Symbolic Pricing Policies for Attended Home Delivery - the Case of an Online Retailer

Created by W.Langdon from gp-bibliography.bib Revision:1.8469

@InProceedings{lunet:2025:GECCO,

• author = "Miguel Lunet and Daniela Fernandes and Fabio Neves-Moreira and Pedro Amorim",
• title = "Symbolic Pricing Policies for Attended Home Delivery - the Case of an Online Retailer",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference",
• year = "2025",
• editor = "Roman Kalkreuth and Alexander Brownlee",
• pages = "1397--1405",
• address = "Malaga, Spain",
• series = "GECCO '25",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, attended home delivery, dynamic pricing, sequential decision-making, vehicle routing, explainable artificial intelligence, Real World Applications",
• isbn13 = "979-8-4007-1465-8",
• URL = "https://doi.org/10.1145/3712256.3726361",
• DOI = "doi:10.1145/3712256.3726361",
• size = "9 pages",
• abstract = "To get products delivered, clients and retailers agree on a delivery time window. We collaborated with an online retailer to develop a real-world application aimed at dynamically determining the delivery fee for each time window while ensuring the explainability of the pricing policy. This sequential decision-making problem arises as new customers continuously arrive. The objective is to maximize the final profit, given by the sum of baskets and delivery fees, discounted by the transportation and fleet costs. As multiple customers share the same delivery route, the costs are distributed among them, complicating the calculation of the marginal cost of each customer. Our study employs Genetic Programming (GP) to create explainable and easy-to-compute pricing policies to determine the delivery fees. These policies, expressed as mathematical formulas, rank price panels - combinations of time slots and corresponding fees - to identify optimal prices for each customer. The inputs to the GP algorithm capture the current state of the system, including factors such as capacity, customer location, and basket value. The resulting expressions offer operational managers a transparent pricing policy that allows them to maximize total profit.",
• notes = "GECCO-2025 RWA A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Miguel Lunet Daniela Fernandes Fabio Neves-Moreira Pedro Sanches Amorim

Citations