Int j simul model 17 (2018) 3, 444-457 ISSN 1726-4529 Original scientific paper https://doi.org/10.2507/IJSIMM17(3)440 444 THE APPLICATION OF SIMULATION MODEL OF A MILK RUN TO IDENTIFY THE OCCURRENCE OF FAILURES Fedorko, G. * ; Molnar, V. * ; Honus, S. ** ; Neradilova, H. *** & Kampf, R. **** * Technical University of Kosice, Letna 9, 042 00 Kosice, Slovak Republic ** VŠB – Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava – Poruba, Czech Republic *** College of Logistics, Palackeho 1381/25, 750 02 Prerov, Czech Republic **** The Institute of Technology and Business in Ceske Budejovice, Okruzni 517/10, 370 01 Ceske Budejovice, Czech Republic E-Mail: gabriel.fedorko@tuke.sk, vieroslav.molnar@tuke.sk, stanislav.honus@vsb.cz, hana.neradilova@vslg.cz, kampf@mail.vstecb.cz Abstract At present, AGV systems are an important part of numerous companies and their internal logistics systems. They are used to efficiently secure different types of transport processes in order to minimize operating costs. However, their reliable operation requires adequate setting and maintenance. Computer simulation is probably the most suitable option from a wide range of engineering methods with which to meet these requirements. This paper describes the development of a simulation model in the Tecnomatix Plant Simulation program to identify critical points of failure within a specific delivery process on the basis of a Milk Run system. Based on the results obtained, an appropriate solution was determined with which to make the whole process function more efficiently and reliably. (Received in February 2018, accepted in June 2018. This paper was with the authors 1 month for 4 revisions.) Key Words: AGV Simulation, Milk Run, Performance Efficiency, Delivery, Failures 1. INTRODUCTION The automation of logistics processes is linked to the introduction of the latest technologies into a wide range of business operations. The primary purpose thereof is to speed up individual processes, reduce costs and enable increases in production. Process automation as such involves a number of processes, such as storage and/or transportation, and is largely implemented through automated logistics systems that perform, for instance, material handling operations [1], replace fork-lift trucks, or provide delivery services to individual workplaces [2, 3]. However, in order for them to operate reliably, it is essential that the whole process is appropriately designed and programmed [4]. Various methods are therefore used for its optimization and effective management, e.g. different evolutionary methodologies [5], also including genetic algorithms [6] or Ant colony algorithms [7] and mathematical models [8]. Automation mostly relates to the implementation and application of automated guided vehicle (AGV) systems [9] that may be generally defined as means of transporting and handling materials without direct human operation [10]. These systems are most frequently used in the form of individual autonomous vehicles or tractors as part of so-called logistics trains. Their great advantage is their ability to cooperate with other systems, e.g. on the basis of robotic logistics systems [11]. Each AGV system must be operated in such a way that the individual vehicles are used as much and as efficiently as possible. This is a challenging issue which is attracting a great deal of attention in the research field of logistics processes and their automation [12]. Cardarelli et al. [13] attempted to solve the given issue by applying cloud robotics architecture to effectively manage certain AGV groups. This resulted in a significant shift in the efficient management of individual vehicle routes aimed at reducing the occurrence of congestion