## 1 Introduction
There are several advantages to our approach:  
* Production capacities can easily be expanded (by adding more modules);
* The system can respond to fluctuating demand (by removing some modules);
* Re-routing of the material flow is simple;
* Modules that are out of order can easily be replaced;
* Start-up times and costs are reduced;
* The system can be dismantled and relocated to another factory. A video that shows by us developing Modular Manufacturing Line with all his components can be seen in Supplementary Materials part as Video S1. The subject of the next chapter is the definition of the objective, targets, and goals. The chapter contains constraints, formulated requirements, a justification of the selection of D-MAS, and a description of research and limitations. Figure 3: An artistic vision of our application. Figure 2: The guidance grid with the mobile robotic system agents (MRSAs) and the modular platform agents (MPAs). Objective, Targets, and Goals_  
The application we have hereinbefore described, requires a continuous, decentralized route planner, suitable for being used in a dynamic environment. Decentralized solutions, such as MAS, can outperform centralized planners, if the application has the following properties: high urgency, medium to high dynamism, and medium to large scale [11]. There are several constraints restricting the use of a route planner in our application:  
* The planning must be highly urgent, in order to meet the requirements of an MML's reconfiguration. * The environment must be highly dynamic due to the possible delays or breakdowns of the hardware prototypes, and adding or removing agents. * The application requires a continuous planner because new transport orders can be requested at any time. * An MRS is bounded by the limited processing capacity and memory and it does not possess a sensor for detecting obstacles or other MRSs. * Movement restrictions resulting from the fact that hardware prototypes physically occupy space. * The presence of deadlock and live-lock situations. We have formulated five requirements (R1-R5) that the route planner should satisfy. It should be able to:  
* **R1**: deal with the highly urgent, space-constrained application and minimize the processing resources. * **R2**: deal with the movement restrictions of MRS and at the same time to be live-lock and deadlock-free. * **R3**: cope with the dynamism of the real-world environment (variable velocity of the agents; disturbances, changes, delays, and breakdowns of the agents; and communication failures)
* **R4**: minimize unnecessarily traveled distances and the travel times of the MRSs, and to maximize the throughput of the GG. * **R5**: be scalable and leave room for further improvements and changes. We chose a decentralized route planner using delegate multi-agent systems (D-MAS) in order to meet R1, R3, and R5. Decentralized planners seem to be comparable to centralized state-of-the-art planners in some cases, yet still offer a decentralized approach without the need of access to global knowledge [7]. A proper implementation of the D-MASs leaves room for further improvements. The routing problem of the kind we describe has been shown to be at least NP-hard [12]. On the other hand, in a D-MAS, the complexity of computations and communication is low-polynomial [13]. The formulation of the requirements R1-R5 sums up the problem definition and their fulfillment can be considered the novelty of this paper. In research, we selected D-MAS because we had a good experience with the application of D-MAS in a previous research project, and at the same time, we did not have experience with commonly used pathfinding algorithms. The implementation of D-MAS provided larger room for changes and the needs of our modular manufacturing line. The first need was the reservation of a time interval in consideration of the nondiscrete movement of an MRS. Most pathfinding algorithms are used for discrete movement.