Search Results (19)

The increasing penetration of distributed renewable energy and the emergence of large-scale, flexible loads such as data centers pose significant challenges to the economic and secure operation of distribution systems. Traditional static pricing mechanisms are often inadequate, leading to inefficient resource dispatch and curtailment of renewable generation. To address these issues, this paper proposes a hierarchical pricing and dispatch framework modeled as a tri-level Stackelberg game that coordinates interactions among an upstream grid, a distribution system operator (DSO), and multiple virtual power plants (VPPs). At the upper level, the DSO acts as the leader, formulating dynamic time-varying purchase and sale prices to maximize its revenue based on upstream grid conditions. In response, at the lower level, each VPP acts as a follower, optimally scheduling its portfolio of distributed energy resources—including microturbines, energy storage, and interruptible loads—to minimize its operating costs under the announced tariffs. A key innovation is the integration of a schedulable data center within one VPP, which responds to a specially designed wind-linked incentive tariff by shifting computational workloads to periods of high renewable availability. The resulting high-dimensional bilevel optimization problem is solved using a Kriging-based surrogate methodology to ensure computational tractability. Simulation results verify that, compared to a static-pricing baseline, the proposed strategy increases DSO revenue by 18.9% and reduces total VPP operating costs by over 28%, demonstrating a robust framework for enhancing system-wide economic and operational efficiency. Full article

This paper addresses the challenges of automated pricing and replenishment strategies for perishable products with time-varying deterioration rates, aiming to assist wholesalers and retailers in optimizing their production, transportation, and sales processes to meet market demand while minimizing inventory backlog and losses. The study utilizes an improved convolutional neural network–long short-term memory (CNN-LSTM) hybrid model, autoregressive moving average (ARIMA) model, and random forest–grey wolf optimization (RF-GWO) algorithm. Using fresh vegetables as an example, the cost relationship is analyzed through linear regression, sales volume is predicted using the LSTM recurrent neural network, and pricing is forecasted with a time series analysis. The RF-GWO algorithm is then employed to solve the profit maximization problem, identifying the optimal replenishment quantity, type, and most effective pricing strategy, which involves dynamically adjusting prices based on predicted sales and market conditions. The experimental results indicate a 5.4% reduction in inventory losses and a 6.15% increase in sales profits, confirming the model’s effectiveness. The proposed mathematical model offers a novel approach to automated pricing and replenishment in managing perishable goods, providing valuable insights for dynamic inventory control and profit optimization. Full article

In the dynamic environment of fresh food supermarkets, managing the short shelf life and varying quality of vegetable products presents significant challenges. This study focuses on optimizing restocking and pricing strategies to maximize profits while accommodating the diverse and time-sensitive nature of vegetable sales. We analyze historical sales, pricing data, and loss rates of six vegetable categories in Supermarket A from 1 July 2020 to 30 June 2023. Using advanced data analysis techniques like K-means++ clustering, non-normal distribution assessments, Spearman correlation coefficients, and heat maps, we uncover significant correlations between vegetable categories and their sales patterns. The research further explores the implications of cost-plus pricing, revealing a notable relationship between pricing strategies and sales volumes. By employing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models, we forecast sales and determine optimal restocking volumes. Additionally, we use price elasticity theories and a comprehensive model to predict net profit changes, aiming to enhance profit margins by 47%. The study also addresses space constraints in supermarkets by proposing an effective assortment of salable items and individual product restocking plans, based on FP-Growth algorithm analysis and market demand. Our findings offer insightful strategies for sustainable and economic growth in the supermarket industry, demonstrating the impact of data-driven decision-making on operational efficiency and profitability. Full article

Price promotions are commonly employed to enhance supermarket performance and the sustainable development of the retail industry, yet their effectiveness may vary among similar supermarket chains. In contrast to Western countries, Chinese supermarkets are typically community-centered, allowing consumers to make frequent visits due to lower transaction costs. This multiple-visit pattern discourages substantial one-time purchases based on promotions. This study aims to investigate how pricing promotions can attract consumers more effectively and which product categories are most suitable for this purpose. Utilizing scanner data from Chinese chain supermarkets, we empirically assess the impact of promotion depth, breadth, and duration on consumer purchasing behavior using fixed effects models, IV, and GMM methods. Furthermore, we identify product category characteristics that are more appealing to consumers based on the relationships between different product category promotions and consumer behavior. Results demonstrate that each of the three price-promotion features has a positive effect on Chinese supermarket performance, with varying degrees of significance. Different promotion methods not only benefit promoted products but also stimulate sales of non-promotional items. At the product level, the impact of supermarket promotions on performance differs across categories. The most attractive category in terms of consumer purchases influenced by discounts is special paper, while small kitchen appliances have the least impact. Promoting categories with lower average prices, higher average sales volumes, fewer products, and better storage durability is conducive to attracting consumer shopping. These empirical findings have implications for academic research on price promotion theory and supermarket managers’ pricing strategy decisions, as well as the sustainable development of the offline retail industry. Full article

This paper focuses on the modeling of automatic pricing and replenishment strategies for perishable products with time-varying deterioration rates based on an improved SVR-LSTM-ARIMA hybrid model. This research aims to support supermarkets in planning future strategies, optimizing category structure, reducing loss rates, and improving profit margins and service quality. Specifically, the paper selects perishable vegetables as the research category and calculates the cost-plus ratio for each vegetable category. Correlation analysis is conducted with total sales, and a non-parametric relationship curve is obtained using support vector regression for nonlinear fitting. The long and short memory recurrent neural network is then used to predict sales volume, and a pricing strategy is calculated based on the fitting curve. Additionally, the paper establishes a correlation between loss rate and shelf life, corrects the daily average sales volume index, and solves the problem of quantity and category of replenishment using a backpack problem approach. By considering multiple constraints, a quantitative category replenishment volume and pricing strategy is obtained. The mathematical model proposed in this paper addresses the replenishment and pricing challenges faced by supermarkets, aiming to improve revenue and reduce loss while meeting market requirements. Full article

The role of the food environment in shaping nutrition and health has gained substantial attention from policymakers, public health researchers, and advocacy groups. To promote equities in food access and nutrition outcomes, understanding factors linked with the utilization of local community food resources is crucial. Using Andersen’s service utilization model, we explained how adults use their neighborhood food resources. In a cross-sectional study design, an online survey was conducted in REDCap Version 13.4.0 via the Amazon Mechanical Turk (MTurk) involving 1830 adults with a mean age of 37.9  ±  12.1 years. Participants answered questions on predisposing, enabling, and need factors that influence their use of different community food resources. The predisposing factors that were statistically significant included age, family size, marital status, race, and ethnicity. The enabling factors included travel time, travel mode, income, and shopping decision motivators (such as being able to use Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) vouchers, delivery services, great sales, and coupons). Food security and community food resources need for lower food price were the significant need factors. However, these factors vary by the types of food resources. In conclusion, enhancing the utilization of community-based food access initiatives and programs among underserved families requires consideration of family composition, racial and ethnic diversity, and transportation access. Full article

The article compares the effects of crisis on the real estate market in two regions of the Czech Republic that differ from a macroeconomic point of view. The region of Prague represents the rich and developed region while the Karlovy Vary region struggles with many socio-economic and structural problems. An analysis was performed for the time period of 2018 to 2022. It analyzed the development of apartment prices in both regions, the availability of housing, the turnover of the real estate market in terms of the number of apartment sales, the development of liens on real estate, the number of apartment transfers from state property to private ownership, and the development of the number of real estate foreclosures. The basis for creating statistical outputs is the EVAL software, which was developed by one of the co-authors of this article. The EVAL software collects price offers of apartments offered for sale and rent throughout the Czech Republic and collects publicly available data from the cadastral office. The authors found that the real estate market experienced a significant turnaround in the volume of mortgage loans granted in 2022. This decline led to a significant drop in the total volume of real estate transactions. The findings suggest that potential buyers should wait for property prices to drop before buying, while rental property owners and investors can take advantage of the increased demand for properties. Full article

Trade credit is generally used by businesses to obtain external funds. This article demonstrates an inventory system from the retailer’s point of view in which (1) the influence of trade credit on expanding small businesses and their consumers is the focus of this research, and (2) the retailer’s on-hand inventory follows the non-instantaneous deterioration. (3) To maximize profit, the demand is disclosed, which is based on not just the sales price, but also on cumulative demand, which indicates saturation and diffusion. (4) The product’s initial price and the permitted discount rate at the time of deterioration are considered to be time-dependent functions of the sales price. In the absence of deterioration, the item is sold at a constant rate, and whenever deterioration occurs, the sales price is assumed to be an exponential function of the discount variable. The main aim is to optimize the total profit of the retailer in terms of cycle time and sales price. The traditional algorithm of optimization is used to address the optimization problem. Finally, the theoretical results are validated by solving three numerical illustrations and conducting a sensitivity analysis of the main factors resulting from the following managerial implications: (1) credit period provides the maximum profit margin of any financing method, and (2) an increase in the initial rate of demand raises sales price while increasing overall profit significantly. Full article

Bangkok suffered from the world’s worst traffic congestion in the 1990s due to rapidly increasing car ownership, reflecting the economic growth and road-dependent transport policy beginning in the 1960s. Due to its monocentric but scattered urban structure, traffic congestion is severe, causing tremendous economic loss, deteriorating air quality, and badly affecting the quality of life. A historical review reveals that the urban and transport plan and development were not efficiently coordinated, resulting in unorganized suburbanization and progressively more severe traffic congestion. It is important to reveal the impact of the transportation project on the housing market in order to incorporate the policies for transportation and urban development. To define the impact, the OLS hedonic price model and the local multiscale geographically weighted regression (MGWR) model were estimated, along with the condominium sales data. The results revealed that the impact of rail transit on a rise in property value significantly varied across the study area. It was estimated that, for the area along the major rail transit corridor in the city center, a premium of a location 100-m closer to the station would be more than 200 USD per square meter. At the same time, the value would be less than 80 USD for the area along the rail corridor in the suburb. These findings provide policy insights for future urban and railway development, including the proper coordination of rail transit development and urban development with subcenters, transit-oriented development, and improved pedestrian flow around transit stations. Full article

When information regarding the effective evaluation of the value of exquisite products is lacking, the market demand function for such products at a given time point is affected by the diffusion of historical transaction price information before the time point. This is because historical transaction prices play an active role in influencing the internal reference price (IRP) of customers, and the continuous diffusion of historical transaction price information leads to the continuous correction, adjustment, and updating of customers’ IRPs. Given the varying rates of such information diffusion, the speed at which customers adjust their IRPs also varies across individuals and contexts. By considering the exponential distribution of potential customers’ IRPs as an example to establish the dynamic demand function that considers the effect of historical transaction prices, this paper discusses the effect of different information diffusion rates on the demand function at a time point. On the basis of this demand function, a sales price control model that maximizes the discounted profitability for businesses in the patent term of an exquisite product is then constructed to provide businesses with an operation method to cultivate prices and increase profits. Full article

In particular business transactions, the supplier usually provides an admissible delay in settlement to its vendor to encourage further sales. Additionally, the demand for the commodity is inversely proportional to the function of the sales price, which is non-linear and, in some situations, a holding cost rises over time. Moreover, many goods often deteriorate consistently and shall not be sold after their expiration dates. This study analyses a model for perishable products with a maximum life span with price-dependent demand and trade credit by assimilating these variations and under the supposition of time-varying holding cost. Furthermore, to diminish the rate of deterioration, investment for preservation technology is often taken into account beforehand. Based on real-life circumstances, shortages are admitted and backlogged partially, with an exponential rise in wait time before the new good emerges. The key ambition is to calculate the optimum investment under preservation, sales price, and cycle time using the classical optimization algorithm to maximize the vendor’s net profit. Additionally, to clarify the outcomes, the numerical illustrations are addressed, and the sensitivity analysis of significant parameters is eventually implemented. Full article

Indonesia is at the epicenter of the Asian Songbird Crisis, i.e., the recognition that the cage bird trade has a devastating impact on numerous imperiled bird species in Asia. The Javan pied starling Gracupica jalla, only in the last five years recognized as distinct from the pied starlings of mainland Southeast Asia, has been declared extinct the wild in 2021. Up until the 1980s, it used to be one of the most common open countryside birds on the islands of Java and Bali, Indonesia. From the early 2000s onwards, the species is commercially bred to meet the demand from the domestic cagebird trade. We conducted 280 market surveys in 25 bird markets in Java and Bali between April 2014 and March 2020, with 15 markets being surveyed at least six times. We recorded 24,358 Javan pied starlings, making it one of the most commonly observed birds in the markets. We established that, conservatively, around 40% of the birds in the market were sold within one week and used this to estimate that at a minimum ~80,000 Javan pied starlings are sold in the bird markets on Java and Bali. The latter represents a monetary value of USD5.2 million. We showed that prices were low in the 1980s, when all birds were sourced from the wild. It became more varied and differentiated in the 2000s when a combination of now expensive wild-caught and cheaper captive-bred birds were offered for sale, and prices stabilized in the 2010s when most, if not all birds were commercially captive-bred. Javan pied starlings are not protected under Indonesian law, and there are no linked-up conservation efforts in place to re-establish a wild population on the islands, although small-scale releases do take place. Full article

China’s wood industry is vulnerable to the COVID-19 pandemic since wood raw materials and sales of products are dependent on the international market. This study seeks to explore the speed of log price recovery under different control measures, and to perhaps find a better way to respond to the pandemic. With the daily data, we utilized the time-varying parameter autoregressive (TVP-VAR) model, which can incorporate structural changes in emergencies into the model through time-varying parameters, to estimate the dynamic impact of the pandemic on log prices at different time points. We found that the impact of the pandemic on oil prices and Renminbi exchange rate is synchronized with the severity of the pandemic, and the ascending in the exchange rate would lead to an increase in log prices, while oil prices would not. Moreover, the impulse response in June converged faster than in February 2020. Thus, partial quarantine is effective. However, the pandemic’s impact on log prices is not consistent with changes of the pandemic. After the pandemic eased in June 2020, the impact of the pandemic on log prices remained increasing. This means that the COVID-19 pandemic has long-term influences on the wood industry, and the work resumption was not smooth, thus the imbalance between supply and demand should be resolved as soon as possible. Therefore, it is necessary to promote the development of the domestic wood market and realize a “dual circulation” strategy as the pandemic becomes a “new normal”. Full article

With the help of the time-varying parameter vector autoregression with stochastic volatility (TVP-SV-VAR) model and the Bayesian dynamic conditional correlational autoregressive conditional heteroscedasticity (Bayesian DCC-GARCH) model, this study analyzes the interaction mechanism and dynamic correlation among financial leverage, house price, and consumer expenditure (the survey data are collected from China’s National Bureau of Statistics from January 2000 to December 2019; the data on financial leverage and consumer expenditure are from the Wind economic database, and the price of commercial housing was calculated based on the sales volume and area of commercial housing on the official website of China’s National Bureau of Statistics). Empirical results show that an increase in financial leverage significantly increases house prices and reduces consumer expenditure, that a rise in house prices inhibits financial leverage and weakens consumer expenditure, and that an increase in consumer expenditure raises financial leverage and stimulates a rise in house prices. In addition, house price and consumer expenditure are most relevant, followed by financial leverage and consumer expenditure, and then by financial leverage and house price. Therefore, systematic analysis of dynamic correlation among the three variables has important practical significance for formulating appropriate financial policies to stabilize house prices and promote the growth of consumer expenditures. Specially, financial leverage is an important factor to hold back soaring house prices and shrinking consumer expenditure. Therefore, monetary and macroprudential policies should be used to deal with financial leverage variables in order to achieve a balanced and sustainable development of the macroeconomy in China. Full article

This study investigates the income and price elasticities of gasoline demand for a fuel subsidizing country case, applying three different time-varying coefficient approaches to the data spanning the period from January 2002 to June 2018. The empirical estimations concluded a cointegration relationship between gasoline demand, income, and gasoline price. The income elasticity found ranges from 0.10 to 0.29, while the price elasticity remains constant over time, being −0.15. Income elasticity increases over time, slightly decreasing close to the end of the period, which is specific for a developing country. In the short run, gasoline demand does not respond to the changes in income and price. The policy implications are discussed based on the findings of the study. Research results show that since the income elasticity of demand is not constant, the use of constant elasticities obtained in previous studies might be misleading for policymaking purposes. An increase in income elasticity might be the cause of the inefficiency of the existing vehicles. The small price elasticity allows to say that if policy makers plan to reduce gasoline consumption then increasing its price would not substantially reduce the consumption. The current situation can be utilized to increase energy efficiency and implement eco-friendly technologies. For this purpose, the quality of existing transport modes can be improved. Meanwhile, to meet households’ needs, policies such as providing soft auto loans need to be formed to balance the recent drop in car sales. Full article