Heuristic Techniques for Real-Time Order Acceptance and Scheduling in Metal Additive Manufacturing

Li, Qiang; Zhang, David; Küçükkoç, İbrahim; He, Naihui

doi:10.1007/978-3-030-37062-6_1

Cited by 1 publication

(1 citation statement)

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“…The objective was to minimize the total delay of the parts to be produced and to maximize the use rate of the additive manufacturing machines [8]. In another research, Li et al considered a real-time order acceptance and scheduling problem in a metal additive manufacturing production environment, where the manufacturer with multiple machines makes decisions on the acceptance and scheduling of dynamic arriving part orders simultaneously [9]. Rohaninejad et al investigated a scheduling problem in an additive manufacturing system with nonidentical parallel selective laser melting machines [10].…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Scheduling Algorithm in Cloud-Based Additive Manufacturing; an Approach to Using Critical Chain Buffer Management Technique

Zohrehvandi,

Knapčíková

2024

Preprint

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In the past few years, cloud-based additive manufacturing has shown its application and potential in industries. For this reason, when the demand for cloud-based additive products increases, efficient scheduling of related tasks becomes more important to achieve optimized duration. This research proposes a Heuristic Scheduling Algorithm (HSA) for use in Cloud-Based Additive Manufacturing (CBAM) with an approach to Critical Chain Buffer Management (CC/BM). This research aims to schedule and optimize the time in cloud-based additive manufacturing, in addition to the parts production process, it also includes the steps of receiving online orders and informing the manufacturing unit and sending them to the customer. The HSA proposed in this research consists of three phases 1- Buffer sizing, 2- Additive manufacturing and logistics scheduling, and 3- Buffer controlling. To evaluate, the performance of the algorithm is simulated. The finding shows that the proposed HAS can effectively solve the CBAM scheduling problem.

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Section: Introductionmentioning

confidence: 99%