52 ISE Magazine | www.iise.org/ISEmagazine
Inside IISE Journals
How can network design save infants’ lives?
Recently, many people have reluctantly become public
health experts. Concepts such as PCR testing, viral load and
reproduction number are now common knowledge. The
global COVID-19 pandemic demonstrated the importance
of an efficient and rapid PCR testing and diagnostic process.
It also highlighted the many logistical and operational chal-
lenges that arise when testing resources are limited.
However, these challenges are not unique to the pandem-
ic. PCR technology has been used for many years to diag-
nose HIV-exposed infants and monitor HIV progression in
older patients. The probability of death for untreated infected
infants exceeds 30% during their first year of life and reaches
60% by age 3. Thus, early diagnosis and treatment of new-
borns with HIV is pivotal to reducing mortality; however, its
implementation is nontrivial.
Motivated by logistics challenges, especially those that pre-
vail in eastern and southern Africa, Reut Noham, Michal
Tzur and Dan Yamin from Tel-Aviv University developed
a mathematical model that aims to determine the optimal
design of sample referral networks. In their paper, “An Indi-
rect Prioritization Approach to Optimizing Sample Referral
Networks for HIV Early Infant Diagnosis,” they propose ap-
plying an indirect samples prioritization approach by restruc-
turing the referral network so that clinics with an elevated
risk to positive results are referred to labs that are less over-
loaded. They show that aggregated data at the clinic level
can be used to shorten the diagnosis time of positive samples,
thereby saving lives of infants via earlier treatment.
The indirect prioritization approach facilitates the use of
the simple and easy-to-implement first-come, first-served
queuing policy at the labs. This policy helps avoid political
challenges – for example, pri-
oritizing deprived areas – or
fairness concerns that may
arise when implementing
direct prioritization. With
no need for comprehensive
data collection and analysis,
policymakers can use the
model or a simplified sug-
gested heuristic to balance
the trade-off between shipping and waiting times.
The suggested approach may imply an advancement to-
ward the UNAIDS (Joint United Nations Programme on
HIV/AIDS) goals that have implications for helping elimi-
nate the AIDS epidemic. Moreover, the results of the paper
are applicable to other systems in which expensive equipment
or limited personnel is required to perform certain opera-
CONTACT: Reut Noham; reutbon@gmail.com; Department of Industrial
Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv
University, P.O. Box 39040, Tel Aviv 6997801, Israel
This month we highlight two articles in IISE Transactions. The first article attempts to address the logistical and operational
challenges that arise when testing resources are limited. The authors’ research leads to optimal designs of sample referral
networks in HIV early infant treatment. They show that aggregated data at the clinic level can be utilized to shorten the
diagnosis time of positive samples, thereby saving lives of infants via earlier treatment. The authors of the second article
ask: Can biofuels be produced profitably? Their question comes after several biorefineries in the U.S. recently failed
financially and the biofuel milestone set for 2022 in the U.S. Renewable Fuel Standard is almost certain to be missed. The
authors show that improving logistics could enable biorefineries to operate profitably under the right conditions. These
articles will appear in the April 2022 issue of IISE Transactions (Volume 54, No. 4).
Reut Noham Michal Tzur
Dan Yamin
March 2022 | ISE Magazine 53
Can biofuels be produced protably?
The United States’ Renewable Fuel Standard mandates that
36 billion gallons of fuel from renewable sources be blended
into transportation fuel by 2022. One reason this milestone
will not be reached is that several bioreneries have recently
failed financially.
In “Optimal Design and Operation of a Second-Genera-
tion Biofuels Supply Chain,” professors Neil Geismar, Bruce
McCarl and Stephen Searcy of Texas A&M University show
how improving logistics can enable bioreneries to operate
profitably, but only under certain conditions.
The short harvest season and the bulk of the biomass that
fuels a biorenery cause storage and transportation to be ma-
jor costs for the industry. These factors also lead to another
cost: Dry matter loss (DML), the exponential decay from
respiration and microbial activity. DML varies from 8% to
38% per year, depending mostly on climatic humidity.
The common supply chain tactic of implementing inter-
mediate consolidation points – called depots in the industry
– may reduce storage and transportation costs. The depots
can also preprocess the biomass to reduce its bulk or to pre-
vent DML. This study shows that the reduction in storage
and transportation costs from consolidation and densification
cannot financially justify constructing depots. However, if
depots’ reduction of DML is combined with the logistical
savings, then their implementation can improve profit, but
only in environments with high DML. Therefore, the value
of adding depots to a biorefinery’s supply chain is driven al-
most entirely by the rate of DML – which is a result of the
regional climate – rather than by the cost of storage or trans-
For these appropriate environments, this study determines
how many depots should be built for a given biorefinery. The
modeling of exact locations for biomass growers enables the
specication of optimal locations for those depots and the
optimal assignment of growers to depots. The algorithm also
species each grower’s optimal percentage of biomass to send
to the depot. This process improves an average biorefinery’s
profit by over 30% under these circumstances.
CONTACT: Neil Geismar; ngeismar@mays.tamu.edu; Department of Infor-
mation and Operations Management, 320 Wehner Bldg., College Station, TX
Computer-aided design to help
target injuries and neck pain
for military helicopter crews
Military helicopter aircrews around the world experience
high rates of neck pain and injury. An increased risk of neck
pain is associated with the use of night vision goggles mount-
ed to the front of helmets. Designing effective countermea-
sures to mitigate neck pain requires the ability to model and
understand how a countermeasure can alter biomechanical
loading on the neck. Countermeasures can be readily devel-
oped in a computer-aided design (CAD) environment, but
how such countermeasures may alter neck biomechanics re-
mains limited to the use of physical lab-based testing.
To enable biomechanical evaluation within a CAD pro-
cess, Christopher Moore, Jeffery Barret, Laura Healey, Jack
Callaghan and Steven Fischer, all from the University of
Waterloo in Canada, developed artificial neural networks
(ANNs) that use CAD-accessible, multibody dynamics tool
outputs to predict cervical spine compression and shear under
different helmet and countermeasure congurations. Their
work is described in the paper, “Predicting Cervical Spine
Compression and Shear in Helicopter Helmeted Conditions
using Artificial Neural Networks.
Training data for the ANNs were gathered from a previ-
ous lab study in which participants performed flight-relevant
head movements about pitch and yaw axes under different
helmet system congurations. Measured data were input into
This month we highlight two articles from IISE
Transactions on Occupational Ergonomics and
Human Factors (Volume 9, Nos. 3 and 4), a special
issue on digital human modeling. In the first, a
team from the University of Waterloo in Canada,
developed artificial neural networks capable of
predicting cervical spine compression and shear to
facilitate proactive consideration of biomechanical
exposures within helicopter helmet design
processes. In the second paper, Alexander Wolf
and colleagues present a framework for interaction
modeling between digital human models and
digital product models, which allows for ergonomic
analysis of versatile product designs while being
suitable for designers who do not have specific
human behavior knowledge.
Neil Geismar
54 ISE Magazine | www.iise.org/ISEmagazine
a musculoskeletal model of the neck to generate time-series
of cervical spine compression and shear acting on the C6 ver-
tebra (lower neck). ANN models were then developed and
tuned to accurately predict peak and cumulative neck load-
ing, using only motion and joint torque data as inputs.
The ANNs developed in this study can be used as a sur-
rogate for joint musculoskeletal modeling when combined
with existing CAD-based multibody dynamics models to
predict cervical spine compression and shear. By iteratively
updating the geometry and mass inertia properties of a coun-
termeasure within a multibody model, designers can quickly
evaluate the effects of design iterations on critical biome-
chanical metrics of injury risk. This approach will greatly
reduce the time and resource requirements for biomechani-
cal assessments within the aircrew helmet
design process.
CONTACT: Steve Fischer, Department of Kinesiology
and Health Sciences, University of Waterloo
Making computer-aided
ergonomic analyses more
accessible for design
Digital human models (DHM) have yet
to reach their full potential for proactive
virtual assessment of ergonomics in en-
gineering and industrial design. In par-
ticular, modeling interactions between
the digital user and the digital product are
often time-consuming, cumbersome, un-
standardized or insufficiently embedded
in a computer-aided engineering envi-
ronment. Existing interaction-modeling
frameworks either focus on simulating
occupational processes, are limited to
specic use cases or have insufficient us-
To help overcome these limitations,
Alexander Wolf, Yvonne Wagner, Marius Oßwald, Jörg
Miehling and Sandro Wartzack from Friedrich-Alexander-
Universität Erlangen-rnberg (FAU) in Germany present
a framework for interaction modeling, its methodological
background and its implementation. It is described in their
paper, “Simplifying Computer Aided Ergonomics: A User-
Product Interaction-Modeling Framework in CAD based
on a Taxonomy of Elementary Affordances.
This framework aims to support ergonomic analyses of
diverse product designs while also being suitable for design-
ers who do not have specific ergonomic or human behavior
knowledge. To resolve these partly contradictory demands,
they utilize affordances, which serve as a tool for interac-
tion modeling.
A team of researchers from the University of Waterloo developed artificial neural networks to predict cervical spine compression
and shear under different helmet and countermeasure configurations for military helicopter flight crews.
A team from Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) present
a framework for interaction modeling aimed to support ergonomic analyses of
diverse product designs.
March 2022 | ISE Magazine 55
The authors posit that many interaction concepts in hu-
man technology interaction can be reduced to a relatively
small set of elementary affordances. They developed a tax-
onomy of elementary affordances from empirical interac-
tion data. They also present the resulting taxonomy, includ-
ing the resulting 31 elementary affordances that describe
mechanical dependencies between product geometries and
human end effectors. Identified elementary affordances are
implemented as affordance features in a CAD environ-
ment (Siemens NX) and comprise an interaction-modeling
framework. A brief application of the functionality of the
framework is presented.
This new framework demonstrates how integration of
interaction modeling into a computer-aided engineer-
ing environment can be achieved in a comprehensible and
straightforward way. The resulting simplicity and acces-
sibility may be a key approach to exploit the potential of
DHM simulations as a computer-aided ergonomics tool in
engineering and industrial design.
CONTACT: Alexander Wolf, Lehrstuhl für Konstruktionstechnik KTmfk
Yu Ding is the Mike and Sugar Barnes Professor of Industrial
and Systems Engineering at Texas A&M University and associate
director for Research Engagement at the Texas A&M Institute of
Data Science. He is editor-in-chief of IISE Transactions and a
fellow of IISE.
Maury Nussbaum is HG Prillaman professor at Virginia Tech in
the Department of Industrial and Systems Engineering, editor-in-
chief of the IISE Transactions on Occupational Ergonomics
and Human Factors and a fellow of IISE.
IIISE Transactions (link.iise.org/iisetransactions) is IISE’s flagship
research journal and is published monthly. It aims to foster
exchange among researchers and practitioners in the industrial
engineering community by publishing papers that are grounded
in science and mathematics and motivated by engineering
IISE Transactions on Occupational Ergonomics and Human Factors
(link.iise.org/iisetransactions_ergonomics) is devoted to compiling and
disseminating knowledge on occupational ergonomics and human
factors theory, technology, application and practice across diverse
areas. You can follow on Twitter at twitter.com/iisetoehf or @iisetoehf.
To subscribe, call (800) 494-0460 or (770) 449-0460.
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