This literature review examines the increasing use of artificial intelligence (AI) in manufacturing systems, in line with the principles of Industry 4.0 and the growth of smart factories. AI is essential for managing the complexities in modern manufacturing, including machine failures, variable orders, and unpredictable work arrivals. This study, conducted using Scopus and Web of Science databases and bibliometric tools, has two main objectives. First, it identifies trends in AI-based scheduling solutions and the most common AI techniques. Second, it assesses the real impact of AI on production scheduling in real industrial settings. This study shows that particle swarm optimization, neural networks, and reinforcement learning are the most widely used techniques to solve scheduling problems. AI solutions have reduced production costs, increased energy efficiency, and improved scheduling in practical applications. AI is increasingly critical in addressing the evolving challenges in contemporary manufacturing environments.

This systematic literature review explores the burgeoning use of Artificial Intelligence (AI) in manufacturing systems, in line with the principles of Industry 4.0 and the growth of smart factories. In this landscape, AI is crucial in addressing the complexity and dynamism of contemporary manufacturing processes, including machine breakdowns, fluctuating orders and unpredictable job arrivals. This systematic literature review, conducted using the Scopus database and bibliometric tools, pursues two primary objectives. First, it identifies the prevailing trends in solving scheduling problems with AI and identifies the most commonly used AI techniques in the literature. Secondly, it analyses how authors have successfully employed AI to address production scheduling challenges in real-world industrial settings and assesses the benefits obtained by companies. The dynamic nature of manufacturing systems requires adaptive scheduling paradigms. AI, including Particle Swarm Optimization, Neural Networks, and Reinforcement Learning, is applied to optimize production processes, predict machine failures, and achieve substantial benefits. In real-world applications, these AI-driven solutions have led to reduced production costs, enhanced energy efficiency, and more efficient scheduling processes. AI is increasingly recognized as an essential tool in addressing the evolving challenges of modern manufacturing environments.

Manufacturing as a Service (MaaS) is an emerging paradigm in which a Provider exposes manufacturing capabilities such as CNC machines, additive manufacturing systems, and other production assets as on-demand services to multiple Consumers. In this setting, Provider side scheduling becomes a critical aspect, as orders arrive dynamically and must be allocated to heterogeneous resources while meeting contractual constraints. This paper proposes an implementable conceptual framework for Provider production scheduling in MaaS, formulating the problem as a Markov Decision Process and enabling Deep Reinforcement Learning based decision making. The proposed Provider Planner models resource allocation as an unrelated parallel machine scheduling problem, where decisions are taken at discrete event-driven decision epochs (e.g., order arrivals and resource releases). The framework explicitly specifies the observation space, action space, and discrete-event transition logic, incorporating practical features such as resource availability and efficiency, operating cost rates, setup states across product families, batch-size constraints, order time windows (earliest/latest start), due dates, and delay penalties. A multi-objective reward formulation is defined to jointly minimise tardiness and associated penalties, overall makespan, and total production cost computed from resource uptimes. The resulting model provides a structured basis for developing and evaluating adaptive scheduling policies for MaaS Providers under demand variability and complex operational constraints.

This paper addresses a Hybrid Flow-shop Scheduling Problem with an Integrated Open-shop Block (HFSIOB), a complex variant that captures the dynamics of industrial assembly systems with operator expertise heterogeneity, finite workstation capacity, and limited auxiliary resources. To tackle this problem, a Deep Reinforcement Learning (DRL) framework based on Proximal Policy Optimisation is developed, modelling scheduling decisions as a Markov Decision Process and optimising a multi-objective function that simultaneously minimises makespan and delivery delays while maximising operator utilisation. The proposed approach is validated on forty test instances, where it outperforms a First-In-First-Out heuristic, an Ant Colony Optimisation meta-heuristic on delay-related metrics achieving near-zero delays while maintaining competitive makespan, and inference times below ten seconds. The proposed framework was validated over a three-month industrial deployment at Multiscan Technologies, a Spanish company specialising in the assembly of inspection machinery, reducing the average assembly cycle by 5.9% and a decrease in the production loss ratio (from 10% to 6.8%) without manual rescheduling. These results demonstrate the practical relevance of the HFSIOB formulation and confirm the effectiveness of DRL as a scalable, data-driven solution for complex scheduling in real manufacturing environments.