Current trends in manufacturing, which are based on customisation and gradually customised production, are becoming the main initiator for the development of new manufacturing approaches. New manufacturing approaches are counted as the application of new behavioural management patterns that calculate the retained competencies of decision-making by the individual members of the system agent; the production becomes decentralised. The interaction of the members of such a system creates emergent behaviour, where the result cannot be accurately determined by ordinary methods and simulation must be applied. Modelling and simulation will, therefore, be an integral part of the planning and control of the processes of factories of the future. The purpose of the article is to describe the use of modelling and simulation processes in factories of the future. The first part of the article describes new manufacturing concepts that will be used in factories of the future, with a description of modelling and simulation routing in the frame of Industry 4.0. The next section describes how simulation is used for the control of manufacturing processes in factories of the future. The included subsection describes the implementation of this suggested pattern in the laboratory of ZIMS (Zilina Intelligent Manufacturing System), with an example of a metamodeling application and the results obtained.

Nowadays, many factories face changes on the global market and manufacturing is unpredictable. This fact creates a demand for developing new concepts of the factory which can represent a solution to these changes. This study presents a way for designing these new factory concepts, particularly a concept of the reconfigurable manufacturing lines. The methodology in this study uses characteristics of reconfigurable manufacturing systems for developing an algorithm for designing the basic factory layout. The methodology also combines classical math operations for designing the production layout with such approaches as simulation, cluster analysis, and LCS algorithm. This combination method with LCS algorithm and an entirely different approach to the design of the manufacturing line, has not yet been used. The accuracy of this methodology is then verified through the results of the complete algorithm containing these features. The main purpose of this study was to find new approaches to designing the reconfigurable factory layout. This article is presenting new ways that differ from the classical design method. The article suggests the new way is possible and the new systems also need new ways for designing and planning.

Featured Application: Reconfigurable logistic system for new generation of Factory of the Future manufacturing systems and modular systems.Abstract: Route planning in a multi-agent system (MAS) is still a complex task, especially if there is need for a continuous, decentralized planner of the routes for physical agents in a dynamic environment. It is a planner of this kind that is required in the application the article considers: the transportation of parts at a modular manufacturing line. Such a planner has to meet several difficult requirements, regarding the physical, time-constrained dynamic environment, live-locks and deadlocks, delayed agents, and last needs to minimize the travel time and the total distance traveled. The article proposes an approach using a delegate multi-agent system (D-MAS) in order to meet these requirements. The approach was verified using virtual reality so as to provide a better understanding of the planner's issues. Several coordination rules were proposed and implemented. As a further verification, the proof-of-concept solution was compared to a non-reservation planner. It was shown, that as the number of agents increases, the approach, including the reservations, outperformed its competitor. Various recommendations for the implementation of the planner were formulated. It was concluded that the performance of the planner is sufficient for its future use. The main objective of article was proof-of-concept and determining the functionality of a prototype based on MAS that was in compliance with a modular manufacturing line developed by us. Appl. Sci. 2019, 9, 4515 2 of 20 these requirements and they offer additional features. These include autonomy, decentralization, scalability, and flexibility [4]. Inclusion of these features can be found in [3,5]. Yet, MASs have not been widely adopted in the industrial domain. Several reasons for this were identified by Karnouskos and Leitao [6], particularly, the demand of the industry for mature technologies, initial investments, missing compliance with existing standards, the lack of development methodologies, insufficient interoperability, and integration with physical systems. There are also several areas in MASs-such as continuous, decentralized route planning for physical agents in a dynamic environment-that can still be challenging [7]. Several works were done in this field [8,9]. This article uses delegates in the context of D-MASs (delegate multi-agent systems). Delegate or delegate agents are simple, reactive agents that are created, sent out, and collected by task and resource agents. Next, the features of delegate agents are that agents are virtual entities, not directly connected with anything physical, and that they communicate with other agents through the environment [8]. Primary agents or delegates use behavioral modules called D-MASs that reduce the agents' internal structural complexities; a definition can be found in [9].The idea of our project was to create an intra-logistic system that can transport parts (i.e., manufa...

In the last decade, simulation software as a tool for managing and controlling business processes has received a lot of attention. Many of the new software features allow businesses to achieve better quality results using optimisation, such as genetic algorithms. This article describes the use of modelling and simulation in shipment and sorting processes that are optimised by a genetic algorithm’s involvement. The designed algorithm and simulation model focuses on optimising the duration of shipment processing times and numbers of workers. The commercially available software Tecnomatix Plant Simulation, paired with a genetic algorithm, was used for optimisation, decreasing time durations, and thus selecting the most suitable solution for defined inputs. This method has produced better results in comparison to the classical heuristic methods and, furthermore, is not as time consuming. This article, at its core, describes the algorithm used to determine the optimal number of workers in sorting warehouses with the results of its application. The final part of this article contains an evaluation of this proposal compared to the original methods, and highlights what benefits result from such changes. The major purpose of this research is to determine the number of workers needed to speed up the departure of shipments and optimise the workload of workers.

Continuous cost reduction is a subject of interest for almost every production company. The cost reflects the competitiveness and sustainability of the business. Many company costs are linked to the effectiveness of production. One such cost is the work-in-progress (WIP) inventory cost. The present article deals with the design of a system for calculating the optimal WIP inventory stored in a manufacturing buffer, which, in the long term, provides the lowest costs. The main goal of the article is to design a new system that allows for the calculation of the optimal capacity of interoperation manufacturing buffers and thus the calculation of the optimal WIP inventory, which influences the lead time and cost. The newly designed system consists of algorithms that describe various steps, many of which use mathematical models. The individual blocks of algorithms are described, and the proposed system is verified and validated by simulation of the production line in the automotive production company.