Optimal Pricing Research Articles

Pricing in spatial classification system in non-symmetric market demand based on the calculations of double interval grey numbers

PurposeThe contemporary landscape of supply chains necessitates a comprehensive integration of multiple components encompassing production, distribution and customer engagement. The pursuit of supply chain harmony underscores the significance of pricing strategies within the framework of dual-channel distribution, particularly when confronted with the dynamics of asymmetric demand performance.Design/methodology/approachThis paper delves into a nuanced decision-making challenge anchored in a dual-channel distribution context featuring a retailer and two distinct products. Notably, the retailer’s decision-making process employs the computational framework of dual grey numbers, a robust tool for handling uncertainty.FindingsThis study revolves around applying game theory to manufacturers. Each manufacturer presents its aggregated price proposition to the retailer. Subsequently, the retailer identifies the optimal pricing configuration among the manufacturers' aggregate prices while adhering to constraints such as spatial classification and inventory costs. This article’s contribution extends to delineating the retailer’s capacity to discern the influence of product market potential and the aggregate product cost on the overall demand.Originality/valueThe model’s innovation lies in its harmonious fusion of spatial classification, pricing strategies and inventory control. Notably, this novel integration provides a platform for unraveling the intricate interplay between non-symmetric market potential, production costs and cross-sensitivity. The investigation is underscored by the utilization of the double interval grey numbers, a powerful computational approach that accommodates the inherent uncertainty pervasive in the domain. This study fills a gap in the existing literature by offering an integrated framework unifying spatial allocation, pricing decisions and inventory optimization.

Implementasi Lelang Barang Jaminan di Pegadaian Syariah KM.6,5 Cabang Bengkulu

This study aims to comprehensively explore the implementation of collateral auctions at Pegadaian Syariah KM.6.5 Bengkulu Branch. Pegadaian Syariah provides financing services based on sharia principles with collateral of valuable goods. When customers fail to repay loans within the specified period, the Islamic pawnshop uses collateral auctions as a last resort to protect the financial rights of the institution. This study analyzes the auction procedures implemented, the obstacles faced, and the impact of auctions on customers. The research method is qualitative descriptive, involving observation, interviews with pawnshops, and document analysis. The results of the study indicate that auctions at Islamic pawnshops follow the principles of fairness, transparency, and sharia; however, challenges such as determining optimal prices and customer education are still obstacles that need to be considered.

The Advance and Spot Selling Strategy Under Quality Information Asymmetry

ABSTRACTWe study firms' optimal pricing and quality selection in the advance period and the spot period under the information asymmetry. Our study theoretically demonstrates that the advance price as a signal of quality information can affect the separating and pooling equilibrium. Importantly, we find that separating equilibrium is more favorable for high cost‐efficiency if consumers have low prior probability that the firm is high cost‐efficiency. For the firm with low cost‐efficiency, the benefit of the separating equilibrium is the same as information is common sense. But no matter which equilibrium firms choose, they will target all consumers during the spot period.

Vegetable Pricing and Replenishment Strategy Based on Multivariate Polynomial Regression and SARIMA Model

Fresh products are one of the important parts of the profitability of superstores, and it is very important for merchants to make effective replenishment and pricing suggestions based on historical data. In order to give a reasonable vegetable pricing and replenishment plan, this paper processes the data and then uses multivariate polynomial regression to regress the sales volume and cost-plus pricing of the category, to obtain the relationship between the sales volume of the category and the cost-plus pricing; and combines with the SARIMA model to further give the recommended replenishment quantity; considering the impact of vegetable freshness on pricing, a freshness-based pricing model is proposed, and the optimal pricing and optimal restocking quantity formula, and proved its correctness in principle. In this study, the pricing and replenishment strategies of reference vegetables are given, and future research can further explore the market forecast of other agricultural products on this basis to broaden the application scope of this model.

A Multi-Model Output Fusion Strategy Based on Various Machine Learning Techniques for Product Price Prediction

In the digital era, precise product price prediction becomes crucial for enhancing competitiveness in the online marketplace. This paper presents a hybrid model framework that enhances the accuracy of online product price predictions by integrating several machine learning algorithms, including Linear Regression, Decision Trees, and Gradient Boosting. The objective of this approach is to leverage the distinct advantages of each model to address their individual limitations and create a robust unified predictive model. This integration allows for improved handling of complex data relationships and diverse market dynamics that are typical in online sales environments. The results demonstrate that the hybrid model achieves superior prediction accuracy, as reflected in reduced Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics, and an exceptionally high R ² value compared to single-model approaches. These outcomes underscore the efficacy of combining multiple predictive models to enhance the precision of price forecasts in the highly competitive online marketplace. This model fusion strategy not only provides more accurate pricing predictions but also offers strategic insights into the optimal pricing strategies for businesses looking to enhance their market position.

The Art of Balancing Price and Plug: Developing a Theoretical Model for Dynamic Pricing in the Electric Vehicle Market

This study presents a novel approach to understanding the complex dynamics of the electric vehicle (EV) market through the lens of differential game theory. We developed a comprehensive model that captures the strategic interactions between EV manufacturers and charging network operators, while incorporating the effects of consumer behavior, market uncertainties, and reference price effects. Using differential game theory, we examined the impact of reference price effects and the charging network’s influence on pricing strategies, focusing on three distinct approaches: basic pricing, static pricing considering reference price effects, and dynamic pricing strategies. Our model offers new insights into consumer behavior and price expectations in the rapidly evolving EV market. The key findings reveal that under static or dynamic pricing strategies, the optimal pricing for EV manufacturers is positively correlated with the initial reference price. When the initial reference price is high (low), the optimal pricing strategy resembles skimming pricing (penetration pricing). As the effort level of charging network operators increases and their influence on consumers’ purchase decisions grows stronger, EV manufacturers tend to set higher prices. Notably, while dynamic pricing strategies can optimize EV manufacturers’ profits, the profits of charging network operators may decrease compared with static pricing strategies. This integrated approach significantly contributes to the field by bridging gaps among market dynamics, pricing strategies, and the infrastructure’s development in the context of electric mobility, providing a comprehensive framework for understanding and optimizing the EV ecosystem. Ultimately, this study advances sustainable business models that balance profitability, consumer behavior, and the infrastructure’s growth in the rapidly evolving EV market.

Interpretable Price Bounds Estimation with Shape Constraints in Price Optimization

Interpretable Price Bounds Estimation with Shape Constraints in Price Optimization

Optimizing Pricing Strategies for Product Lines and Value‐Added Services: Accounting for Reference Prices and Network Effects

ABSTRACTCustomer choice behavior is increasingly influenced by the reference price effect and negative network effect as customers become more rational and personalized. This study, considering customers as pivotal stakeholders for enterprise development, employs the multinomial logit (MNL) model and develops pricing and strategy optimization models for pure bundling and mixed bundling strategies of an enterprise's product line. The main focus is to analyze how these effects impact optimal pricing and strategy selection. The results indicate that stronger effects lead to lower enterprise profits, and ignoring them can cause substantial losses. Generally, mixed bundling strategies yield higher profits.

A strategic optimizing carbon neutrality within profit and emission reduction efficiency: a stackelberg game analysis under a cap-and-trade scheme.

Carbon emissions (CO2e) are a main problem, and many countries are committed to reducing CO2e to attain carbon neutrality. To narrow this gap, this research considers green technology implementation (GTI) and CO2e quota in dual green gaming. The objective is to explore the integration of GTI and CO2e quotas into optimal production and pricing decisions in emission-generating companies (EGCs). To achieve this, a two-party simulation Stackelberg game model involving the CO2e strategy of EGCs and governments is proposed. We formulate a mathematical model and experimental methods to optimize EGCs' decision-making using neutral indicators. EGCs optimize production quantity, achieving CO2e reduction through ERE maximization. EGCs exhibit a strong commitment to ERE enhancement practices, enhancing profit and carbon-neutral goals. Our findings demonstrate the effectiveness of CO2e quotas and GTI as carbon-neutral indicators in EGCs, contributing to global CO2e reduction. Our research provides both literature and inspiration for global CO2e reduction efforts. Moreover, it aids governments in exploring optimal incentives and decisions for pricing and production. Last, this work enables EGCs to maximize profits and move toward a carbon-neutral environment.

Partial hedging in credit markets with structured derivatives: a quantitative approach using put options

This study develops a novel method for mitigating credit risk through the use of structured derivatives, focusing in particular on the use of European put options as a strategic hedging tool. Inspired by the work of Merton (1974), our approach introduces the concept of default triggered by the stock price ST breaching a predefined barrier B. By establishing a distributional equivalence between an existing default model and P(ST<B) for a given time T, we demonstrate the potential for reducing the necessary capital allocation for a projected loss X(T) by partially hedging with a European put option. We formulate and solve an optimization problem w.r.t. a specific risk measure to determine the optimal strike price for the option, and our numerical analysis confirms a reduction in the Solvency Capital Requirement (SCR) in markets with and without jumps. Our findings provide (insurance) companies with a pragmatic approach to mitigating losses while maintaining their current risk management framework.

Pricing policies of dual-channel green supply chain: considering manufacturers’ dual behavioural preferences and government subsidies

As the green economy and e-commerce evolve, some manufacturers begin to engage in green production and use online direct channels to enhance competitiveness. However, the additional cost of green production makes manufacturers often show dual behavioural preferences of fairness concern and risk aversion when making decisions. Based on this, this study constructs the optimal pricing model of a dual-channel green supply chain, considering dual behavioural preferences of manufacturers’ fairness concern and risk aversion. In addition, no government subsidy, government research and development cost subsidy and green degree subsidy are also considered. This paper uses the Stackelberg game to find the optimal solution for the model. The findings illustrate that (1) Government subsidy mechanisms cannot coordinate the effects of manufacturers’ dual behavioural preferences on pricing policies of a dual-channel green supply chain. (2) Under the three different government subsidy conditions, manufacturers’ fairness concern and risk aversion will all reduce the green degree of products and online direct price of products. (3) The government research and development cost subsidy and green degree subsidy can both improve the green degree of products, offline retail price, market demand and members’ expected profits.

Sharing channel choices for electric vehicle battery manufacturers when considering range anxiety among heterogeneous consumers

PurposeThis study aims to investigate the optimal pricing decisions and shared channel strategy selection of battery manufacturers considering heterogeneous consumers' range anxiety.Design/methodology/approachAmidst the rapid growth of the electric vehicle sector, countries are promoting upgrades in the automotive industry. However, insufficient driving range causes consumer range anxiety. The study utilizes the Stackelberg game model to assess how range anxiety influences battery manufacturers' pricing and channel strategy decisions across three strategies.FindingsWe find that electric vehicle battery manufacturers' decisions to cooperate with third-party sharing platforms (TPSPs) are primarily influenced by fixed costs and consumer range anxiety levels. As range anxiety increases, the cost threshold for joining shared channels rises, reducing cooperation likelihood. However, considering diverse consumer needs, especially a higher proportion of leisure-oriented consumers, increases the likelihood of cooperation. Furthermore, higher battery quality makes direct participation in shared channels more probable.Originality/valueIn the electric vehicle industry, range anxiety is a significant concern. While existing literature focuses on its impact on consumer behavior and charging infrastructure, this study delves into battery manufacturers' strategic responses, offering insights into channel options and pricing strategies amidst diverse consumer segments.

Optimal management of park-and-ride points and shared parking spaces in the context of autonomous driving

This paper studies how to set the location of park-and-ride and the pricing of shared parking spaces to achieve the desired goals in the context of autonomous vehicles (AV). Assuming that a linear city does not allow single-occupancy private cars to enter the city center, travelers can only choose to park and ride or take a shared autonomous vehicle to reach the city center. Under the condition of autonomous driving, since the car can park autonomously, there are three ways to park: park the car home, park the car in a free parking space near the transfer point, and park the car in a shared parking space provided by residents near the transfer point. Starting from the two goals of profit maximization and social cost minimization, this paper explores the optimal parameter settings under the two goals respectively. The results show that the optimal park-and-ride point and the optimal shared parking space pricing under the two goals are both obtained when the supply of shared parking spaces is equal to the demand. This study can provide theoretical reference and decision-making basis for the optimal allocation of resources in the era of autonomous driving.

What is the impact of demand patterns on integrated online-offline and buy-online-pickup in-store (BOPS) retail in a smart supply chain management?

Retailers aim to provide in-person and remote interactions to accelerate purchases through hybrid methods, which introduce additional delivery and emissions loads but create a seamless shopping experience. The challenges of unified inventory, customer information, pricing, demand forecasting complications, and order fulfillment are met successfully in the hybrid methods. Here, a smart supply chain management that integrates traditional physical presence with cloud-based and hybrid buy-online-pickup in-store channels is optimized to meet customer demand in different demand patterns. The demand patterns considered are linear, cubic, and normalized functions of the selling price, the advertising cost, and the product lifetime. The article incorporates sustainable supply chain and omnichannel strategies to optimize supply chain performance and profitability. The paper compares the results of the proposed models with traditional supply chain models and provides some managerial insights and suggestions for online retailers. Numerical simulation reveals that nonlinear price dependence in demand provides an opportunity for higher promotional investment with a substantial rise in optimal selling price and overall profit. The shift-to-imperfect probability is seen to be the volatile component in the solution surface. The markup of the offline channel has a greater impact than buy-online-pickup in-store markup.

Determining the optimal bid direction of a generation company using the gradient vector of the profit function in the network constraints of the electricity market

AbstractOne of the primary challenges faced by generation companies (GenCos), which operate multiple generation units within the electricity market, is the determination of the optimal bid price for these units to maximize profit. This paper proposes a novel approach to ascertain the optimal bid price direction for GenCos by leveraging the gradient vector of the profit function within the constraints of the electricity market. First, the Jacobian matrix of unit profits is computed using the electricity market structural decomposition method. This matrix highlights how the profit of generation units is affected by market input parameters, including the bid prices of the units. Then, the gradient vector of the GenCos' profit function and the optimal bid price direction are derived from the Jacobian matrix. The methodology is applied to a 24‐bus IEEE network, with results validated against those from a simulation method to confirm the efficacy of the proposed approach. The simulation results show that the highest and lowest profit changes with a step increase of 0.1$/MWh are observed for GenCo 4 and GenCo 6 with values of 60.28 and 2.20 $/h, respectively. The proposed approach can be effective in the changes of bid direction of the units of a GenCo to achieve the highest possible profit.

Price optimization in supply chain agreements: a comparative analysis of buyback and put option contracts for inventory risk management

AbstractThis paper aims to provide a model of a supply chain in the integrated system and obtain its optimal decision variables. The paper introduces buyback and put option contracts to reduce inventory risk. These contracts were compared in three different cases via a numerical analysis approach. In the first case, the holding cost (h) of a retailer for surplus orders in the buyback contract is equal to the option price (o) in the put option model. The relationship between exercise price (e) in the put option model and buyback price (b) in the buyback contract was obtained by comparing the optimal values in the models. This study found that the exercise price in the put option contract will be greater than the buyback price. Furthermore, it is more likely that the retailer gave more benefits under the buyback agreement than the time the retailer chooses the put option contract. Therefore, it can be concluded that if the retailer chooses the buyback agreement in this situation, can gain more benefits. The study provides essential managerial insights to compare agreements and presents recommendations to choose a suitable contract.

Social Cost of Carbon as an International Benchmark to Drive Countries’ Carbon Pricing during the Transition

Building on updated estimates of the social cost of carbon obtained from the most recent literature, this article proposes a social cost of carbon-based benchmark for carbon pricing to drive world countries’ carbon pricing policies up to 2050, consistent with the Paris Agreement targets. By using a dataset on net effective carbon rates developed by the Organisation for Economic Co-operation and Development (OECD), we firstly compare both explicit and implicit carbon pricing in 2021 in 71 OECD and non-OECD countries with the social cost of carbon benchmark for 2021 and calculate the degree of internalization of the social cost of carbon averagely related to their carbon pricing instruments. We find that there is a serious gap in current climate policies, which are far from creating optimal pricing conditions to reduce global emissions to levels needed by the Paris Agreement. The economic and distributional feasibility of a full implementation of the carbon pricing benchmark is tested in the same set of countries using two indicators, which are calculated for 2025, 2030, 2040 and 2050. Since the test results are income-regressive among income country groups, benchmark implementation by countries within the cooperative approaches of Paris Agreement art. 6 should be accompanied by the creation of an international cooperative fund aimed to recycle at least part of the revenues collected by high-income countries to compensate affected population in lower-income countries.

Optimal financing strategies for low‐carbon supply chains: A Stackelberg game perspective

AbstractThis study constructs a Stackelberg game model between low‐carbon manufacturers and capital‐constrained retailers. Considering the manufacturer's financing model, the manufacturer's investment model, and the internal blended financing model, it studies the optimal wholesale price of manufacturers. This paper investigates the impact of consumers' low‐carbon preference, the financing interest rate of manufacturers, investment ratio, investment return on carbon emission reduction investment, and supply chain pricing. The conditions for low‐carbon supply chain participants to choose the best financing mode and the impact of different financing models on the performance of low‐carbon supply chains were obtained.

APPLYING SOFT COMPUTING METHODS IN BUSINESS ANALYTICS USING HYBRID GENETIC ALGORITHMS

In the era of big data and advanced analytics, the application of soft computing techniques has emerged as a powerful tool in solving complex business problems. This paper presents the use of hybrid genetic algorithms (HGAs) in business analytics to address challenges related to optimization, prediction, and decision-making processes. Traditional algorithms often struggle with large, nonlinear, and dynamic datasets typical of business environments. The incorporation of soft computing techniques such as genetic algorithms (GAs) and their hybridization with other methods like fuzzy logic and neural networks can help overcome these limitations. The problem addressed in this research is optimizing decision-making in marketing strategies, focusing on maximizing return on investment (ROI). Standard methods face difficulties in navigating through vast datasets and discovering optimal solutions. The hybrid genetic algorithm proposed in this study combines the exploration strength of GAs with the exploitative precision of local search techniques. The model was tested using a real-world dataset of marketing expenditures and revenues from a retail company. The HGA achieved an ROI improvement of 25%, significantly outperforming standard GAs and traditional optimization methods, which yielded only a 12% improvement. The flexibility and efficiency of this approach make it ideal for various business applications, including supply chain optimization, customer segmentation, and product pricing.

Determinants of Business Model Choices for New Entrant in the Pharmaceutical E-commerce Environment

The COVID-19 pandemic has significantly impacted the pharmaceutical retail market, leading to rapid growth in the pharmaceutical e-commerce industry. Currently, the new entrant has three options for business models: offline pharmacy model, normal e-pharmacy model, and Direct-To-Patient (DTP) pharmacy model. To address this issue, this study constructs a dual-channel model that considers the enhancement coefficient of medication experience and incorporates a hospital that dominate the pharmaceutical retail market using the Hotelling model for each of the three models. The study utilizes dual-channel competition theory to compare and analyze optimal pricing and maximum profit of supply chain members under the different models, providing a decision-making mechanism for the new entrant. By comparing profits, the study finds that the DTP pharmacy model outperforms the traditional pharmacy and normal e-commerce models, providing the highest profit and the best medication experience for patients.