This paper explores the potential application of Deep Reinforcement Learning in the furniture industry. To offer a broad product portfolio, most furniture manufacturers are organized as a job shop, which ultimately results in the Job Shop Scheduling Problem (JSSP). The JSSP is addressed with a focus on extending traditional models to better represent the complexities of real-world production environments. Existing approaches frequently fail to consider critical factors such as machine setup times or varying batch sizes. A concept for a model is proposed that provides a higher level of information detail to enhance scheduling accuracy and efficiency. The concept introduces the integration of DRL for production planning, particularly suited to batch production industries such as the furniture industry. The model extends traditional approaches to JSSPs by including job volumes, buffer management, transportation times, and machine setup times. This enables more precise forecasting and analysis of production flows and processes, accommodating the variability and complexity inherent in real-world manufacturing processes. The RL agent learns to optimize scheduling decisions. It operates within a discrete action space, making decisions based on detailed observations. A reward function guides the agent's decision-making process, thereby promoting efficient scheduling and meeting production deadlines. Two integration strategies for implementing the RL agent are discussed: episodic planning, which is suitable for low-automation environments, and continuous planning, which is ideal for highly automated plants. While episodic planning can be employed as a standalone solution, the continuous planning approach necessitates the integration of the agent with ERP and Manufacturing Execution Systems. This integration enables real-time adjustments to production schedules based on dynamic changes.

Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractional optimal solution while not affecting the optimal integer solution. In this work, we explore a novel approach within cutting plane methods: instead of only adding new cuts, we also consider the removal of previous cuts introduced at any of the preceding iterations of the method under a learnable parametric criteria. We demonstrate that in fundamental combinatorial optimization settings such cut removal policies can lead to significant improvements over both human-based and machine learning-guided cut addition policies even when implemented with simple models.

Manufacturing companies face challenges when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches that encapsulate production functions as services have shown to be promising in order to increase the flexibility of Cyber-Physical Production Systems. But an existing challenge of such approaches is finding a production plan based on provided functionalities for a demanded product, especially when there is no direct (i.e., syntactic) match between demanded and provided functions. While there is a variety of approaches to production planning, flexible production poses specific requirements that are not covered by existing research. In this contribution, we first capture these requirements for flexible production environments. Afterwards, an overview of current Artificial Intelligence approaches that can be utilized in order to overcome the aforementioned challenges is given. For this purpose, we focus on planning algorithms, but also consider models of production systems that can act as inputs to these algorithms. Approaches from both symbolic AI planning as well as approaches based on Machine Learning are discussed and eventually compared against the requirements. Based on this comparison, a research agenda is derived.

An efficient supply chain planning is the fundamental block for building a successful and well-organized supply chain mechanism. Many businesses are unable to achieve the desired operational excellence due to manual operative approaches, lack of visibility and poor supply chain planning. This restricts brands from creating synchronized, smooth and responsive supply chains. The most crucial activity in supply chain management is planning. Supply chain planning is the process of accurately planning a product flow from raw material sourcing to reaching the final consumer.

Actionable Cognitive Twins are the next generation Digital Twins enhanced with cognitive capabilities through a knowledge graph and artificial intelligence models that provide insights and decision-making options to the users. The knowledge graph describes the domain-specific knowledge regarding entities and interrelationships related to a manufacturing setting. It also contains information on possible decision-making options that can assist decision-makers, such as planners or logisticians. In this paper, we propose a knowledge graph modeling approach to construct actionable cognitive twins for capturing specific knowledge related to demand forecasting and production planning in a manufacturing plant. The knowledge graph provides semantic descriptions and contextualization of the production lines and processes, including data identification and simulation or artificial intelligence algorithms and forecasts used to support them. Such semantics provide ground for inferencing, relating different knowledge types: creative, deductive, definitional, and inductive. To develop the knowledge graph models for describing the use case completely, systems thinking approach is proposed to design and verify the ontology, develop a knowledge graph and build an actionable cognitive twin. Finally, we evaluate our approach in two use cases developed for a European original equipment manufacturer related to the automotive industry as part of the European Horizon 2020 project FACTLOG.

The digital revolution has made way for many innovative and efficient technologies that have allowed organizations to revamp their business process. These technologies have helped to bridge the gap between the service providers and the customers. The supply chain industry is also leveraging many intelligent tools like machine learning (ML) to rev-up their services. Let's delve deeper to understand the significance of ML techniques in streamlining and optimizing the operation of supply chain industry: Scalability: The fierce competition among companies requires them to scale up or scale down their services according to the requirement. Companies need to be prepared for any upcoming challenges to stay relevant in the market.

Machine learning has been successfully applied to demand planning, but leading suppliers of supply chain planning are beginning to work on using machine learning to improve production planning. But architecturally and culturally, this is a much tougher problem than machine learning applied to demand planning. In the $2 billion-plus supply chain planning market, ARC Advisory Group's latest market study shows production planning as being a critical application SCP solution representing over 25 percent of the total market. Production planning applications are used for both planning daily production at a factory to creating weekly or monthly plans to divvy up the production tasks that need to be accomplished across multiple factories. Machine learning is a form of continuous improvement.

Microelectronics supplier TRW optimizes the combustion process Clothing manufacturer Wrangler (Redondo Beach, CA) is using virtual in a coal-fired utility boiler, (Greensboro, NC) has developed a reality (VR) to decontaminate nuclear reducing nitrogen oxide emissions neural network system to improve facilities. The company has developed and loss on ignition while improving production planning and forecasting. An applications to its 36,000 Group (Washington, DC) has expert system makes recommendations employees worldwide. Pacific Gas & Electric (PG&E) (San provides real-time restoration of NeuralWare (Pittsburgh, PA), a Francisco, CA), a public utility, has telecommunications services in areas provider of neural network software, affected by disaster or accidents. The system allows PG&E outage through a series of tests, 24 for target and path optimization to offer customers flexible energy hours a day, 7 days a week.