Formulating and solving the integrated online order batching and delivery planning with specific due dates for orders

Today with the outbreak of the COVID-19 many people prefer to stay home and buy their required products from online sellers and receive them in their home or office at their desired times. This change has increased the workload of online retailers. In an online retailing system, lots of orders containing different products arrive dynamically and must be delivered in the due dates requested by customers, so there is a limited time to retrieve products from their storage locations, pack them, load them on trucks, and deliver to their destinations. In this study, we deal with the integrated order batching and delivery planning of an online retailer that stores a variety of products in a warehouse and sells them online. A mixed-integer nonlinear programming model is proposed that decides on order batching, scheduling of batches, assigning orders to trucks, and scheduling and routing of trucks simultaneously in an offline setting. This model clarifies the domain of the problem and its complexity. Two rule-based heuristic algorithms are developed to solve the problem in the online setting. The first algorithm deals with two sub-problems of order batching and delivery planning separately and sequentially, while the second algorithm considers the relationship between two sub-problems. An extensive numerical experiment is carried out to evaluate the performance of algorithms in different problem sizes, demonstrating that the second algorithm by integrating two sub-problems leads to a minimum of 14% reduction in cost per delivered order, as the main finding of this study. Finally, the effect of several parameters on the performance of algorithms is analyzed through a sensitivity analysis, and some managerial insights are provided to help the retail managers with their decision-making that are the other findings of this paper.

• Publication year

  2021

• Venue

  Journal of Intelligent & Fuzzy Systems

• Publication date

  2021-01-27

• Fields of study

  Medicine, Business, Engineering, Computer Science

• Identifiers
  DOI 10.3233/JIFS-201690
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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