Applied Intelligence

, Volume 48, Issue 2, pp 482–498 | Cite as

Hybrid cost and time path planning for multiple autonomous guided vehicles

  • Hamed Fazlollahtabar
  • Samaneh Hassanli


In this paper, simultaneous scheduling and routing problem for autonomous guided vehicles (AGVs) is investigated. At the beginning of the planning horizon list of orders is processed in the manufacturing system. The produced or semi-produced products are carried among stations using AGVs according to the process plan and the earliest delivery time rule. Thus, a network of stations and AGV paths is configured. The guide path is bi-direction and AGVs can only stop at the end of a node. Two kinds of collisions exist namely: AGVs move directly to a same node and AGVs are on a same path. Delay is defined as an order is carried after the earliest delivery time. Therefore, the problem is defined to consider some AGVs and material handling orders available and assign orders to AGVs so that collision free paths as cost attribute and minimal waiting time as time attribute, are obtained. Solving this problem leads to determine: the number of required AGVs for orders fulfillment assign orders to AGVs schedule delivery and material handling and route different AGVs. The problem is formulated as a network mathematical model and optimized using a modified network simplex algorithm. The proposed mathematical formulation is first adapted to a minimum cost flow (MCF) model and then optimized using a modified network simplex algorithm (NSA). Numerical illustrations verify and validate the proposed modelling and optimization. Also, comparative studies guarantee superiority of the proposed MCF-NSA solution approach.


Scheduling Routing Autonomous guided vehicles (AGVs) Path planning Network simplex algorithm (NSA) 


  1. 1.
    Das PK, Behera HS, Das S, Tripathy HK, Panigrahi BK, Pradhan SK (2016) A hybrid improved PSO-DV algorithm for multi-robot path planning in a clutter environment. Neurocomputing 207:735–753CrossRefGoogle Scholar
  2. 2.
    Das PK, Behera HS, Jena PK, Panigrahi BK (2016) Multi-robot path planning in a dynamic environment using improved gravitational search algorithm. J Electr Syst Inform Technol 3(1):295–313CrossRefGoogle Scholar
  3. 3.
    Drobouchevitch IG, Sidney JB (2012) Minimization of earliness, tardiness and due date penalties on uniform parallel machines with identical jobs. Comput Oper Res 39:1919–1926MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Fazlollahtabar H, Mahdavi-Amiri N (2013) Producer’s behavior analysis in an uncertain bicriteria AGV-based flexible jobshop manufacturing system with expert system. Int J Adv Manuf Technol 65(9/12):1605–1618CrossRefGoogle Scholar
  5. 5.
    Fazlollahtabar H, Olya MH (2013) A cross-entropy heuristic statistical modeling for determining total stochastic material handling time. Int J Adv Manuf Technol 67(5/8):1631–1641CrossRefGoogle Scholar
  6. 6.
    Fazlollahtabar H, Saidi-Mehrabad M (2015a) Methodologies to optimize automated guided vehicle scheduling and routing problems: a review study. J Intell Robot Syst 77:525–545CrossRefGoogle Scholar
  7. 7.
    Fazlollahtabar H, Saidi-Mehrabad M (2015b) Autonomous guided vehicles: Methods and models for optimal path planning. Springer International Publishing, Switzerland. ISBN 978-3-319-14746-8CrossRefGoogle Scholar
  8. 8.
    Fazlollahtabar H, Rezaie B, Kalantari H (2010) Mathematical programming approach to optimize material flow in an AGV-based flexible jobshop manufacturing system with performance analysis. Int J Adv Manuf Technol 51(9-12):1149–1158CrossRefGoogle Scholar
  9. 9.
    Fazlollahtabar H, Saidi-Mehrabad M, Balakrishnan J (2015a) Mathematical optimization for earliness/tardiness minimization in a multiple automated guided vehicle manufacturing system via integrated heuristic algorithms. Robot Auton Syst 72:131–138CrossRefGoogle Scholar
  10. 10.
    Fazlollahtabar H, Saidi-Mehrabad M, Masehian E (2015b) Mathematical model for deadlock resolution in multiple AGV scheduling and routing network: a case study. Ind Robot: Int J 42(2):252–263CrossRefGoogle Scholar
  11. 11.
    Gerstl E, Mosheiov G (2013) Scheduling problems with two competing agents to minimized weighted earlines–tardiness. Comput Oper Res 40:109–116MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Gómez JV, Vale A, Garrido S, Moreno L (2015) Performance analysis of fast marching-based motion planning for autonomous mobile robots in ITER scenarios. Robot Auton Syst 63(1):36–49CrossRefGoogle Scholar
  13. 13.
    Hamidinia A, Khakabimamaghani S, Mahdavi Mazdeh M, Jafari M (2012) A genetic algorithm for minimizing total tardiness/earliness of weighted jobs in a batched delivery system. Comput Ind Eng 62:29–38CrossRefGoogle Scholar
  14. 14.
    Jose K, Pratihar DK (2016) Task allocation and collision-free path planning of centralized multi-robots system for industrial plant inspection using heuristic methods. Robot Auton Syst 80:34–42CrossRefGoogle Scholar
  15. 15.
    Kostavelis I, Gasteratos A (2015) Semantic mapping for mobile robotics tasks: a survey. Robot Auton Syst 66:86–103CrossRefGoogle Scholar
  16. 16.
    Latombe JC (1991) Robot motion planning. Kluwer Academic Publishers, DordrechtCrossRefMATHGoogle Scholar
  17. 17.
    Lee J, Kim DW (2016) An effective initialization method for genetic algorithm-based robot path planning using a directed acyclic graph. Inf Sci 332:1–18CrossRefGoogle Scholar
  18. 18.
    M’Hallah R (2007) Minimizing total earliness and tardiness on a single machine using a hybrid heuristic. Comput Oper Res 34:3126–3142CrossRefMATHGoogle Scholar
  19. 19.
    Mac TT, Copot C, Tran DT, De Keyser R (2016) Heuristic approaches in robot path planning: a survey. Robot Auton Syst 86:13–28CrossRefGoogle Scholar
  20. 20.
    Olya MH (2014a) Applying Dijkstra’s algorithm for general shortest path problem with normal probability distribution arc length. Int J Oper Res 21(1):143–154MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Olya MH (2014b) Finding shortest path in a combined exponential–gamma probability distribution arc length. Int J Oper Res 21(1):25–37MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Roy D, Krishnamurthy A, Heragu S, Malmborg C (2015) Queuing models to analyze dwell-point and cross-aisle location in autonomous vehicle-based warehouse systems. Eur J Oper Res 242(1):72–87CrossRefMATHGoogle Scholar
  23. 23.
    Wu X, Shen W, Lou P, Wu B, Wang L, Tang D (2016) An automated guided mechatronic tractor for path tracking of heavy-duty robotic vehicles. Mechatronics 35:23–31CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Industrial Engineering, College of EngineeringDamghan UniversityDamghanIran
  2. 2.Department of Industrial EngineeringMazandaran University of Science and TechnologyBabolIran

Personalised recommendations