arXiv research

Analyzes fashion demand to optimize style mix in retail.

Demand response is designed to motivate electricity customers to modify their loads at critical time periods. The accurate estimation of impact of demand response signals to customers' consumption is central to any successful program. In practice, learning these response is nontrivial because operators can only send a …

Study examines retailer responses to stockouts in wholesale environments.

Proposes a pricing agent using reinforcement learning to balance renewable energy demand.

Learning customer preferences from an observed behaviour is an important topic in the marketing literature. Structural models typically model forward-looking customers or firms as utility-maximizing agents whose utility is estimated using methods of Stochastic Optimal Control. We suggest an alternative approach to stud…

This paper addresses dynamic price discrimination with fairness constraints.

Develops a Bayesian model to predict business revenue and demand.

Whenever customers' choices (e.g. to buy or not a given good) depend on others choices (cases coined 'positive externalities' or 'bandwagon effect' in the economic literature), the demand may be multiply valued: for a same posted price, there is either a small number of buyers, or a large one -- in which case one says …

System uses TDA for user segmentation and demand forecasting.

The paper develops loss functions for pricing models using observational data.

Paper develops privacy-preserving dynamic pricing policy for e-commerce.

Predicts next item in sequential bundles using Transformers.

Considering a lead-time-and price-sensitive demand, we investigate whether a client rejection policy, modeled as M/M/1/K system, can be more profitable than an all-client acceptance policy, modeled as M/M/1 system. We provide analytical insights for the cases with and without holding and penalty costs by comparing M/M/…

Study improves retail demand forecasting by integrating macroeconomic data.

Suppliers (including companies and individual prosumers) may wish to protect their private information when selling items they have in stock. A market is envisaged where private information can be protected through the use of differential privacy and option contracts, while privacy-aware suppliers deliver their stock a…

Deep learning model reduces food waste by stabilizing online food delivery supply chains.

Custom narrow-precision representations boost DNN inference speed by 7.6x with minimal accuracy loss.

Improved forecast accuracy for Knitwear by 20% using adaptive AI/ML model.

We propose the new Top-Dog-Index to quantify the historic deviation of the supply data of many small branches for a commodity group from sales data. On the one hand, the common parametric assumptions on the customer demand distribution in the literature could not at all be supported in our real-world data set. On the o…

Study analyzes how discounts affect train ticket purchases and rescheduling in Switzerland.

System optimizes product images for e-commerce, enhancing customer engagement.

Automated neural network synthesis engine for custom designs.

The paper proposes a demand prediction model for e-commerce sites using machine learning and stacking.

Network models assume unrealistic idiosyncratic risk, which can be mitigated by allowing for correlated shocks.

Optimizes profit in targeted marketing across multiple markets with varying marketing expenditures.

Study examines pricing strategies in competitive supply chains with discrete prices.

RNN models improve demand forecasting for diverse products.

CodeX improves DNN acceleration on FPGAs by encoding and customizing bitwidth.

This paper tackles fair same-day delivery service by optimizing regional service rates.

Proposes RTL model for sentiment classification and key word detection in online reviews.

Study online pricing with contextual elasticity and heteroscedastic valuation.

The paper proposes a new DR model to better predict EUCs' responses in real-time pricing.

Deep RL methods improve resource allocation in uncertain environments.

Model improves fraud detection for new scenes with limited data.

One key requirement for effective supply chain management is the quality of its inventory management. Various inventory management methods are typically employed for different types of products based on their demand patterns, product attributes, and supply network. In this paper, our goal is to develop robust demand pr…

New loss functions optimize pricing policies using transaction data, ensuring expected revenue guarantees.

This paper optimizes revenue and resource balance in network revenue management.

We describe an agent-based simulation of a fictional (but feasible) information trading business. The Gas Price Information Trader (GPIT) buys information about real-time gas prices in a metropolitan area from drivers and resells the information to drivers who need to refuel their vehicles. Our simulation uses real wor…

Optimal vehicle repositioning policy found for shared mobility services.

Efficient sequential matching of supply and demand is a problem of interest in many online to offline services. For instance, Uber, Lyft, Grab for matching taxis to customers; Ubereats, Deliveroo, FoodPanda etc for matching restaurants to customers. In these online to offline service problems, individuals who are respo…

This study designs a financial risk control platform using big data and machine learning.

Starfield optimizes satellite links for LEO mega-constellations by aligning ISLs with regional traffic patterns.

Paper evaluates how forecast errors affect optimal utilisation in production planning.

Deriving the optimal safety stock quantity with which to meet customer satisfaction is one of the most important topics in stock management. However, it is difficult to control the stock management of correlated marketable merchandise when using an inventory control method that was developed under the assumption that t…

This study enhances sales forecasts by integrating market indicators into forecasting models.

The purpose of this study was to build a customer selection model based on 20 dimensions, including customer codes, total contribution, assets, deposit, profit, profit rate, trading volume, trading amount, turnover rate, order amount, withdraw amount, withdraw rate, process fee, process fee submitted, process fee retai…

Study develops ML emulators for generator models from terminal bus data.

DCE learns customer embeddings from digital activity and financial context.