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Integrated Tomato Picking and Distribution Scheduling Based on Maturity

Author

Listed:
  • Anqi Zhu

    (School of Information Management, Nanjing Agricultural University, Nanjing 210031, China)

  • Bei Bian

    (School of Information Management, Nanjing Agricultural University, Nanjing 210031, China)

  • Yiping Jiang

    (School of Information Management, Nanjing Agricultural University, Nanjing 210031, China)

  • Jiaxiang Hu

    (College of Economics and Management, Nanjing Agricultural University, Nanjing 210095, China
    China Center for Food Security Studies, Nanjing Agricultural University, Nanjing 210095, China)

Abstract

Agriproducts have the characteristics of short lifespan and quality decay due to the maturity factor. With the development of e-commerce, high timelines and quality have become a new pursuit for agriproduct online retailing. To satisfy the new demands of customers, reducing the time from receiving orders to distribution and improving agriproduct quality are significantly needed advancements. In this study, we focus on the joint optimization of the fulfillment of online tomato orders that integrates picking and distribution simultaneously within the context of the farm-to-door model. A tomato maturity model with a firmness indicator is proposed firstly. Then, we incorporate the tomato maturity model function into the integrated picking and distribution schedule and formulate a multiple-vehicle routing problem with time windows. Next, to solve the model, an improved genetic algorithm (the sweep-adaptive genetic algorithm, S-AGA) is addressed. Finally, we prove the validity of the proposed model and the superiority of S-AGA with different numerical experiments. The results show that significant improvements are obtained in the overall tomato supply chain efficiency and quality. For instance, tomato quality and customer satisfaction increased by 5% when considering the joint optimization, and the order processing speed increased over 90% compared with traditional GA. This study could provide scientific tomato picking and distribution scheduling to satisfy the multiple requirements of consumers and improve agricultural and logistics sustainability.

Suggested Citation

  • Anqi Zhu & Bei Bian & Yiping Jiang & Jiaxiang Hu, 2020. "Integrated Tomato Picking and Distribution Scheduling Based on Maturity," Sustainability, MDPI, vol. 12(19), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:7934-:d:419472
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    References listed on IDEAS

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    1. Behzadi, Golnar & O’Sullivan, Michael Justin & Olsen, Tava Lennon & Zhang, Abraham, 2018. "Agribusiness supply chain risk management: A review of quantitative decision models," Omega, Elsevier, vol. 79(C), pages 21-42.
    2. Liu, Hengyu & Zhang, Juliang & Zhou, Chen & Ru, Yihong, 2018. "Optimal purchase and inventory retrieval policies for perishable seasonal agricultural products," Omega, Elsevier, vol. 79(C), pages 133-145.
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    5. Kangzhou Wang & Shulin Lan & Yingxue Zhao, 2017. "A genetic-algorithm-based approach to the two-echelon capacitated vehicle routing problem with stochastic demands in logistics service," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1409-1421, November.
    6. Ahumada, Omar & Villalobos, J. Rene, 2009. "Application of planning models in the agri-food supply chain: A review," European Journal of Operational Research, Elsevier, vol. 196(1), pages 1-20, July.
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    Cited by:

    1. Linlu Zu & Mingzheng Han & Jiuqin Liu & Pingzeng Liu & Tianhua Li & Fei Su, 2022. "Design and Experiment of Nondestructive Post-Harvest Device for Tomatoes," Agriculture, MDPI, vol. 12(8), pages 1-19, August.
    2. Yongmao Xiao & Jincheng Zhou & Xiaoyong Zhu & Fajun Yu, 2022. "Research on Optimization Method and Algorithm Design of Green Simultaneous Pick-up and Delivery Vehicle Scheduling under Uncertain Demand," Sustainability, MDPI, vol. 14(19), pages 1-25, October.

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