cell maturation antigen (BCMA)-directed bispecific T-cell engager approved for relapsed-refractory multiple myeloma (RRMM). Cytokine release syndrome (CRS) and Immune effector cell-associated neurotoxicity syndrome (ICANS) are well-documented treatment -related adverse events of teclistamab. The prescribing information recommends step-up dosing on days 1, 4, and 7 with 48-72 h of inpatient observation after each dose to monitor for CRS. This leads to a more than weeklong hospital stay, adding to the cost of therapy, resource utilization, and patient inconvenience. Here, we present a single center retrospective analysis addressing the safety and utility of a condensed step-up dosing schedule for teclistamab. All patients who were treated with teclistamab from November 2022 to August 2023 at the Medical University of South Carolina were included in the analysis. Patients received subcutaneous (SC) teclistamab with step-up doses (0.06 and 0.3 mg/kg) separated by either 2 or 3 (48-72 h) before the administration of the first full (1.5 mg/kg) dose (days 1, 3, and 5 'condensed' schedule or days 1, 4, and 7 'standard' schedule, respectively). All patients were hospitalized for the two step-up doses and first full dose of teclistamab and received pre-medications prior to each dose. Patients could be discharged after a minimum of 24 h following the full dose, if they did not have any CRS or ICANS. Relevant data regarding incidence, severity, and onset of CRS was collected. Statistical analysis was completed to assess the probability of fever with the first full dose of teclistamab based on incidence of fever with previous doses. A total of 25 patients were included in the analysis. Twenty-eight percent (7/25) of patients underwent the standard step up while the remaining 72% (18/25) underwent a condensed step up of teclistamab. More than half (53%, 13/25) of the patients experienced CRS during step up dosing. Grades 1 and 2 CRS occurred in 48% (12/25) and 4% (1/25) patients, respectively. Of the 13 patients that experienced CRS, 30% (4/13) fevered with the first dose, 84% (11/13) fevered with the second dose, and one patient developed fever after the third dose. The negative predictive value of being 'fever free' after doses 1 and 2 and remaining 'fever free' throughout hospitalization was 0.92. The median length of hospital stay among the 1, 3, and 5 step up group was 6 days (6-25) and 70% (14/20) of patients were discharged from the hospital within 7 days of treatment initiation. This report demonstrates the utility of a condensed step-up schedule for teclistamab initiation. The schedule was found to be safe and reduced hospital length of stay. These results should prompt consideration of shorter hospital stays for patients who do not experience CRS and raise the possibility of outpatient administration with close observation.

Exploring the role of Mobility-as-a-Service in morning commuting trips

Promising the seamless integration of multiple transportation modes, Mobility-as-a-Service (MaaS) has gained popularity over the years, yet its effectiveness in enticing private car users and improving travel efficiency remains uncertain. This study explores the competitiveness of MaaS-enabled multi-modal travel options versus private car usage through equilibrium analysis. In addition to pricing that affects the fixed cost for travel, we examine the often overlooked inconvenience cost associated with multi-modal trips. We establish our analysis for the commuting problem of a one-origin-one-destination network, where a highway and a mass transit line connect the residential area and the central business district (CBD) area. Travelers choose their departure time and travel mode among auto, Park-and-Ride (PnR), ride-hailing and transit (RnT) to minimize their total travel cost. Inconvenience costs associated with searching for parking and waiting for ride-hailing are explicitly modeled. We analytically provide the mode share and departure time windows under all possible equilibria. Our findings reveal the complex nature of mode choice, distinctly affected by fixed and inconvenience costs, with demand playing an even more significant role. Notably, fixed costs set an entry fee to adopt different modes, and the feature of inconvenience costs affects the utilization of available transportation resources. More importantly, to benefit the overall system, we encourage maintaining a balanced mode share by implementing pricing and capacity strategies rather than aiming for a completer transition of private car users to MaaS.

Mail back or in-store dropoff? Optimal design of product-exchange policies in omnichannel retailing systems

Online retailing has been booming over the past several years as people grow increasingly comfortable with it and accustomed to its ease and speed. A major drawback of online retailing is the lack of consumer–product interaction before a purchase is finalized, which often leads to consumer dissatisfaction due to mismatched expectation of the received product. In response, retailers usually promise that online orders may be returned or exchanged free of extra charges, which can be processed either online (e.g., by mail) or onsite i.e., in-store dropoff. In this work, we study the implications of both online and onsite exchange policies in the setting of a queueing model that offers omnichannel services. Taking into account customers’ behavioral responses to these policies, we aim to inform the retailer of the one that generates a higher system revenue. Our results reveal that the online exchange policy is a double-edged sword: On the one hand, it helps eliminate the inconvenience cost for exchange customers to revisit the store; on the other hand, it can trigger more feedback orders and render a higher system congestion level, which in turn, deters future customers from placing orders. Specifically, we discover that online exchange becomes an inferior policy relative to in-store exchange when the market size is large.

Live streaming channel strategy of an online retailer in a supply chain

With the increasing development of live streaming selling, it is important for retailers to determine whether and how to introduce such channel. We develop a theoretical model to address these issues by considering three scenarios, i.e., without live streaming channel, with an influencer channel and a third-party channel. Our results show that the retailer will always benefit from adding a live streaming channel in the face of two channel choices, but not always do so only facing an influencer channel. Within a certain slotting fee, if the relative utility enhancement effect and the inconvenience cost are both small or both large, the retailer is better off choosing the third-party channel, but the influencer channel otherwise. Notably, adding a live streaming channel will not always benefit the upstream supplier. Furthermore, the presence of more loyal followers of the influencer may not always benefit the retailer if choosing the influencer channel.

Modeling strategic walk-in patients in appointment systems: Equilibrium behavior and capacity allocation

We consider an outpatient clinic with strategic patients who choose between making an appointment with an indirect wait cost (advance patients) and walking in with an inconvenience cost that includes the risk of being rejected and waiting in the clinic (walk-ins). Patients have different indirect waiting costs and show up with some probability. The clinic allocates slots to advance and walk-in patients to minimize the expected blockage of walk-in patients. We characterize the equilibrium behavior of patients and investigate the optimal capacity allocation, for unobservable (patients know the expected waiting time) and observable (patients know their exact waiting time) cases. For the unobservable case, one of the three options is optimal: allocating all slots to advance patients, allocating all slots to walk-ins, or allocating a certain number of slots to advance patients so that only urgent patients would choose the walk-in option. In contrast, for the observable case, no such structure exists. We investigate the value of information numerically. Finally, we develop a simulation platform to examine the effects of model assumptions. We find the optimal capacity allocation for the simulation model to benchmark the performance of the theoretical models and two simple policies. These analyses verify that our models work well in realistic simulations, offering a useful tool in practice. In contrast to the common practice of allocating some slots to walk-ins, our results suggest that the clinics should prefer a system that allocates all slots to advance patients in certain environments due to the strategic behavior of patients.

Designing shipping policies with top‐up options to qualify for free delivery

Motivated by the booming online grocery market and the extensive use of contingent free‐shipping (CFS) policies in the e‐grocery industry, we investigate the optimal CFS and pricing decisions for online grocers. Under a CFS policy, consumers enjoy free shipping for orders exceeding a certain threshold value; otherwise, they are charged a flat fee for orders below this threshold. We adopt a utility‐based model to capture consumers' behavior of purchasing additional items to qualify for free shipping under a CFS policy and analyze its impact on policy structure and consumer surplus. We characterize the e‐grocer's optimal pricing and CFS policy and find that consumer heterogeneity and demand distribution lead to different forms of the optimal shipping policy. When consumer heterogeneity is large enough, the optimal policy induces some consumers to top up and may allow some others to ship for free. In this case, the e‐grocer can charge a high‐profit margin. Otherwise, a top‐up option is unnecessary, and a flat‐rate shipping fee policy is optimal. Moreover, while the optimal policy never induces all consumers to top up when they are rational, it is possible to do so when consumers associate some psychological disutility with the shipping fee. Surprisingly, the total consumer surplus under the optimal policy may increase in the latter case. We further model a Stackelberg game between an e‐grocer and an offline channel and find that the difference between the e‐grocer's internal shipping cost and consumers' inconvenience cost of shopping offline is a main driver for market segmentation. Lastly, we show that a subscription‐based free‐shipping program, in addition to the jointly optimized CFS and pricing policy, cannot improve profits when consumers' order size and frequency are independent. Our findings help online grocers make operational and marketing decisions under the impact of consumers' top‐up behavior.

Game Theory Based Optimal Pricing Strategy for V2G Participating in Demand Response

Aggregators, as the interface of electric vehicles (EVs) and distribution system, are the market agents of EVs to participate in demand response (DR) and other balancing services. Aggregators can enlarge its benefit room from vehicle-to-grid (V2G) by providing flexible V2G price to EV users as long as they can secure a certain amount of V2G energy. There is a need to study Aggregator's V2G pricing strategy due to the fact that distribution companies, aggregators, and EV users have their own independent benefit maximization objectives as independent entities, and the decision variables of different entities interact and restrict each other. In this study, an optimal V2G pricing strategy for each aggregator is obtained by using Stackelberg game during the gaming of the involved multiple entities. The benefits of aggregator and EV users are set as the game factors, and benefit models of EV user's and aggregator's are established with consideration of the historical charging cost, and users' inconvenience cost. A simulation of V2G price for 750 EVs and 3 aggregators is carried out, and the impact of the market volume of EVs on each aggregator under this pricing strategy is studied. Simulation results show the feasibility of the proposed model and the interaction among the randomness of driving behavior and the market volume of EV groups, loading level of the distribution system, V2G strategies, as well as the DR capabilities.

An analysis of pricing strategy for bike-sharing services: The role of the inconvenience cost

In actual bike-sharing services, an operator can adopt any of three pricing strategies, namely, pay-per-use (Strategy P), flat-fee (Strategy F) and a hybrid of pay-per-use and flat-fee (Strategy H). When the operator launches the mode of picking up and returning bikes at designated shared bike parking points, users often perceive the inconvenience cost because of having to go to the designated parking points to pick up and return shared bikes. This paper investigates operators’ pricing strategies in monopoly and duopoly markets and analyzes the effects of the inconvenience cost on different pricing strategies. We find that in the monopoly market, it is advantageous for the operator to choose Strategy H if the inconvenience cost is relatively small; otherwise, it is advantageous to choose Strategy F. In the duopoly market, the operator can choose Strategy P. In particular, when the two operators bring the same inconvenience cost to users, there exist two pricing strategy combinations in equilibrium, namely, P/F and F/P. When the two operators bring different inconvenience costs to users, there exist five pricing strategy combinations in equilibrium, namely, P/F, F/P, H/F, F/H and F/F.

Marketplace Leakage

A key issue for the design of online marketplaces is addressing leakage. Buyers may use the marketplace to discover a seller or to obtain certain conveniences, but the seller may then want to take transactions off the marketplace to avoid transaction fees. Assuming buyers are heterogenous in their switching cost or inconvenience cost of purchasing directly, we provide a model in which there is partial leakage in equilibrium. We use the model to analyze the trade-offs associated with different strategies the marketplace can use to attenuate the effects of leakage: investing in transaction benefits, limiting communication, charging referral fees, using price-parity clauses, introducing seller competition on the marketplace, and hiding sellers that try to induce too much leakage. This paper was accepted by Joshua Gans, business strategy. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4757 .

Incentive-compatible mechanisms for online resource allocation in Mobility-as-a-Service systems

In the context of Mobility-as-a-Service (MaaS), the transportation sector has been evolving towards user-centric business models, which put the user experience and tailored mobility solutions at the center of the offer. The emerging concept of MaaS emphasizes that users value experience-relevant factors, e.g., service time, inconvenience cost, and travel delay, over segmented travel modes choices. This study proposes an auction-based mechanism and tractable optimization models for the demand-side management of MaaS systems wherein users’ trip requests are represented as mode-agnostic mobility resources. Users’ requests arrive dynamically in the MaaS system and users compete for mobility resources by bidding for mobility services based on their willingness to pay and experience-relevant preferences. We take the perspective of a MaaS platform regulator who aims to maximize social welfare by optimally allocating mobility resources to users in real-time. The MaaS regulator first decides whether to offer each user a MaaS bundle and identifies the optimal allocation of mobility resources for the selected users. Users have the possibility to accept or reject offered MaaS bundles by comparing the associated utility obtained from MaaS with a reserve utility obtained from other travel options. We introduce mixed-integer programming formulations for this online mobility resource allocation problem. We show that the proposed MaaS mechanism is incentive-compatible, individually rational, budget balanced, and computationally efficient. We propose a polynomial-time online algorithm and derive its competitive ratio relative to an offline algorithm. We also explore rolling horizon configurations with varying look-ahead policies to implement the proposed mechanism. Extensive numerical simulations conducted on large-scale instances generated from realistic mobility data highlight the benefits of the proposed mechanism.

A multi-objective approach for reducing Patient’s inconvenience in a generalized home healthcare delivery setup

In this work, we present a mixed-integer programming model for a multi-objective home healthcare delivery problem. The proposed problem is modeled with minimum assumptions about the procedure attributes and can handle most of the commonly imposed restrictions in the field of home healthcare delivery. Under the imposed restrictions, the model is designed to provide selection (for caregivers and patients), assignment, scheduling, and routing decisions. In addition to some minor modifications in ‘workload balance’ constraints, a major focus of the work is to improve the quality of the schedule for the selected patients. To achieve this, we define and calculate the inconvenience caused by the unnecessarily scattered visits and their overlap with the patient-specific inconvenient time window. The model minimizes the total inconvenience cost for patients against the competitive goals of other stakeholders. Higher net profit, minimum loss of employed labor, balanced workload among staff, and maximization of fully served patients have been included as the other objectives. To solve the instances of the proposed home healthcare delivery problem, an efficient implementation for a reference point-based non-dominated sorting genetic algorithm (NSGA-III) is developed. After extensive parameter tuning using the Taguchi method of experimental design, the algorithm is used for generating a diverse set of non-dominated solutions for the decision-maker. In addition to the comparing the performance of NGSA-III with Multi-objective particle swarm optimization and Multi-objective grey wolf optimizer, experiments are also carried out to establish the relationship between patient convenience and net profit.

Pumping up adoption: The role of policy awareness in explaining willingness to adopt heat pumps in Canada

Heat pumps are a key technology for decarbonising residential buildings, yet their current market share in Canada remains very low at approximately 5 %. To promote heat pump adoption, governments in Canada have introduced supportive policies such as purchase subsidies, and it is often assumed that increasing consumer awareness of such policies increases heat pump adoption. Using a survey of Canadian homeowners who do not own heat pumps (n = 3138), we assess: (1) levels of willingness to adopt air source and ground source heat pumps across Canada, (2) the effect of information provision on willingness to adopt heat pumps, (3) levels of heat pump policy awareness across Canada, (4) whether perceived technical characteristics of heat pumps can be categorized as functional or symbolic, and as private or societal, and (5) the role of policy awareness and other drivers in explaining willingness to adopt heat pumps. We find that a third of Canadian homeowners express willingness to adopt heat pumps. These homeowners are found predominantly within the Atlantic region and show higher levels of adoption willingness for air source rather than ground source heat pumps. Awareness of existing heat pump supportive policy is low, with only 5 % of respondents able to name any policies from memory. Awareness tends to be higher in British Columbia, and for heat pump subsidies and carbon taxes. Policy awareness without cues (i.e., being able to freely recall policies) is a positive predictor of willingness to adopt air source heat pumps only. Policy awareness with cues (i.e., when respondents were provided with a list of policies that served as a memory prompt) is not associated with willingness to adopt either type of heat pump. Other significant predictors include perceptions of heat pumps' functionality and their environmental benefits, having a technology-oriented lifestyle, having higher support for policies, and the financial and inconvenience costs during installation. Based on findings, insights into targeted policy designs to accelerate residential building decarbonisation are provided.

Store density in omnichannel implementation while considering the environmental impact of packaging materials

A staggering amount of packaging materials consumption in e-commerce has become a significant threat to the environment. The option of ordering online and fulfillment by offline stores significantly decreases the use of packaging materials. The aim of this paper is to introduce an analytical model to support the decision of an omnichannel strategy implementation while simultaneously optimizing the offline stores' density, pricing strategy, and environmental impact of packaging materials. Two omnichannel models are compared, the Buy-Online-and-Pick-Up in-Store (BOPS) mode and a combination of the BOPS mode plus a Ship-From-Store (SFS) mode (BOPS + SFS). The BOPS and BOPS + SFS modes consider an inconvenience cost for consumers who decide to pick up their product at the offline store. The consumers' inconvenience cost is a function of the distance to the offline stores, which suggests that a greater stores' density results in a lower inconvenience cost. The proposed analytical model shows that both the BOPS and the BOPS + SFS modes support a reduction of the environmental impact of packaging materials when compared with the online-only mode. Moreover, there is an optimal offline stores' density that maximizes profits while reducing packaging materials. The density of offline stores is a strategic planning decision to balance operating costs and consumer inconvenience costs. The fulfillment of online orders through physical stores is an encouraging strategy to solve the problem of over-packaging and improves the customer's experience through fast and convenient order pickup; furthermore, it is aligned with the interests of environmentally aware customers. The research findings can guide decision makers when analyzing an omnichannel implementation or searching for improvement opportunities in sustainable e-commerce.

Capacity allocation and tolling-rewarding schemes for the morning commute with carpooling

This study investigates the road capacity allocation scheme and step tolling-rewarding scheme for carpooling. Different from most studies that assume a single or a continuous carpooling ratio, this study models multiple scenarios explicitly considering two-person, three-person, and four-person carpooling, as well as shared vans or shuttle buses. The proposed capacity allocation schemes reserve road service capacity during designated time windows for specific carpooling vehicles. During these time windows, the road service capacity is exclusively allocated to carpooling vehicles with a designated number of commuters (or carpoolers), while other vehicles are only allowed to pass the roadway bottleneck outside these time windows. Multiple reserved time windows can be designed and allocated to carpooling vehicles with different numbers of carpoolers. We examine the commuting equilibrium profile under given temporal capacity allocation schemes. We also model the potential “braking or tactical waiting” behavior due to temporal changes of right-of-way under the capacity allocation schemes. On the one hand, the temporal capacity allocation scheme encourages more carpoolers to share one vehicle and thus the number of vehicles on the road is potentially reduced; on the other hand, the temporal capacity allocation scheme yields inconvenience costs due to carpooling and road capacity waste due to braking or tactical waiting behavior under the capacity allocation scheme. We model this trade-off and quantify the system performance under the temporal road capacity allocation scheme. We also extend the analysis to the cases with heterogeneous inconvenience costs due to carpooling and with both temporal and spatial road capacity allocation (i.e., a proportion of road capacity is used for carpooling during certain time windows). Furthermore, we integrate the step tolling-rewarding scheme with the capacity allocation scheme, which further manages the carpooling choices and reduces total system cost. Numerical studies are presented to illustrate the analytical results. We found that carpooling should not be rewarded in some occasions, and instead should be penalized to avoid overcrowded carpooling flows in order to improve system efficiency.

Two-sided pricing strategies for a parking sharing platform: Reselling or commissioning?

A parking sharing platform plays the role of a matchmaker between parking ‘slot owners/sharers’ and ‘slot renters’ who demand parking. The platform can act as either a reseller or a commissioner, and can have different economic objectives when setting prices for owners and renters. This paper is the first to address a platform operator’s pricing strategy in a two-sided parking sharing market under different business formats (reselling and commissioning) and different economic objectives (profit-maximization and social welfare-maximization). The number of parking slots rented out is assumed to be an increasing function of the number of suppliers (owners) who offer a slot for rent, and the number of demanders (parkers) who request a slot. We derive the supply–demand matching equilibrium and the platform’s two-sided pricing strategies for the alternative business formats and objectives. We also analytically and numerically examine how the buying and selling prices, and numbers of participating owners, renters, and transactions vary with parameter values in each business format. We find that a welfare-maximizing platform earns a positive profit if the matching function has decreasing returns to scale, and incurs a deficit if the matching function has increasing returns to scale. If the cost of inconvenience to slot owners from renting a slot equals their cost of disappointment from failing to rent it out, then the reselling and commissioning formats can replicate each other (i.e, they can yield identical platform profit and social welfare). If the cost of inconvenience is greater then the commissioning format is superior, whereas if the disappointment is greater the reselling format is preferred.

A data driven policy to minimise the tuberculosis testing cost among healthcare workers.

IntroductionThe Centres for Disease Control and Prevention (CDC) mandates that healthcare employees at high‐risk exposure to Tuberculosis (TB) undergo annual testing. Currently, two methods of TB testing are used: a two‐step skin test (TST) or a whole‐blood test (IGRA). Healthcare leadership's test selection must account for not only direct costs such as procedure and resources but also indirect costs, including employee workplace absence.MethodsA mathematical model based on Upstate South Carolina's largest health system affecting over 18,000 employees on six campuses was developed to investigate the value loss perspective of these testing methods and assist in decision‐making. A process flow map identified the varied direct and indirect costs for each test for four employee types, and 6 travel‐to‐testing‐site times were calculated.ResultsThe switching point between testing procedures that minimised total system costs was most influenced by employee salary compared to travel distance. Switching from the current hospital policy to an integrated TST/IGRA testing could reduce TB compliance costs by 28%.ConclusionsThis study recommends an integrated approach as cost‐effective for large health systems with multiple campuses while considering the direct and indirect costs. When accounting for ‘inconvenience costs’ (stress, etc.) associated with visits, IGRAs are recommended irrespective of employee salary.

Comparison of Inconvenience Costs Between Influenza Antivirals for Japanese Pediatric Patients: A Conjoint Analysis of Parental Responses.

IntroductionCertain drug characteristics, including dosage and form, are associated with either convenience or inconvenience for the patients taking them, and any inconvenience can be considered as a “cost” in disease treatment. Multiple antivirals are available for influenza in Japan, with various dosages and forms. This study evaluated the inconvenience costs associated with influenza antivirals for pediatric patients by using conjoint analysis on responses from their parents.MethodsAn online survey (May 2021) was conducted for parents whose child took antivirals for influenza at 6–11 years during the 3 years until March 2021. Attributes of the conjoint analysis were administration routes and formulation (tablet, capsule, dry syrup, or inhalant), duration of administration, frequency of administration per day, and out-of-pocket expenses. We assumed the efficacy and safety to be equivalent among the antivirals. A logistic regression model was applied to the analysis. We also asked parents about their recent experiences with antiviral treatment for their child.ResultsWe collected responses from 3161 eligible individuals. The mean age (standard deviation) of the children when taking the antivirals and percentage of female children were 8.27 (1.63) years old and 53.2%, respectively. The tablet was the most preferred formulation; the inconvenience costs for each administration route and formulation, relative to the tablet as zero, were Japanese yen (JPY) 515 (US dollar 4.61, as of October 2021) for the inhalant, JPY 775 for the capsule, and JPY 804 for the dry syrup. The inconvenience costs for 5 days relative to 1 day and for twice a day relative to once a day were JPY 2150 and JPY 399, respectively.ConclusionBased on the conjoint analysis, a single-dose tablet antiviral was suggested to have the lowest inconvenience cost for pediatric patients.Trial RegistrationUMIN000044243.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12325-022-02062-z.

Unlock the Sharing Economy: The Case of the Parking Sector for Recurrent Commuting Trips

This study examines the pricing strategy of a parking sharing platform that rents the daytime-usage rights of private parking spaces from parking owners and sells them to parking users. In an urban area with both shared parking and curbside parking, a choice equilibrium model is proposed to predict the number of shared parking users under any given pricing strategy of the platform. We analytically analyze how the pricing strategy of the platform (price charged on users and rent paid to parking owners or sharers) would affect the parking choice equilibrium and several system efficiency metrics. It is shown that the platform is profitable when some parking owners have a relatively small inconvenience cost from sharing their spaces, but its profit is always negative at minimum social cost. Numerical studies are conducted to illustrate the analytical results and provide further understanding.

Incorporating User Behaviour Into System Dynamics Modelling: A Case Study of Private vs. Public Transport in Latvia

Abstract In this paper, a newly developed dynamic simulation model for transport user behaviour analysis is presented. The study aims to evaluate the impact of various factors on human behavioural intentions, identify the inconvenience costs of public transport, and identify the necessary improvements to shift the demand for population mobility from private cars to public transport. The assessment of such aspects as travel time and travel cost is presented. Modelling assumptions are based on a specially designed public survey allowing to improve the reliability of the developed system dynamics model. Modelling results highlight the multiplicative effect of factors influencing mode shift.

Smart Charging of Electric Vehicles: An Innovative Business Model for Utility Firms

Problem definition: By providing an environmentally friendly alternative to traditional vehicles, electric vehicles will transform urban mobility, particularly in smart cities. In practice, after an electric vehicle is plugged in, the charging station completes charging as soon as possible. Given that the procurement cost of electricity and associated emissions vary significantly during a day, substantial savings can be achieved by smart charging—delaying charging until the cost is lower. In this paper, we study smart charging as an innovative business model for utility firms. Academic/practical relevance: Utility firms are already investing in charging stations, and they can achieve significant cost savings through smart charging. Methodology: We consider a mechanism design problem in which a utility firm first announces pairs of charging price and completion time. Then, each customer selects the pair that maximizes their utility. Given the selected completion times, the utility firm solves the optimal control problem of determining the charging schedule that minimizes the cost of charging under endogenous, time-varying electricity procurement cost. We assume that there are ample parking spots with chargers at the charging station. Results: We devise an intuitive and practically implementable policy for scheduling charging of electric vehicles under given completion times. We prove that this policy is optimal if all customers arrive at the station simultaneously. We also characterize the optimal pairs of charging price and completion time. By using real electricity demand and generation data from the largest electricity market in the United States, we find that cost and emissions savings from smart charging are approximately 20% and 15%, respectively, during a typical summer month. Managerial implications: In contrast to the current practice of charging vehicles without delay, we show that it is economically and environmentally beneficial to delay charging some vehicles and to set charging prices based on customers’ inconvenience cost of delays. We also find that most of the savings from implementing smart charging can be achieved during peak-demand days, highlighting the effectiveness of smart charging.