Optimal Revenue Research Articles

Gacha Game Analysis and Design

Gacha game is a special opaque selling approach, where the seller is selling gacha pulls to the buyer. Each gacha pull provides a certain probability for the buyer to win the gacha game reward. The gacha game has been enthusiastically embraced in numerous online video games and has a wide range of potential applications.In this work, we model the complex interaction between the seller and the buyer as a Stackelberg game, where the sequential decision of the buyer is modeled as a Markov Decision Process (MDP). We define the whale property in the context of gacha games. Then, we show that this is the necessary condition to achieve optimal revenue. Moreover, we provide the revenue-optimal gacha game design and show that it is equivalent to the single-item single-bidder Myerson auction.We further explore two popular multi-item gacha games, namely, the sequential multi-item gacha game and the banner-based multi-item gacha game. We also discuss the subsidies in the gacha game and demonstrate how subsidies may encourage the buyer to engage in grinding behavior. Finally, we provide a case study on blockchain systems as gacha games.

An Enhanced Predictive Analytics Model for Tax-Based Operations

In order to meet its basic responsibilities of governance such as provision of infrastructure, governments world over require significant amount of funds. Consequently, citizens and businesses are required to pay certain legislated amounts as taxes and royalties. However, tax compliance and optimal revenue generation remains a major source of concern. Measures such as penalties and in the current times Data and Predictive Analytics have been devised to curb these issues. Such effective Analytics measures are absent in Bauchi State and Nigeria as a whole. Previous studies in Nigeria have done much in the area of tax compliance but have not implemented Data Analytics solutions to unearth the relationships which this study will cover. A Combined Sequential Minimal Optimisation (CSMO) model has been developed to analyse co-relation of Tax-payers, classification and predictive traits which uncovers trends on which to base overall decisions for the ultimate goal of revenue generation. Experimental validation demonstrates the advantages of CSMO in terms of classification, training time and prediction accuracy in comparison to Sequential Minimal Optimisation (SMO) and Parallel Sequential Minimal Optimisation (PSMO). CSMO recorded a Kappa Statistics measure of 0.916 which is 8% more than the SMO and 7.8% more than the PSMO; 99.74% correctly classified instances was compared to 98.28% in SMO and 98.35 in parallel SMO. Incorrectly classified instances of CSMO recorded a value of 0.25% which is better than 1.72% of SMO and 1.68% of PSMO. Training time of 223ms was recorded when compared to 378ms in SMO and 286ms in PSMO. A better value of 0.9981 for CSMO was achieved in the ROC Curve plot against 0.944 in SMO and 0.913 in PSMO. CSMO takes advantage of powerful Analytics techniques such as prediction and parallelisation in function-based classifiers to discover relationships that were initially non-existent

Information heterogeneity in queueing service systems: Differentiated pricing decision with word-of-mouth rating information on service value

<p style='text-indent:20px;'>In many service settings, customers are usually unaware of service-related information. Therefore, in such service systems, customers can only resort to online ratings posted on the review platforms to obtain the user-generated service information and make their joining/balking decisions depending on their service value assessment (consumption utility). Motivated by such phenomenon, we consider a single-server M/G/1 queue with two types of customers with heterogeneous access to service-related information, namely experienced and first-time customers, and price them differently. We first characterize the equilibrium behavior of experienced customers and investigate how the market size of first-time customers and the number of ratings collected by first-time customers affect the queueing behavior of experienced customers. We then construct the revenue function and obtain the differentiated pricing strategy of the service provider. Interestingly, through numerical examples, we show that when the service price for experienced customers is fixed, the impact of the number of ratings collected by first-time customers on optimal revenue and pricing is related to the variance of service value. When the service price for first-time customers is fixed, the effect of number of ratings collected by first-time customers on optimal revenue and pricing is related to the mean service value (real service value). In a word, compared to the full rationality setting, the bounded rationality setting of first-time customers who have imperfect knowledge of the real service value could bring different effects on the strategic behavior of customers and the pricing decision of the service provider.</p>

Fresh food supply chain coordination with competition and differentiated freshness preservation methods

For a secondary supply chain consisting of a supplier and two competing retailers, freshness spoilage rate, competitive behavior and market capacity constraint are introduced into the traditional demand-substitution model. Under different preservation methods such as chilling and freezing, the Gounod double oligopoly game and the conspiracy game competition models between retailers are constructed with the benchmark of the centralized decision-making. The optimal sales price and ordering decision are secured, and the influence of market volume, freshness deterioration rate and demand substitution rate on the optimal decision is analyzed under different competition models. Meanwhile, an optimal revenue coordination contract is constructed to maximize the benefits of each subject in the fresh supply chain. The results show that the optimal sales price and optimal order quantity are positively correlated with the respective market volume and demand substitution rate, but negatively correlated with the deterioration rate of goods. The optimal coordination degree of the proposed revenue-sharing contract is 100%, which indicates that this contract can effectively coordinate the interests of all parties in the fresh supply chain and achieve the overall optimal supply chain.

USAGE OF ACCOUNTING SOFTWARE ON COST CONTROL OF LISTED DEPOSIT MONEY BANKS IN NIGERIA

The ability to implement and maximize the use of accounting software to ensure reliable and efficient cost control among firms have become one of the complex challenges faced by many firms in contemporary business operations. The impact of accounting software on cost control among firms in the service sectors of Nigerian economy using listed deposit money banks was examined in this study. The study employed field survey design, via structured questionnaire administered to 120 respondents in Nigeria’s financial services sector. A total of 107 representing 89.7% were retrieved usable copies. Cronbach’s alpha test showed that the instrument had a value of 0.967 which is greater than 0.70, which implied that the research instrument was reliable. Purposive sampling technique was used for sampling size estimation of the descriptive and inferential statistics while regression analysis was used for the data analysis. The results of the study revealed that accounting software proxied by (software efficiency, software reliability, software easiness, software accuracy and data quality) significantly affected responsibility accounting (R2 = 0.600; F(5, 114) = 32.758; p-value =0.000. as well as activity based costing (R2 = 0.810; F(5, 114) = 91.489; p-value = 0.000). The study therefore concluded that accounting software deployment and implementation has a significant positive impact on cost control in listed deposit money banks. In particular, the study found that software operational easiness and its associated accuracy are the two principal elements that drive cost control effectiveness of listed deposit money banks in Nigeria. Consequently, the study recommended that when considering selection of accounting software for organization-wide deployment and implementation, owners and management of deposit money banks should ensure that software operational easiness and accuracy are used as the primary selection criterion to facilitate cost control effectiveness and by extension optimal revenue returns.

A Reinforcement Learning Approach to Price Cloud Resources With Provable Convergence Guarantees.

How to generate more revenues is crucial to cloud providers. Evidences from the Amazon cloud system indicate that "dynamic pricing" would be more profitable than "static pricing." The challenges are: How to set the price in real-time so to maximize revenues? How to estimate the price dependent demand so to optimize the pricing decision? We first design a discrete-time based dynamic pricing scheme and formulate a Markov decision process to characterize the evolving dynamics of the price-dependent demand. We formulate a revenue maximization framework to determine the optimal price and theoretically characterize the "structure" of the optimal revenue and optimal price. We apply the Q -learning to infer the optimal price from historical transaction data and derive sufficient conditions on the model to guarantee its convergence to the optimal price, but it converges slowly. To speed up the convergence, we incorporate the structure of the optimal revenue that we obtained earlier, leading to the VpQ-learning ( Q -learning with value projection) algorithm. We derive sufficient conditions, under which the VpQ-learning algorithm converges to the optimal policy. Experiments on a real-world dataset show that the VpQ-learning algorithm outperforms a variety of baselines, i.e., improves the revenue by as high as 50% over the Q -learning, speedy Q -learning, and adaptive real-time dynamic programming (ARTDP), and by as high as 20% over the fixed pricing scheme.

Implementing optimal outcomes through sequential auctions

AbstractWe study sequential auctions as optimal mechanisms for selling multiple heterogeneous items to unit‐demand buyers. We find that as long as the items can be ordered in decreasing variation of the buyers' values, any combination of static standard auctions in this sequence achieves full efficiency and the constrained optimal revenue, subject to (BIC), (IIR), and the all‐sold constraint. In addition, with increasing threshold types and optimal static reserve prices, any l‐th price auctions in the same sequence can achieve the optimal revenue subject to (BIC) and (IIR). Our results have implications for practical auction design, such as sponsored search auctions.

Investigate an imperfect green production system considering rework policy via Teaching-Learning-Based Optimizer algorithm

Green products have achieved a wide reputation in the current competitive market in the last few decades. Again, the imperfect production rate during production imposes a minor issue in generating optimal revenue for a manufacturing firm. Primarily focusing on these two factors, the current study demonstrates an imperfect production inventory system of green products by considering two activities regarding business accommodation of imperfectly manufactured products: (i) rework and (ii) salvage. The primary purpose of this work is to study the optimal policy of a manufacturing firm based on production control, the greenness of the products, and the rework or salvage of defective products. Here, consumers’ demand is green level and selling price dependent. Further, production costs are green-level and production rate dependent. The average profits of the system (corresponding to both cases) appear to be highly non-linear. They necessitate the employment of a meta-heuristic algorithm (viz., the Teaching-Learning-Based Optimizer Algorithm). From the numerical example, it is observed that the reworked policy appears to be more profitable than the salvage policy for the firm. Finally, a sensitivity analysis is carried out to draw some fruitful conclusions.

Investigating cooperative strategies in low-carbon public–private partnership projects through evolutionary game

PurposePromoting low-carbon in the construction industry is important for achieving the overall low-carbon goals. Public–private partnership is very popular in public infrastructure projects. However, different perceptions of low-carbon and behaviors of public and private sectors can hinder the realization of low-carbon in these projects. In order to analyze the willingness of each stakeholder to cooperate towards low-carbon goals, an evolutionary game model is constructed.Design/methodology/approachAn evolutionary game model that considers the opportunistic behavior of the participants is developed. The evolutionary stable strategies (ESSs) under different scenarios are examined, and the factors that influence the willingness to cooperate between the government and private investors are investigated.FindingsThe results illustrate that a well-designed system of profit distribution and subsidies can enhance collaboration. Excessive subsidies have negative impact on cooperation between the two sides, because these two sides can weaken income distribution and lead to the free-riding behavior of the government. Under the situation of two ESSs, there is also an optimal revenue distribution coefficient that maximizes the probability of cooperation. With the introduction of supervision and punishment mechanism, the opportunistic behavior of private investors is effectively constrained.Originality/valueAn evolutionary game model is developed to explore the cooperation between the public sector and the private sector in the field of low-carbon construction. Based on the analysis of the model, this paper summarizes the conditions and strategies that can enable the two sectors to cooperate.

A STUDY OF INVENTORY CONTROL TECHNIQUES TO OPTIMISE REVENUE IN SMALL RETAIL STORES

According to an estimation, there were around 12.65 million retail grocery stores in India in 2020. These Stores typically covered 100-200 square feet and kept stock of around 500-1000 items. These small retail shops generate yearly average revenue of 18-25 lac, of this the profit margins are around 3-4% i.e., Rs 54,000-72,000 yearly. From discussions with shop owners running small to medium retail stores from two-three generations, the challenge seems to be either overstocking leading to wastage, or stock-out leading to customer diversion. Small retail stores usually are managed using the primitive hit and trial method when it comes to inventory management, that only becomes moderately efficient in case of extensive experience of the manager. The following paper presents a study of three inventory control techniques in order to optimize revenue by controlling the inventory efficiently for small retail stores in India. The purpose of this study is to figure out a pattern connecting inventory control with optimal revenue which may further be converted into an algorithm for software that may help small retail store owners all over India.

Stochastic dynamic production planning in hybrid manufacturing and remanufacturing system with random usage durations

ABSTRACT This paper studies a multi-period dynamic production planning problem in a hybrid manufacturing and remanufacturing system (HMRS), where new and remanufactured products are perfect substitutes. The HMRS encounters uncertain return amounts due to products' random usage durations. As returns are from previous sales, production planning in each period impacts both current sales and future returns. The random return amount and the correlation among different periods make it a complex system. To solve the problem, we first utilise the hazard rate function and in-use products' information to derive an estimator of the return amount. Then, we formulate a dynamic programming model and prove a threshold policy is optimal under uniformly distributed demand. We employ marginal analysis to derive an approximation of the optimal threshold value. Through simulating all alternatives, the derived threshold is verified to be a good approximation as it achieves more than 99% of the optimal revenue in most scenarios. In addition, the calculated return amount based on the hazard rate function is almost identical to the return amount obtained via simulation. Compared to other return measurements, our method achieves the highest revenue in all considered scenarios, including heterogeneous usage durations and general demand distributions.

Research on P2P product portfolio strategy based on term structure under risk reserve system

This paper studies the portfolio strategy of P2P products based on the term structure under the risk reserve system. It constructs a Lagrange revenue function for each type of product to indicate the relationship between the optimal loan interest rate and the Lagrange revenue, and further proposes the optimal portfolio strategy model based on the term structure under the risk reserve system. A simulation analysis is carried out and the results show that. (i) Under the risk reserve system, the revenue is increasing with the loan interest rate. (ii) The optimal term will change due to the adjustment of supervision, and then the optimal portfolio will be transformed. (iii) The optimal revenue is increasing with the amount of risk reserve, and excess risk reserves will lead to an increase in the penalty cost, which makes it less likely for the platform to obtain profits from the excess. (iv) If the loan interest rate remains fixed, the cost minimization model is equivalent to the revenue maximization model, and the revenue is the largest when the total cost is minimal. (v) When the loan interest rate reaches a certain threshold, the marginal default cost will be greater than the marginal benefit, which will reduce the revenue. (vi) The differentiation of default risk with different terms will result in the change of the optimal revenue, which will cause the alteration of the optimal portfolio.

Technical Note—Approximation Schemes for Capacity-Constrained Assortment Optimization Under the Nested Logit Model

This paper proposes a carefully crafted dynamic programming approach for capacitated assortment optimization under the nested logit model in its utmost generality, potentially including partially captured nests and possibly synergistic products. Specifically, we show that the optimal revenue can be efficiently approached within any degree of accuracy through synthesizing ideas related to continuous-state dynamic programming, state space discretization, and sensitivity analysis of modified revenue functions. These developments allow us to devise the first fully polynomial-time approximation scheme in this context, thus resolving fundamental open questions posed in earlier literature.

High-Low Promotion Policies for Peak-End Demand Models

In-store promotions are a highly effective marketing tool that can have a significant impact on revenue. In this research, we study the question of dynamic promotion planning in the face of Bounded-Memory Peak-End demand models. In order to determine promotion strategies, we establish that a High-Low pricing policy is optimal under diagonal dominance conditions (so that the current period price dominates both past period price effects and competitive product price effects on the demand), as well as conditions on the price dispersion. We show that finding the optimal High-Low dynamic promotion policy is NP-hard in the strong sense. Nevertheless, for the special case of promotion planning for a single item, we propose a compact Dynamic Programming (DP) approach that can find the optimal promotion plan that follows a High-Low policy in polynomial time. When the diagonal dominance conditions do not hold, and, hence, a High-Low policy is not necessarily optimal, we show that the optimal High-Low policy that is found by our proposed DP can find a provably near-optimal solution. Using the proposed DP as a subroutine, for the case of multiple items, we propose a Polynomial-Time-Approximation Scheme (PTAS) that can find a solution that can capture at least [Formula: see text] of the optimal revenue and runs in time that is exponential only in [Formula: see text]. Finally, we test our approach on data from large retailers and demonstrate an average of [Formula: see text] increase in revenue relative to the retailer’s current practices. This paper was accepted by Chung Piaw Teo, optimization. Funding: Financial support from the Oracle Corporation [External Research Office grant] and the National Science Foundation [Grant CMMI-1162034] is gratefully acknowledged.

A differential game for basin ecological compensation mechanism based on cross-regional government-enterprise cooperation

Cross-regional cooperation among upstream and downstream governments and enterprises is crucial for the efficiently implementation of basin ecological compensation mechanism. In this study, we propose and study a differential game model for basin ecological compensation mechanism based on cross-regional cooperation among governments and enterprises. We have considered three different cooperation settings: mechanisms with no cost sharing, mechanisms with cost sharing and collaborative cooperation mechanism. Under these three settings, all the players' optimal ecological protection efforts, cost sharing rates and optimal revenues are derived. Comparison analysis shows that (1) all the players’ revenues are increasing in the partially ordered set of vectors of cost sharing; (2) the total revenue of the whole system achieves optimal under the collaborative cooperation mechanism. Finally, we conduct several numerical experiments to verify the performance of the solutions to the model, and reveal many interesting managerial insights after hence.

A MODEL TO DEVELOP HOTEL MANAGEMENT SYSTEM TO OPTIMIZE REVENUE

Hotel provides facilities for customers and earns revenue. In this sector of business, many challenges might be faced by the hotel management since there arise price competition between hotels. Hotel management has to follow some strategies to earn maximum revenue. Though it is difficult to estimate actual profit in any epidemic situations, we showed the optimal revenue is possible even within this kind of epidemic situations. And to do this, a revenue management model was developed in terms of pricing and room allocation capacity. Price is the sole factor in this model. Two types of demand models are used, one is deterministic demand model and another is stochastic demand model and both are used for pricing model and capacity model formulation. This work showed how hotels can gain more revenue by using proposed capacity and pricing model, even in the time of global crisis situation like COVID-19 epidemic.

MODERN TOOLS FOR FORMING THE OPTIMAL STRUCTURE OF REVENUES OF TERRITORIAL COMMUNITIES' BUDGETS

The influence of budgetary risks on the formation of revenues of budgets of territorial communities is investigated and a methodical approach to estimating the structure of revenues of budgets of territorial communities in the conditions of financial decentralization is developed. It is noted that today the introduction of the latest methods for optimizing the revenues of the budgets of territorial communities with permanent control over the level of budgetary risks becomes especially important. Mathematical forecasting of the probability of budget risks and its empirical measurement requires special attention. The main approaches of domestic and foreign scientists to forecasting the probability of budget risks with the help of economic and mathematical tools are considered. Its practical use, on the one hand, will ensure their permanent control, and on the other - will allow optimization of the revenues of the budgets of territorial communities. It is established that the existing methods are difficult to implement in domestic practice due to their significant mathematical calculation. Calculations were made using the postulates of portfolio theory, in particular the portfolio approach of J. Hicks, which allowed to form a rational structure of revenues of budgets of territorial communities in terms of general and special funds, as well as to identify ways to minimize budgetary risks to ensure optimal revenue structure without interbudgetary transfers in conditions of permanent crisis phenomena. This will allow local governments of territorial communities to increase the performance of their budgets. The latest tools of financial decision-making using J. Hicks' portfolio approach to the formation of the optimal structure of budget revenues of territorial communities without intergovernmental transfers, taking into account the level of budgetary risk, were obtained by applying the tools of economic and mathematical modeling. It is concluded that the latest tools for the formation of the optimal structure of budget revenues of territorial communities with permanent forecasting of the maximum possible level of their growth under the control of budget risks allow the development of ways to minimize, forming many scenarios for the optimal structure of future budget revenues and budget risk. It is established that the proposed toolkit allows to generate alternative financial solutions for local governments and assess the results and consequences of their possible adoption in determining strategic guidelines for the development of local community finances. It is proposed to further use the developed scientific and methodological approach to optimize the revenue structure of budgets of territorial communities in the context of various budgetary risks, which, of course, will allow local governments to make effective financial decisions in developing programs of socio-economic development of territorial communities.

Efficient Mobile Vehicle Data Sharing Scheme Based on Consortium Blockchain

Efficient data sharing schemes are one of the key technologies in the Internet of Vehicles (IoV). However, the insufficient willingness of vehicle users to provide data makes the traditional blockchain-based IoV network have low throughput. The income of IoV providers decreases when the vehicle density increases on the road. In this paper, we investigated a mobile vehicle data sharing scheme based on the consortium blockchain. In detail, the consortium blockchain was used to limit the degree of decentralization and openness, and the optimal revenue strategy approach between vehicles and data-demand devices was obtained through the Stackelberg game. The load test library based on Node.js was used to simulate and compare the data transmission performance of the proposed consortium blockchain with traditional blockchain schemes. Simulation results show that the proposed scheme had higher buyer’s revenue, and the block transmission performance was significantly higher than that of traditional blockchain schemes.

An Approximation Scheme for Data Monetization

The unprecedented rate at which data are being generated has led to the growth of data markets where valuable data sets are bought and sold. A salient feature of this market is that a data‐buyer (agent) is endowed with multidimensional private information, namely, her “ideal” record that she values the most and how her valuation for a given record changes as its distance from her ideal record changes. Consequently, the revenue‐maximization problem faced by a data‐seller (principal), who serves multiple buyers, is a multidimensional mechanism‐design problem, which is well recognized as being difficult to solve. Our main result in this paper is an approximation scheme that guarantees a revenue within as close a positive amount from the optimal revenue as desired. The scheme generates a posted‐price menu consisting of a set of item–price pairs—each entry in the menu consists of an item, that is, a set of records from the data set, and the price corresponding to that item. As a trade‐off, the length of the menu resulting from the scheme increases as the desired guarantee gets closer to zero. For convenience in practice, data‐sellers may want the ability to limit the length of the menu used by the scheme. To facilitate this, we extend our analysis to obtain a general approximation guarantee corresponding to a menu of any given length. We also demonstrate how the seller can exploit buyers' preferences to generate intuitive and useful rules of thumb for an effective practical implementation of the scheme.

Pengaruh Iklan dan Brand Image MS. Glow terhadap Minat Beli Konsumen

In the era of globalization, it is very important to continue to look for the right formula in business activities, innovation from each business is often shown as part of business competition. The emergence of business structures and strategies using modern innovative approaches contributes to the creation of models that determine the achievement of competitive advantage and optimal revenue generation, so the company must be able to implement the right strategy in order to attract more visitors. The purpose of this study was to determine the effect of advertising and brand image variables simultaneously or partially on consumer buying interest in MS products. Glow and to find out which mix of Advertising and Brand Image MS. Glow is the most dominant in influencing consumer buying interest. Respondents were 100 people. Data were collected using an instrument in the form of a questionnaire. The data analysis technique used is multiple linear regression analysis.
 The conclusion of this study is that together the independent variables have a positive and significant effect on consumer buying interest in MS products. glow. On its own, the variables that have a positive effect are advertising and brand image. The dominant variable of the mix on consumer buying interest is advertising.