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Ageel Abdulaziz Alogla
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RESEARCH ARTICLE

Dynamic scheduling of hybrid additive manufacturing–injection molding for surge capacity in polymer supply chains

Ageel Abdulaziz Alogla1*
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1 Department of Mechanical and Industrial Engineering, College of Engineering and Computing at Al-Qunfudhah, Umm Al Qura University, Makkah , Saudi Arabia
IJOCTA, 026260137 https://doi.org/10.36922/IJOCTA026260137
Received: 27 June 2026 | Revised: 21 August 2026 | Accepted: 24 August 2026 | Published online: 4 September 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Demand volatility creates a persistent challenge for manufacturers seeking to balance the cost efficiency of conventional high-volume production with the responsiveness offered by additive manufacturing. This study develops a data-driven decision-support framework to deploy additive manufacturing (AM) as strategic surge capacity in a polymer pipe-fitting supply chain facing volatile demand. Following a 48-month screening of four unplasticized polyvinyl chloride tee joints, the 3/4-in. variant was selected, and a validated stream of 77 stochastic orders was used in a bi-objective order-assignment model that minimizes production cost and tardiness across injection molding (IM) and AM. The novelty lies in combining order-level hybrid routing, build-level parallel AM capacity, practical heuristic benchmarks, due-window sensitivity analysis, and a multi-run stability assessment within one industrially grounded framework. Under the baseline 48 h service target, the IM-only approach cost the least (EUR 296,396.19) but produced 244.15 h of tardiness and 23 late orders, whereas the AM-only approach eliminated tardiness at EUR 1,008,780.00. The non-dominated sorting genetic algorithm II Pareto frontier identifies a balanced solution that assigns 12 peak orders to AM, reduces tardiness by 69.2%, and improves on-time service from 70.1% to 85.7% at an 85.9% cost premium over the IM-only approach. The zero-tardiness endpoint requires 23 AM-assigned orders and costs EUR 759,250.77. Sensitivity analysis shows that the value of hybridization depends on the responsiveness requirement: at 24 h, AM capacity is stressed, whereas at 72 h or longer, the IM-only approach meets all due dates. The results therefore position AM as a selective surge capacity rather than a universal substitute for IM.

Graphical abstract
Keywords
Additive manufacturing
Injection molding
Hybrid manufacturing
Demand volatility
Non-dominated sorting genetic algorithm II
Multi-objective optimization
Pipe fittings
Smart manufacturing
Funding
This research work was funded by Umm Al-Qura University, Saudi Arabia under grant number 26UQU4340306GSSR03.
Conflict of interest
The author declares no competing interests.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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