A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The paper tackles revenue management with time-varying demand using posterior sampling.
Deep RL agent secures 2nd place in CityLearn Challenge for district demand management.
We consider a continuous-time model for inventory management with Markov modulated non-stationary demands. We introduce active learning by assuming that the state of the world is unobserved and must be inferred by the manager. We also assume that demands are observed only when they are completely met. We first derive t…
Improved algorithm reduces regret in NRM with unknown demand.
New algorithm controls large groups of devices to match energy demand signals.
New framework forecasts both supply and demand in rental markets.
Risk, including economic risk, is increasingly a concern for public policy and management. The possibility of dealing effectively with risk is hampered, however, by lack of a sound empirical basis for risk assessment and management. The paper demonstrates the general point for cost and demand risks in urban rail projec…
We propose a contextual-bandit approach for demand side management by offering price incentives. More precisely, a target mean consumption is set at each round and the mean consumption is modeled as a complex function of the distribution of prices sent and of some contextual variables such as the temperature, weather, …
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…
Study optimizes smart contract adoption under high demand variability using Negative Binomial models.
Study examines how COVID-19 intensified demand variability in U.S. supply chains.
This paper optimizes revenue and resource balance in network revenue management.
Demand functions for goods are generally cyclical in nature with characteristics such as trend or stochasticity. Most existing demand forecasting techniques in literature are designed to manage and forecast this type of demand functions. However, if the demand function is lumpy in nature, then the general demand foreca…
We consider a repeated newsvendor problem where the inventory manager has no prior information about the demand, and can access only censored/sales data. In analogy to multi-armed bandit problems, the manager needs to simultaneously "explore" and "exploit" with her inventory decisions, in order to minimize the cumulati…
Deep learning model reduces food waste by stabilizing online food delivery supply chains.
New method for disaggregate electricity demand forecasting at household level.
Predicting ambulance demand accurately at a fine resolution in time and space (e.g., every hour and 1 km) is critical for staff / fleet management and dynamic deployment. There are several challenges: though the dataset is typically large-scale, demand per time period and locality is almost always zero. The demand …
MaxCOSD algorithm tackles non-i.i.d. demands and stateful dynamics in online inventory control.
Optimal market making strategy with price forecasts reduces inventory costs and spreads.
A new approach integrates inventory prediction and routing optimization for better supply chain management.
Paper tackles online learning for DR management with incentives.
CONTINA provides adaptive confidence intervals for traffic demand prediction.
Proposes a pricing agent using reinforcement learning to balance renewable energy demand.
Proposes a new model to predict travel demand with zero-inflated and long-tail characteristics.
Paper combines RL with policy regularization for inventory policies.
We consider a stochastic inventory control problem under censored demands, lost sales, and positive lead times. This is a fundamental problem in inventory management, with significant literature establishing near-optimality of a simple class of policies called ``base-stock policies'' for the underlying Markov Decision …
Traditional centralized energy systems have the disadvantages of difficult management and insufficient incentives. Blockchain is an emerging technology, which can be utilized in energy systems to enhance their management and control. Integrating token economy and blockchain technology, token economic systems in energy …
The paper addresses uncertainty in demand prediction for dynamic pricing.
Many economic applications including optimal pricing and inventory management requires prediction of demand based on sales data and estimation of sales reaction to a price change. There is a wide range of econometric approaches which are used to correct a bias in estimates of demand parameters on censored sales data. T…
Project promoters, forecasters, and managers sometimes object to two things in measuring inaccuracy in travel demand forecasting: (1) using the forecast made at the time of making the decision to build as the basis for measuring inaccuracy and (2) using traffic during the first year of operations as the basis for measu…
In retailer management, the Newsvendor problem has widely attracted attention as one of basic inventory models. In the traditional approach to solving this problem, it relies on the probability distribution of the demand. In theory, if the probability distribution is known, the problem can be considered as fully solved…
We present a comparative study of different probabilistic forecasting techniques on the task of predicting the electrical load of secondary substations and cabinets located in a low voltage distribution grid, as well as their aggregated power profile. The methods are evaluated using standard KPIs for deterministic and …
Paper optimizes demand aggregation for low-level electricity markets.
This paper develops a stochastic learning-optimization model for resilient automotive supply chains.
Paper uses RL to optimize SFC deployment and VNF management in NFV networks.
System uses TDA for user segmentation and demand forecasting.
Paper presents a method for probabilistic load forecasting using adaptive online learning.
Smart grid uses deep learning to optimize household energy use.
Study develops smart contract framework for procurement under demand variability.
This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributio…
CPAS uses machine learning to plan hospital resources for COVID-19.
Overprocuring reserves can improve network efficiency by using excess reserves for congestion management.
Fund2Persona creates personalized financial advisor personas from fund data, improving investment advice and manager interpretation.
Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we pr…
Motivated by recent advancements in Deep Reinforcement Learning (RL), we have developed an RL agent to manage the operation of storage devices in a household and is designed to maximize demand-side cost savings. The proposed technique is data-driven, and the RL agent learns from scratch how to efficiently use the energ…
A major source of risk in project management is inaccurate forecasts of project costs, demand, and other impacts. The paper presents a promising new approach to mitigating such risk, based on theories of decision making under uncertainty which won the 2002 Nobel prize in economics. First, the paper documents inaccuracy…
Paper presents forecasting models for platelet demand.
Network slicing promises to provision diversified services with distinct requirements in one infrastructure. Deep reinforcement learning (e.g., deep -learning, DQL) is assumed to be an appropriate algorithm to solve the demand-aware inter-slice resource management issue in network slicing by regarding the …