Paper applies RL to optimize inventory management across multiple products and nodes.
problem Optimizing inventory management for a large number of products with shared capacity in a multi-node supply chain.
method Novel multi-agent hierarchical reinforcement learning framework with A2C algorithm and quantised action spaces.
result The approach optimizes for maximizing product sales and minimizing wastage of perishable products.
This work uses reinforcement learning to optimize task scheduling and execution in a dynamic multi-agent warehouse environment.
problem Optimizing task scheduling and execution in a dynamic multi-agent warehouse environment with limited observability.
method Deep reinforcement learning to solve both high-level scheduling and low-level multi-agent execution problems.
result Demonstrates the effectiveness of reinforcement learning in optimizing task scheduling and execution in a dynamic multi-agent environment.
Study compares MAPF and MARL algorithms for warehouse automation.
problem Optimizing multi-agent pickup and delivery in warehouse settings.
method Compared conflict-based search (MAPF) and shared experience actor-critic (MARL).
result Comprehensive benchmarking of MAPF and MARL in a simulated warehouse environment.
Paper solves trade-off between internalisation and externalisation in stochastic trade flows.
problem Managing risk in stochastic trade flows between internalisation and externalisation.
method Derives almost-closed-form solutions using Almgren-Chriss framework for quadratic execution costs. Uses numerical methods for more general cases. Proposes reinforcement learning as an alternative.
result Almost-closed-form solutions and numerical methods for optimal strategies.
This paper intends to present the opportunities emerging for the national economy, out of the financial crisis. In particular the management of those, which arise from the commercial real estate owned property sector, defined by the author as crisis heritage management. On one hand, as real estate property prices are s…
DeepCO uses deep learning for offline combinatorial optimization in warehouse operations.
problem Optimizing warehouse operation sequences in offline settings.
method DeepCO framework utilizing distribution regularized optimization for TSP.
result DeepCO reduces route length by 5.7% on average for TSP problems.
Novel ML approach solves complex warehouse routing problem.
problem Efficiently routing pickers in mixed-shelves warehouses.
method Hierarchical and parallel multi-agent reinforcement learning.
result State-of-the-art performance in solution quality and inference speed.
Optimizing storage assignment is a central problem in warehousing. Past literature has shown the superiority of the Duration-of-Stay (DoS) method in assigning pallets, but the methodology requires perfect prior knowledge of DoS for each pallet, which is unknown and uncertain under realistic conditions. The dynamic natu…
Reducing the incidence of surgical site infections (SSIs) is one of the objectives of the French nosocomial infection control program. Manual monitoring of SSIs is carried out each year by the hospital hygiene team and surgeons at the University Hospital of Bordeaux. Our goal was to develop an automatic detection algor…
The use of CVA to cover credit risk is widely spread, but has its limitations. Namely, dealers face the problem of the illiquidity of instruments used for hedging it, hence forced to warehouse credit risk. As a result, dealers tend to offer a limited OTC derivatives market to highly risky counterparties. Consequently, …
Study optimal strategies for unwinding uncertain order flows in financial trading desks.
problem Optimizing strategies for handling uncertain order flows in financial trading desks.
method Modeling and solving the problem for a general class of in-flow processes, enabling an analytic solution.
result Optimal strategies depend on the autocorrelation of orders; only truth-telling flow is unwound myopically.
An evolutionary game model analyzes e-commerce and traditional retail trends during the pandemic.
problem Understanding the dynamics between e-commerce and traditional retail during the pandemic.
method Developed an evolutionary game model to study consumer-producer interactions on e-commerce platforms.
result Investment in logistics and warehouses in e-commerce led to faster delivery and consumer trends.
Having the right assortment of shipping boxes in the fulfillment warehouse to pack and ship customer's online orders is an indispensable and integral part of nowadays eCommerce business, as it will not only help maintain a profitable business but also create great experiences for customers. However, it is an extremely …
Depth perception is a key component for autonomous systems that interact in the real world, such as delivery robots, warehouse robots, and self-driving cars. Tasks in autonomous robotics such as 3D object recognition, simultaneous localization and mapping (SLAM), path planning and navigation, require some form of 3D sp…
Robotics has proved to be an indispensable tool in many industrial as well as social applications, such as warehouse automation, manufacturing, disaster robotics, etc. In most of these scenarios, damage to the agent while accomplishing mission-critical tasks can result in failure. To enable robotic adaptation in such s…
Many real-world prediction tasks have outcome variables that have characteristic heavy-tail distributions. Examples include copies of books sold, auction prices of art pieces, demand for commodities in warehouses, etc. By learning heavy-tailed distributions, "big and rare" instances (e.g., the best-sellers) will have a…
Two heuristics solve dynamic multiple travelling salesmen problems.
problem Dynamic routing with unknown customers.
method Balanced dynamic closest vehicle heuristic and balanced dynamic assignment vehicle heuristic.
result Continuous approximation models for strategic dynamic routing.
This paper explores portfolio management strategies to maximize alpha and minimize beta.
problem Maximizing returns while minimizing risk in investment portfolios.
method Examines asset allocation, diversification, active management, and risk management strategies.
result Combining these strategies optimizes portfolio performance.
Paper introduces a framework for managing cyber risk with insurance and cybersecurity models.
problem Pervasive challenges in managing cyber risk, especially for capital allocation.
method Combines insurance frequency-severity models with cybersecurity cascade models for comprehensive cyber risk assessment. Facilitates informed capital allocation through a two-pillar framework.
result Demonstrates the necessity of comprehensive cost-benefit analysis for budget-constrained companies.
Modular RL modules solve complex 3D Sokoban tasks.
problem Solving complex, integrated tasks combining visual, physical, and abstract reasoning.
method Compose RL modules in a sense-plan-act hierarchy, using only model-free methods.
result Modular RL outperforms state-of-the-art monolithic RL on Mujoban.
The basic financial purpose of a firm is to maximize its value. An inventory management system should also contribute to realization of this basic aim. Many current asset management models currently found in financial management literature were constructed with the assumption of book profit maximization as basic aim. H…
Study finds Indian mutual funds adjust cash holdings based on inflows, impacting stock purchases.
problem Active liquidity management by mutual funds in India.
method Examined cash holdings and stock purchases of Indian equity mutual funds.
result Funds with active liquidity choices outperform, highlighting the importance of this strategy.
Framework for managing cyber risks in networks.
problem Managing systemic cyber risks in digital networks.
method Three components: acceptable configurations, risk mitigation interventions, and cost function.
result Effective decision-making for network resilience.
Research identifies risks in selecting project managers for civil engineering projects.
problem Lack of awareness of project manager selection criteria and associated risks.
method Combined ANP-FMEA approach for risk analysis.
result ANP-FMEA model identifies more significant risks than traditional FMEA.
Deep learning improves portfolio management by optimizing asset weights.
problem Traditional portfolio managers are outperformed by deep learning models in trading.
method Proposes a deep reinforcement learning portfolio manager that allocates weights to assets.
result The proposed portfolio manager outperforms conventional managers in risk-adjusted returns.
This research develops a dynamic risk management system for industrial companies.
problem Risk assessment and management in industrial enterprises.
method Qualitative and quantitative analysis, systematic risk classification, dynamic system development.
result Effective risk management strategies formed through dynamic risk management system and risk assessment methods.
Study finds managers' tenure and education influence their choice between in-court and out-of-court restructuring.
problem Exploring managers' characteristics and their impact on restructuring decisions.
method Empirical investigation using upper echelons theory and data from 342 managers of French firms.
result Managers with longer tenure and higher education levels prefer private restructuring over court involvement.
The paper fits cash management models to data using stochastic and linear programming.
problem Cash flow probability distribution assumptions in cash management models are relaxed.
method Stochastic and linear programming to fit models to data.
result A small random sample of data is sufficient to fit bound-based models.
Decision tool helps manage biofouling risks for ships in the Baltic Sea.
problem Biofouling of ships causes environmental and economic issues.
method Bayesian networks to identify biofouling management strategies.
result Optimal biofouling management includes biocidal-free coating and in-water cleaning.
Model cash management under ambiguity using maxmin preferences and diffusion.
problem Optimizing cash reserves in the presence of ambiguity.
method Singular control model with maxmin preferences, verified using Dynkin games.
result Higher expected costs and narrower inaction region under increased ambiguity.
This review classifies electricity price models for risk management.
problem Choosing suitable models for risk management in electricity markets.
method Classification of models based on their ability to represent price behavior.
result Helps users select appropriate models for risk management.
Study improves machine learning for long-term financial portfolio management.
problem Machine learning precision declines with long-term data.
method Data augmentation using multiple time scales and learning data.
result Generalization performance can be maintained for long-term tasks.
The paper analyzes portfolio management in the Heston model, proposing new strategies.
problem Investment performance influenced by asset diversity and cash inclusion.
method Monte Carlo simulations in the Heston model, MACD and RSI technical analysis.
result New portfolio management strategies based on MACD and RSI.
Paper proposes real-time risk metrics for stablecoin protocols.
problem Lack of risk management frameworks for stablecoins.
method Developed two risk metrics: capitalization and liquidity.
result Demonstrated practical benefits of real-time on-chain data.
A fund manager invests both the fund's assets and own private wealth in separate but potentially correlated risky assets, aiming to maximize expected utility from private wealth in the long run. If relative risk aversion and investment opportunities are constant, we find that the fund's portfolio depends only on the fu…
Paper discusses how financial institutions' model risk management can benefit academic research.
problem Improving academic research process and mitigating limitations.
method Adopting financial institutions' model risk management practices.
result Lessons from financial institutions can enhance academic research reliability.
To predict the employee attrition beforehand and to enable management to take individualized preventive action. Using Ensemble classification modeling techniques and Linear Regression. Model could predict over 91% accurate employee prediction, lead-time in separation and individual reasons causing attrition. Prior inti…
Third part of a study on liquidity risk in asset management, focusing on managing the asset-liability liquidity risk.
problem Managing the asset-liability liquidity risk in asset management.
method Develops a methodological and practical framework for liquidity stress testing programs.
result Proposes measurement, management, and monitoring tools for controlling the liquidity gap.
This paper provides a ML framework for diabetes prediction and care management.
problem Diabetes prediction and care management challenges in real-world healthcare.
method Illustrates a Machine Learning framework for T2DM prediction and risk stratification.
result ML models align with physician's disease management steps.
Banks must manage their trading books, not just value them. Pricing includes valuation adjustments collectively known as XVA (at least credit, funding, capital and tax), so management must also include XVA. In trading book management we focus on pricing, hedging, and allocation of prices or hedging costs to desks on an…
The paper introduces deep learning for ALM, enhancing asset and liability management.
problem Optimizing asset and liability management for treasurers and other applications.
method Deep learning applied to ALM for optimal decision making.
result Enhanced ALM approach for better asset and liability management.
The basic financial purpose of an enterprise is maximization of its value. Trade credit management should also contribute to realization of this fundamental aim. Many of the current asset management models that are found in financial management literature assume book profit maximization as the basic financial purpose. …
Active management is a term that has many meanings and we have found the defining characteristics needed for success as an "active manager" elusive within the literature. In this paper we offer a set of criteria that defines an active manager and his success. In order to facilitate this, we introduce several definition…
Cash management is concerned with optimizing the short-term funding requirements of a company. To this end, different optimization strategies have been proposed to minimize costs using daily cash flow forecasts as the main input to the models. However, the effect of the accuracy of such forecasts on cash management pol…
This paper asks, "Do classics exist in megaproject management?" We identify three types of classic texts: conventional, Kuhnian, and citation classics. We find that the answer to our question depends on the definition of "classic" employed. First, "citation classics" do exist in megaproject management, and they perform…
Deep RL optimizes goal-based investing strategies.
problem Optimizing investment strategies for achieving financial goals.
method Novel deep reinforcement learning approach for goal-based investing.
result Superior performance compared to benchmarks.
Study finds risk management significantly improves pension scheme efficiency in Kenya.
problem Improving efficiency of pension schemes in Kenya.
method Panel data analysis of 128 pension schemes from 2015-2021.
result Risk management significantly mediates the relationship between corporate governance and pension scheme efficiency.
Optimizes fund manager's wealth with partial information on market risk.
problem Maximizing wealth with incomplete information about market risk.
method Formulated as optimization under partial information, solved via martingale method and concavification.
result Shows how learning about market risk affects optimal investment strategy.