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Intelligent Vehicle Scheduling and Routing for a Chain of Retail Stores: A Case Study of Dhaka, Bangladesh

Author

Listed:
  • M. Azizur Rahman

    (Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh)

  • Al-Amin Hossain

    (Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh)

  • Binoy Debnath

    (Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh)

  • Zinnat Mahmud Zefat

    (Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh)

  • Mohammad Sarwar Morshed

    (Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh)

  • Ziaul Haq Adnan

    (Department of Management, North South University, Dhaka 1229, Bangladesh)

Abstract

Background : Retail chains aim to maintain a competitive advantage by ensuring product availability and fulfilling customer demand on-time. However, inefficient scheduling and vehicle routing from the distribution center may cause delivery delays and, thus, stock-outs on the store shelves. Therefore, optimization of vehicle routing can play a vital role in fulfilling customer demand. Methods : In this research, a case study is formulated for a chain of retail stores in Dhaka City, Bangladesh. Orders from various stores are combined, grouped, and scheduled for Region-1 and Region-2 of Dhaka City. The ‘vehicle routing add-on’ feature of Google Sheets is used for scheduling and navigation. An android application, Intelligent Route Optimizer, is developed using the shortest path first algorithm based on the Dijkstra algorithm. The vehicle navigation scheme is programmed to change the direction according to the shortest possible path in the google map generated by the intelligent routing optimizer. Results : With the application, the improvement of optimization results is evident from the reductions of traveled distance (8.1% and 12.2%) and time (20.2% and 15.0%) in Region-1 and Region-2, respectively. Conclusions : A smartphone-based application is developed to improve the distribution plan. It can be utilized for an intelligent vehicle routing system to respond to real-time traffic; hence, the overall replenishment process will be improved.

Suggested Citation

  • M. Azizur Rahman & Al-Amin Hossain & Binoy Debnath & Zinnat Mahmud Zefat & Mohammad Sarwar Morshed & Ziaul Haq Adnan, 2021. "Intelligent Vehicle Scheduling and Routing for a Chain of Retail Stores: A Case Study of Dhaka, Bangladesh," Logistics, MDPI, vol. 5(3), pages 1-21, September.
    • Handle: RePEc:gam:jlogis:v:5:y:2021:i:3:p:63-:d:634792
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    References listed on IDEAS

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    1. Muhammad Azmat & Sebastian Kummer & Lara Trigueiro Moura & Federico Di Gennaro & Rene Moser, 2019. "Future Outlook of Highway Operations with Implementation of Innovative Technologies Like AV, CV, IoT and Big Data," Logistics, MDPI, vol. 3(2), pages 1-20, June.
    2. Stefan Ropke & David Pisinger, 2006. "An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows," Transportation Science, INFORMS, vol. 40(4), pages 455-472, November.
    3. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    4. Stavros T. Ponis & Orestis K. Efthymiou, 2020. "Cloud and IoT Applications in Material Handling Automation and Intralogistics," Logistics, MDPI, vol. 4(3), pages 1-17, September.
    5. Phan Nguyen Ky Phuc & Nguyen Le Phuong Thao, 2021. "Ant Colony Optimization for Multiple Pickup and Multiple Delivery Vehicle Routing Problem with Time Window and Heterogeneous Fleets," Logistics, MDPI, vol. 5(2), pages 1-13, May.
    6. Ghiani, Gianpaolo & Guerriero, Francesca & Laporte, Gilbert & Musmanno, Roberto, 2003. "Real-time vehicle routing: Solution concepts, algorithms and parallel computing strategies," European Journal of Operational Research, Elsevier, vol. 151(1), pages 1-11, November.
    7. Kaffash, Sepideh & Nguyen, An Truong & Zhu, Joe, 2021. "Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 231(C).
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