Better On-Demand Production and Scheduling in Additive Manufacturing


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International researchers have come together to discuss challenges in meeting the needs of ‘prosumers,’ in additive manufacturing production around the world—particularly in relation to powder bed fusion systems. In ‘A dynamic order acceptance and scheduling approach for additive manufacturing on-demand production,’ authors David Zhang, Shilong Wang, and Ibrahim Kucukkoc examine new ways that manufacturers can schedule orders and maximize profits.

Currently, there are challenges due to ‘multiple interactional subproblems’ such as

  • Bin packing
  • Batch processing
  • Dynamic scheduling
  • Decision making

The researchers experiment with different ideas for a higher-level strategy to improve on-demand production and streamline the decision-making process, explaining that it all begins with the mastery of operations: preparation, productions, collection. Jobs must be set up, parts made, and then delivered. There is much to be considered though by individual service providers, and the researchers delve further into how they can maximize their business when considering typical issues like order acceptance and scheduling of batch processing machines to complete the work at hand.

Illustration of the general production process with a powder bed fusion system

“It is vitally important to appropriately determine which part orders should be accepted and how they should be scheduled simultaneously so as to maximize the average profit-per-unit-time corresponding to the whole makespan,” state the authors.

As is common in so many different types of commerce, issues arise when the company is becoming overwhelmed with orders and a lack of clear organization. Production planning is key, but these types of businesses are new in most cases, and with little previous roadmap to work from as they engage in new technology, new materials, and a new type of customer.

“Although various approaches have been developed for various OAS problems and BPP problems, it is hard to adopt these approaches directly in on-demand production with PBF systems due to the unique nature of AM production,” state the researchers, who realize the complicated nature of scheduling in PBF systems.

The overall objective is to make everyone happy—from the customer who makes an order and receives a quality product in a timely manner, to the company able to function in a structured profitable system with good workflow. The authors explain that the makespan includes the difference between the last completion time and the rest of the projects scheduled. The bottom line is fairly to easy to calculate, consisting of powder material, the cost of layering, and the cost of setting up the job.

There are constraints, which play a major role in organizational problems and decision-making, with the most central limitation being capacity of the AM machine.

“A part order can be assigned to a job on the machine only when it can be placed on the machine’s building platform without overlapping with other parts already assigned to this job and the height of the part must be smaller than the maximum height supported by the machine,” explain the authors.

“As the extreme case is that all the part orders are assigned to one machine and each AM job only processes one part, the number of all scheduled jobs should be no more than the number of all the part orders. Also, an AM machine can only handle one AM job at a time; thus, the AM jobs should be scheduled to the machine in sequence. That is, the second AM job cannot be scheduled if the first job on the machine has not been scheduled yet. Meanwhile, a scheduled AM job can be started only when the previous job has been completed on this machine.”

The researchers proposed that the AM machine should be able to produce a reasonable number of completed jobs within a reasonable amount of time—but strategy is key to narrowing down the maximum net profit.

Available time slot for an AM job in scheduling

For the study, the researchers created six different decision-making strategy sets overall, ‘based on the influence of selective behaviors on the scheduling results.’ Ultimately, they found that with their proposed operation plans, business owners and service providers should be able to follow through with suitable production procedures, while making sought-after net profits. They foresee the strategies and approach as realistic for true industrial practices with ‘limited modification.’

“The problem will be more complex and challenging where the service providers should make attractive offers to compete for as many profitable orders as possible to maximize the average profit-per-unit-time while the decision on acceptance of offers would be made by customers,” conclude the researchers. “Therefore, the decision-making strategies for both service providers and customers should be investigated and a novel simulation-based heuristic approach needs to be developed to solve the problem efficiently.”

Commerce related to 3D printing and additive manufacturing offers a great way for those interested in the technology to help others and earn a living, while many users want to design and enjoy fabricated products without investing in expensive hardware. Many different companies and organizations have turned to service bureaus, and many suppliers of such services offer niche products like medical models or electronics. What do you think of this news? Let us know your thoughts! Join the discussion of this and other 3D printing topics at

[Source / Images: A dynamic order acceptance and scheduling approach for additive manufacturing on-demand production]


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