Decision-making in supply chains is challenged by high complexity, a combination of
continuous and discrete processes, integrated and interdependent operations, dynamics …

Abstract The integration of Artificial Intelligence (AI) into supply chain management has
emerged as a pivotal avenue for enhancing efficiency and resilience in contemporary …

Modeling and optimizing dynamic job shop scheduling problems (DJSSP) without ample
assumptions is inherently challenging due to the increasing complexity and uncertainty …

Purpose This paper aims to investigate how manufacturing firms behave to mitigate
business risk during and post-COVID-19 coronavirus disease (COVID-19) on the global …

Disassembly is a decisive step in the remanufacturing process of End-of-Life (EoL) products.
As an emerging semi-automatic disassembly paradigm, human–robot collaborative …

Curiously, there are few contributions in the scientific literature on the subject of artificial
intelligence (AI) and its impact on aeronautics. However, many communications and reports …

This study conducts a comprehensive analysis of deep reinforcement learning (DRL)
algorithms applied to supply chain inventory management (SCIM), which consists of a …

Personalized product demands have made the production mode of many varieties and small
batches mainstream. Self-organizing manufacturing systems represented by multi-agent …

Herein, we present a real-time multi-agent deep reinforcement learning model as a
disassembly planning framework for human–robot collaboration. This disassembly plan …

Modern supply chains are complex and interlinked, resulting in increased network risk
exposure for companies. Digital Supply Chain Surveillance (DSCS) has emerged as a …