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Modeling and optimization for static-dynamic routing of a vehicle with additive manufacturing equipment

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  • Cui, Weiwei
  • Yang, Yiran
  • Di, Lei

Abstract

Additive manufacturing (AM) offers apparent advantages compared with traditional subtractive manufacturing, such as enhanced manufacturing capability and complexity, increased design freedom, and reduced production time/cost. This has subsequently affected the behavior and acquisition of different participants in the supply chain. In this paper, an AM-enabled supply chain with an integrated production-inventory-transportation (PIT) structure is studied focusing on delivery route design. Specifically, two different orders are considered including the orders received or accumulated prior to the studied workday and the orders received in real-time during the workday. To accommodate such a unique hybrid static-dynamic operation, two mathematical models are established to address the offline static and online dynamic optimization. Two optimization algorithms (one based on dynamic programming and the other one based on heuristic local search) are designed to solve the offline mixed-integer linear model; An agent-based rule with high efficiency is designed to solve the online problem. Numerical case studies show that it is feasible to leverage AM to achieve same-day delivery where the majority of the orders can be delivered within 3 h. In addition, case study results also show that service radius and product complexity level have a great impact on the averaged delivery cost per order, indicating the need for properly coordinating the visiting/fabricating sequence as opposed to simply adopting the first-order-first-delivery policy. The results of this paper will provide useful insights on how to incorporate AM in an integrated PIT supply chain in an effective, efficient manner.

Suggested Citation

  • Cui, Weiwei & Yang, Yiran & Di, Lei, 2023. "Modeling and optimization for static-dynamic routing of a vehicle with additive manufacturing equipment," International Journal of Production Economics, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:proeco:v:257:y:2023:i:c:s0925527322003383
    DOI: 10.1016/j.ijpe.2022.108756
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    References listed on IDEAS

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