Revenue Maximization Research Articles

ANÁLISE BIBLIOMÉTRICA SOBRE APOIO A DECISÃO MULTICRITÉRIO PARA PRIORIZAÇÃO DE TROCA DE HIDRÔMETROS

A bibliometric analysis was carried out motivated by a research that aims to identify the existence of scientific publications with the theme of multicriteria decision support method for the prioritization of the replacement of water meters in a given territorial region of operation of a state sanitation company, which provides the increase (maximization) of revenue, through the effective reduction (minimization) of apparent losses in water supply systems, caused by the under-measurement of these meters. Thus, the present work uses the Knowledge Development Process-Constructivist – ProKnow-C method, which establishes the procedures, in a structured way, for the selection of the bibliographic portfolio (BP) and its respective analysis, allowing the development of the necessary knowledge to fulfill the motivation of the research. 44 relevant articles were identified and aligned with scientific research and recognition, published between 2005 and 2024. In addition to the selection of articles, the present study carried out a bibliometric analysis, which consists of counting the occurrence of certain variables (characteristics) in the final publications of the BP. With the results, it is possible to quantify existing information and map the structure of knowledge in a scientific field, also serving to build the initial base of research information and identify future scientific opportunities.

Thermodynamic Modeling and Research for Processing Complex Concentrate Blends in Custom Copper Smelters for Maximum Revenue

Custom copper smelters, which are dependent on purchased concentrates, are facing increasing economic pressures amid falling treatment and refining fees. With the declining availability of high-grade, low-impurity concentrates, copper demand is expected to surge to support the transition to renewable energy. This study, which is based on recent observations of Chinese custom smelters, examines their strategies to address the challenge of purchasing concentrates at record-low treatment and refining charges. By investing in slag flotation technology, smelters can enhance copper, gold, and silver recovery. By blending high-grade and low-grade concentrates, they can capitalize on the gap between the recoverable and payable metals, which are often referred to as “free metals”, while also benefiting from byproducts, mainly sulfuric acid. While this approach offers economic opportunities, it introduces operational complexities. To mitigate these, laboratory testing, combined with advanced digital predictive tools based on thermodynamics, is crucial. This study demonstrates the use of thermodynamic models supported by experimental work for analyzing furnace operations. FactSage® software and a custom database are employed to define the operating window of two common flowsheets: (1) flash smelting + flash converting and (2) bottom-blown smelting + bottom-blowing converting.

Revenue Maximization Under Unknown Private Values with Nonobligatory Inspection

Rethinking Informational Assumptions in Mechanism Design Mechanism design has proven to be very effective is soliciting buyers’ incentives and finding the optimal way of allocating scares resources. A common assumption in classic mechanism design, however, is that buyers have full information about their values. Motivated by application in e-commerce, Alaei et al. (2024) consider settings without this assumption and finds the optimal selling mechanism for them.

Strategic behavior, social optimization, and revenue maximization in queues with infinite servers

Motivated by the impact of emerging technologies on (toll) parks, this paper studies a problem of customers’ strategic behavior, social optimization, and revenue maximization for infinite-server queues. More specifically, we assume that a customer’s utility consists of a positive reward for receiving service minus a cost caused by the other customers in the system. In the observable setting, we show the existence, uniqueness, and expressions of the individual equilibrium threshold, the socially optimal threshold, and the optimal revenue threshold, respectively. Then, we prove that the optimal revenue threshold is smaller than the socially optimal threshold, which is smaller than the individual one. Furthermore, we also extend the cost functions to any finite polynomial function with nonnegative coefficients. In the unobservable setting, we derive the joining probabilities of individual equilibrium and optimal revenue. Finally, using numerical experiments, we complement our results and compare the social welfare and the revenue under these two information levels.

Dynamic pricing and demand forecasting: Integrating time-series analysis, regression models, machine learning, and competitive analysis

Abstract. Dynamic pricing is a critical strategy for businesses seeking to optimize revenue and stay competitive in fluctuating markets. This paper explores the integration of various demand forecasting techniques, including time-series analysis, regression models, and machine learning algorithms, with competitive analysis methodologies to enhance dynamic pricing strategies. Time-series analysis focuses on decomposing data into trend, seasonality, and random fluctuations, using ARIMA models for accurate demand prediction. Regression models delve into the complexities of variable interactions, extending beyond linear relationships to include advanced techniques like Ridge, Lasso, and Elastic Net regression. Machine learning algorithms, such as decision trees, random forests, gradient boosting, and neural networks, revolutionize demand forecasting by uncovering complex patterns in large datasets. Competitive analysis incorporates market scanning, price elasticity estimation, and competitor behavior modeling to inform dynamic pricing decisions. Optimization algorithms, including linear programming, genetic algorithms, and simulated annealing, are employed to refine pricing strategies, while revenue management techniques like yield management and overbooking ensure maximum revenue from perishable goods and services. This comprehensive approach enables businesses to dynamically adjust prices in real-time, maintaining competitiveness and maximizing profitability in an ever-evolving market landscape.

PENGARUH UKURAN PERUSAHAAN, PROFITABILITAS, DAN LEVERAGE TERHADAP TARIF PAJAK EFEKTIF (Studi Kasus pada Sektor Energi yang Terdaftar di Bursa Efek Indonesia Periode 2019-2022)

Taxes are one of the biggest contributions for the country. Therefore, the government tries to maximize tax revenue to finance the country's development needs. However, this is contrary to the company's objectives, which want the company's effective tax rate to decrease further away from the statutory tax rate. This research aims to know the effect of firm size, profitability, and leverage on effective tax rate. The sample selection used a purposive sampling technique and obtained 52 samples. Data analysis using multiple linear regression analysis with the IBM SPSS version 26 program. The research results show that firm size has a positive effect on the effective tax rate, profitability and leverage have a negative effect on the effective tax rate. Simultaneously company size, profitability and leverage influence the effective tax rate.

Solving Multi-objective Bi-item Capacitated Transportation Problem with Fermatean Fuzzy Multi-Choice Stochastic Mixed Constraints Involving Normal Distribution

The transportation problem (TP) is an important type of linear programming problem. The aim of TP is to optimize the objective function by allocating product shipments from various sources to multiple destinations. When limitations exist during transportation in the number of products transported from a source to destination due to a variety of factors, such as storage limitations, budgetary constraints, and so on, then TP becomes the capacitated transportation problem (CTP). The business entrepreneurs are keen on transporting multiple items with multiple objectives to generate maximum revenue for an organization, which leads to the development of a multi-objective bi-item capacitated transportation problem (MOBICTP). If a supplier's resources significantly increase or decrease, and the demand needs also significantly increase or decrease, the MOBICTP transforms into a MOBICTP with mixed constraints. Most real-world problems have uncertain parameters due to insufficient data, fluctuating market costs for an item, variation in weather conditions, and so on. Such uncertain parameters are addressed using fuzzy, multi-choice, or stochastic programming. In this article, we examined a novel integrated model for a multi-objective bi-item capacitated transportation problem, which includes a fermatean fuzzy multi-choice stochastic mixed constraint that follows normal distribution. First, the Fermatean fuzzy multi-choice stochastic parameter in the constraints is transformed into a multi-choice stochastic parameter in the constraints by using the (α, β )-cut technique and accuracy function. Then the improved chance-constrained method is developed using Newton divided difference interpolation polynomial. The improved chance-constrained method is used to transform the multi-choice stochastic parameter in the constraints into its equivalent deterministic constraints. Secondly, we propose a novel approach, the improved global weighted sum method, which transforms a multi-objective problem into a single objective problem and utilizes Lingo 18.0 software to find the optimal compromise solution to the equivalent deterministic problem. The main aim of this paper is to help business entrepreneurs improve their profit margins by optimizing the quantity of multiple items while minimizing damage costs, labor costs, transportation time, and transportation costs, and maximizing discounts. To show the model’s validity and significance, a numerical example is solved using the Lingo 18.0 software. In order to emphasize the proposed method, a comparative analysis is conducted with other existing methods. The final component includes a sensitivity analysis and conclusions with future research directions.

Optimization operation strategy of wind–pumped storage integrated system participation in spot market considering dual uncertainties

Abstract With the gradual increase in the penetration rate of renewable energy, the multifunctional role of pumped storage is becoming increasingly prominent, and the joint operation of “renewable energy + pumped storage” is a current research hotspot. However, during the joint system operation, there are dual risks from internal (renewable energy output) and external (market prices) factors, which significantly impact the system’s overall revenue. Therefore, an analysis is conducted around the operational mechanism of the “wind power–pumped storage” joint operation, and the uncertain factors faced during the system’s operation are identified. Second, an optimization model for wind power–pumped storage under deterministic scenarios is constructed, employing robust optimization theory and information gap decision theory to describe the uncertainty of electricity prices and wind power, thus forming a hybrid of the information gap decision theory and the robust optimization model for wind power–pumped storage. Finally, the results show that: (1) The total revenue of the model proposed in the paper has increased by 2.36% compared to the robust optimization model and by 9.04% compared to the deterministic model, significantly enhancing the model’s robustness and risk resistance capabilities. (2) From the perspective of the economic feasibility of different energy storage system configurations, the wind plant equipped with pumped storage has the highest economic feasibility, with an internal rate of return of 9.8% and net present value of 872 million Chinese Yuan, which is higher than that of compressed air energy storage and electrochemical energy storage systems. (3) Decision makers can set the risk deviation coefficient and the uncertainty budget according to their risk preferences, thereby changing the robustness of the model for differentiated decision making. However, an increase in the uncertainty budget coefficient will cause the total revenue of the joint operation system first to increase and then decrease, with the maximum revenue achievable within the range of 500–625; the total revenue reaches its maximum when the risk deviation coefficient is between 0.1 and 0.125.

Hydropower Plant Available Energy Forecasting Using Artificial Neural Network and Particle Swarm Optimization

Accurate forecasting of the available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for hydropower plants in energy production planning for revenue maximization, as well as for environmental impact prevention and flood control upstream and downstream of a basin. Therefore, a reliable forecasting tool or model is deemed necessary and crucial. Considering the fluctuation and nonlinearity of data which significantly influence the forecasting results, this study develops an effective hybrid model by integrating an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) called “PSO-ANN” model based on the hydrological and meteorological data pre-processed by cross-correlation function (CCF), autocorrelation function (AFC), and normalization techniques for predicting the available energy portion corresponding to the reservoir inflow mentioned above for a case study hydropower plant in Laos, namely, the Theun-Hinboun hydropower plant (THHP). The model was evaluated by using correlation coefficient (r), relative error (RE), root mean square error (RMSE), and Taylor diagram plots in comparison with popular single-algorithm approaches such as ANN, and NARX models. The results demonstrated the superiority of the proposed PSO-ANN approach over the other two models, in addition to being comparable to those proposed by previous studies.

Agile project management for sustainable residential construction: A study of critical success factors

Sustainability principles should be incorporated into all decision-making stages for residential construction projects to ensure maximum revenue while maintaining essential residential building services. This study identifies and analyzes the critical success factors (CSFs) necessary for implementing agile project management (APM) in residential construction projects. Data were collected from 120 professionals in the Nigerian construction industry through questionnaire surveys to understand the implementation of APM. The CSFs were obtained from previous research and analyzed within the specific context of the Nigerian construction industry through questionnaire surveys. The CSFs were grouped into two main categories using exploratory factor analysis: dynamic project optimization and agile project foundations. The model for the CSFs was developed using partial least squares structural equation modeling. The study found that the dynamic project optimization element had the most significant impact on the model, highlighting its importance as a key CSF in APM implementation. These results support the adoption of APM within Nigeria’s construction industry as stakeholders and professionals seek effective strategies to reduce costs and improve sustainability.

Maximizing Profitability and Occupancy: An Optimal Pricing Strategy for Airbnb Hosts Using Regression Techniques and Natural Language Processing

In the competitive landscape of Airbnb hosting, optimizing pricing strategies for properties is a complex challenge that requires revenue maximization with high occupancy rates. This research aimed to introduce a solution that leverages big data and machine learning techniques to help hosts improve their property’s market performance. Our primary goal was to introduce a solution that can augment property owners’ understanding of their property’s market value within their urban context, thereby optimizing both the utilization and profitability of their listings. We employed a multi-faceted approach with diverse models, including support vector regression, XGBoost, and neural networks, to analyze the influence of factors such as location, host attributes, and guest reviews on a listing’s financial performance. To further refine our predictive models, we integrated natural language processing techniques for in-depth listing review analysis, focusing on term frequency-inverse document frequency (TF-IDF), bag-of-words, and aspect-based sentiment analysis. Integrating such techniques allowed for in-depth listing review analysis, providing nuanced insights into guest preferences and satisfaction. Our findings demonstrated that AirBnB hosts can effectively utilize both state-of-the-art and traditional machine learning algorithms to better understand customer needs and preferences, more accurately assess their listings’ market value, and focus on the importance of dynamic pricing strategies. By adopting this data-driven approach, hosts can achieve a balance between maintaining competitive pricing and ensuring high occupancy rates. This method not only enhances revenue potential but also contributes to improved guest satisfaction and the growing field of data-driven decisions in the sharing economy, specially tailored to the challenges of short-term rentals.

A Framework for 5G Network Slicing Optimization using 2-Edge-Connected Subgraphs for Path Protection

Emerging telecommunications technologies require robust frameworks for efficient network slicing. We propose a network-slicing model that aims to optimize the deployment of virtual networks on a physical network topology. Our model ensures compliance with 5G requirements, incorporating latency and capacity constraints on virtual links. Selecting slices with cost and resource requirements on the computing nodes is optimized using a Knapsack problem with revenue maximization. We propose a path protection algorithm to deal with link failures by constructing a 2-edge-connected subgraph (or two link-disjoint Steiner trees) for each slice to provide both primary and backup paths. Simulation results include comparison with existing solutions by metrics such as latency, revenue, resource utilization, number of protected slices, and computation time, providing valuable insights for network planners operating in diverse and dynamic environments. Key contributions include efficient resource allocation using the Knapsack problem, enhanced network resilience via 2-edge-connected subgraphs for path protection, and realistic simulation experiments on SNDlib dataset topologies. The simulation results show that the proposed framework improves computational efficiency compared to the recent related solutions, particularly in large network topologies where k-connected function slicing (KC- FS) subgraph embeddings take approximately 3.5 times more computation time.

Order-sensitive competitive revenue maximization for viral marketing in social networks

The Competitive Influence Maximization (CIM) problem is a critical issue in viral marketing, focusing on selecting a set of influential individuals, known as seed users, for competitors to maximize their revenue. These seed users have significant sway in social networks and serve as valuable marketing resources provided by the platform. They are often displayed in a certain order launched by the platform and the potential information hidden in the order can profoundly affect the final marketing outcomes. However, current CIM research predominantly emphasizes designing effective algorithms for seed selection while ignoring the impact of the seed order launched by the platform. Therefore, this paper focuses on identifying the optimal seed order to maximize platform revenue in a competitive market environment. Specifically, we introduce a new problem called Order-Sensitive Competitive Revenue Maximization (OSCRM) to investigate the CIM problem from a new practical perspective. We prove the problem to be NP-hard and present a simple greedy algorithm with a 1/3-approximate ratio. To address it more efficiently, we further propose an enhanced greedy algorithm called GMST. This algorithm leverages the maximum spanning tree (MST) and achieves a 1/2-approximate ratio. Extensive experiments on four real-world datasets demonstrate the effectiveness of our proposed GMST algorithm.

Anchoring decisions: the role of decoy pricing in consumer choices

PurposeHospitality firms aim to increase their profits by implementing a variety of marketing activities, including using decoy pricing to provide alternative choices for consumers. Decoys are relatively higher-priced offerings that signal lower value than the other offerings in the consideration set. The purpose of this research is to investigate the influence of decoy pricing on consumer choices across various contexts in the foodservice and hotel industries.Design/methodology/approachAcross the pilot and four main studies, the current research employs a sequential exploratory mixed-method design to investigate the influence of decoy pricing in the foodservice and lodging industries. The qualitative part of this research was based on two focus groups, followed by a pilot study and four main study experiments.FindingsThe results show that decoy pricing escalates consumers’ choices of more expensive product bundles in both restaurant and hotel cancellation policy contexts. However, decoy pricing does not increase the selection of more expensive hotel product bundles.Originality/valueWhile decoy pricing has been utilized as an effective revenue maximization strategy for product placement in retail stores, less is known about how promotional advertisements with decoy offers influence hotel and restaurant customers to choose more costly options. Specifically, this is the first study that explores whether decoy pricing and product/service bundling can encourage customers to select more expensive offers in hotel and restaurant contexts, considering the types of hospitality bundles that may limit this effect.

Revenue Maximization for a Battery Storage With Optimal Capacity Revamping Due to Cyclic Fade

Revenue Maximization for a Battery Storage With Optimal Capacity Revamping Due to Cyclic Fade

Rental Housing Supply and Build-to-Rent Conundrum in Australia

Traditionally, rental housing has been supplied by a large pool of individual investors who own a few units and invest their savings, with some leverage, to take advantage of the tax regime in Australia. The last five years have seen the emergence of build-to-rent (BTR) units, which are supplied by investors who own a large number of units. The state of Victoria in Australia has the largest share of these projects. In the current market and regulatory environment, the financial viability of BTR projects is low for investors and hinges on the ability of units to be leased at higher than market rents. This paper examines four groups of levers, including those already being pursued by the building industry, that can be used to improve the financial viability of BTRs. These include: (i) revenue maximization, (ii) cost reduction (iii) fiscal and (iv) planning incentives. An archetypical BTR project which mimics current practice is considered, assumed to be in Docklands, Victoria, where several BTR projects are planned. For the robustness check, a feasibility analysis is conducted for a site in North Melbourne, a neighbourhood in Victoria with several BTR projects. The results indicate that for revenue maximization, the mix of unit types in a BTR project should be location-specific, as market preferences (and the characteristics of renters) determine the rent for different types of units that can be achieved. In a conventional BTR project development, where land is bought upfront and the project is developed, the mixed-use BTR (residential in combination with commercial) does not provide significant financial benefits though including small retail (3–4% of the net lettable area) may provide complementary benefits. Incurring large capital costs upfront and having the revenue stream spread over long periods reduces financial viability. While construction costs are more difficult to reduce, ways to reduce land costs could be through zoning land for BTR use, through mechanisms such as joint ventures with landowners, and land leasing. Exemptions on income, land tax, and rates (like CHPs) can result in a higher return for investors. A full GST refund, an incentive that industry is lobbying for, results in a similar IRR as an exemption on income, land tax and rates would offer. These results will assist in determining priorities for policies that are aimed at BTR.

The motivations and practices of commercial short-term rental operators in the short-term/long-term “regulatory gap”

ABSTRACT Despite the rapid proliferation of short-term rentals (STRs) over the last decade, research contacting commercial operators to explore their business practices, motivations, and attitudes remains rare. Leveraging a comprehensive dataset of STR activity in Montreal alongside key-informant interviews, this study presents an in-depth portrait of commercial STR operators. We find that the experience of commercially operating STRs is marked by contradictions. While operating STRs can be time-consuming, the chief motivating factors are the potential for revenue maximization and the increased flexibility provided to property owners driven by a ‘regulatory gap’ between the long- and short-term rental markets.

Antecedents and consequences of automated VAT solution adoption in Gulf cooperation countries: the case of the United Arab Emirates

Purpose This paper aims to examine the antecedents of the Automated VAT Solution (AVS) and its eventual consequence on value-added tax (VAT) compliance costs among the small and medium enterprises (SMEs) in Gulf Cooperation Countries (GCC), with the United Arab Emirates (UAE) as context. Design/methodology/approach A quantitative research design was deployed through a survey of 576 SMEs in the UAE. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings The findings revealed that technological factors (IT complexity and IT competency) and organizational factors (management support and size of SME) significantly influence AVS adoption. However, only consumer pressure was found to be significant among the environmental factors, and AVS adoption was found to have a significant negative effect on the VAT compliance cost. Research limitations/implications A lower coefficient of determination for the effect of AVS adoption on VAT compliance cost meant that there may be other accounting-related technologies that improve operational efficiency and process automation and, in the long run, lower the cost of VAT compliance. These technologies should be included in future studies. Practical implications The findings imply that the adoption of AVS among SMEs is highly desirable, as it reduces VAT compliance costs. Increased regulatory pressure by the UAE’s policymakers is also desirable to accelerate AVS adoption for enhanced cost reduction and revenue maximization from the perspectives of both the government and SMEs. Originality/value To the best of the authors’ knowledge, this study could be the first to expand the Technology-Organization-Environmental (TOE) Framework through the integration of determinants of AVS adoption and VAT compliance costs among SMEs in GCC countries.

Research on Pricing and Replenishment Strategies for Supermarkets Based on Revenue Maximization

Based on the perspectives of time series and the supply chain, this paper investigates pricing and replenishment strategies for fresh supermarkets with the aim of maximizing profits. Through visual analysis, the vegetable category is thoroughly examined, revealing seasonal sales patterns. Furthermore, the correlation between different categories is explored using ADF tests and the DPCCA model. To optimize the replenishment quantity for the upcoming week, sales data from the past month is employed for nonlinear fitting. Assuming a fixed purchase price, the ARIMA model is utilized to forecast pricing, and optimal pricing and daily replenishment strategies for the next seven days are formulated through nonlinear programming methods.

Modeling Comprehensive Deficit Irrigation Strategies for Drip-Irrigated Cotton Using AquaCrop

The development of a crop production strategy through the use of a crop model represents a crucial method for the assurance of a stable agricultural yield and the subsequent enhancement thereof. There are currently no studies evaluating the suitability of the AquaCrop model for the drip irrigation of Gossypium barbadense in Southern Xinjiang, which is the primary planting region for Gossypium barbadense in China. In order to investigate the performance of the AquaCrop model in simulating the growth of cotton under mulched drip irrigation, the model was locally calibrated and validated according to different irrigation thresholds during a key growth period of two years. The results of the simulation for total soil water (TSW), crop evapotranspiration (ETc), canopy coverage (CC), aboveground biomass (Bio), and seed cotton yield demonstrated a high degree of correlation with the observed data, with a root mean square error (RMSE) of <11.58%. The Bio and yield simulations demonstrated a high degree of concordance with the corresponding measured values, with root mean square error (RMSE) values of 1.23 t ha−1 and 0.15 t ha−1, respectively. However, the predicted yield declined in the verification year, though the prediction error remained below 15%. Furthermore, the estimated evapotranspiration (ETc) value demonstrated a slight degree of overestimation. Generally, the middle and late stages of cotton growth led to an overestimation of the TSW content. However, the prediction error was less than 13.99%. Through the calculation of each performance index of the AquaCrop model, it is found that they are in the acceptable range. In conclusion, the AquaCrop model can be employed as a viable tool for predicting the water response of cotton to drip irrigation under mulched film in Southern Xinjiang. Based on 64 years of historical meteorological data, three years were selected as scenarios for simulation. Principal component analysis (PCA) showed that, in a local wet year in Southern Xinjiang, the irrigation quota was 520 mm, and the irrigation cycle was 6 days/time. In normal years, the irrigation quota was 520 mm, with an irrigation cycle of 6 days/time. In dry years, the irrigation quota was 595 mm, with an irrigation cycle of 10 days/time. This allowed for higher seed cotton yields and irrigation water productivity, as well as the maximization of cotton yields and net revenue in the arid oasis area of Southern Xinjiang.