Inconvenience Cost Research Articles

Network user equilibrium problems with infrastructure-enabled autonomy

Although autonomous vehicle (AV) technology is expected to bring dramatic societal, environmental, and economic benefits, the high vehicle cost might slow the adoption of AVs. This paper explores an infrastructure-enabled autonomous driving system, which is a promising remedy to the high cost of AVs. Specifically, the system combines vehicles and infrastructure in the realization of autonomous driving. Equipped with roadside sensing, computing, and communicating devices, an ordinary road can be upgraded into an “automated road” enabling autonomous driving service for vehicles with the minimum required on-board devices. The vehicle costs can thus be significantly reduced. We envision that automated roads will be deployed in transportation networks to serve a new type of vehicle called infrastructure-enabled autonomous vehicles (IEAVs), which can be driven autonomously only on automated roads but manually on ordinary roads. Therefore, IEAV users may experience inconvenience costs due to transitions between autonomous driving and manual driving, and the frequent switch will inevitably yield high inconvenience costs. Therefore, to minimize their individual travel cost, they have to decide whether to switch to the autonomous driving mode when heading to an automated road. Considering such a unique feature of IEAVs, we proposed a group of driving-mode-choice equilibrium conditions to describe IEAV drivers’ driving mode choice behaviors, in which we considered drivers’ travel time costs, service charges of autonomous driving, and inconvenience costs due to driving mode change. Combining traditional route-choice equilibrium conditions with the proposed driving-mode-choice equilibrium conditions, we developed a new user equilibrium (UE) model to describe the equilibrium flow distributions in a road network with automated roads and mixed-autonomy traffic. The UE model is formulated as a novel non-linear complementarity problem, and its solution existence is discussed. To solve the UE model, a network expansion method is proposed to reformulate the UE model as a standard path-based UE model. A route-swapping-based solution algorithm is then used to solve the reformulated UE model. Numerical studies are presented to demonstrate the proposed models and algorithms.

Cost-sharing mechanism design for ride-sharing

In this paper, we focus on the cost-sharing problem for ride-sharing: determining how to allocate the total ride cost between the driver and the passengers. In particular, we focus on the scenario where drivers are also commuters with the goal of cost recovery. We identify the properties that a desirable cost-sharing mechanism should have and develop a general framework that can be used to create specific cost-sharing mechanisms. We propose specific mechanisms and analyze their relative advantages and disadvantages so that service providers can select a mechanism according to their different needs. In addition, we incorporate the value of time by allowing passengers to have inconvenience costs due to extra travel time caused by detours for picking up the passengers, and provide discount methods to compensate for these costs. We evaluate our approach using real traffic data from the downtown Los Angeles area. Our results show that each proposed mechanism has its unique advantages and that the discount methods can successfully reduce the number of no-passenger vehicles for a large ride-sharing system.

Dynamic Pricing and Matching for Two-Sided Markets with Strategic Servers

Motivated by applications in online marketplaces such as ridesharing, we study dynamic pricing and matching in two-sided queues with strategic servers. We consider a discrete-time process in which, heterogeneous customers and servers arrive. Each customer joins their type's queue, while servers might join a different type's queue depending on the prices posted by the system operator and an inconvenience cost. Then the system operator, constrained by a compatibility graph, decides the matching. The objective is to maximize the profit minus the expected waiting times. We develop a general framework that enables us to analyze a broad range of strategic behaviors. In particular, we encode servers' behavior in a properly defined cost function that can be tailored to various settings. Using this general cost function, we introduce a novel probabilistic fluid problem as an infinite dimensional optimization program. The probabilistic fluid model provides an upper bound on the achievable profit. We then study the system under a large market regime in which the arrival rates are scaled by η and present a probabilistic two-price policy and a max-weight matching policy which results in $O(η^1/3 )$ profit-loss. In addition, under a broad class of customer pricing policies, we show that any matching policy has profit-loss Ω(η1/3). Conditional on a given expected waiting time, we also establish scale-free lower and upper bounds for the profit. Our asymptotic analysis provides insight into near-optimal pricing and matching decisions, and our scale-free bounds provide insights into how different service levels impact the firm's profit.

Integrated U.S. nationwide corridor charging infrastructure planning for mass electrification of inter-city trips

This study introduces an integrated modeling framework to evaluate long-term national corridor charging infrastructure requirements in the United States to support the growing inter-city charging demand with the rapid growth in the battery electric vehicle (BEV) market. The core model is an optimization model that considers spatial and temporal dimensions and models heterogeneous behaviors between travelers. The model also introduces the travelers’ inconvenience cost function by linking travelers’ acceptance of the charging infrastructure with exogenous technology and social factors. The inconvenience cost function simulates mode choice between BEVs and alternative modes by heterogenous travelers. We applied the framework to assess the inter-regional charging infrastructure requirements for the entire U.S. mainland interstate highway network. We evaluated impacts on the infrastructure design and its public acceptance with changes in policy, technology, and demographic characteristics, and we also quantified the importance of modeling full-scale inter-regional charging infrastructure requirements compared to the conventional regional level analyses.

Impact of Penalty Cost on Customers' Booking Decisions

We study a novel newsvendor‐type problem where the information on demand quantity is not exogenously given. The customer needs to make the booking decision based on her estimation on demand, which is affected by the value of shortage penalty cost. The problem is motivated by low‐cost airline service practices where passengers need to book baggage allowance for their travel. The baggage overweight price affects the accuracy of passengers' baggage weight estimation and thus their booking quantities. Through stochastic decision models, we analytically characterize the impact of shortage penalty cost on passengers' booking decisions as well as airline's profit. We consider various modeling settings, including a system with multiple passengers and a system where passengers have stochastic inconvenience costs on not‐carrying overweight baggage. The results and insights from our study provide guidelines for firms to set their optimal penalty prices.

An Analysis of Incentive Schemes for Participant Retention in Clinical Studies

Participant retention is one of the significant issues faced by clinical studies. Various factors, such as expected health benefits, logistics expenses, and inconvenience during the study, impact retention rate. In this paper, we focus on analyzing mechanisms that combine both monetary payments and the effort of a clinic to improve retention. In particular, we consider a clinic that conducts a clinical study and a set of participants characterized by their health conditions, logistics expenses, and inconvenience costs. The clinic offers monetary payments to participants and exerts effort to reduce inconvenience. The participants decide to complete or leave the study based on their utilities. Considering the participants' decisions, the clinic minimizes his cost of achieving a desirable retention rate. Given a participant-specific linear payment scheme, we model the clinic's problem as a non-linear 0--1 integer program and propose a polynomial-time algorithm to solve it under full information. We further modify this algorithm to solve the clinic's problem under two commonly observed incentive schemes--- the Fixed Payment scheme and the Logistics Reimbursement scheme. Then, we conduct a comprehensive computational study to gain insights on how participant population, clinic, and study characteristics affect the relative performance of these schemes. Our analysis highlights that by optimally designing a participant-specific scheme, the clinic can reduce his cost by about 47\% on average compared to that under the reimbursement and fixed payment schemes. We also extend our analysis to a setting where the clinic does not observe participants' characteristics and show that the performance of the reimbursement scheme is robust to this information asymmetry. Further, if the clinic prefers a fixed payment scheme, it benefits significantly from acquiring information on participants' characteristics.

Potential of carpool for network traffic management

This study examines the impact of carpool on network traffic in a highly idealized futuristic world, where all travelers are willing to participate in carpool arranged by a Transportation Network Company. We build a parsimonious carpool model that focuses on the trade-off between inconvenience costs and travel cost savings. Underlying the model is a nonlinear bipartite matching problem that seeks to maximize commuters’ welfare. By assuming the congestion effect is negligible, we derive several useful analytical results. When the inconvenience cost is less than the median trip valuation of a rider, the platform could always achieve an almost perfect match while maximizing commuters’ welfare, which corresponds to a 50% reduction in vehicular traffic flow. In the case of perfect match, if there is an even number of travelers, we propose a pricing policy that possesses all desired properties of the Vickrey-Clark-Groves (VCG) policy – a benchmark truthful policy for achieving socially optimal solution – but runs a lower deficit. Otherwise, we show the VCG policy always generates a profit. If the inconvenience cost is too high, the perfect match is no longer socially optimal, but the VCG policy still yields a positive profit. Solutions from numerical experiments generally agree with the analytical results. They also suggest that matching across O-D pairs occurs only when it has a significantly lower inconvenience cost than matching within, an unlikely event in reality. Moreover, when cross O-D matching does become prevalent, it leads to higher vehicle miles travelled, hence worse congestion. Thus, from the point of view of traffic management, cross O-D carpool should not be encouraged.

Can a Manufacturer Get Better off When Providing Product Sharing Services?

The emerging product sharing trend poses challenges for manufacturers. Although the cost of purchasing products is decreased by adopting product sharing, consumers must accept that their demand cannot be responded to instantly and must use unfamiliar shared products. There are unavoidable inconveniences that are inherent in product sharing that directly affect consumers’ choices and further affect manufacturers’ decisions about providing sharing services. However, few papers have focused on the impact of such factors. In this paper, we aim to study how the unavoidable inconvenience costs and operational costs affect the manufacturer’s decision on whether to provide sharing services. First, low inconvenience cost and operational cost encourage the manufacturer to provide sharing services. High product quality weakens the influence of these two factors, while strengthening the influence of the pooling effect on the manufacturer’s provision decision. Second, the sharing market cannibalizes the sales demand when inconvenience cost is low and product quality is high. Third, the provision of sharing services is a win-win situation for consumers and the manufacturers of low-quality products.

Optimal scheduling of a micro-grid with multi-period islanding constraints using hybrid CFCS technique

This paper proposes an efficient technique for optimum scheduling of micro-grids with multi-period islanding restrictions. The proposed method is joint implementation of Cuttle Fish Algorithm (CFA) and Crow Search Algorithm (CSA); is known CFCS method. Here, the CFA search behavior is modified by the CSA update position. The proposed CFCS system is utilized for optimal scheduling of micro-grid and considerably decreases the calculation load. The main purpose of proposed work is diminish the micro-grid operation cost together with the dispatch-able units operation cost, cost of power transmission as main grid with inconvenience cost recognized with consumers. The cost of power transmission from main phase can be positive or negative based on the flow direction of transmission line linking the microcircuit to main phase. Negative cost that represents an export of power to main grid emerges as economic advantage of micro-grid. Cost of inconvenience implies the penalty of modifiable loads programming outside the time intervals particular by the consumers. Constant penalty factor is utilized for prioritizing loads with respect to operation sensitivity inside the particular time intervals, in which a superior value of the penalty factor implies a least flexible load based on time interval operation settings. The value of the penalty factor is chosen reasonably greater to unit generation cost and market price. In proposed technique, the CFCS is used for establishing the correct program of the MG combinations according to load side power range. The CFCS method, objective function can be defined as system data subject to equality and inequality limitations. At that time, CFCS model is performed at MATLAB/Simulink working platform and performance is estimated to existing techniques.

A Differentially Private Incentive Design for Traffic Offload to Public Transportation

Increasingly large trip demands have strained urban transportation capacity, which consequently leads to traffic congestion and rapid growth of greenhouse gas emissions. In this work, we focus on achieving sustainable transportation by incentivizing passengers to switch from private cars to public transport. We address the following challenges. First, the passengers incur inconvenience costs when changing their transit behaviors due to delay and discomfort, and thus need to be reimbursed. Second, the inconvenience cost, however, is unknown to the government when choosing the incentives. Furthermore, changing transit behaviors raises privacy concerns from passengers. An adversary could infer personal information (e.g., daily routine, region of interest, and wealth) by observing the decisions made by the government, which are known to the public. We adopt the concept of differential privacy and propose privacy-preserving incentive designs under two settings, denoted as two-way communication and one-way communication. Under two-way communication, passengers submit bids and then the government determines the incentives, whereas in one-way communication, the government simply sets a price without acquiring information from the passengers. We formulate the problem under two-way communication as a mixed integer linear program and propose a polynomial-time approximation algorithm. We show the proposed approach achieves truthfulness, individual rationality, social optimality, and differential privacy. Under one-way communication, we focus on how the government should design the incentives without revealing passengers’ inconvenience costs while still preserving differential privacy. We formulate the problem as a convex program and propose a differentially private and near-optimal solution algorithm. A numerical case study using the Caltrans Performance Measurement System (PeMS) data source is presented as evaluation. The results show that the proposed approaches achieve a win-win situation in which both the government and passengers obtain non-negative utilities.

Dynamic channel decision-making of fresh agricultural product companies considering consumer convenience preferences

This paper studies the strategic channel decision-making of fresh agricultural product enterprises from the perspective of consumer convenience preferences. This research is carried out under the background of the upgrading of domestic consumption and the explosion of stay-at-home economy in China in recent years. In this paper, consumers are segmented firstly according to their different preference for consumption time, namely, convenience preference. Then, a channel decision model which considers the consumer convenience sensitivity and the inconvenience cost is proposed. Finally, the optimal wholesale price and delivery lead time of the supplier and the service level decision of the retailer are obtained by using backward induction method. The results show that under different market conditions, the supplier can adopt three equilibrium strategies: NR (No Retailer), CP (Capture Profit) and SP (Share Profit). With the change of inconvenience cost, the equilibrium state of supply chain will also change. Fresh agricultural product companies can choose different strategies to maximize their revenue according to different markets.

The role of energy storage and demand response as energy democracy policies in the energy productivity of hybrid hub system considering social inconvenience cost

Energy liberation policies are very effective in the development of energy democracy in society. The Demand Response Programs (DRPs) and Electrical Energy Storages (EESs) are the core activities of energy democracy in the energy and social science policies. Furthermore, energy democracy programs have an influential role in the energy productivity. Moreover, the energy productivity concept divides into energy effectiveness and energy efficiency programs. Therefore, the contribution of EESs and DRPs in the effectiveness aspect of energy productivity is significant. However, the implementation of DRPs from the system operator perspective in the smart society causes to increase the social inconvenience of customers. Therefore, this study provides a novel multi-objective probabilistic economic and environmental energy management structure to evaluate the role of EESs and social inconvenience of DRPs on the energy hub system incorporating various carriers such as electrical, heat, gas, and water infrastructure. The energy interdependency causes to increase the scheduling flexibility in the energy hub system. Furthermore, the seawater desalination units make the significant potential of water management in the remote energy hub areas that are near to the seawater. Therefore, the proposed model considers the role of programs and technologies (such as EESs and DRPs) in the optimal power and water nexus problem. Also, the proposed multi-objective function is modeled as a MILP function and the problem is solved using the CPLEX solver in the GAMS software. The results show that the Sensitivity Coefficient of Customers (SCCs) in DRPs has significant effects on the effectiveness of DRPs and EESs in the energy hub system. Indeed, considering social energy democracy policies increases the energy productivity of the hub system.

Free contents vs. inconvenience costs: Two faces of online video advertising

With the growth of online video platforms such as YouTube and Netflix, not only the video content market, but also the online video advertising market are growing rapidly. However, as the video content industry expands, the inconvenience caused by video advertisements is increasing, leading to ad-blocking by consumers. To identify the causes of ad-blocking, this study collects consumer preference data using a conjoint survey and estimates the inconvenience cost of advertisements using a mixed logit model. In addition, we performed computational experiments by changing advertising attributes to identify strategies for increasing the market share of online video platform companies. Our results show that the use of personal information affects consumers most seriously, and that platforms can increase market share by reducing the length of advertisements, by reducing the use of personal information, or by blocking all advertising.

Build or join a sharing platform? The choice of manufacturer's sharing mode

Facing the upsurge of sharing economy, in order to regain market opportunities, traditional manufacturers have provided product sharing services through two sharing modes. One mode is the self-built mode, the manufacturer will build a sharing platform on which consumers' rental is relatively inconvenient. The other mode is the joined mode, the manufacturer will join a third-party sharing platform on which the manufacturer is charged a commission fee. In this paper, we develop an analytical framework consisting of a manufacturer and a third-party sharing platform to study the optimal choice of manufacturer's sharing mode, in which consumers' preference toward renting manufacturer's products is heterogeneous. Our analysis shows that the manufacturer will not provide product sharing service when the commission rate, the consumers' inconvenient cost and the marginal cost are all high. Further, we obtain the optimal choice of sharing mode, that is, when the consumers' inconvenient cost is relatively low but the marginal cost is relatively high, the manufacturer will build a sharing platform, while when the consumers' inconvenient cost is relatively high but the marginal cost is relatively low, the manufacturer will join a third-party sharing platform. We also find that the choice of sharing mode is influenced by the proportion of consumers who prefer to rent manufacturer's products. As the proportion increases, the manufacturer is more likely to join the third-party sharing platform.

A path-based equilibrium model for ridesharing matching

This paper examines how the ridesharing program will reshape the spatial distribution of traffic congestion. With ridesharing services, travelers make multi-dimensional decisions concerning mode choices, route choices, and matching decisions. We build a path-based equilibrium model to describe the decision-making of travelers in the presence of the ridesharing program and thereby to forecast the stable congestion distribution in the long run. Specifically, our model can explicitly track the path information of the matched drivers and riders by solving the proposed equilibrium model. The model is formulated as variational inequalities due to the non-separable cost functions and asymmetric link interactions. We first demonstrate the non-uniqueness of mode-specific link flows and derive the condition for the uniqueness of link flows. We then derive sufficient conditions for a matching failure. A matching failure occurs if the lowest total inconvenience cost of a pair of rider and driver exceeds a threshold. In this situation, matching between the ridesharing supply (drivers) and demand (riders) cannot be established by adjusting the compensation scheme. Moreover, we derive the sufficient and necessary conditions for the existence of equilibrium with a positive ridesharing ridership. These conditions help operators gain insights into how to appropriately design compensation schemes. We reformulate the variational inequalities as the equivalent mixed complementarity problem, and propose a heuristic routing algorithm to identify the feasible routing paths for shared rides that interest both ridesharing drivers and riders. Finally, Our numerical experiments show that ridesharing is effective in relieving the overall congestion but leads to high congestion on some links. In the scenarios where the demand into the central business district (CBD) is high, the commuters entering CBD are all better off with ridesharing service. In contrast, the commuters traveling to certain non-CBD areas are worse off. Our experiments also reveal various path choice behaviors of ridesharing participants. The solo drivers always use the paths with short travel times, while the ridesharing drivers tend to use paths with short travel distances. By considering a commission to the platform, we demonstrate the regime in which increasing the commission rate improves the platform’s revenue without harming the social welfare.

Optimal purchase subsidy design for human-driven electric vehicles and autonomous electric vehicles

This paper studies the purchase subsidy design problem for human-driven electric vehicles (HDEVs) and autonomous electric vehicles (AEVs). The proposed range- and mode-specific purchase subsidy aims to maximize the social benefits from vehicle electrification and automation. In this study, we first classify electric vehicles (EVs) into several classes based on electric driving ranges. Each EV class contains two driving modes, i.e., human driving and automated driving. We provide a simplified model to estimate the greenhouse gas (GHG) emission and the inconvenience costs of vehicle charging. The nested logit model is used to characterize users’ vehicle choice behaviors. A mixed integer nonlinear programming (MINLP) model is formulated for the purchase subsidy design problem. A customized branch-and-bound (B&B) method is developed to seek a globally optimal solution to the formulated MINLP model. The numerical examples show that the developed solution method can effectively solve the proposed problem in a reasonable time. The local search strategy embedded in the customized B&B method helps reduce 7% computation time on average. Some managerial insights obtained from the numerical experiments are discussed, which can help the government agency to achieve a reasonable budget allocation between HDEVs and AEVs with different electric driving ranges.

Joint Decision on Pricing and Ordering for Omnichannel BOPS Retailers: Considering Online Returns

Uncertainty in demand and high online return rates tend to generate a large inventory backlog after a hot selling season. Some well-known companies such as Burberry and H&M choose to burn these backlogs, which is very unfavorable to the sustainable development of enterprises and society. The omnichannel BOPS (Buy Online and Pickup in Store) provides consumers with a new and convenient choice of purchasing channels. Considering the probability of online return, we build expected profit models for retailers before and after opening a BOPS channel based on consumer surplus and purchase ratio via an online channel, store channel, and BOPS channel. Then, we prove the existence of optimal solutions and obtain the joint optimization decision on pricing and ordering. The results show that, under certain conditions, when the proportion of online channel buyers increases, the retailers’ optimal decision before opening a BOPS channel is to reduce the price and increase the order quantity, and the retailers’ optimal decision after opening a BOPS channel is to simultaneously reduce the price and the order quantity. Whether or not a BOPS channel is opened, when the purchase proportion of store channel increases, the optimal decision of retailers is to increase the price and order quantity at the same time. Furthermore, when the online return rate, the cost of online shopping, the inconvenient cost of the store channel, and the inconvenience of the BOPS channel relative to store channel increase, the optimal decision of retailers is to simultaneously reduce the price and order quantity. Moreover, when the maximum psychological value of the product increases, the optimal decision is to reduce the order quantity while increasing the price. In addition, the opening of a BOPS channel increases optimal price, optimal order quantity, and maximum expected profit.

The economic value of sustainable recycling and waste management policies: The case of a waste management crisis in South Korea

The price of recyclable waste has declined, and China is refusing solid waste imports. As a result, recycling companies in South Korea are refusing to collect recyclable waste, which caused a solid waste management crisis in April 2018. Due to this crisis, many people are unable to discard their recyclable waste. This study aims to assess the economic value of a sustainable recycling and waste management policy to solve the waste management crisis. The analysis in this study is divided into two parts: The first part estimates the inconvenience costs to residents using a contingent valuation method, whereas the second part analyzes the preferences for a new policy using a mixed logit model with a choice experiment. The results show that mean willingness to pay (WTP) per household is 41,234 (USD 36.96) KRW/year; the total WTP would be able to increase the total disposal capacity by up to 4.51%. In addition, at an adequate price level for the policy alternative, the policy acceptance rate suggests that the price of biodegradable volume-rate disposal bags should increase by no more than KRW 66 (USD 0.06) per bag. This study provides guidance to policy makers who wish to consider both validity and acceptability.

A preference-based demand response mechanism for energy management in a microgrid

In this work, a preference-based, demand response (DR) multi-objective optimization model based on real-time electricity price is presented to solve the problem of optimal residential load management. The purpose of such a model is threefold: 1) to minimize the costs associated with consumption; 2) to minimize the inconvenience caused to consumers; and 3) to minimize environmental pollution. Potential solutions to the underlying multi-objective optimization problem are subject to a set of electrical and operational constraints related to home appliances categories and the utilization of distributed energy resources (DER) and energy storage systems (ESS). The use of the proposed model is illustrated in a realistic microgrid scenario where suitable solutions were found by the Non-Dominated Sorting Genetic Algorithm III (NSGA-III). These solutions determine new operational periods for home appliances as well as the utilization of DER and ESS for the planning horizon while considering consumer preferences. Besides helping consumers to take advantage of the benefits offered by DR, the experimental results show that the multi-objective DR model together with the NSGA-III algorithm can effectively minimize energy-consumption costs as well as reduce inconvenience costs and environmental pollution.

Dynamic modelling of consumers’ inconvenience associated with demand flexibility potentials

Demand flexibility, involving the potential to reduce or temporally defer electricity demand, is regarded as a key enabler for transitioning to a secure, cost-efficient and low-carbon energy future. However, previous work has not comprehensively modelled the inconvenience experienced by end-consumers due to demand modifications, since it has focused on static modelling approaches. This paper presents a novel model of inconvenience cost that simultaneously accounts for differentiated preferences of consumer groups, time and duration of interruptions, differentiated valuation of different units of power and temporal redistribution of shiftable loads. This model is dynamic and future-agnostic, implying that it captures the time-coupling characteristics of consumers’ flexibility and the temporal evolution of interruptions, without resorting to the unrealistic assumption that time and duration of interruptions are foreknown. The model is quantitatively informed by publicly available surveys combined with realistic assumptions and suitable sensitivity analyses regarding aspects excluded from existing surveys. In the examined case studies, the developed model is applied to manage an aggregator’s portfolio in a scenario involving emergence of an adequacy issue in the Belgian system. The results illustrate how considering each of the above factors affects demand management decisions and the inconvenience cost, revealing the value of the developed model.