17 papers · ranked by Valyu relevance
Mohit Apte, Kale, Ketan, Pranav Datar + 1 more
—This paper explores the application of a reinforcement learning (RL) framework using the Q-Learning algorithm to enhance dynamic pricing strategies in the retail sector. Unlike traditional pricing methods, which often rely on static demand models, our RL approach continuously adapts to evolving market dynamics…
Qian Shao, Tien Mai, Shih-Fen Cheng
We consider a dynamic pricing problem in network revenue management where customer behavior is predicted by a choice model, i.e., the multinomial logit (MNL) model. The problem, even in the static setting (i.e., customer demand remains unchanged over time), is highly non-concave in prices. Existing studies mostly rely…
Lev Razumovskiy, Mariya Gerasimova, Nikolay Karenin
We study a mathematical model for the optimization of the price of real estate (RE). This model can be characterised by a limited amount of goods, fixed sales horizon and presence of intermediate sales and revenue goals. We develop it as an enhancement and upgrade of the model discussed in [BM12] now also taking into…
Andrei M. Bandalouski, Natalja G. Egorova, Mikhail Y. Kovalyov, Erwin Pesch + 1 more
'Erwin Pesch' 'S. Armagan Tarim'] In this paper we present a novel approach to the dynamic pricing problem for hotel businesses. It includes disaggregation of the demand into several categories, forecasting, elastic demand simulation, and a mathematical programming model with concave quadratic objective function and…
Ravi Ganti, Mátyás A. Sustik, Quoc Nhat Han Tran, Brian Seaman
In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing decisions to update prices of the items. Most popular approaches to dynamic pricing use a passive…
Meixian Tang, Junfeng Tian, Jinxia Kong, Zhenzhen Ren + 2 more
This study considers a two-stage dynamic pricing framework with demand learning, where a seller sells a finite number of products to strategic consumers in an uncertain market. Based on Bayesian updating, we reveal the applicable conditions of demand learning for dynamic pricing with and without price guarantee. If a…
Le-Bin Wang, Jian Chai, Ying Yang, Nikolay K. Vitanov
This paper studies a dynamic price adjustment system in platform markets, where sellers continuously revise prices, and examines its implications for market stability. We develop a platform-led discrete-time Stackelberg game model to describe the evolution of sellers’ prices and price adjustment speeds under bounded…
Biman Barua, M. Shamim Kaiser
Industry: Algorithms, Scalability, and Impact on Revenue and Customer Satisfaction Authors: ['Biman Barua' 'M. Shamim Kaiser'] Abstract: This research investigates the implementation of a real-time, microservices-oriented dynamic pricing system for the travel sector. The system is designed to address factors such as…
Hazenberg, Thomas, Ma, Yao + 4 more
This study investigates how Multi-Agent Reinforcement Learning (MARL) can improve dynamic pricing strategies in supply chains, particularly in contexts where traditional ERP systems rely on static, rule-based approaches that overlook strategic interactions among market actors. While recent research has applied…
Jiren CAO, Lei NIE, Lu TONG, Zhenhuan HE + 2 more
'Levent ÇALLI'] In order to improve the operation efficiency and market competitiveness, how to optimize the ticket pricing strategy of high-speed railway to match the dynamic supply-demand relationship was an urgent problem to be studied. Taking differentiated passenger demand and supply trains as the research object…
Shima Roosta, Seyed Jafar Sadjadi, Ahmad Makui, Natraj N. A.
In the world of omnichannel retail, where customers seamlessly switch between online and offline channels, pricing and inventory management decisions have become more complex than ever. Customer purchasing behavior is influenced by uncertainty, market fluctuations, and competitive interactions, which traditional models…
Rainer Schlößer, Martin Boissier
Most sales applications are characterized by competition and limited demand information. For successful pricing strategies, frequent price adjustments as well as anticipation of market dynamics are crucial. Both effects are challenging as competitive markets are complex and computations of optimized pricing adjustments…
Murat Cihan Sorkun, Baptiste Saliou, Süleyman Er
The open-access movement in chemistry has led to a surge in publicly accessible data repositories such as PubChem and ChemSpider, housing more than 100 million chemical compounds. These repositories are invaluable for large-scale virtual screening but lack crucial pricing information, a vital economic aspect…
Mario Martinez-Saito, Rodion Konovalov, Michael A. Piradov, Anna Shestakova + 2 more
Competition for resources is a fundamental characteristic of evolution. Auctions have been widely used to model competition of individuals for resources, and bidding behavior plays a major role in social competition. Yet how humans learn to bid efficiently remains an open question. We used model-based neuroimaging to…
Mario Martinez-Saito, Alexis Belianin, Anna Shestakova, Boris Gutkin + 1 more
In games of incomplete information individual players make decisions facing a combination of structural uncertainty about the underlying parameters of the environment, and strategic uncertainty about the actions undertaken by their partners. How well are human actors able to cope with these uncertainties, and what…
Chet Birger, Megan Hanna, Edward Salinas, Jason Neff + 11 more
FireCloud, one of three NCI Cloud Pilots, is a collaborative genome analysis platform built on a cloud computing infrastructure. FireCloud aims to solve the many challenges presented by the increasingly large data sets and computing requirements employed in cancer research. However, cost uncertainty associated with…
Ruben Sanchez-Garcia, Dávid Havasi, Gergely Takács, Matthew C. Robinson + 3 more
Compound availability is a critical property for design prioritization across the drug discovery pipeline. Historically, and despite their multiple limitations, compound-oriented synthetic accessibility scores have been used as proxies for this problem. However, the size of the catalogues of commercially available…