In the rapidly evolving landscape of Industry 4.0 and the forthcoming Industry 5.0, the integration of intelligent systems into industrial automation has become a cornerstone for achieving efficiency, adaptability, and sustainability. In particular, deep learning (DL) has been central to this transformation, by empowering machines to extract meaningful patterns from complex, high-dimensional data, DL has demonstrated remarkable success in tackling industrial challenges such as anomaly detection, predictive maintenance, quality control, and process optimization However, as industrial systems become increasingly interconnected and dynamic, classic supervised learning paradigms often fail to meet the practical demands of real-world environments and operate under assumptions that are frequently violated in real-world industrial deployments.

A qualification management problem arising in semiconductor wafer fabrication facilities (wafer fabs) is discussed. The stepper equipment needs to be qualified to process lots that belong to different families. Stepper- and family dependent qualification time windows exist. Time windows can be reinitialized if required and can be extended by on-time processing of lots from qualified families. A hierarchical approach is proposed. The base-level provides a dispatching strategy that takes into account qualification decisions, while the mid-level consists of a mixed integer linear program (MILP) for making qualification decisions. The top-level comprises a linear program (LP) that computes target quantities for the families on the steppers in each period of the planning window taking into account fab-wide objectives. In addition to an LP formulation with conventional capacity constraints, we provide a formulation where clearing functions are used to represent the congestion of the wafer fab. We also discuss how to anticipate the behavior of the mid-level on the top-level. Results of simulation experiments where the hierarchical approach is applied in a rolling horizon manner demonstrate that the LP-based approaches outperform a heuristic to determine the target quantities. Among the LP formulations, the ones with clearing functions perform best. We also demonstrate the value of anticipation. Note to Practitioners—A hierarchical approach is designed to decide when tools in wafer fabs have to be qualified for a certain lot family. Qualification-related decisions are highly relevant because qualifications are costly and take a lot of time. The top-level determines target quantities for each family based on linear programming. The mid-level is formed by a MILP. A dispatching scheme to implement the qualification decisions provides the base-level. The hierarchical approach is assessed in a rolling horizon setting using a simulation model of a 200mm wafer fab. The benefit of the different levels of the hierarchy is demonstrated by simulation experiments.

Most traditional industries or emerging countries may not be capable of directly transiting to Industry 4.0. To fill the gap between as-is Industry 3.0 and to-be Industry 4.0, some disruptive innovations from automation and industrial engineering identify best practice with adopting cost-effective semi-automated systems to manage the potential socio-economic impacts of infrastructure disruptions, while considering total resource management for sustainability. This is the so-called “hybrid strategy (HS),” or “Industry 3.5.” On the other hand, the current Industry 4.0-related technologies should also emphasize quality enhancement to achieve “zero-defect manufacturing (ZDM),” also referred to as “Industry 4.1.” ZDM is a systematic strategy to realize the goal of Zero Defects, which includes two phases. Phase I: accomplish Zero Defects of all thedeliverablesby applying efficient and economical total-quality-inspection techniques; and Phase II: further ensure Zero Defects of all theproductsgradually by improving the yield with big data analytics and continuous improvement. Both the challenges and opportunities from HS to ZDM have significantly expanded the scope of traditional automation science and engineering.

Machines need to be individually qualified to run lots of certain families in semiconductor wafer fabrication facilities (wafer fabs). A qualification time window that lasts a couple of days is associated with each family and each tool. The time window can be reinitialized with additional qualification effort as needed and can be extended by on-time processing of qualified families. A mixed-integer linear programming formulation is proposed. The objective function takes into account the backlog costs and inventory holding costs. Moreover, it penalizes qualifications. The optimization approach is applied in a rolling horizon setting using a wafer fab simulation model. The demand for a planning window of several periods is derived using feedback from the shop floor. The results of the simulation experiments demonstrate that an appropriate tradeoff between production objectives and performed qualifications can be achieved by an appropriate configuration of the model. Note to Practitioners-In this article, we propose a mathematical programming-based approach to decide when the tools in wafer fabs have to be qualified for a certain family. These decisions have high practical relevance because qualifications are expensive and time-consuming. A dispatching scheme to implement the qualification decisions is designed. The overall approach is assessed in a rolling horizon setting using a simulation model of a large-scale wafer fab. An appropriate tradeoff between production and qualification goals can be achieved using the proposed approach.

The composition of services is used to implement complex functionality. The quality of service (QoS) is a demanding issue for the management of service compositions. A QoS-aware service selection recommends the services to be composed to account for the quality of a service composition and its execution cost incurred by requests. It is shown that in the presence of sophisticated service charging a cost minimization objective imposes the need for a request- and service composition-comprehensive service selection.This type of selection is called a tactical service selection. Existing tactical service selection models assume a deterministic execution environment. The need to adjust a service composition during the execution of a request to react on uncertain QoS attributes and service failures is neglected. Service reconfiguration approaches are proposed to deal with service failures, uncertain QoS attributes, and their impact on QoS restrictions. The challenge addressed in this paper is to propose a hierarchical service selection that integrates a tactical service selection with a service reconfiguration to satisfy the cost minimization objective and to maintain a successful execution of requests. It is shown that the tactical service selection can be efficiently combined with an existing service reconfiguration method to achieve both runtime-related goals and tactical objectives.

The contribution of this paper is a conceptual architecture based on intelligent services for manufacturing support systems. Current approaches cannot cope with the dynamism inherent in the manufacturing domain, where unexpected and drastic changes occur at any time and affect cross-boundary systems. Our approach alleviates these issues and focuses on the need for supporting real-time automatic negotiation, planning, scheduling and optimisation, intra and across factories. We present an execution example of our approach in the aerospace industry, instantiating the conceptual architecture using different technologies. With this we push forward a new definition of Intelligent Services and indicate future work in the area.

In this paper, we extend the FABMAS ontology towards its usage in adaptive production control systems for complex manufacturing systems. FABMAS is a hierarchically organized multi-agent-system for production control in the semiconductor manufacturing domain. It consists of decision-making and staff agents. Staff agents support the decision-making agents. We introduce adaptive staff agents. Adaptive staff agents make sure that the parameters of production control algorithms are properly adjusted in a situation-dependent manner. We derive necessary extensions of the FABMAS ontology based on the architecture of adaptive staff agents and their interaction with other agents of the manufacturing control system. We present an example for using the ontology within FABMAS.