Proposal of Methodology to Calculate Necessary Number of Autonomous Trucks for Trolleys and Efficiency Evaluation

Abstract The introduction of the paper highlights best practice in the area of deploying autonomous trucks in warehouses and the automotive industry, including the current technical possibilities of selected autonomous trucks. The next chapter presents the selected outputs of the scientific project “Center of Excellence for Intelligent Transport Systems” focused on a proposal of the methodology for calculating the necessary number of autonomous trucks and trolleys deployed in logistics warehouses. The methodology is based on the requirement that autonomous trucks do not have downtime. This represents a model solution with possible application in warehouse logistics but also in the automotive industry. The follow-up chapter proposes a methodological procedure to evaluate the efficiency of introducing autonomous trucks to pull trolleys in a logistics warehouse compared to conventional trucks operated by trained personnel. Autonomous trucks can theoretically be operated 365 days and 24 hours depending on the technology of their operation, battery charging, etc. On the other hand, there is generally a shortage of logistics personnel in the European Union as well as reliability and performance have been declining in recent years. The conclusion of the paper includes a discussion of the research results obtained and possibilities for future research.

[1]  Nils Boysen,et al.  Warehousing in the e-commerce era: A survey , 2019, Eur. J. Oper. Res..

[2]  V. Jaiganesh,et al.  Automated Guided Vehicle with Robotic Logistics System , 2014 .

[4]  Mariagrazia Dotoli,et al.  A Survey on Petri Net Models for Freight Logistics and Transportation Systems , 2018, IEEE Transactions on Intelligent Transportation Systems.

[5]  Štefánia Semanová,et al.  Logistics of Entry and Parking of Vehicles at Large Production Companies , 2017 .

[6]  J. Gnap,et al.  Research on Relationship between Freight Transport Performance and GDP in Slovakia and EU Countries , 2018 .

[8]  Qingguo Li,et al.  A λ-rough set model and its applications with TOPSIS method to decision making , 2019, Knowl. Based Syst..

[9]  Huafeng Wu,et al.  Impact Analysis of Travel Time Uncertainty on AGV Catch-Up Conflict and the Associated Dynamic Adjustment , 2018 .

[10]  Min Dai,et al.  Distributed control of multi-AGV system based on regional control model , 2013, Prod. Eng..

[11]  Chonglin Gu,et al.  Time Window Based Path Planning of Multi-AGVs in Logistics Center , 2017, 2017 10th International Symposium on Computational Intelligence and Design (ISCID).

[12]  Józef Matuszek,et al.  Simulation of Human Effect to the Adaptive Logistics System Used in Public Facilities , 2017 .

[13]  Satoshi Hoshino,et al.  Hybrid Design Methodology and Cost-Effectiveness Evaluation of AGV Transportation Systems , 2007, IEEE Transactions on Automation Science and Engineering.

[14]  Ondrej Stopka,et al.  Proposal of the Inventory Management Automatic Identification System in the Manufacturing Enterprise Applying the Multi-criteria Analysis Methods , 2019 .

[15]  Denisa Hrušecká,et al.  Challenges in the introduction of AGVS in production lines: Case studies in the automotive industry , 2019, Serbian Journal of Management.

[16]  Arkadiusz Gola,et al.  Computational Intelligence in Control of AGV Multimodal Systems , 2018 .

[17]  Dejan Mirčetić,et al.  Logistics Response to the Industry 4.0: the Physical Internet , 2016 .

[18]  João Marcos Travassos Romano,et al.  Application of independent component analysis and TOPSIS to deal with dependent criteria in multicriteria decision problems , 2019, Expert Syst. Appl..

[19]  Feng Liu,et al.  Multi-AGV path planning with double-path constraints by using an improved genetic algorithm , 2017, PloS one.

[20]  Hiroshi Yoshitake,et al.  New Automated Guided Vehicle System Using Real-Time Holonic Scheduling for Warehouse Picking , 2019, IEEE Robotics and Automation Letters.

[21]  Bing Hai Zhou,et al.  An adaptive large neighbourhood search-based optimisation for economic co-scheduling of mobile robots , 2018 .

[22]  Kai Wang,et al.  A group decision making sustainable supplier selection approach using extended TOPSIS under interval-valued Pythagorean fuzzy environment , 2019, Expert Syst. Appl..

[23]  Ondrej Stopka,et al.  Inventory Model Design by Implementing New Parameters into the Deterministic Model Objective Function to Streamline Effectiveness Indicators of the Inventory Management , 2019, Sustainability.

[24]  Yong-Tae Kim,et al.  Design of Autonomous Logistics Transportation Robot System with Fork-Type Lifter , 2017, Int. J. Fuzzy Log. Intell. Syst..

[25]  Aroop K. Mahanty,et al.  THEORY OF PRODUCTION , 1980 .

[26]  Gabriel Fedorko,et al.  Simulation of the Supply of Workplaces by the AGV in the Digital Factory , 2017 .

[27]  Rudolf Kampf,et al.  The application of ABC analysis to inventories in the automatic industry utilizing the cost saving effect , 2016 .

[29]  Peter Nielsen,et al.  Two strategies of two-level facility network design for autonomous ground vehicle operations , 2018 .

[30]  Chen Guo,et al.  A Dynamic Scheduling Method for Logistics Tasks Oriented to Intelligent Manufacturing Workshop , 2019 .

[31]  Millek Jiří The Robustness of TOPSIS Results Using Sensitivity Analysis Based on Weight Tuning , 2018, IFMBE Proceedings.