The principal aim of this dissertation is to develop a discrete events simulation model through the use of the Flexsim software, which represents a significant support instrument in the designing of an AGV plant. In particular, the design stage of an automation solution elaborated with AGV vehicles requires a pre-sales engineer to take numerous complex decisions concerning different elements of the project itself. In fact, using a 3D simulation tool, able to reproduce the dynamics of the real system in a simulating environment, allows not only to investigate and examine a multitude of aspects of the suggested solution, but also to make more conscious choices.
More specifically, the prototype developed at System Logistics S.p.a. constitutes a dynamic, stochastic, and discrete events simulation model. This simulated system is, indeed, a storage and material handling plant, which is currently under investigation for a specific customer belonging to the food sector, where AGVs are held accountable for the handling of loading units. This thesis presents a more in-depth analysis of three distinct factors that must be defined in the designing of an AGV plant; namely, the fleet sizing, the layout configuration of the so-called dwell points or parking spots, and the dispatching system that has to be adopted.
Generally speaking, this study required a comprehensive investigation of the customer’s real plant through a formalisation of main functioning dynamics of such a system, and, simultaneously, a detailed study of Flexsim as a simulation tool. On this basis, the simulation model under investigation here was implemented and tested through various developing steps so as to accurately verify its proper functioning. Thereafter, the prototype was validated through highly sophisticated performance measures deriving from a corporate-owned 2D simulator, as well as through the outputs of a static spreadsheet, employed in the dimensioning of the fleet.
Subsequently, different configurations of the model were implemented. These proved to be useful in carrying out a scenario analysis, within which the three above-mentioned factors where alternatively modified on multiple levels. A comparison of the simulation results was then made through the use of some KPI, by implementing them into the model in order to determine which scenario was most suited to the customer’s needs, and hence, in order to identify the most helpful solution configuration for the design engineer in the realisation of the design itself.
Finally, a series of statistical tests, conducted on numerous and diverse settings, demonstrated how all three of the examined factors present a considerable treatment effect on the performances of the system.