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A cross-entropy method for optimising robotic automated storage and retrieval systems

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  • Mehdi Foumani
  • Asghar Moeini
  • Michael Haythorpe
  • Kate Smith-Miles

Abstract

In this paper, we consider a robotic automated storage and retrieval system (AS/RS) where a Cartesian robot picks and palletises items onto a mixed pallet for any order. This robotic AS/RS not only retrieves orders in an optimal sequence, but also creates an optimal store ready pallet of any order. Adapting the Travelling Salesman Problem to warehousing, the decision to be made includes finding the optimal sequence of orders, and optimal sequence of items inside each order, that jointly minimise total travel times. In the first phase, as a control problem, we develop an avoidance strategy for the robot (or automatic stacker crane) movement sequence. This approach detects the collision occurrence causing unsafe handling of hazardous items and prevents the occurrence of it by a collision-free robot movement sequence. Due to the complexity of the problem, the second phase is attacked by a Cross-Entropy (CE) method. To evaluate the performance of the CE method, a computational analysis is performed over various test problems. The results obtained from the CE method are compared to those of the optimal solutions obtained using CPLEX. The results indicate high performance of the solution procedure to solve the sequencing problem of robotic AS/RSs.

Suggested Citation

  • Mehdi Foumani & Asghar Moeini & Michael Haythorpe & Kate Smith-Miles, 2018. "A cross-entropy method for optimising robotic automated storage and retrieval systems," International Journal of Production Research, Taylor & Francis Journals, vol. 56(19), pages 6450-6472, October.
  • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:19:p:6450-6472
    DOI: 10.1080/00207543.2018.1456692
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    Citations

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    Cited by:

    1. Ratko Stanković & Kristijan Rogić & Mario Šafran, 2022. "Saving Energy by Optimizing Warehouse Dock Door Allocation," Energies, MDPI, vol. 15(16), pages 1-14, August.
    2. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    3. Zhi Li & Ali Vatankhah Barenji & Jiazhi Jiang & Ray Y. Zhong & Gangyan Xu, 2020. "A mechanism for scheduling multi robot intelligent warehouse system face with dynamic demand," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 469-480, February.
    4. Yi Li & Zhiyang Li, 2022. "Shuttle-Based Storage and Retrieval System: A Literature Review," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    5. Polten, Lukas & Emde, Simon, 2022. "Multi-shuttle crane scheduling in automated storage and retrieval systems," European Journal of Operational Research, Elsevier, vol. 302(3), pages 892-908.
    6. Bibi Aamirah Shafaa Emambocus & Muhammed Basheer Jasser & Angela Amphawan & Ali Wagdy Mohamed, 2022. "An Optimized Discrete Dragonfly Algorithm Tackling the Low Exploitation Problem for Solving TSP," Mathematics, MDPI, vol. 10(19), pages 1-24, October.
    7. Florin Leon & Marius Gavrilescu, 2021. "A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving," Mathematics, MDPI, vol. 9(6), pages 1-37, March.
    8. Janusz Szpytko & Yorlandys Salgado Duarte, 2021. "A digital twins concept model for integrated maintenance: a case study for crane operation," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1863-1881, October.
    9. Baniasadi, Pouya & Foumani, Mehdi & Smith-Miles, Kate & Ejov, Vladimir, 2020. "A transformation technique for the clustered generalized traveling salesman problem with applications to logistics," European Journal of Operational Research, Elsevier, vol. 285(2), pages 444-457.

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