As productivity advances, the demand of customers for multi-variety and small-batch production is increasing, thereby putting forward higher requirements for manufacturing systems. When production tasks frequent changes due to this demand, traditional manufacturing systems often cannot response promptly. The multi-agent manufacturing system is proposed to address this problem. However, because of technical limitations, the negotiation among agents in this kind of system is realized through predefined heuristic rules, which is not intelligent enough to deal with the multi-variety and small batch production. To this end, a Large Language Model-based (LLM-based) multi-agent manufacturing system for intelligent shopfloor is proposed in the present study. This system delineates the diverse agents and defines their collaborative methods. The roles of the agents encompass Machine Server Agent (MSA), Bid Inviter Agent (BIA), Bidder Agent (BA), Thinking Agent (TA), and Decision Agent (DA). Due to the support of LLMs, TA and DA acquire the ability of analyzing the shopfloor condition and choosing the most suitable machine, as opposed to executing a predefined program artificially. The negotiation between BAs and BIA is the most crucial step in connecting manufacturing resources. With the support of TA and DA, BIA will finalize the distribution of orders, relying on the information of each machine returned by BA. MSAs bears the responsibility for connecting the agents with the physical shopfloor. This system aims to distribute and transmit workpieces through the collaboration of the agents with these distinct roles, distinguishing it from other scheduling approaches. Comparative experiments were also conducted to validate the performance of this system.

To stay competitive, the Low- or Middle-Income Countries (LMICs) need to embrace Industry 4.0 and Logistics 4.0. This requires government-level interventions and policy-making to incentivize quality product solutions and drive innovation in traditionally resistant economic sectors. In this position paper, we support the establishment of Smart Industrial Parks (SIPs) with a focus on enhancing operational efficiencies and bringing together MSMEs and startups targeting niche clientele with innovative Industry 4.0 solutions. SIPs along with the phased deployment of well-planned robotic automation technologies shall enable bringing down India's untenable logistics costs. Toward the successful execution of SIPs, we are required to implement the efficient allocation of manufacturing resources and capabilities within SIPs. Thus, we emphasize the importance of efficient resource utilization, collaboration, and technology adoption in industrial parks to promote industrial development and economic growth. We advocate the use of a cloud-based cyber-physical system for real-time data access and analysis in SIPs. Such centralized cloud-based monitoring of factory floors, warehouses, and industrial units using IoT infrastructure shall improve decision-making, efficiency, and safety. Digital Twins (DTs), which are cyber-replicas of physical systems, could play a significant role in enabling simulation, optimization, and real-time monitoring of smart manufacturing and distributed manufacturing systems. However, there are several challenges involved in implementing DTs in distributed manufacturing systems, such as defining data schemas and collaboration protocols, ensuring interoperability, the need for effective authentication technology, distributed machine learning models, and scalability to manage multiple DTs.

Manufacturing is transitioning from a mass production model to a manufacturing as a service model in which manufacturing facilities'bid' to produce products. To decide whether to bid for a complex, previously unseen product, a manufacturing facility must be able to synthesize, 'on the fly', a process plan controller that delegates abstract manufacturing tasks in the supplied process recipe to the appropriate manufacturing resources, e.g., CNC machines, robots etc. Previous work in applying AI behaviour composition to synthesize process plan controllers has considered only finite state ad-hoc representations. Here, we study the problem in the relational setting of the Situation Calculus. By taking advantage of recent work on abstraction in the Situation Calculus, process recipes and available resources are represented by Con-Golog programs over, respectively, an abstract and a concrete action theory. This allows us to capture the problem in a formal, general framework, and show decidability for the case of bounded action theories. We also provide techniques for actually synthesizing the controller.

In this paper, we model manufacturing processes and facilities as transducers (automata with output). The problem of whether a given manufacturing process can be realized by a given set of manufacturing resources can then be stated as an orchestration problem for transducers. We first consider the conceptually simpler case of uni-transducers (transducers with a single input and a single output port), and show that synthesizing orchestrations for uni-transducers is EXPTIME-complete. Surprisingly, the complexity remains the same for the more expressive multi-transducer case, where transducers have multiple input and output ports and the orchestration is in charge of dynamically connecting ports during execution.