Short answer

Implement a dynamic system that considers both order acceptance and scheduling simultaneously, guided by well-defined decision-making strategies, to maximize profit in on-demand additive manufacturing.

Field: Commercial Production
Source: The International Journal of Advanced Manufacturing Technology (2019)
Method: Metaheuristic decision-making approach with experimental study
Evidence: Strong effect

A metaheuristic approach for simultaneously accepting and scheduling on-demand orders in additive manufacturing can significantly increase average profit per unit time. This commercial production research insight is drawn from a 2019 study published in The International Journal of Advanced Manufacturing Technology. Using Metaheuristic decision-making approach with experimental study, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Implement a dynamic system that considers both order acceptance and scheduling simultaneously, guided by well-defined decision-making strategies, to maximize profit in on-demand additive manufacturing.

Study

Commercial ProductionHigh ImpactStrong effect

Dynamic order acceptance and scheduling boosts additive manufacturing profitability

A metaheuristic approach for simultaneously accepting and scheduling on-demand orders in additive manufacturing can significantly increase average profit per unit time.

The International Journal of Advanced Manufacturing Technology · 2019

Key Findings

01It is practicable to achieve promising profitability in AM on-demand production.
02A properly designed decision-making strategy within a metaheuristic approach is crucial for success.

Application

Design takeaway

How to apply

Develop or adopt a software solution that integrates order intake with real-time scheduling capabilities, using algorithms that can adapt to incoming orders and production constraints to prioritize profitable jobs.

Project actions

01When designing a production system, consider how to make real-time decisions about orders.
02Explore how different algorithms can help manage dynamic workloads.

Method & Evidence

AimTo develop and evaluate a dynamic order acceptance and scheduling (OAS) approach for additive manufacturing (AM) to maximize average profit per unit time.

MethodMetaheuristic decision-making approach with experimental study

ProcedureThe study proposed a strategy-based metaheuristic approach to address the complex problem of simultaneously accepting and scheduling dynamic incoming orders in AM production. The performance of different strategy sets was then evaluated through a comprehensive experimental study.

ContextAdditive manufacturing (AM) on-demand production, specifically Powder Bed Fusion (PBF) systems.

Variables

IVDecision-making strategies within the metaheuristic approach.

DVAverage profit per unit time.

CVAdditive manufacturing system type (PBF), order characteristics (profit, processing time), makespan.

Strengths & Limitations

Strengths

+Addresses a complex and highly relevant problem in modern manufacturing.
+Proposes a practical, strategy-based metaheuristic solution.
+Validates the approach through comprehensive experimental studies.

Limitations

The computational complexity of real-time dynamic scheduling can be a challenge for simpler systems. The effectiveness of the chosen metaheuristic strategy is critical and may require significant tuning.

Reliability & validity

The study's validity relies on the comprehensiveness of its experimental setup and the robustness of the metaheuristic algorithm. Reliability would be demonstrated by consistent performance across various simulated scenarios and strategy sets.

Think critically

How might the 'prosumer' model, enabled by additive manufacturing, further complicate dynamic order acceptance and scheduling due to potentially unpredictable order volumes and specifications?

Design Principles

"Optimize dynamic order acceptance and scheduling to maximize throughput and profitability in on-demand production environments."

Effective order management is critical for additive manufacturing (AM) facilities, especially those operating on-demand. This research offers a practical method to optimize decisions, leading to better resource utilization and financial returns.

What This Means for Your Design

This study shows that by using a smart computer system to decide which 3D printing jobs to take and when to do them, companies can make more money.

How to use in your project

1.This research can inform the development of a simulation model for a production system, where different scheduling strategies are tested for their impact on profit.

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Quick Cite

Paragraph starter

The research by Li et al. (2019) highlights the critical role of dynamic order acceptance and scheduling (OAS) in maximizing profitability for on-demand additive manufacturing. Their proposed metaheuristic approach demonstrates that a well-designed strategy for simultaneously managing incoming orders and production schedules can lead to significant improvements in profit per unit time, addressing the inherent complexity of such systems.

Source

The International Journal of Advanced Manufacturing Technology

A dynamic order acceptance and scheduling approach for additive manufacturing on-demand production

journal · 2019

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Questions About This Research

What does the research say about dynamic order acceptance and scheduling boosts additive manufacturing profitability?: Implement a dynamic system that considers both order acceptance and scheduling simultaneously, guided by well-defined decision-making strategies, to maximize profit in on-demand additive manufacturing. Evidence: The International Journal of Advanced Manufacturing Technology (2019).
Why does "Dynamic order acceptance and scheduling boosts additive manufacturing profitability" matter for design?: Effective order management is critical for additive manufacturing (AM) facilities, especially those operating on-demand. This research offers a practical method to optimize decisions, leading to better resource utilization and financial returns.
How can designers apply this research?: Implement a dynamic system that considers both order acceptance and scheduling simultaneously, guided by well-defined decision-making strategies, to maximize profit in on-demand additive manufacturing.
What were the main findings?: It is practicable to achieve promising profitability in AM on-demand production.. A properly designed decision-making strategy within a metaheuristic approach is crucial for success.
What research method was used?: Metaheuristic decision-making approach with experimental study.
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2019 journal from The International Journal of Advanced Manufacturing Technology.
What should I do differently in my next project?: Develop or adopt a software solution that integrates order intake with real-time scheduling capabilities, using algorithms that can adapt to incoming orders and production constraints to prioritize profitable jobs.
What are the limitations?: The complexity of the problem (NP hard) means that optimal solutions may not always be found, and the effectiveness of the metaheuristic approach can depend heavily on the quality of the chosen strategies.