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Improving Energy Consumption and Order Tardiness in Picker-to-Part Warehouses with Electric Forklifts: A Comparison of Four Evolutionary Algorithms

Author

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  • Ahmad Ebrahimi

    (Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USA)

  • Hyun-woo Jeon

    (Department of Industrial & Management Systems Engineering, Kyung Hee University, Yongin-si 17104, Republic of Korea)

  • Sang-yeop Jung

    (Department of Industrial & Management Systems Engineering, Kyung Hee University, Yongin-si 17104, Republic of Korea)

Abstract

Improving energy consumption (EC) and order tardiness (OT) for a warehouse picker-to-parts system is a challenging task since these two objectives are interrelated in a complex way with forklift activities. Thus, this research aims to minimize EC and OT with a multi-objective mixed-integer mathematical model by considering electric forklift operations. The proposed model addresses a lack of studies by controlling (i) order batching, (ii) batch assignment, (iii) batch sequencing, (iv) forklift routing, and (v) forklift battery charging schedule. The feasibility of the presented mathematical model is validated by solving small-sized examples. To solve medium- to large-sized case studies, we also propose and compare four multi-objective evolutionary algorithms (MOEAs). In illustrative examples, this study identifies the number of battery charging, orders, and forklifts as significant parameters affecting EC and OT. Our analysis also provides regression models connecting EC and OT from Pareto-optimal frontiers, and these results can help industrial practitioners and academic researchers find and investigate the relationship between EC and OT for making relevant decisions in warehouses served by electric forklifts. Among the four MOEAs developed, we show that the NSGA-II non-dominated sorting variable neighborhood search dynamic learning strategy (NSGA-VNS-DLS) outperforms other algorithms in accuracy, diversity, and CPU time.

Suggested Citation

  • Ahmad Ebrahimi & Hyun-woo Jeon & Sang-yeop Jung, 2023. "Improving Energy Consumption and Order Tardiness in Picker-to-Part Warehouses with Electric Forklifts: A Comparison of Four Evolutionary Algorithms," Sustainability, MDPI, vol. 15(13), pages 1-28, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10551-:d:1186887
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    References listed on IDEAS

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    1. Žulj, Ivan & Kramer, Sergej & Schneider, Michael, 2018. "A hybrid of adaptive large neighborhood search and tabu search for the order-batching problem," European Journal of Operational Research, Elsevier, vol. 264(2), pages 653-664.
    2. Ardjmand, Ehsan & Shakeri, Heman & Singh, Manjeet & Sanei Bajgiran, Omid, 2018. "Minimizing order picking makespan with multiple pickers in a wave picking warehouse," International Journal of Production Economics, Elsevier, vol. 206(C), pages 169-183.
    3. Chen, Tzu-Li & Cheng, Chen-Yang & Chen, Yin-Yann & Chan, Li-Kai, 2015. "An efficient hybrid algorithm for integrated order batching, sequencing and routing problem," International Journal of Production Economics, Elsevier, vol. 159(C), pages 158-167.
    4. Fekete, Patrick & Lim, Sirirat & Martin, Steve & Kuhn, Katja & Wright, Nick, 2016. "Improved energy supply for non-road electric vehicles by occasional charging station location modelling," Energy, Elsevier, vol. 114(C), pages 1033-1040.
    5. Raffaele Carli & Mariagrazia Dotoli & Salvatore Digiesi & Francesco Facchini & Giorgio Mossa, 2020. "Sustainable Scheduling of Material Handling Activities in Labor-Intensive Warehouses: A Decision and Control Model," Sustainability, MDPI, vol. 12(8), pages 1-25, April.
    6. Henn, Sebastian & Wäscher, Gerhard, 2012. "Tabu search heuristics for the order batching problem in manual order picking systems," European Journal of Operational Research, Elsevier, vol. 222(3), pages 484-494.
    7. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Pasdeloup, Bastien & Meyer, Patrick, 2023. "Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1296-1330.
    8. İbrahim Muter & Temel Öncan, 2015. "An exact solution approach for the order batching problem," IISE Transactions, Taylor & Francis Journals, vol. 47(7), pages 728-738, July.
    9. Zhengyu Yao & Hwan-Sik Yoon & Yang-Ki Hong, 2023. "Control of Hybrid Electric Vehicle Powertrain Using Offline-Online Hybrid Reinforcement Learning," Energies, MDPI, vol. 16(2), pages 1-18, January.
    10. Yang, Peng & Zhao, Zhijie & Guo, Huijie, 2020. "Order batch picking optimization under different storage scenarios for e-commerce warehouses," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    11. Jianbin Li & Rihuan Huang & James B. Dai, 2017. "Joint optimisation of order batching and picker routing in the online retailer’s warehouse in China," International Journal of Production Research, Taylor & Francis Journals, vol. 55(2), pages 447-461, January.
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    Cited by:

    1. Hyun-woo Jeon & Ahmad Ebrahimi & Ga-hyun Lee, 2023. "A Simulation-Based Experimental Design for Analyzing Energy Consumption and Order Tardiness in Warehousing Systems," Sustainability, MDPI, vol. 15(20), pages 1-25, October.

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