Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions

It is difficult to automatically produce trading signals based on previous transaction data and the financial status of assets because of the significant noise and unpredictability of capital markets. This paper proposes an innovative algorithm to solve the optimal portfolio problem in stock market trading activities. Our novel portfolio trading strategy utilizes three features to outperform other benchmark strategies in a real-market environment. First, we propose a mean-VaR portfolio optimization model, the solution of which is based on the actor-critic architecture. Unlike the existing literature that learns the expectation of cumulative returns, the critic module learns the cumulative returns distribution by quantile regression, and the actor module outputs the optimal portfolio weight by maximizing the objective function of the optimization model. Secondly, we use a linear transformation function to realize short selling to ensure investors have profit opportunities in the bear market. Third, A multi-process method, called Ape-x, was used to accelerate the speed of deep reinforcement learning training. To validate our proposed approach, we conduct backtesting for two representative portfolios and observe that the proposed model in this work is superior to the benchmark strategies.

A Mean-VaR Based Deep Reinforcement Learning Framework for Practical Algorithmic Trading

148940

Human-Centric CBM Solution for Machine Tools: From Development to Deployment

Abstract Computer Numerical Control (CNC) milling is a commonly used manufacturing process with a high level of automation. Nevertheless, setting up a new CNC milling process involves multiple development steps relying heavily on human expertise. In this work, we focus on positioning and orientation of the workpiece (WP) in the working space of a CNC milling machine and propose a deep learning approach to speed up this process significantly. The selection of the WP’s setup depends on the chosen milling technological process, the geometry of the WP, and the capabilities of the considered CNC machining. It directly impacts the milling quality, machine wear, and overall energy consumption. Our approach relies on representation learning of the milling technological process with the subsequent use of reinforcement learning (RL) for the WP positioning and orientation. Solutions proposed by the RL agent are used as a warm start for simple hill-climbing heuristics, which boosts overall performance while keeping the overall number of search iterations low. The novelty of the developed approach is the ability to conduct the WP setup optimization covering both WP positioning and orientation while ensuring the axis collision avoidance, minimization of the axis traveled distances and improving the dynamic characteristics of the milling process with no input from human experts. Experiments show the potential of the proposed learning-based approach to generate almost comparably good WP setups order of magnitude faster than common metaheuristics, such as genetic algorithms (GA) and Particle Swarm Optimisation (PSA). 

https://link.springer.com/content/pdf/10.1007/s11740-023-01209-3.pdf

Deep representation learning and reinforcement learning for workpiece setup optimization in CNC milling

In diesem Artikel wird ein reaktives Job-Shop-Scheduling-Verfahren für eine flexible Fertigung vorgestellt. Flexible  Fertigungssysteme können eine hohe Anzahl an Produktvarianten effizient fertigen. Deshalb liegt der Fokus des Beitrags auf dem Skalieren der Lösung auf eine hohe Produktvarianz durch ein spezielles State Encoding. Mit einem Deep-Q-Networks-Agenten-Ansatz werden Produkte durch die Fertigung navigiert und zu Resourcen zugewiesen. Nach einem Training mit 600 Produktvariantenwird bewiesen, dass die Agenten mit unbekannten Produkten umgehen können. Mit dem State Encoding stellen wir eine Lösung für ein selbstlernendes reaktives Job Shop Scheduling vor, welches sich für eine hohe Produktvarianz skalieren lässt.

Skalierung der Online-Job-Shop-Planung durch Reinforcement Learning

Generic multi-agent reinforcement learning approach for flexible job-shop scheduling

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Deep representation learning and reinforcement learning for workpiece setup optimization in CNC milling

Skalierung der Online-Job-Shop-Planung durch Reinforcement Learning

Generic multi-agent reinforcement learning approach for flexible job-shop scheduling