Revenue Gain Research Articles

A column and row generation approach to the crowd-shipping problem with transfers

Crowd-shipping is a last-mile delivery concept in which commuters pick up and deliver parcels on their pre-existing paths. In urban areas, crowd-shipping circumvents problems that traditional last-mile delivery systems suffer from, such as road congestion and lack of parking spaces, especially if more sustainable modes of transport are utilized, like bikes or e-bikes. Using transfers between crowd-shippers allows for expanding the service area and improving the overall performance. However, as this requires synchronization over space and time, it makes the problem more complex. In this work, we develop a model that can encompass fully heterogeneous crowd-shippers and parcels. Thereby, it allows for both direct time-synchronized transfers as well as intermediate storage at designated parcel lockers. We design a column generation algorithm to solve large-scale realistic instances to optimality. We extend the problem to allow crowd-shippers to carry multiple parcels at the same time and for this, we extend the algorithm to simultaneous column and row generation. We evaluate the performance of our algorithm as well as the potential of crowd-shipping with transfers on a realistic case study of a bike-based crowd-shipping system in Washington DC. Our methods solve realistic instances with 1000 crowd-shippers and 1000 parcels within minutes. The results show that a gain in revenue and service level of 30% can be obtained by allowing transfers. By letting part of the population of crowd-shippers carry two or three parcels at the same time, the revenue and service level can be further increased by 30 to 50%. Maximum locker capacities are shown to be reasonable and are the highest in areas where there is a large gap between the moment when parcels are dropped off and when they are picked up from parcel points, which are mainly in the city center.

Learning Personalizable Clustered Embedding for Recommender Systems

In recent years, recommender systems have advanced rapidly, where embedding learning for users and items plays a critical role. A standard method learns a unique embedding vector for each user and item. However, such a method has two important limitations in real-world applications: 1) it is hard to learn embeddings that generalize well for users and items with rare interactions; and 2) it may incur unbearably high memory costs when the number of users and items scales up. Existing approaches either can only address one of the limitations or have flawed overall performances. In this paper, we propose Clustered Embedding Learning (CEL) as an integrated solution to these two problems. CEL is a plug-and-play embedding learning framework that can be combined with any differentiable feature interaction model. It is capable of achieving improved performance, especially for cold users and items, with reduced memory cost. CEL enables automatic and dynamic clustering of users and items in a top-down fashion, where clustered entities could jointly learn a shared embedding. The accelerated version of CEL has an optimal time complexity, which supports efficient online updates. Theoretically, we prove the identifiability and the existence of a unique optimal number of clusters for CEL in the context of nonnegative matrix factorization. Empirically, we validate the effectiveness of CEL on three public datasets and one business dataset, showing its consistently superior performance against current state-of-the-art methods. In particular, when incorporating CEL into the business model, it brings an improvement of \(+0.6\% \) in AUC, which translates into a significant revenue gain; meanwhile, the size of the embedding table gets 2650 times smaller. Additionally, we demonstrate that, if there is enough memory, learning a personalized embedding for each user and item around their clustering centers is feasible and can further boost the performance.

Global Minimum Taxation: A Strategic Approach for Developing Countries

The world has seemingly embraced the altruistic idea of ensuring a minimum level of corporate income taxation worldwide, consolidating a “benefits for all” narrative by which both developed and developing countries apparently gain. However, this altruistic narrative proves to be quite unrealistic for many developing countries. As argued in this article, the perceived benefits of a global minimum corporate income tax in developing countries rest exclusively upon three unconvincing premises. These include the assumption that all corporate income tax incentives provided by developing countries are equally inefficient, the idea that all developing countries can seamlessly transition from corporate income tax competition to alternative forms of tax and non-tax competition, and most notably, the notion that supporting or opposing a global minimum corporate income tax could either boost or diminish tax revenue for developing countries. This article urges a departure from these premises and elaborates upon three strategic recommendations for developing countries, which include: first, viewing a global minimum corporate income tax as a concept divorced from the assumption of revenue gain or loss; second, using the global minimum corporate income tax as an opportunity to reassess their tax and non-tax incentives, encompassing alternative competitive strategies; and third, striving for simplicity and ease of administration in designing and implementing a minimum tax approach. In doing so, developing countries could perhaps find an opportunity to refine their general action plan to attract foreign direct investment (FDI) more effectively while they still try to ride the wave of minimum global corporate income taxation that the world seems to be in.

Do luxury brands have to trade off for sustainability?

ABSTRACT In today’s global landscape, sustainability emerges as a paramount concern for both individuals and businesses worldwide, including Vietnam. Vietdata reports that luxury distribution and trading enterprises in Vietnam saw a 67% revenue gain to about 25,000 billion copper from 2021. Twelve companies selling 34 luxury brands including Louis Vuitton, Chanel, and Dior generated this data. Thus, luxury sector stakeholders may be interested in Vietnamese consumers’ tastes and purchase behavior, which local research rarely examines. This study explores how sustainability and high-end fashion intersect to meet consumer tastes that increasingly prioritize sustainability in purchase decisions. Despite this trend, the research scrutinizes nuanced reactions when juxtaposing sustainability with luxury. Investigating the influence of factors like exclusivity, conformity, hedonic value, and attitude on the purchase intentions of Vietnamese luxury consumers toward sustainable fashion brands, the study surveyed around 300 luxury consumers in HoChiMinh City. Utilizing PLS-SEM for analysis, findings underscore the pivotal role of attitude in driving intention towards sustainable high-end fashion, followed by hedonic value, exclusivity, and conformity. This exploration aims to guide luxury brands in navigating the sustainability-traditional luxury dichotomy, offering theoretical and practical insights and profiling potential influential customers in sustainable high-end fashion.

Qualified Domestic Minimum Top-Up Tax: Should Developing Countries Adopt It?

The Qualified Domestic Minimum Top-up Tax (QDMTT) is presented as the preferred choice for developing countries. However, given its unfamiliar design, complexity, limited revenue gain, peer review and resource burdens, the QDMTT is unsuitable for developing countries. Instead, such countries should reform their tax incentives, and consider an across-the-board minimum tax.

The Examination of the User Engagement Scale (UES) in Small Medium Enterprise Social Media Usage: A Survey-Based Quantitative Study

Social networks have proven to be an essential marketing tool for the success of any product, service, or business. User participation affects the increase in revenue gain and creates long-term profit. The User Engagement Scale (UES) is one of the tools developed to measure user engagement and has been used in various digital domains. The UES intends to compute six dimensions of engagement: aesthetic appeal, perceived usability, focused attention, novelty, felt involvement, and endurability. This study investigates and verifies the three-factor structure of the UES. We used PCA to perform the analysis. The original data will be reanalyzed using UES, which consists of 220 valid responses. The result shows that the UES examination indicates good reliability in three factors. Factor 1 encompasses the feeling of involvement (FI), aesthetic appeal (AE), novelty (NO), and endurability (EN). Factor 2 aggregates the perceived usability (PU) elements. Factor 3 pertains to focused attention (FA) items. Our findings indicate that the User Engagement Scale is a valuable and suitable measurement tool for assessing user engagement in the context of social media within small and medium enterprises.

Smart and optimization-based operation scheduling strategies for maximizing battery profitability and longevity in grid-connected application

Lithium-ion battery storage has emerged as a promising solution for various energy systems. However, complex degradation behavior, relatively short lifetime, high capital, and operational costs, and electricity market volatility are critical factors that challenge its practical viability. Thus, to ensure sustained profitability of Lithium-ion batteries in real-life applications, a smart and optimal management strategy considering key influencing factors is imperative for achieving efficient battery utilization.This study proposes two day-ahead battery-behavior-aware operation scheduling strategies to maximize profitability and longevity in residential grid-connected applications with dynamic electricity pricing. Each scenario employs unique approaches to make optimal decisions for optimal battery utilization. The first scenario optimizes short-term profitability by prioritizing revenue gains under three charge/discharge rates (high, moderate, low), considering daily charge and discharge timings as decision variables. Conversely, the second scenario proposes a smart strategy capable of making intelligent decisions on a wide range of variables to simultaneously maximize revenue and minimize degradation costs, ensuring short-term and long-term profitability. Decision variables include the cycle frequency for each specific day, timings as well as durations for charging and discharging per cycle. To ensure effective long-term assessment, both scenarios accurately estimate battery performance, calendric and cyclic capacity degradations, remaining-useful-lifetime, and internal states under real operational conditions until battery reaches its end-of-life criteria. The scenarios are assessed economically using various indicators. Furthermore, the impact of battery price and size on optimization outcomes are examined.The key findings indicate that, among the first set of scenarios, the strategy with low charge/discharge rate extends the battery lifetime most efficiently, estimated at 14.8 years. However, it proved to be the least profitable, resulting in negative profit of −3€/kWh/yr. On the other hand, strategies with high and moderate charge/discharge rates resulted in positive profit of 8.3 €/kWh/year and 9.2 €/kWh/year, despite having shorter battery lifetimes, estimated at 10.1 years and 13.6 years, respectively. Furthermore, from a payback perspective, the strategy with fast charge/discharge capability led to 1.5 years shorter payback period than that of the moderate rate strategy. The findings highlight that the first set of scenarios limits the strategy’s flexibility in achieving both sustainability and profitability. In contrast, the second scenario achieves impressive profit (18 €/kWh/yr), shortest payback period (7.5 year), a commendable lifespan (12.5 years), contrasting revenue-focused scenarios, emphasizing the importance of striking optimal balance between revenue gain and degradation costs for charging/discharging actions, ensuring sustained profitability. The findings offer valuable insights for decision-makers, enabling informed strategic choices and effective solutions.

Airline revenue management with segmented continuous pricing: methods and competitive effects

With the introduction of IATA’s New Distribution Capability (NDC), airlines will no longer be limited to discrete fare classes for their fare product distribution but could show fare quotes from continuous ranges to booking requests. NDC will also allow airlines to present different fare quotes to passengers from different demand segments as identified by the airline. In theory, airlines can better extract passenger willingness to pay, and thus, see gains in revenue, by offering segmented continuous fare quotes to different passengers requesting to book. This paper describes the revenue management (RM) methods for Segmented Continuous Pricing and examines their potential effects on airlines’ revenue through simulations in the Passenger Origin–Destination Simulator (PODS). We describe a class-based algorithm for continuous pricing, a straightforward extension from the traditional methods used with existing RM systems. Our simulation results show that in a calibrated scenario in which only one airline adopts Segmented Continuous Pricing and has an 80% accuracy in identifying business versus leisure passenger booking requests, the first-mover airline can see as much as a 17% revenue gain, at the expense of competitors. The revenue gains come primarily from the leisure passenger segment by offering lower fares than competitors closer to departure. The first-mover airline loses bookings but does not see losses in revenue from the business passenger segment. We also explore potential response strategies by the competing airlines. We discover that competitors can reverse the first-mover’s revenue gain by removing their fare restrictions while still using traditional RM methods. We conclude that although adopting Segmented Continuous Pricing is promising in theory, its gains in practice will depend heavily on the competitive situation and the responses made by competing airlines.

Trade-ins and Transaction Costs in the Market for Used Business Jets

Manufacturers of durable goods can encourage consumers facing transaction costs to upgrade by accepting used units as trade-ins. These “buyback schemes” increase demand for new units, but increase the supply of used units if trade-ins are resold. I investigate the equilibrium effects of buyback schemes in the market for business jets. I find that buyback increases manufacturer revenue by 7.2 percent at fixed prices. However, in equilibrium this revenue gain is diminished by 43 percent due to substitution away from new jets among first time buyers. I show how the size of this cannibalization effect depends on preference heterogeneity. (JEL D23, G34, L13, L62)

Quantitative and qualitative impact of physician assistants in a Canadian urology setting.

Physician assistants (PAs) are healthcare professionals who act as physician extenders. PAs are being used more and more in a wide variety of clinic settings throughout Canada to increase access to healthcare and reduce cost. We set out to determine the impact of PAs on a tertiary care center urologic oncology practice. We reviewed Ontario Health Insurance Plan (OHIP) billing codes since the introduction of PAs for two attending urologists at Princess Margaret Cancer Centre. Data were grouped into early experience and established experience. In addition, questionnaires were electronically distributed among nurses, physicians, residents, and fellows who work with PAs in clinic. Patient visits conducted by PAs were tracked for one quarter to estimate the amount of annual patients seen by PAs. The costs associated with PAs are presented as recommendations for a new graduate PA hire. On average, PAs increased clinic volume by 11.3 patient visits per day. Furthermore, they individually care for an average of 24 patients per day. PAs did not represent a financial burden on the urology practice plan (revenue gain of $16 800). Our questionnaire demonstrated that PAs were capable healthcare professionals, who decreased workload and contributed to resident/fellow education. PAs in a Canadian urology practice allow for more patient visits, decrease in physician workload, and positively impact trainee education. PAs saw more patients in clinic than clinic growth, thereby decreasing physician, fellow, and resident workload. The offset of the increase in patient visits made the PAs a cost-neutral investment.

Should higher education be subsidized more?

SUMMARY We investigate in which countries fiscal externalities provide a justification for increasing subsidies to higher education. First, we show that the marginal fiscal recovery rate, i.e., the ratio of the change in total net fiscal revenues and the change in total subsidy costs caused by a small change in tuition subsidies, is the key statistic: if larger than a well-specified threshold value, then a small increase in subsidies is desirable from a welfare point of view. We also show that the marginal fiscal recovery rate depends on three statistics: the elasticity of participation with respect to subsidies, the success probability of the marginal student, and the ratio of the net fiscal revenue gain and the subsidy level of a degree in higher education. Second, we use this formula to approximate the marginal fiscal recovery rate in twenty OECD countries. The average marginal fiscal recovery rate is equal to 0.94, meaning that, on average, 0.94 euro is recovered of an increase in subsidies by one euro. This average hides substantial heterogeneity between countries. In six countries, the marginal fiscal recovery rate is larger than one, implying that an increase in subsidies to higher education is unambiguously desirable (i.e., a Pareto improvement) in these countries. In the other 14 countries, the marginal fiscal recovery rate is below one. Yet, if the degree of inequality aversion is not extreme, then increasing subsidies is also desirable in 12 additional countries. Third, to check the quality of our approximation of the marginal fiscal recovery rate, we simulate it for Belgium (region of Flanders) on the basis of a more detailed model. This simulation provides a somewhat lower, yet fairly similar result compared to the approximation.

Coordinated Slicing and Admission Control Using Multi-Agent Deep Reinforcement Learning

5G Cloud Radio Access Networks (C-RANs) facilitate new forms of flexible resource management as dynamic RAN function splitting and placement. Virtualized RAN functions can be placed at different sites in the substrate network based on resource availability and slice constraints. Due to limited resources in the substrate network and variability in revenue of slices, the Infrastructure Provider (InP) must perform network slicing in a strategic manner, and accept or reject slice-requests to maximize long-term revenue. In this paper, we propose to use multi-agent Deep Reinforcement Learning (DRL) to jointly solve the problems of network slicing and slice Admission Control (AC). Multi-agent DRL along with reward shaping is a promising choice, which is well-suited to problems where multiple distinct tasks have to be performed optimally. The proposed DRL approach can learn the dynamics of slice-request traffic and effectively address these joint problems. We compare multi-agent DRL to approaches that use: (i) simple heuristics to address the problems, and (ii) DRL to address either slicing or AC. Our results show that the proposed approach achieves up to 30% and 5.18% gain in long-term InP revenue when compared to approaches (i) and (ii), respectively. Additionally, we show that multi-agent DRL is preferable to a single-agent DRL approach for the joint problems in terms of convergence time and InP revenue. Finally, we evaluate the robustness of the trained agents in scenarios that differ from training, such as different arrival rates and real dynamic traffic patterns.

Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network

An optimal method for resource allocation based on contract theory is proposed to improve energy utilization. In heterogeneous networks (HetNets), distributed heterogeneous network architectures are designed to balance different computing capacities, and MEC server gains are designed based on the amount of allocated computing tasks. An optimal function based on contract theory is developed to optimize the revenue gain of MEC servers while considering constraints such as service caching, computation offloading, and the number of resources allocated. As the objective function is a complex problem, it is solved utilizing equivalent transformations and variations of the reduced constraints. A greedy algorithm is applied to solve the optimal function. A comparative experiment on resource allocation is conducted, and energy utilization parameters are calculated to compare the effectiveness of the proposed algorithm and the main algorithm. The results show that the proposed incentive mechanism has a significant advantage in improving the utility of the MEC server.

Use of Cryopreserved Amniotic Membrane During Pterygium Excision: Health Economic Analysis.

To determine the health economic opportunity cost or gain associated with performing pterygium excision surgery using the TissueTuck technique with cryopreserved amniotic membrane (AM) instead of conjunctival autograft (CAU). We performed a literature review to determine the average surgical duration of pterygium surgery using CAU with fibrin glue or sutures to calculate the average time saved with the TissueTuck technique. Such time savings was then used to determine the opportunity revenue gain per national average Medicare reimbursement if adjusted to the average surgical duration of cataract surgery. The time savings achieved using the TissueTuck technique over CAU with fibrin glue is 8.9 min per procedure, which can be applied to additional MSICS or phacoemulsification procedures to generate an opportunity revenue gain of $1167 or $762 per 2022 National Average Medicare reimbursement, respectively. After subtracting the current list cost of AmnioGraft (ie, $645), the opportunity gain is $522 or $117 if the time saving is applied to the above procedures, respectively. Alternatively, the time savings achieved by using the TissueTuck technique over CAU with sutures is 23.4 min per procedure, which can be applied to additional MSICS or phacoemulsification procedures to generate an opportunity revenue gain of $3068 and $2004 per TissueTuck procedure or $2423 or $1359 when accounting for the list cost of AmnioGraft, respectively. The TissueTuck surgical technique using cryopreserved AM for pterygium takes less time, has lower recurrence rates, and provides an opportunity gain compared to pterygium excision with CAU.

Income tax credits for consumer services: A tool for tackling VAT evasion?

This paper analyzes the effects of an income tax credit for hard-to-tax consumer services on evasion of the value-added-tax (VAT). Based on the individual tax files of the universe of VAT payers in Germany, our analysis shows that harnessing incentives for consumers through tax credits fosters firms’ compliance with VAT by bringing in an element of third-party reporting at the last VAT stage. Our results point at strong stimulating effects of the introduction of the tax credit on reported sales as well as on the ratio of reported sales to inputs and we find limited price effects. While two thirds of the revenue losses in the income tax are recovered by an increase in VAT revenues, at least a quarter and up to a half of the revenue gain is associated with a response at the VAT evasion margin. The policy thus fosters considerable formalization effects.

Commentary on Brennan et al.: Pros and cons of minimum unit price for alcohol.

Commentary on Brennan et al.: Pros and cons of minimum unit price for alcohol.

Analyzing the enablers of circular supply chain using Neutrosophic-ISM method: lessons from the Indian apparel industry

PurposeThis research identifies the enablers for implementing circular supply chains (CSCEs) and analyzes interrelationships between them to quantify their driving and dependence power to understand the critical CSCEs.Design/methodology/approachInitially, 10 CSCEs are identified for the Indian apparel industries through an extant literature review and validated using the Delphi method by seeking experts' opinions. The identified CSCEs are subjected to a novel neutrosophic interpretive structural modeling (N-ISM) method to capture the interrelationships between CSCEs and compute the driving and dependence power of CSCEs.FindingsThe findings of the present research work revealed that “supportive legislative framework, awareness of circular economy's potential for revenue gain and availability of trained research and development (R&D) team” are the critical CSCEs that need to be considered while implementing a circular supply chain in apparel industries.Research limitations/implicationsThis study offers insightful implications to guide practitioners in implementing the circular economy in apparel supply chains.Originality/valueThis research work is one of the earlier studies to analyze the enablers for implementing circular supply chains. This study has explored CSCEs in the context of apparel industries. From a methodological perspective, the novel N-ISM method is worth highlighting as the originality of the work.

The savings associated with decriminalization of drug use in New South Wales, Australia: A comparison of four drug policies

IntroductionMost Australian states and territories have established some form of scheme to divert minor drug offenders from court. However, the number charged with drug possession continues to rise. We examine the costs of four alternatives to existing policy in relation to people apprehended by police using or in possession of a prohibited drug. MethodsWe construct a Markov micro-simulation model to examine four policies: (1) current policy; (2) expanding the existing cannabis cautioning scheme to all drug use/possession offences; (3) issuing an infringement notice to all those found using or in possession of a prohibited drug; (4) prosecuting all drug use/possession offences in the courts. The cycle length is one month. Since our aim is to examine the cost to the government, all costs are taken from the Government perspective and are in 2020 Australian dollars. ResultsThe current estimated annual cost per offence is $977 (SD: $293). Policy 2 costs $507 per offence per year (SD: $106). Policy 3 turns into a net revenue gain of $225 (SD: $68) per offence per annum. Policy 4 lifts the current cost of processing from $977 to $1282 per offence per year (SD: $321). ConclusionsExtending the cannabis cautioning scheme to all drugs would reduce the cost of current policy by more than 50 %. A policy of issuing infringement notices or cautions for drug use/possession would save costs and generate income for the government.

DRL-Based Slice Admission Using Overbooking in 5G Networks

In 5G-and-beyond networks, the concept of Network Slicing is used to support multiple independent and co-existing logical networks on a physical network infrastructure. The infrastructure provider (InP) owns the set of virtual and physical resources that are used to support the tenant slice requests. Each slice request specifies a service level agreement (SLA) that contains the required slice-level resources (computation and communication) and the revenue provided by the tenant. Due to limited resources, the InP cannot accommodate all requests made by the tenants. In general, it has been found that tenants tend to overestimate their resource demands (e.g., for 5G Core computation) to reduce possible SLA violations. In this paper, we consider two major slice types: Elastic (low priority and low revenue) and Inelastic (high priority and high revenue). We apply the concept of overbooking, where the InP accepts more slices while considering slice priorities in order to maximize the overall revenue and utilization. We consider a multi-tenant environment and propose a slice admission system named PRLOV, which is based on Reinforcement Learning (RL) and prediction methods. The system predicts the future resource demands of Elastic slices and applies an opportunistic overbooking technique to overbook InP for accepting more slices. In addition, the admission decision is formulated as an Markov Decision Process (MDP) problem and solved using standard RL techniques (Policy Iteration, Q-Learning, DQN). The performance of our proposed work is compared against three other heuristics (Basic, Prediction, PRL) that do not use overbooking. Data traces from the Materna data center networks were used for prediction purposes. The important performance metrics measures include InP total revenue, the acceptance rate of respective slices and overall resource utilization for different slices. The results show that the proposed work significantly outperforms the other mechanisms in terms of revenue gain and resource utilization. Simulation results show that PRLOV provides a revenue gain of 6%, 26%, and 102% compared to the PRL, Prediction and Basic scheme.

Time and Labor Costs of Compression Therapy Wrap System for Lower Extremity Edema: Results From a Single Center

Introduction. Compression therapy is the standard of care for the treatment of lower extremity edema. However, compression therapy systems can be time-consuming to apply, which adds costs to the health care system and further strains human resources. Objective. The purpose of this study was to assess time and labor costs associated with the application of a 2LB versus 4LB compression therapy system. Materials and Methods. Time and labor cost data associated with the application of a 2LB system for the treatment of lower extremity edema of all etiologies were collected from a single high-volume wound care center located in Dayton, Ohio. The time and labor costs were compared to a 4LB system over the course of a single day (n = 38). Results. The application time and associated costs were 50% lower for the 2LB system. The expected savings of a 2LB compression system over the course of a month was 16:27 hours and $427 compared to a 4LB compression system, and the revenue gain was estimated at $15 210 revenue per month over the course of the month for the practice. Conclusion. The use of a 2LB compression system may be associated with substantial time and cost savings compared to a 4LB system.