Variable Pricing Research Articles

Optimal Early-Bird Discounts and the Challenges of Diversion

Variable pricing and early-bird discounts can shift some demand or the time of purchase from higher-priced periods to lower-priced periods without increasing the total demand. This diversion effect influences optimal price levels, and the present paper presents a model for calculating optimal early-bird discounts when incorporating diversion. The model also reveals the differences in price elasticities and the feasible price interval for positive profit. The paper contributes to the literature on early-bird pricing strategies and offers practical insights for service providers seeking to optimize their pricing strategies. The empirical relevance is documented using survey and real-life experimental data among skiers at a major ski resort in Norway. The main insights of the paper are that the profit-maximizing discounts and the gap between the optimal price with and without diversion increase with the length of the pre-booking period.

The effects of price framing and magnitude of price differences on perceived fairness when switching from static to variable pricing

The long-term success of variable pricing relies on existing and new customers accepting the pricing scheme implemented. We use experimental data from the alpine skiing industry to examine how the magnitude of price differences and price framing affect perceived fairness in cases of intra-weekly price differentiations and early-bird discounts. The results show that both the price framing and the magnitude of the price differences between the high- and low-price scenarios have strong effects on perceived fairness. Framing the price difference as either a loss or a gain has an asymmetrical effect on perceived fairness. Increasing the price difference between the high- and low-price alternatives has a negative impact on perceived fairness, but the effect differs across framing (gain or loss) and for various early-bird horizons.

Comparing pricing mechanisms for managed lanes

Two commonly used methods for pricing managed lanes (MLs) are adjusting tolls based on the time of day or dynamically changing them in response to real-time traffic congestion. It is unclear which of these tolling options is more effective in controlling the usage of MLs. This study employed nine different performance measures with the goal to evaluate the toll's capacity to manage traffic on six MLs across the United States, including if dynamic tolling or variable tolling is better at managing ML traffic. Among the MLs examined, two were priced variably (SR-91 and I-25), while four were priced dynamically (I-35W, I-394, I-35E, and MoPac). Overall, both pricing approaches were found to effectively maintain traffic flow on MLs, with neither method demonstrating clear superiority. However, dynamic pricing did exhibit a slight advantage over variable pricing in most performance measures. Our results imply that both pricing mechanisms are similarly effective in ensuring the smooth operation of MLs and are therefore viable options for ML operators.

Ticketing and Crowd Management System for Attraction Facilities: An Aquarium Case Study

In tourist facilities, managing ticket sales can reduce congestion imbalances. This study reports on the results of a pilot experiment conducted at Kaiyukan in Osaka, one of the largest aquariums in Japan. The experiment utilized pre-sale ticket data for controlling admission time intervals, smoothing the number of admissions through dynamic pricing, and predicting visitor numbers. The study reviewed the effectiveness of each of these approaches to alleviate congestion inside Kaiyukan. We then report on a method to predict the number of visitors from the pre-sale conditions of Kaiyukan admission tickets. It was found that setting entry times to 15-minute intervals was most operationally advantageous for the Kaiyukan. Moreover, the behavioral effects induced by variable pricing were more effective when prices were changed based on time slots rather than on a daily basis. Compared to the Holt–Winters’ method, we were able to maintain stable prediction accuracy even during consecutive holidays and long school vacation seasons.

Techstreet Enterprise

Techstreet Enterprise is a subscription-based platform dedicated to industry codes and standards, offering users online access to complete standards documents in PDF format. Delving into its features and functionality, this review highlights Techstreet Enterprise’s content customization options, including the innovative Building Blocks feature. It also examines the platform’s variable pricing structure, interface, and competitive products and discusses the platform’s strengths and considerations. This review is intended to guide academic libraries in deciding whether to adopt Techstreet Enterprise as their standards resources. Overall, Techstreet Enterprise is an excellent resource for engineering standards access, and academic libraries are strongly encouraged to consider it as a top choice when navigating purchasing decisions.

A Generative AI Approach to Pricing Mechanisms and Consumer Behavior in the Electric Vehicle Charging Market

The electrification of transportation is a growing strategy to reduce mobile source emissions and air pollution globally. To encourage adoption of electric vehicles, there is a need for reliable evidence about pricing in pub-lic charging stations that can serve a greater number of communities. However, user-entered pricing information by thousands of charge point operators (CPOs) has created ambiguity for large-scale aggregation, increasing both the cost of analysis for researchers and search costs for consumers. In this paper, we use large language models to address standing challenges with price discovery in distributed digital data. We show that generative AI models can effectively extract pricing mechanisms from unstructured text with high accuracy, and at substantially lower cost of three to four orders of magnitude lower than human curation (USD 0.006 pennies per observation). We exploit the few-shot learning capabilities of GPT-4 with human-in-the-loop feedback—beating prior classification performance benchmarks with fewer training data. The most common pricing models include free, energy-based (per kWh), and time-based (per unit time), with tiered pricing (variable pricing based on usage) being the most prevalent among paid stations. Behavioral insights from a US nationally representative sample of 13,008 stations suggest that EV users are commonly frustrated with the slower than expected charging rates and the total cost of charging. This study uncovers additional consumer barriers to charging services concerning the need for better price standardization.

Simulation of power consumption control of receivers at underground iron ore mining enterprises

The article presents the results of developing a concept for controlling the process of power consumption by electrical receivers with a voltage of up to 1000 V at underground mining enterprises in the function of hourly tariffs, which are characterized by variable pricing for the day ahead in current market conditions. The following parameters are considered: limitations on operation duration of a separate electrical unit during the day, the maximum load on underground substation transformers, and the amount of power ordered by the enterprise, the excess of which leads to the application of penalties. To solve the control problem, it is proposed to apply a heuristic optimization method based on a genetic algorithm. System efficiency is studied by determining settings of the evolutionary search algorithm that would ensure the lowest cost of power purchase. In particular, the crossover function (one-point, two-point, or Laplace) and the number of elite individuals in the population are modified. The experiments are carried out on the basis of the Global Optimization Toolbox in the MATLAB software package. Simulation of system efficiency through different settings of the genetic algorithm demonstrates that the minimum power cost can be ensured by using the Laplace crossover method with 10 individuals in a population of 100, of which 10 are elite and pass to the next generation unchanged. This option allows obtaining an average of 2.62 % lower daily power cost than the other parameters studied. The proposed method of power consumption control allows us to identify the achievable potential for reducing the energy component in the final product cost of iron ore mining at underground mining enterprises. It can be recommended for practical implementation at both operating and projected enterprises

Optimal pricing strategies and decision‐making systems in e‐commerce using integrated fuzzy multi‐criteria method

AbstractE‐commerce online stores are now virtual platforms for connecting with millions of potential clients worldwide in the age of digitization. The marketing teams develop digital marketing tactics to attract traffic to their e‐commerce sites and increase sales volume. With the vast amount of data provided by the cloud, decisions that were previously made with a significant level of intuition based on the knowledge and experience of decision‐makers can now be backed using artificial intelligence algorithms. To identify the variables influencing pricing decisions for products launched on e‐commerce shopping sites and to develop variable pricing techniques for each product found on an e‐commerce site. This paper introduces a novel approach that applies Fuzzy association rule mining (FARM) and Fuzzy TOPSIS MCDM methodology. A B2B e‐commerce marketing store based in Hong Kong has created and implemented Smart‐Quo, a pricing decision support system for B2B e‐commerce retail businesses. After a six‐month trial period, there has been a substantial advance in the effectiveness and efficiency of choosing prices for each product. The case study illustrates the viability and potential advantages of implementing artificial intelligence tools in marketing management in the digital era.

Exact separation of the rounded capacity inequalities for the capacitated vehicle routing problem

AbstractThe family of Rounded Capacity (RC) inequalities is one of the most important sets of valid inequalities for the Capacitated Vehicle Routing Problem (CVRP). This paper considers the problem of separation of violated RC inequalities and develops an exact procedure employing mixed integer linear programming. The developed routine is demonstrated to be very efficient for small and medium‐sized problem instances. For larger‐scale problem instances, an iterative approach for exact separation of RC inequalities is developed, based upon a selective variable pricing strategy. The approach combines column and row generation and allows us to introduce variables only when they are needed, which is essential when dealing with large‐scale problem instances. A computational study demonstrates scalability of the proposed separation routines and provides exact RC‐based lower bounds to some of the publicly available unsolved CVRP instances. The same computational study provides RC‐based lower bounds for very large‐scale CVRP instances with more than 3000 locations obtained within appropriate computational time limits.

Platelet inventory management with demand and supply uncertainty and variable pricing considerations

Managing the inventory of blood is a crucial operation in hospitals owing to its significance in medical treatment. At the same time, blood is characterized by unique attributes, such as perishability and the unpredictable nature of its supply and demand. While models have been developed to optimize the said process, gaps in literature exist in terms of considering the possibility of variable pricing and extensively accounting for uncertainties in the supply chain. In this light, the present study proposes a stochastic multi-period mixed integer linear programming cost minimization model that determines the optimal inventory plan for a hospital purchasing platelets, assuming that prices fluctuate along with the blood center’s supply. To implement uncertain supply and demand, the model considers a discrete set of scenarios for each parameter. A study was performed, and the results indicate a promising direction as inventory costs decreased relative to models without the new considerations.

The Effect of Pricing Policy and Service Quality on Customer Satisfaction at Bunga Laundry in Surabaya Village, Bengkulu City (Case Study on Customers of Bunga Laundry in Surabaya Village, Bengkulu City)

This study aims to determine the effect of pricing policies and service quality on customer satisfaction in laundry flowers in the Surabaya urban village, Bengkulu city (a case study in laundry interest customers in the Surabaya urban village, Bengkulu city), either partially or simultaneously. There are several factors that will be discussed in this study, including pricing policies and service quality. The objects in this study were customers at Laundry Flowers in the Surabaya Village, Bengkulu City, totaling 140 people. In this research the writer uses data collection methods by means of observation, interviews and questionnaires.
 There are several data analysis techniques used in this study, namely instrument testing namely validity test, reliability test, classical assumption test, coefficient of determination, and also hypothesis testing. From the results of the multiple linear regression test, the regression equation is as follows: Y = 2.242 + 0.567 (X1) + 0.416 (X2). From the hypothesis test, namely the T test that has been carried out, it can be seen that the variable Pricing Policy (X1) obtained a tcount of 7.180 (higher than the value of ttable, or ttable > 1.97743 (see distribution in table t), and a significance level of 0.000 (significant <0.05) and the variable Quality of Service (X2) obtained a tcount of 7.350 (lower than the value of ttable, or ttable <1.97743 (see distribution in table t), and a significance level of 0.000 (significant > 0.05). So it can be concluded that the hypothesis is accepted, meaning that there is a partial influence between the Pricing Policy (X1) and Service Quality (X2) on the Customer Satisfaction variable (Y).From the F test it is known that the Pricing Policy variables (X1) and Service Quality (X2) are obtained fcount of 114.545 (higher than the ftable value, or ftable > 2.67 (see distribution in table f), and a significance level of 0.000 (significant <0.05). This means that together the variables l Pricing Policy (X1) and Service Quality (X2) have an effect on Customer Satisfaction (Y) on Laundry Flowers in the Surabaya Village, Bengkulu City.

Thinking Beyond the Gas Tax: A Case Study Analysis of a Road User Charge System for Texas

Surface transportation funding in the U.S. is heavily reliant on fuel taxes that are becoming less effective as fuel efficiency increases and alternative fuel vehicles become more common. This report examines the benefits and challenges of using road user charges (RUCs) of various types to address this issue, before analyzing the potential revenue from a hypothetical RUC on four urban corridors within Texas. Common issues for RUCs are discussed, which include privacy, interstate coordination, and program administration. A range of possible options for RUCs are discussed, followed by an analysis of four corridors in the following counties: Bexar, Collin, Harris, and Lubbock. Data from TTI’s Urban Mobility Report on VMT were utilized along with vehicle registration data from TTI’s TRENDS model to estimate an average miles per gallon for each county based on the local fleet. Three scenarios were then developed including a revenue recovery scenario, a variable pricing scenario, and a carbon tax scenario. This report concludes with a discussion of the scenario results. Specific challenges and barriers for each scenario are discussed along with a discussion of the data sources and privacy trade-offs.

THE INFLUENCE OF PRICE AND PRODUCT VARIETY ON PURCHASING DECISIONS FOR MOUNTEA

The aim of this study was to examine how Mountea's purchase decisions were affected by product prices and product variants. Quantitative primary data were employed in the analysis in this study, and the following tests were run: validity test, reliability test, normality test, multicollinearity test, heteroscedasticity test, multiple linear regression test, T test, F test, and coefficient of determination. The study's valid data were gathered by 100 participants in the form of a Google Form, which was utilized to administer the questionnaire. Non-probability sampling using the purposive sample technique was the sampling strategy used in this investigation. SPSS version 26 was used as the testing software. The findings indicated that Mountea's purchase decisions were partially influenced by the results of the variable pricing and product variety. Purchase decisions on Mountea are influenced simultaneously by product variety and price changes.

AGGREGATION PRICE MODELS FOR MICROGRIDS WITH DISTRIBUTED ENERGY RESOURCES

The development of Microgrid states the problem of choosing the optimal technologies for its composition.This paper is to present cost based demand-side management methods for the Microgrids with DistributedGeneration sources to optimize their operation. While implementing variable pricing models the aggregatorshould take into account characteristics of different Distributed Energy Resources (DER) (diesel engines, gasturbines, fuel cells, solar panels, small hydropower plants and wind turbines) and LCOE for each type of DER tostimulate each local electricity market participant separately. The system Microgrid using three diesel generatorsand the option of replacing one generator with a solar electric and wind generator installation was considered.

Energy-Consumption Pattern-Detecting Technique for Household Appliances for Smart Home Platform

Rising electricity prices and the greater penetration of electricity consumption in end-uses have prompted efforts to set up data-driven methodologies to optimise energy consumption and foster user engagement in demand-side management strategies. The performance of energy-management systems is greatly affected by the consumer behaviors and the adopted energy-management methodology. Consequently, it is necessary to develop appliance-level, detailed energy-consumption information models to inform citizens to improve behaviors toward energy use. The goal of the Home Energy Management System (HEMS) is to foster an ecosystem that is energy-optimized and can manage Internet of things (IoT) equipment over its network. HEMS allows consumers to reduce energy costs by adapting their consumption to variable pricing over the day. With the use of descriptive data-mining techniques, we have developed a numerical model that gives consumers access to information on their domestic appliances with regard to the number and duration of operations, cycles disaggregation for appliances that have cyclic operation (e.g., washing machine, dishwasher), and energy consumption throughout various time periods basing on 15-min monitoring data. The model has been calibrated and validated on two datasets collected by ENEA by real-time monitoring of Italian dwellings and has been tested over several appliances showing effective analysis of the energy-consumption patterns. Therefore, it has been integrated in the DHOMUS IoT platform, developed by ENEA to monitor and analyse the energy consumption in dwellings in order to increase citizens’ engagement and awareness of their energy consumption. The results indicate that the developed model is sufficiently accurate, and that it is possible to promote a more virtuous and sustainable use of energy by end users, as well as to reduce the energy demand as required by the current European Council Regulation (EU) 2022/1854.

ANALYSIS OF THE FACTORS AFFECTING THE COST OF SHIP REPAIRS IN THE ISLAND OF JAVA AND KALIMANTAN

The national shipbuilding industry is primarily responsible for maintaining and repairing ships at ship repair facilities to ensure their seaworthiness. Each shipyard offers variable pricing for ship repair, mainly if the work is performed on different islands, in Java and outside of Java. By understanding the differences in ship repair costs between shipyards in Java and those outside of Java and the factors that influence these discrepancies, a ship owner can choose a repair shipyard for docking his vessel. Ship repair data were collected from shipyards in Java and Kalimantan, with data collection and direct surveys conducted at three Industries which are located in the island of Java, and three industries in Kalimantan Island, it is processed to determine the difference between the average cost of ship repairs at shipyards in Java Island and Kalimantan Island, revealing that the cost of repairing ships at shipyards in Java Island is higher than the cost of repairs at shipyards on Kalimantan Island. However, there are also increased costs associated with ship maintenance activities in shipyards in the island of Kalimantan. After processing the difference data, the identification of the factors that cause the difference in repair costs forms, and the identification results indicate that the difference in ship repair costs at the shipyards in Java and Kalimantan is influenced by differences in service costs, productivity figures, geographical location of the shipyards, supply chains. materials, as well as the completeness, quality, and capacity of repair support facilities at the shipyards.

Scoring a Touchdown with Variable Pricing: Evidence from a Quasi-Experiment in the NFL Ticket Markets

Although variable pricing enables sports teams to respond to changing demand across games, reports point to somewhat limited implementation. In this paper, we study the implications of a switch to variable pricing on the primary and resale ticket markets using quasi-experimental data from the National Football League. Applying a difference-in-differences technique with propensity score weighting, we first show that the adoption of variable pricing increases primary market ticket sales by 1.59% per game. We then explore the mechanism behind this positive impact. We find that lower prices for the less attractive games are well received by customers, and customers do not have an unfavorable response to higher prices for the more attractive games. Our analysis of the customers’ activities in the resale market shows that the minimum resale market prices increase for the more attractive games following the switch to variable pricing. This analysis provides evidence for the increase in the option value created by the resale market and explains the positive response to higher prices for the more attractive games. Finally, we investigate when variable pricing is likely to be the most effective. We find that variable pricing has greater success in team hometowns with lower income levels and higher income diversity, and creating more game tiers in variable pricing leads to higher sales.This paper was accepted by Gabriel Weintraub, revenue management and market analytics.Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4588 .

ONLINE MARKETING ON PRICING STRATEGIES FOR THE INDONESIAN TOURISM INDUSTRY PRODUCTS DURING THE COVID-19 PANDEMIC

The purpose of this study is to find out the relationship between the tourism products offered, the tourism services offered and the promotional activities offered whether directly or indirectly affect the interest of tourists to visit during the Pandemic through product pricing strategies. In this study, the only variables that exist are marketing activities of tourism services or services such as products offered, tourist services offered and promotional activities, which only focus on explaining the effect of these three variables on tourist interest in visiting tourist attractions during a pandemic, while the variable intervening marketing activities or the marketing mix of product pricing strategies that are the connecting variables that affect tourist interest in visiting tourist attractions during a pandemic. The research method used in this research is descriptive quantitative research method using path analysis. In this study, the independent variables are marketing activities or marketing mix such as: tourism services such as, products offered, tourism services offered and promotional activities to be carried out, while for the intervening variable is the marketing activity variable, namely the product pricing strategy and the dependent variable is the interest of tourists visiting during a pandemic using SPSS 20. Partially, only the variables of promotional activities carried out have a positive and significant effect on the variableproduct pricing strategies during a pandemic as well as variables of tourist interest in visiting during a pandemic. Meanwhile, simultaneously, the variables of the products offered, the tourism services offered and the promotional activities that will be carried out have a positive and significant effect on the variable of tourist interest in visiting during the pandemic through the variable pricing strategy of the product during the pandemic as an intervening variable. Through the results of research, it has been stated that the products and services offered through online marketing during the pandemic have not been able to increase the interest of tourists to visit tourist attractions during the COVID-19 pandemic, while the owners of tourist attractions must carry out vigorous promotional activities so that tourists are interested in visiting. Tourist attractions, thus influencing the pricing strategy that will be applied to tourism products and services.

Variable Pricing in Restaurant Revenue Management: A Priority Mixed Bundle Strategy

The restaurant industry has historically been limited in its ability to adopt traditional revenue management pricing practices (e.g., variable pricing across tables and times) because of three specific challenges: (a) inability to segment customers by willingness to pay prior to seating, (b) limited ability to price discriminate (i.e., prioritize limited seating for the highest paying customers), and (c) inability to communicate menu price variances in advance. This article reviews common restaurant pricing strategies and discusses how each strategy cannot sufficiently address these three challenges. This work proposes a new strategy, the Priority Mixed Bundle ( PMB) Strategy, which addresses all three of these challenges. The PMB states that customers can make reservations if they are willing to commit to dining from a prix-fixe menu while walk-ins can dine a la carte. The article argues for why PMB is theoretically viable and could be superior to existing menu pricing strategies. A field study shows that the PMB generates more revenue than a la carte strategies. Survey results suggest that customers perceive PMB as fair. Overall, this research advances theory in restaurant revenue management and proposes a pricing strategy for restaurants.

Modeling and analysis of cost-effective energy management for integrated microgrids

A microgrid concept is an innovative approach for integrating hybrid and renewable energy sources into the utility grid. The uncertainties because of the intermittent nature of renewable energy resources, the load, and market price are significant challenges. In the traditional heuristic method, data is forecast but not known perfectly. Improving energy storage systems and energy management systems (EMS) development using optimization-based methods is a possible solution to improve the performance of microgrid operations. The EMS is an essential part of the distributed energy resources in the microgrid system, especially when power generation, transmission, distribution, utilization, and variable pricing are involved. This optimization process developed in this paper uses forecasted costs and loading conditions to store or sell the energy from an integrated grid battery system. Two approaches are introduced in this research work: the heuristic method using state flow (chart flow) and the optimization method based on linear programming (LP), which minimizes operation costs (savings of around 19% cost) subject to operational constraints. The LP optimization saves roughly 3.44–5.01% of excess grid energy. Several plausible outcomes of this research study simplify the comprehensive, integrated microgrid simulation for EMS optimization algorithm validation. The suggested integrated microgrid management system might be a testbed for smart grid technology research. • This paper uses linear programming optimization methods to develop a microgrid energy management system (EMS). • A constrained linear program (LP) optimization minimizes the total cost of electricity consumption. • The case studies are presented to show the performance of the proposed optimization and compare it with the heuristic approach. • The method predicts pricing and loading conditions and optimally stores/sells energy from a grid-scale battery system. • The cost of electricity consumption in the optimization method is around 19% less than in the heuristic method.