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Multi-Stove Scheduling for Sustainable On-Demand Food Delivery

Author

Listed:
  • Tao Dai

    (Glorious Sun School of Management, Donghua University, Shanghai 200051, China)

  • Xiangqi Fan

    (Glorious Sun School of Management, Donghua University, Shanghai 200051, China)

Abstract

Ordering food through mobile apps and crowdsourcing resources has become increasingly popular in the digital age. Restaurants can improve customer satisfaction to satisfy on-demand food orders by shortening waiting time and achieving sustainability through fuel reduction. In the present study, we construct a double-layer scheduling model, which is developed using the characteristics of on-demand food preparation, including the use of multiple stoves, a variety of dishes in one order, and the integration of the same dishes from different customers. The bottom layer is a multi-stove dish package scheduling model based on parallel machine scheduling. The upper layer is an order selection model based on the knapsack problem. To identify the optimal solution, four strategies for calculating the weight coefficient of the dish package are proposed to shorten the waiting time and realize sustainability. Numerical experiments are designed to analyze the differences of the final scheduling results under the four strategies. The bottom layer is extended to another model based on the vehicle routing optimization model, given the switch time between different dishes. The extension of the model is also compared in the numerical experiments. Our paper confirms the necessity of using a double-layer model for multi-strategy comparison in order to achieve sustainable on-demand scheduling.

Suggested Citation

  • Tao Dai & Xiangqi Fan, 2021. "Multi-Stove Scheduling for Sustainable On-Demand Food Delivery," Sustainability, MDPI, vol. 13(23), pages 1-13, November.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13133-:d:688983
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    References listed on IDEAS

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    1. Witteman, Max & Deng, Qichen & Santos, Bruno F., 2021. "A bin packing approach to solve the aircraft maintenance task allocation problem," European Journal of Operational Research, Elsevier, vol. 294(1), pages 365-376.
    2. Peng Liu & Jun Lv & Tao Jiang & Xudong Chai, 2020. "Equilibrium Joining Strategies of Delay-Sensitive Customers in a Queueing System with Service Quality Feedback," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-11, February.
    3. de Lima, Vinícius L. & Alves, Cláudio & Clautiaux, François & Iori, Manuel & Valério de Carvalho, José M., 2022. "Arc flow formulations based on dynamic programming: Theoretical foundations and applications," European Journal of Operational Research, Elsevier, vol. 296(1), pages 3-21.
    4. Lotfi Hidri & Ali Alqahtani & Achraf Gazdar & Belgacem Ben Youssef, 2021. "Green Scheduling of Identical Parallel Machines with Release Date, Delivery Time and No-Idle Machine Constraints," Sustainability, MDPI, vol. 13(16), pages 1-30, August.
    5. Chen, Cheng & Demir, Emrah & Huang, Yuan, 2021. "An adaptive large neighborhood search heuristic for the vehicle routing problem with time windows and delivery robots," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1164-1180.
    6. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    7. Naderi, Bahman & Roshanaei, Vahid, 2020. "Branch-Relax-and-Check: A tractable decomposition method for order acceptance and identical parallel machine scheduling," European Journal of Operational Research, Elsevier, vol. 286(3), pages 811-827.
    8. Yepes-Borrero, Juan C. & Perea, Federico & Ruiz, Rubén & Villa, Fulgencia, 2021. "Bi-objective parallel machine scheduling with additional resources during setups," European Journal of Operational Research, Elsevier, vol. 292(2), pages 443-455.
    9. Jun-Ho Lee & Hoon Jang, 2019. "Uniform Parallel Machine Scheduling with Dedicated Machines, Job Splitting and Setup Resources," Sustainability, MDPI, vol. 11(24), pages 1-23, December.
    10. Baldacci, Roberto & Mingozzi, Aristide & Roberti, Roberto, 2012. "Recent exact algorithms for solving the vehicle routing problem under capacity and time window constraints," European Journal of Operational Research, Elsevier, vol. 218(1), pages 1-6.
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