Energy Efficient Routing and Task Scheduling for Autonomous Transport Vehicles in Intra Logistics

Authors

DOI:

https://doi.org/10.48129/kjs.v49i1.10194

Keywords:

Autonomous transport vehicles, energy efficient routing, pickup and delivery tasks, vrp with backhauls, hybrid simulated annealing

Abstract

An energy efficient routing and scheduling system is proposed to minimize the total energy that the vehicles spend. Not only travelled distance but also the load of the vehicle is considered between two points. The routes of vehicles are obtained by using the proposed Hybrid Simulated Annealing Algorithm. An algorithm for the initial solution is also proposed to determine the minimum number of vehicles for pickup and delivery requests. The performance of the algorithm is compared with the best solutions of the test problems in the literature. Besides, the proposed energy efficient routing and task scheduling model is compared with the classical distance model for routing and scheduling with backhauls. An analysis of trade-offs between energy and distance is proposed for intra logistics. Experimental results show that while energy saving is 18.11%, the total distance increases as 12.08% in total for test problems. 

Author Biographies

İnci Sarıçiçek, Eskisehir Osmangazi University

Industrial Engineering

Sinem Bozkurt Keser, Eskisehir Osmangazi University

Computer Engineering

Azmi Cibi, Eskisehir Osmangazi University

Computer Engineering

Tahir Özdemir, Eskisehir Osmangazi University

Computer Engineering

Ahmet Yazıcı, Eskisehir Osmangazi University

Computer Engineering

References

Brandao, J. (2006). A new tabu search algorithm for the vehicle routing problem with backhauls. European Journal of Operational Research, 173, 540–555.

Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1, 269–271.

Eksioglu, B., Vural A.V. & Reisman, A. (2009). The vehicle routing problem: A taxonomic review, Computers and Industrial Engineering, 57(4), 1472-1483.

Emilio, F., Dahleh, M.A. & Feron, E. (2002). Real-time motion planning for agile autonomous vehicles. Journal of Guidance, Control, and Dynamics, 25(1), 116-129.

Goetschalckx, M. & Jacobs-Blecha, C. (1989). The vehicle routing problem with backhauls. European Journal of Operational Research 42, 39–51.

Herrero-Pérez, D. & Martínez-Barberá, H., (2010). Modeling distributed transportation systems composed of flexible automated guided vehicles in flexible manufacturing systems. IEEE Transactions on Industrial Informatics. 6(2), 166–180.

Hussein, A. et al. (2012). Metaheuristic optimization approach to mobile robot path planning. IEEE International Conference on Engineering and Technology (ICET), 1-6.

Lin, C. et al. (2014). Survey of Green Vehicle Routing Problem: Past and future trends. Expert Systems with Applications, 41, 1118–1138.

Liu, S., Linbo, M. & Jinshou, Y. (2006). Path planning based on ant colony algorithm and distributed local navigation for multi-robot systems. IEEE International Conference on Mechatronics and Automation, 1733-1738.

Magdy, Y. et al. (2017). Metaheuristic optimization in path planning of autonomous vehicles under the ATOM framework. 2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Vienna, 32-37.

Xidias, E.K., Nearchou, A.C. & Aspragathos, N.A. (2009). Vehicle scheduling in 2D shop floor environments, Industrial Robot: An International Journal. Vol.36 (2), 176-183.

Xidias, E.K. Paraskevi, Z. & Andreas, N. (2016). Path planning and scheduling for a fleet of autonomous vehicles. Robotica, 34(10), 1-17.

Xidias, E.K. (2018). On designing near-optimum paths on weighted regions for an intelligent vehicle. International Journal of Intelligent Transportation Systems Research, 17(2), 89-101.

Published

02-12-2021