1,720,962 research outputs found
Advancing Supply Chain Inventory Management through Mathematical and Deep Reinforcement Learning Approaches
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains
Full text linkIn this study, we analyze and compare the performance of state-of-the-art deep reinforcement learning algorithms for solving the supply chain inventory management problem. This complex sequential decision-making problem consists of determining the optimal quantity of products to be produced and shipped across different warehouses over a given time horizon. In particular, we present a mathematical formulation of a two-echelon supply chain environment with stochastic and seasonal demand, which allows managing an arbitrary number of warehouses and product types. Through a rich set of numerical experiments, we compare the performance of different deep reinforcement learning algorithms under various supply chain structures, topologies, demands, capacities, and costs. The results of the experimental plan indicate that deep reinforcement learning algorithms outperform traditional inventory management strategies, such as the static (s, Q)-policy. Furthermore, this study provides detailed insight into the design and development of an open-source software library that provides a customizable environment for solving the supply chain inventory management problem using a wide range of data-driven approaches
Performance of deep reinforcement learning algorithms in two-echelon inventory control systems
Full text linkThis study conducts a comprehensive analysis of deep reinforcement learning (DRL) algorithms applied to supply chain inventory management (SCIM), which consists of a sequential decision-making problem focussed on determining the optimal quantity of products to produce and ship across multiple capacitated local warehouses over a specific time horizon. In detail, we formulate the problem as a Markov decision process for a divergent two-echelon inventory control system characterised by stochastic and seasonal demand, also presenting a balanced allocation rule designed to prevent backorders in the first echelon. Through numerical experiments, we evaluate the performance of state-of-the-art DRL algorithms and static inventory policies in terms of both cost minimisation and training time while varying the number of local warehouses and product types and the length of replenishment lead times. Our results reveal that the Proximal Policy Optimization algorithm consistently outperforms other algorithms across all experiments, proving to be a robust method for tackling the SCIM problem. Furthermore, the (s, Q)-policy stands as a solid alternative, offering a compromise in performance and computational efficiency. Lastly, this study presents an open-source software library that provides a customisable simulation environment for addressing the SCIM problem, utilising a wide range of DRL algorithms and static inventory policies
Deep Survival Analysis for Healthcare: An Empirical Study on Post-Processing Techniques
Full text linkSurvival analysis is a crucial tool in healthcare, allowing us to understand and predict time-to-event occurrences using statistical and machine-learning techniques. As deep learning gains traction in this domain, a specific challenge emerges: neural network-based survival models often produce discrete-time outputs, with the number of discretization points being much fewer than the unique time points in the dataset, leading to potentially inaccurate survival functions. To this end, our study explores post-processing techniques for survival functions. Specifically, interpolation and smoothing can act as effective regularization, enhancing performance metrics integrated over time, such as the Integrated Brier Score and the Cumulative Area-Under-the-Curve. We employed various regularization techniques on diverse real-world healthcare datasets to validate this claim. Empirical results suggest a significant performance improvement when using these post-processing techniques, underscoring their potential as a robust enhancement for neural network-based survival models. These findings suggest that integrating the strengths of neural networks with the non-discrete nature of survival tasks can yield more accurate and reliable survival predictions in clinical scenarios
Bridging the gap: improve neural survival models with interpolation techniques
Full text linkSurvival analysis is an essential tool in healthcare for risk assessment, assisting clinicians in their evaluation and decision making processes. Therefore, the importance of using expressive and high-performing survival models is crucial. With the advent of neural networks and deep learning, a new generation of survival models has emerged, offering state-of-the-art capabilities to capture the non-linear and complex relationships inherent in multimodal patient data for survival prediction. However, these models often produce discrete outputs, resulting in survival functions that are coarse-grained and difficult to interpret. This study advances previous research by further exploring interpolation techniques as a post-processing strategy to improve the predictive accuracy of survival models. Our results show how the use of specific interpolation techniques significantly improves the concordance and calibration of survival estimates. This analysis encompasses a wide array of medical datasets, models, and interpolation techniques, demonstrating the effectiveness of the proposed approach and providing actionable insights for survival model design
Drug Inventory Control: Human Decisions Versus Deep Reinforcement Learning
Full text linkWe investigate whether and how deep reinforcement learning (DRL) can be exploited for managing inventory systems with a specific reference to perishable pharmaceutical products. A real-world case study is formulated as a Markov decision process, where states, actions, and rewards are defined. We then developed a DRL agent based on the Proximal Policy Optimization algorithm and compared its performance with a human decision-maker with several years of experience. Our findings reveal that the DRL agent outperforms the human policy by 11%, optimizing storage space and leading to growing profitability. Such incremental improvements can translate into substantial value for pharmaceutical companies operating in complex scenarios, and patients also stand to benefit. Finally, the study highlights the strategic advantage of integrating DRL into inventory management business operations, particularly for its ability to estimate uncertainty and manage corresponding supply chain risks
Hard and soft EM in Bayesian network learning from incomplete data
Full text linkIncomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations. BN parameter learning from incomplete data is usually implemented with the Expectation-Maximisation algorithm (EM), which computes the relevant sufficient statistics (“soft EM”) using belief propagation. Similarly, the Structural Expectation-Maximisation algorithm (Structural EM) learns the network structure of the BN from those sufficient statistics using algorithms designed for complete data. However, practical implementations of parameter and structure learning often impute missing data (“hard EM”) to compute sufficient statistics instead of using belief propagation, for both ease of implementation and computational speed. In this paper, we investigate the question: what is the impact of using imputation instead of belief propagation on the quality of the resulting BNs? From a simulation study using synthetic data and reference BNs, we find that it is possible to recommend one approach over the other in several scenarios based on the characteristics of the data. We then use this information to build a simple decision tree to guide practitioners in choosing the EM algorithm best suited to their problem
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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