2019 conference paper
Impact of scheduling policies on the performance of an additive manufacturing production system
Procedia Manufacturing, 39, 447–456.
Contributors: M. Kapadia n, B. Starly n, A. Thomas n, R. Uzsoy n & D. Warsing n
author keywords: Iterative Optimization-based Simulation; Additive Manufacturing; Genetic Algorithm
TL;DR:
Simulation experiments using an Iterative Optimization-based Simulation (IOS) model integrating a simulation engine with a computational engine show that the scheduling policies have a significant impact on cycle time and order lateness.
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open access via doi.org (publisher)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure
(Web of Science)
Source: ORCID
Added: October 23, 2020
We study the impact of scheduling policies on the cycle time and throughput of an Additive Manufacturing (AM) facility. Orders for irregularly shaped parts with specified due dates and surface quality requirements arrive randomly at the facility. The parts are fabricated in one of several AM build chambers, followed by post-processing operations based on surface finishing requirements. We combine a part placement heuristic with different scheduling policies (FIFO, Earliest Due Date) to allocate parts to AM build chambers and lay out the parts within the chambers to approximately optimize delivery performance. Simulation experiments using an Iterative Optimization-based Simulation (IOS) model integrating a simulation engine (implemented in SIMIO) with a computational engine (implemented in MATLAB) show that the scheduling policies have a significant impact on cycle time and order lateness. Selection and peer review under the responsibility of ICPR25 International Scientific & Advisory and Organizing Committee members.