Inconvenience Cost Research Articles

Smart Charging of Electric Vehicles

Problem Definition: By providing an environmentally friendly alternative to traditional vehicles, electric vehicles will transform urban mobility, particularly in smart cities. In practice, once 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. 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 in the charging station. Results: We devise an intuitive and easy-to-implement 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 U.S., 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 due to delays. We also find that most of the savings of smart charging can be achieved by implementing it only a few peak-demand days in a month, highlighting its practical relevance.

Near-Infrared Image-Based Periocular Biometric Method Using Convolutional Neural Network

The biometric technique of iris recognition is considerably limited by the cost of optical devices and user inconvenience. Periocular-based methods are an alternative means of biometric authentication because they do not require expensive equipment. Moreover, the resulting data are suitable for biometrics because they include features such as eyelashes, eyebrows, and eyelids. However, conventional periocular-based biometric authentication methods use limited sets of features that are dependent on the selected feature extraction method, resulting in relatively poor performance. Therefore, we propose a deep-learning-based method that actively utilizes the various features contained in periocular images. The method maintains the mid-level features of the convolutional layers and selectively utilizes features useful for classification. We compared the proposed method with previous methods using public and self-collected databases. The experimental results show that the equal error rate is less than 1%, which is superior to the previous methods. In addition, we propose a new method to analyze whether mid-stage features have been utilized. As a result, it was confirmed that this approach, which utilizes the mid-level features, can effectively improve the feature extraction performance of the network.

Power structure and channel integration strategy for online retailers

With the boom of e-commerce, express delivery has been increasingly regarded as a bottleneck and key factor for achieving success. Additionally, whether to include such express delivery service or not is an important yet outstandingly unsolved problem for online retailers. In this regard, this paper uses a game-theoretic framework to investigate the channel structure, in which an offline retailer competes with an online retailer selling products to consumers through its partner express company. The consumers purchase from either an online or offline channel considering the delivery service as well as the inconvenience of shopping from physical stores. We consider three power structures: online retailer Stackelberg game, offline retailer Stackelberg game and Nash game. Under each power structure, we characterize the channel integration strategy for the online retailer. Interestingly, our results show that the online channel integration is not beneficial for the online retailer in most cases. Online retailers prefer to use express companies as intermediaries to avoid large logistics operations costs in the offline retailer Stackelberg game and Nash game. Only in the online retailer Stackelberg game, where the online retailer has the first-move advantage in the market, together with a moderate store-visiting inconvenience cost and a delivery service cost coefficient, will vertical integration improve the online channel’s profit. Dominant market power ensures sufficient profit to cover the logistics cost, and the moderate inconvenience cost and service cost coefficient promise a moderate logistics cost. Under this condition, the online retailer will choose the vertical integration strategy. We show in the extension that this strict condition can be relaxed when the online retailer owns a mixed channel. The online retailer with a mixed channel has more incentive to integrate than a pure online retailer does, as the mixed channel adds his power and helps to gain more market shares and profit. Our analysis generates managerial insights into the relationship between online retailers and express companies and provides a guide for implementing the vertical integration strategy in the online retailing industry.

Branch-and-Cut-and-Price for the Vehicle Routing Problem with Time Windows and Convex Node Costs

Two critical yet frequently conflicting objectives for logistics and transportation service companies are improving customer satisfaction and reducing transportation cost. In particular, given a network of customer requests with preferred service times, it is very challenging to find vehicle routes and service schedules simultaneously that respect all operating constraints and minimize the total transportation and customers’ inconvenience costs. In this paper, we introduce the vehicle routing problem with time windows and convex node costs (VRPTW-CNC), in which we model each customer’s inconvenience cost as a convex function of the service start time at that customer. The VRPTW-CNC combines and extends both the standard vehicle routing problem with time windows and some previous results on the optimal service scheduling problem over a fixed route. We propose a branch-and-cut-and-price algorithm to solve the VRPTW-CNC with general convex inconvenience cost functions. To solve the pricing problem, our labeling algorithm only generates labels that possibly lead to optimal schedule times over a route, which significantly improves the effectiveness of pricing. Extensive computational results demonstrate the effectiveness of our approach.

An inconvenient cost: The effects of climate change on municipal bonds

Counties more likely to be affected by climate change pay more in underwriting fees and initial yields to issue long-term municipal bonds compared to counties unlikely to be affected by climate change. This difference disappears when comparing short-term municipal bonds, implying the market prices climate change risks for long-term securities only. Higher issuance costs for climate risk counties are driven by bonds with lower credit ratings. Investor attention is a driving factor, as the difference in issuance costs on bonds issued by climate and nonclimate affected counties increases after the release of the 2006 Stern Review on climate change.

Understanding the Impact of Environmental Education on Tourists’ Future Visit Intentions to Leisure Farms in Mountain Regions

The research problem of this study is to understand whether people with higher level of EE are more likely to choose farm stays for their vacation. The main aim of this study is to investigate the causal relationship between an individual’s environmental education (EE) and their theory of planned behavior (TPB) decision-making process that leads to future visit intention. A convenient sampling method was used to survey visitors of Toucheng Leisure Farm in the mountain region of Taiwan; and a total of 600 responses were collected. Structural equation modeling (SEM) was used to analyze the data. The results showed that five of the six hypotheses were supported. This finding suggests that environmental education makes an individual more pro-environmental and susceptible to the social norms about environmental protection. Environmental education, however, does not affect the inconvenience or extra cost incurred by purchasing green products.

Perception of inconvenience costs: Evidence from seven ferry services in Norway

Transport projects are assessed mainly by cost-benefit analysis (CBA). Despite its wide use, this method has some weaknesses, such as using simulated traffic volumes as an input. These traffic volumes for future projects are estimated according to user travel patterns based on the new generalised costs (GC), which include travel time, distance, and direct costs. Nevertheless, many large transport projects dramatically change accessibility; thus, other factors could play important roles in the process. Notably, ferry replacement projects reduce travel times, eliminate waiting times, and provide a reliable and flexible connection 24/7. Some of these changes, known as inconvenience costs (IC), are not included in the GC.This study contributes to the scarce literature on the IC concept and relates IC to the factors that different users value. The results, which are based on 9,878 answers from onboard ferry surveys in 2011 and 2016, revealed that inconvenience factors, such as trip planning and the lack of accessibility to family/friends, leisure activities and a broader labour market, are important. Thus, projects that improve these factors might trigger additional changes in user travel patterns. Four partial proportional odds models were estimated to relate the inconvenience factors to the trip and the socioeconomic features of users. The results showed that the waiting time, frequency of trips via the connection, trip purpose, gender, age and income played important roles in the perception of different factors. In addition, a quantification of the differences in the perceptions among the categories of the variables was described based on the average marginal effects (AME).

Optimal Power Control Strategy of a Hybrid Energy System Considering Demand Response Strategy and Customer Interruption Cost

Background: The integration of distributed renewable energy sources into the conventional power system network has created opportunities for electricity customers to reduce their electricity cost. This paper investigates the optimal power scheduling of a hybrid energy system connected to the grid in the presence of demand response strategy and inconvenience cost. Methods: A new proposed method of calculating the inconvenience cost which is dependent on total home appliance load, Customer Interruption Cost (CIC) and delay time operation of home appliances is proposed. The hybrid energy system consists of solar photovoltaic (PV) module and battery bank storage system. The home appliance scheduling is formulated as a non-convex mixed integer programming with a binary decision variable to switch ON/OFF the home appliances. The optimization objective is to minimize both the total daily electricity cost and inconvenience cost of a residential customer with different time-shiftable, power shiftable home appliances and customer time preference constraints. Results: The results show that it is important to schedule home appliances and include their inconvenience cost so that home appliances are not only shifted to the lower electricity tariff periods but can also start at their customer preferred operation times. Conclusion: The results also show that the hybrid energy system is able to cater for all the energy requirements of home appliances during the day, reducing power demand from the grid by a significant percentage and thus, relieve the power system network and afford electricity consumers significant monetary savings.

An Overview of Pricing Models for Using Cloud Services with analysis on Pay-Per-Use Model

Emergence of Cloud computing in recent years has provided various options to end-users w.r.t. cloud services. Different end users have different requirements for cloud services such as IAAS, PAAS & SAAS, but these services can be availed using different pricing mechanisms such as PPU, PFR, leased based, subscription based and dynamic pricing based on factors such as initial cost, lease period, QoS, Age of resources and cost of maintenance. The authors work focusses on ‘pay-per-use’ model of cloud pricing by studying various aspects of this model and comparing the current pricing rates of leading cloud service provider. Through this paper, the authors try to analyse the pricing model used by provider by comparing similar pricing offered by competitors. Authors will also try to establish the fairness of pricing as basis for designing better model for such services. The idea of pay per use has emerged to counter the rampant software piracy, while capturing the marginal and heterogeneous users who have been often found to use pirated software, as the acquisition costs for perpetual usage are too high. The marginal usage does not justify the huge capital investment of perpetual license, thereby leading to software piracy. In this paper the authors have also discussed on the Pay per use SaaS model and how it is better than the perpetual licencing, Pay per use is primarily dependent upon certain market conditions, like higher potential for piracy, lower inconvenience costs, majority of marginal users, and strong cloud network presence. Whereas perpetual licensing is important for heavy users, a market having the above-mentioned conditions will always benefit the SaaS pay per use model. So while the developer finds advantages of increased authorized user network, lower costs of marketing, enhanced customer reliability, and lesser impact of piracy, the rewards for users are even greater as they get to use the licensed full & updated versions for a small fee, even for minor everyday usage without incurring the huge expenditure on acquisition.

Software Licensing: Pay-Per-Use versus Perpetual

Piracy has been a major problem for perpetually licensed software. Usage-based licensing architecture such as pay-per-use or software-as-a-service can offer technology-based protection against piracy. We provide an analytical framework to examine the economic implications of pay-per-use versus perpetual licensing in a market with potential piracy, network effect, and heterogeneous consumers in terms of marginal usage benefit and acquisition costs for pirated software. We show that the potential piracy rate, the user inconvenience cost of pay-per-use licensing, consumer heterogeneity, and the network strength are important factors determining a vendor’s optimal choice of licensing architecture. While perpetual licensing tends to be optimal when consumers have homogeneous valuations, pay-per-use is more profitable than perpetual licensing or mixed licensing in markets with heterogeneous consumers and low user inconvenience costs. If the inconvenience cost is low enough, pay-per-use will be more profitable than perpetual licensing even if the market has no potential piracy. The presence of network effect also favors pay-per-use over perpetual licensing; if the network effect is strong, pay-per-use will always dominate perpetual licensing regardless of the inconvenience cost or the potential piracy. With more heterogeneous consumers, higher potential piracy, lower inconvenience costs, and stronger network effects, pay-per-use licensing yields not only higher vendor profits but also a higher social surplus than perpetual licensing. Important managerial implications are also discussed.

Optimal Preventive Law Enforcement and Stopping Standards

Preventive law enforcement increases social welfare by hindering the infliction of criminal harm, but produces inconvenience costs to the general public, because it requires interfering with the acts of innocents as well as attempters. The optimal amount of investment in preventive enforcement is greater than that which maximizes deterrence, but, smaller than that which minimizes criminal harm. Thus, ignoring preventive benefits and/or inconvenience costs results in an inefficient investment portfolio over enforcement methods, and in a predictable manner. Stopping standards, which determine the threshold suspicion required to trigger a stop, are tools that can be used to optimally trade-off the costs and benefits associated with preventive enforcement. The optimal stopping standard is weaker than its analogs in the trial context, namely standards of proof, which generally require preponderance of the evidence in civil trials and proof beyond a reasonable doubt in criminal trials. Finally, suspicionless stops can be optimal in a variety of circumstances, and are more likely optimal when enforcers perform poorly in forming suspicions; inconvenience costs are small; the population is unresponsive to deterrence measures; and the attempt rate is high.

Optimal Preventive Law Enforcement and Stopping Standards

Preventive law enforcement increases social welfare by hindering the infliction of criminal harm, but produces inconvenience costs to the general public, because it requires interfering with the acts of innocents as well as attempters. The optimal amount of investment in preventive enforcement is greater than that which maximizes deterrence, but, smaller than that which minimizes criminal harm. Thus, ignoring preventive benefits and/or inconvenience costs results in an inefficient investment portfolio over enforcement methods, and in a predictable manner. Stopping standards, which determine the threshold suspicion required to trigger a stop, are tools that can be used to optimally trade-off the costs and benefits associated with preventive enforcement. The optimal stopping standard is weaker than its analogs in the trial context, namely standards of proof, which generally require preponderance of the evidence in civil trials and proof beyond a reasonable doubt in criminal trials. Finally, suspicionless stops can be optimal in a variety of circumstances, and are more likely optimal when enforcers perform poorly in forming suspicions; inconvenience costs are small; the population is unresponsive to deterrence measures; and the attempt rate is high.

Inconvenience cost of mobile communication failure: The case of South Korea

Mobile communication failure can occur when mobile traffic exceeds the manageable level. This depends on frequency bandwidth. Mobile communication failure causes inconveniences in a user's daily life that lead to social and economic damage. To address this issue, mobile telecommunications companies deploy additional bandwidths and develop new technologies, but these are costly strategies. This study applies a spike model based on a contingent valuation method (CVM) to measure the inconvenience cost resulting from mobile communication failure. The mean monthly willingness-to-pay (WTP) to avoid communication failure per user is estimated to be KRW 898.14 (USD 0.80) over a period of five years in our study. The inconvenience cost borne by the population is estimated to be KRW 2.97 trillion (USD 2.61 billion). Users experiencing greater frequency of communication failure are found to be willing to pay more to avoid the inconvenience. When excluding respondents citing zero-WTP, the mean WTP per user was calculated to be KRW 3426.41 (USD 3.01). Data traffic usage and frequency at which communication failure is experienced are variables that exhibit statistically significant effects on WTP to avoid mobile communication failure. Overall, estimation results show that a price discrimination based on data traffic usage or quality can be considered by mobile telecommunications companies and regulators to address the issue of data traffic inducing mobile communication failure.

Price Discounts and Consumer Load-Shifting Behavior in the Smart Grid

This paper studies the impact of consumers' individual attitudes towards load shifting in electricity consumption in an electricity market that includes a single electricity provider and multiple consumers. A Stackelberg game model is formulated in which the provider uses price discounts over a finite number of periods in order to induce incentives for consumers to shift their peak period loads to off-peak periods. The equilibrium outcomes are investigated and the analytical results are derived for this type of market, where not only the response behaviors of independent consumers are diverse but also an individual consumer's valuation of electricity consumption varies across periods. The obtained results demonstrate that consumer sensitivities to price discounts significantly impact price discounts and load-shifts, which are not necessarily monotonic. The authors also observe that a diverse market leads to lower peak-to-average values and provider payoffs compared to a homogenous market unless the latter one is composed of consumers with relatively lower inconvenience costs during the peak periods.

Impact of Bilateral Rating System on Ride-Sharing Platforms

Traditional online platforms (e.g., Amazon Marketplace) use Unilateral Rating System (URS), where customers rate sellers. However, sharing economy platforms (e.g., Uber, Airbnb) have adopted Bilateral Rating System (BRS) that also allows service providers to rate customers, and even selects customers based on their ratings. BRS is often purported to be better than URS, as BRS unlocks more hidden information than URS by revealing ratings of customers to service providers before they make acceptance/rejection decisions. We compare URS and BRS in the context of the ride-sharing service to study its impact on all stakeholders in the system. Conventional wisdom suggests that BRS would harm riders, as riders can be rejected by drivers while platforms should benefit from BRS, since it unlocks the value in the riders’ rating that can be potentially extracted. We find that contrary to this intuition, BRS may increase riders’ welfare under some conditions, as drivers rejecting low-rating riders can reduce the excess demand and the associated inconvenience cost born by all riders in the system. BRS can both improve or reduce the platform’s revenue, as there is a tug-of-war between the platform and drivers on the revenue share. Under the right conditions, BRS can result in a win-win-win situation for the drivers, riders and the platform.

Integrating wet appliances with delay functionality in distribution network operation and planning

Due to the lack of observability and controllability in existing distribution networks, the traditional operation and planning framework makes the oversimplifying assumption that customers' demand can be either supplied or shed at a cost determined by a benchmark value of lost load. For most load appliances, however, the users are more likely to shift their operation in time than completely avoiding using them. Driven by the advanced metering and control capabilities of the emerging smart grid, this study develops a model of distribution network operation and planning integrating this time-shifting flexibility and accounting for the associated customers' inconvenience cost. The study focuses specifically on wet appliances with delay functionality, given their significant penetration, energy consumption and time-shifting flexibility. Case studies on a typical UK high-voltage (HV) distribution network demonstrate the value of such flexibility in reducing network operating costs and postponing network reinforcements.

Airline delay management problem with airport capacity constraints and priority decisions

This paper deals with the Airline Delay Management Problem (ADMP), which can be described as the task of dealing with daily airline operational delays and deciding whether to delay subsequent flights at a hub airport or to have them departing on time. An innovative integer linear programming approach is presented to the capacitated case of the ADMP and airport limitations in terms of bay availability, taxiway capacity and runway separation are incorporated to represent capacity constraints. Fuel cost, passenger compensation, and passenger inconvenience costs are included in the objective function. The decision variables include the re-timing of flight departures and arrivals, the use of the airport capacity over time and the rebooking of passengers in case of missed connections. To guarantee the linearity of the optimization model and fast computational times, a receding horizon modeling framework is adopted. The approach is applied to a case study using real operational and passenger data from an international hub-and-spoke carrier. The case study shows the capability of the linear model to deal with a complete day of operations within a few minutes. The results suggest that the proposed approach can lead to cost reductions of almost 30% during recovery, when compared with the solution from the airline. In addition, a sensitivity analysis is provided to investigate the impact of not including passenger inconvenience costs and of reducing runway capacity.

Inconvenience cost of waste disposal behavior in South Korea

Pro-environmental activities, such as waste sorting, are considered inconveniencing; the higher the inconvenience, the more difficult it becomes to encourage active public participation. This study defines waste sorting behavior considering certain attributes and estimates the inconvenience costs associated with each attribute. The definition also considers how and when waste is disposed of as well as the hygiene of a disposal spot. We apply a conjoint analysis for data collection and latent class logit model to calculate the inconvenience costs. The model incorporates consumers' heterogeneity as a finite number of homogenous groups. The results show that the inconvenience cost for the hygiene of the disposal spot is generally higher than that of sorting itself; this tendency is strongest among young women. Moreover, older people report lower inconvenience costs than do younger ones. Further, some groups prefer manual sorting to an automated sorting service for food waste. Our findings offer policy implications considering such inconvenience costs.

Valuing the risk and social costs of road traffic accidents – Seasonal variation and the significance of delay costs

By using a conventional risk model, and a time loss model for delay, the risk, severity, and social costs of road traffic accidents have been estimated on a rural transport corridor in an area with large seasonal variations. The novelty of the study lies in the comparison of the estimates between seasons, and the inclusion of delay costs when assessing the total social costs of accidents for private motor vehicles and heavy vehicles. Increasing congestion in urban areas has motivated researchers’ interest in studying the cost of delays due to accidents. However, still many countries, such as Norway, do not include delay costs when estimating the social costs of road accidents. In this study, we show that these costs can constitute a significant proportion of the social costs of accidents in rural areas, particularly during winter in regions with strong seasonal variations. The delay costs on the studied road section constituted on average 10% of total annual social costs of accidents, and were nearly 70% higher than the accidents’ material costs. By including these inconvenience costs, we would achieve better estimates of the social costs of accidents, which would in turn give rise to more accurate assessments of the costs and benefits of accident reduction measures, as well as measures reducing the response time when accidents happen. Many road safety measures have been aimed at reducing accidents involving death and serious injury. This analysis shows that it can also be beneficial to take measures to reduce the number of less severe accidents, particularly in rural areas where delay costs can be high when the roads are closed because of accidents. It is thus, particularly important that such costs are included in project assessment tools to ensure that rural areas do not lose the fight for road investments.

Pricing scheme design of ridesharing program in morning commute problem

This paper examines the dynamic user equilibrium of the morning commute problem in the presence of ridesharing program. Commuters simultaneously choose departure time from home and commute mode among three roles: solo driver, ridesharing driver, and ridesharing rider. Considering the congestion evolution over time, we propose a time-varying compensation scheme to maintain a positive ridesharing ridership at user equilibrium. To match the demand and the supply of ridesharing service over time, the compensation scheme should be set according to the inconvenience cost functions and the out-of-pocket cost functions. When the price charged per time unit is higher than the inconvenience cost per time unit perceived by the ridesharing drivers, the ridesharing participants will travel at the center of peak hours and solo drivers will commute at the two tails. Within the feasible region with positive ridership, the ridesharing program can reduce the congestion and all the commuters will be better off. To support system optimum (SO), we derive a time-varying toll combined with a flat ridesharing price from eliminating queuing delay. Under SO toll, the ridesharing program can attract more participants and have an enlarged feasible region. This reveals that the commuters are more tolerant to the inconvenience caused by sharing a ride at SO because of the lower travel time. Compared with no-toll equilibrium, both overall congestion and individual travel cost are further reduced at SO.