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Individualisation of products and ever-shorter product lifecycles require manufacturing companies to quickly reconfigure their production and adapt to changing requirements. While most of the existing literature focuses on organisational structures or hardware requirements for reconfigurability, requirements and best practices for logical reconfigurations of automated production systems are only sparsely covered. In practice, logical system reconfigurations require adjustments to the software, which is often done manually by experts. With the ongoing automation and digitisation of manufacturing systems in the context of Industry4.0, the need for automated software reconfigurations is increasing. However, heterogeneous and proprietary technologies in the field of industrial automation pose a hurdle to overcome for generally applicable approaches for logical reconfigurations in the industrial domain. Therefore, this paper reviews available technologies that can be used to solve the problem of automated software reconfigurations. For this purpose, an architecture and a procedure are proposed on how to use these technologies for automatic adaptation and virtual commissioning of control software in industrial automation. To demonstrate the interoperability of the approach, collective cloud manufacturing is used as a composing platform. The presented approach further includes a domain-specific capability model for the specification of software artefacts to be generated, allowing jobs to be described and matched on the platform. The core element is a code generator for generating and orchestrating the control code for process execution using the reconfigurable digital twin as a validator on the platform. The approach is evaluated and demonstrated in a real-world use case of a modular disassembly station.

Reconfigurable manufacturing systems (RMS) are capable of adjusting their operating point to the requirements of current customer demand with high degrees of freedom. In light of recent events, such as the covid crisis or the chip crisis, this reconfigurability proves to be crucial for efficient manufacturing of goods. Reconfigurability aims thereby not only at adjust production capacities but also for fast integration of new product variants or technologies. However, the operation of such systems is linked to high efforts concerning manual work in production planning and control. Simulation-based optimization provides the possibility to automate processes in production planning and control with the advantage of relying on mostly existing models such as material flow simulations. This paper studies the capabilities of the meta heuristics evolutionary algorithm, linear annealing and tabu search to automate the search for optimal production reconfiguration strategies. Two distinct use cases are regarded: an increase of customer demand and the introduction of a previously unknown product variant. A parametrized material flow simulation is used as function approximator for the optimizers, whereby the production system's structure as well as logic are target variables of the optimizers. The analysis shows that meta-heuristics find good solutions in a short time with only little manual configuration needed. Thus, metaheuristics illustrate the potential to automate the production planning of RMS. However, the results indicate that the performance of the three meta-heuristics considering optimization quality and speed differs strongly.

End-of-Life (EOL) products in remanufacturing are prone to a high degree of uncertainty in terms of product quantity and quality. Therefore, the industrial shift towards a circular economy emphasizes the need for agile and hybrid disassembly systems. These systems feature a dynamic material flow. Besides that, they combine the endurance of robots with the dexterity of human operators for an effective and economically reasonable EOL-product treatment. Moreover, being reconfigurable, agile disassembly systems allow an alignment of their functional and quantitative capacity to volatile production programs. However, changes in both the system configuration and the production program to be processed call for adaptive approaches to production control. This paper proposes a multi-priority rule heuristic combined with an optimization tool for adaptive re-parameterization. First, domain-specific priority rules are introduced and incorporated into a weighted priority function for disassembly task allocation. Besides that, a novel metaheuristic parameter optimizer is devised to facilitate the adaption of weights in response to evolving requirements in a reasonable timeframe. Different metaheuristics such as simulated annealing or particle swarm optimization are incorporated as black-box optimizers. Subsequently, the performance of these metaheuristics is meticulously evaluated across six distinct test cases, employing discrete event simulation for evaluation, with a primary focus on measuring both speed and solution quality. To gauge the efficacy of the approach, a robust set of weights is employed as a benchmark. Encouragingly, the results of the experimentation reveal that the metaheuristics exhibit a notable proficiency in rapidly identifying high-quality solutions. The results are promising in that the metaheuristics can quickly find reasonable solutions, thus illustrating the compelling potential in enhancing the efficiency of agile disassembly systems.