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Mathematics
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Data-Driven Approaches in Revenue Management and Pricing Analytics
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Data-Driven Approaches in Revenue Management and Pricing Analytics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 16136

Special Issue Editors

Dr. Dong Li

E-Mail Website
Guest Editor
School of Business and Economics, Loughborough University, Loughborough LE11 3TT, UK
Interests: revenue management and pricing; choice-modelling; resource allocation; approximate dynamic programming; multi-armed bandits
Dr. Xinan Yang

E-Mail Website
Guest Editor
Department of Mathematical Sciences, University of Essex, Colchester CO4 3SQ, UK
Interests: dyanmic programming; revenue management; vehicle routing; meta-heuristics; stochastic optimzation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Revenue management (RM) was first introduced in the airline sector after its deregulation in the late 1970s but has since seen a widespread adoption in other industries such as the hotel, car rental, ferry, manufacturing, logistics, attended home delivery sectors, amongst others. The main processes in a typical RM system includes customer segmentation, demand forecasting, and revenue optimisation. Pricing analytics, which uses historical data to determine the best prices to set in the future, has found its applications in a wide range of industries. It is also a primary lever used in RM to drive revenue. Beyond revenue, RM and pricing analytics have been applied to reducing carbon emissions and energy consumption as the world races to net zero. The continuous advances made in big data technique over the last couple of decades has led to the development of novel methodologies and applications of RM and pricing analytics in both traditional and emerging industries. This Special Issue provides a forum for researchers to present and share their recent novel works in data-driven approaches in any areas within the realm of RM and pricing analytics. These approaches can be either descriptive, predictive, or prescriptive methods of analysis.

Dr. Dong Li
Dr. Xinan Yang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • revenue management
  • customer segmentation
  • choice models
  • behavioural analysis
  • demand forecasting/management
  • demand unconstraining
  • availability controls
  • bid price controls
  • pricing analytics
  • stochastic/dynamic pricing
  • pricing optimisation
  • inventory management
  • transportation
  • attended home delivery
  • last-mile logsitics
  • carbon reduction
  • descriptive analytics
  • predictive analytics
  • prescriptive analytics
  • Markov decision process
  • (approximate) dynamic programming
  • mathmatical programming
  • machine learning
  • reinforcement learning

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Published Papers (4 papers)

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Research

32 pages, 5045 KiB  
Article
Ensemble-Based Machine Learning Algorithm for Loan Default Risk Prediction
by Abisola Akinjole, Olamilekan Shobayo, Jumoke Popoola, Obinna Okoyeigbo and Bayode Ogunleye
Mathematics 2024, 12(21), 3423; https://doi.org/10.3390/math12213423 - 31 Oct 2024
Cited by 2 | Viewed by 4867
Abstract
Predicting credit default risk is important to financial institutions, as accurately predicting the likelihood of a borrower defaulting on their loans will help to reduce financial losses, thereby maintaining profitability and stability. Although machine learning models have been used in assessing large applications [...] Read more.
Predicting credit default risk is important to financial institutions, as accurately predicting the likelihood of a borrower defaulting on their loans will help to reduce financial losses, thereby maintaining profitability and stability. Although machine learning models have been used in assessing large applications with complex attributes for these predictions, there is still a need to identify the most effective techniques for the model development process, including the technique to address the issue of data imbalance. In this research, we conducted a comparative analysis of random forest, decision tree, SVMs (Support Vector Machines), XGBoost (Extreme Gradient Boosting), ADABoost (Adaptive Boosting) and the multi-layered perceptron, to predict credit defaults using loan data from LendingClub. Additionally, XGBoost was used as a framework for testing and evaluating various techniques. Moreover, we applied this XGBoost framework to handle the issue of class imbalance observed, by testing various resampling methods such as Random Over-Sampling (ROS), the Synthetic Minority Over-Sampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), Random Under-Sampling (RUS), and hybrid approaches like the SMOTE with Tomek Links and the SMOTE with Edited Nearest Neighbours (SMOTE + ENNs). The results showed that balanced datasets significantly outperformed the imbalanced dataset, with the SMOTE + ENNs delivering the best overall performance, achieving an accuracy of 90.49%, a precision of 94.61% and a recall of 92.02%. Furthermore, ensemble methods such as voting and stacking were employed to enhance performance further. Our proposed model achieved an accuracy of 93.7%, a precision of 95.6% and a recall of 95.5%, which shows the potential of ensemble methods in improving credit default predictions and can provide lending platforms with the tool to reduce default rates and financial losses. In conclusion, the findings from this study have broader implications for financial institutions, offering a robust approach to risk assessment beyond the LendingClub dataset. Full article
(This article belongs to the Special Issue Data-Driven Approaches in Revenue Management and Pricing Analytics)
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29 pages, 660 KiB  
Article
The Factors Influencing User Satisfaction in Last-Mile Delivery: The Structural Equation Modeling Approach
by Vijoleta Vrhovac, Dušanka Dakić, Stevan Milisavljević, Đorđe Ćelić, Darko Stefanović and Marina Janković
Mathematics 2024, 12(12), 1857; https://doi.org/10.3390/math12121857 - 14 Jun 2024
Cited by 3 | Viewed by 7026
Abstract
The primary goal of this research is to identify which factors most significantly influence customer satisfaction in the last-mile delivery (LMD) process. The sample comprised 907 participants (63.4% female) with a mean age of 34.90. All participants completed three questionnaires regarding LMD, customer [...] Read more.
The primary goal of this research is to identify which factors most significantly influence customer satisfaction in the last-mile delivery (LMD) process. The sample comprised 907 participants (63.4% female) with a mean age of 34.90. All participants completed three questionnaires regarding LMD, customer satisfaction, and trust in courier service. Furthermore, participants answered questions related to significant aspects of the delivery process: speed, price, and courier call before delivery. To determine which factors most significantly influence customer satisfaction in LMD, structural equation modeling (SEM) was applied. The tested SEM model showed a good fit. The results indicated that within the LMD dimension, visual appeal was a significant predictor in a negative direction, and all other LMD dimensions (except parcel tracking) were positive and significant predictors of customer satisfaction. Trust in courier service, delivery price, speed, and courier call before delivery were statistically significant predictors of customer satisfaction in last-mile delivery, all in a positive direction. Full article
(This article belongs to the Special Issue Data-Driven Approaches in Revenue Management and Pricing Analytics)
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17 pages, 3120 KiB  
Article
Optimization of Vegetable Restocking and Pricing Strategies for Innovating Supermarket Operations Utilizing a Combination of ARIMA, LSTM, and FP-Growth Algorithms
by Haoyang Ping, Zhuocheng Li, Xizhu Shen and Haizhen Sun
Mathematics 2024, 12(7), 1054; https://doi.org/10.3390/math12071054 - 31 Mar 2024
Cited by 6 | Viewed by 2230
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Data-Driven Approaches in Revenue Management and Pricing Analytics)
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21 pages, 1257 KiB  
Article
Pricing Analysis for Railway Multi-Ride Tickets: An Optimization Approach for Uncertain Demand within an Agreed Time Limit
by Yu Wang and Jiafa Zhu
Mathematics 2023, 11(23), 4818; https://doi.org/10.3390/math11234818 - 29 Nov 2023
Viewed by 1171
Abstract
A multi-ride ticket with a certain period of validity and maximum number of uses has been introduced into railway transport. The key to pricing the railway multi-ride ticket is determining the uncertain demand within an agreed time limit. Unfortunately, limited studies have focused [...] Read more.
A multi-ride ticket with a certain period of validity and maximum number of uses has been introduced into railway transport. The key to pricing the railway multi-ride ticket is determining the uncertain demand within an agreed time limit. Unfortunately, limited studies have focused on this pricing issue. Therefore, we focused on railway multi-ride ticket pricing optimization in two different scenarios: a single train with multiple stops and multiple trains with multiple stops. First, the expected coefficient and incentive coefficient were introduced to describe the decision-making process for multi-ride tickets and simulate the change in passengers’ travel behavior after purchasing multi-ride tickets. Then, passenger demand functions based on a normal distribution were developed to establish the pricing models with maximized revenue. Finally, we adopted improved particle swarm optimization (PSO) to solve the models. Two numerical cases were used to verify the models separately for two application scenarios. The results revealed that the multi-ride ticket pricing problem is not a simple summation of pricing for one-time travel of passengers. In the situation of a single train with multiple stops, the expected coefficient is positively related to the total income, whereas the incentive coefficient has limited influence on the optimal price and total revenue. Furthermore, a multi-ride ticket should allow the passenger to take trains eight times at most in 8 days at the price of CNY 4922 (abbreviated as 4922 (8, 8)) rather than 3785 (8, 6). Railway enterprises should cautiously limit the scope of trains available for multi-ride tickets in the case of multiple trains with multiple stops. Full article
(This article belongs to the Special Issue Data-Driven Approaches in Revenue Management and Pricing Analytics)
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