Data-Driven Approaches in Revenue Management and Pricing Analytics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 1334

Special Issue Editors


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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
Department of Mathematical Sciences, University of Essex, Colchester CO4 3SQ, UK
Interests: dyanmic programming; revenue management; vehicle routing; meta-heuristics; stochastic optimzation
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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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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

Published Papers (2 papers)

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Research

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
Viewed by 441
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 509
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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