52 ISE Magazine | www.iise.org/ISEmagazine
Inside IISE Journals
How to efciently manage
warehouse order picking activities
Warehouses are crucial for the efficiency of distribution
networks since they balance the variation in demand, con-
solidate various products and help with rapid response to
customer requests. In a classical warehouse with a picker-
to-parts system, pickers walk or ride along aisles and col-
lect the requested items. In this type of warehouse, batch
picking is a common practice where a set of orders, called
a batch, must be formed. A picker collects the items of the
orders belonging to a batch in a single tour with the objec-
tive of minimizing the total travel distance of the pickers.
On the other hand, the assignment of selected batches to
the pickers is nearly always made under time considerations.
Hence, the wave picking systems have important implica-
tions in delivery operations especially when the orders to
be collected constitute a truckload and the truck will not
be dispatched until all orders have been handed over by the
This paper addresses the integration of order batching
and picker scheduling decisions by taking into account the
minimization of both the total travel time to collect all or-
ders by the pickers and the makespan of the orders. The in-
tegrated problem not only occurs naturally in wave picking
systems in which the latest picking time of orders becomes
the key performance metric, but also arises when there is a
limit on the picker operating time.
In their work, “Order Batching and Picker Scheduling in
Warehouse Order Pickingİbrahim Muter from Amazon
Web Services and Temel Öncan from the Galatasaray Uni-
versity in İstanbul propose a column generation-based exact
algorithm for the integrated problem. The novelty of the
proposed approach lies in the ability of efficiently solving
the integrated problem to optimality by designing a column
generation subproblem based on the set of batches.
The major finding is that the upper bound for the order
batching and scheduling problem obtained by solving the
order batching problem is very tight for instances with 50 or
more orders. For many instances, solving the order batch-
ing problem is sufficient to find the optimal solution for the
integrated problem.
CONTACT: Temel Öncan; ytoncan@gsu.edu.tr; Department of Industrial
Engineering, Galatasaray University, 34349 Ortaköy, İstanbul, Turkey
How to compress deep learning?
Try tensor decomposition
Deep learning represents a family of artificial intelligence
methods based on deep neural networks. It has attracted
much attention from industrial practitioners. It has been
This month we highlight two articles in IISE Transactions. The first article looks into the question of how to efficiently
manage order picking activities in the warehouses. The authors focus on an integrated problem involving both order
batching and picker scheduling. Their research reveals that the upper bound for the combined order batching and
scheduling problem obtained by solving the order batching problem alone is very tight when there are a sufcient
amount of orders. This insight suggests solving the order batching problem is sufficient to find the optimal solution for
the integrated decision problem. The second article asks, “How to compress deep learning?” The number of parameters
involved in a deep learning model increases too fast as the number of layers increases. Compressing deep learning
models and making them smaller will therefore make them run faster and more interpretable. The authors show that the
convolutional layers in deep learning can be reformulated and compressed by using tensor decomposition. These articles
will appear in the May 2022 issue (Volume 54, No. 5).
Temel ÖncanIbrahim Muter
April 2022 | ISE Magazine 53
applied to many fields, such
as computer vision, speech
recognition, anomaly detec-
tion, production prediction
and quality control, where
deep learning can achieve
high prediction or classi-
cation accuracy, sometimes
surpassing human expert
Although deep learning
is favorable in dealing with
large-scale and high-dimensional datasets due to its ac-
curacy, the huge number of parameters in deep learning
models will increase exponentially as the layers grow. This
limitation increases the model complexity, decays the fea-
ture interpretability and hinders its applications in devices
with limited memory space. Therefore, compressing deep
learning models is desirable for making them smaller, faster
and more interpretable.
In their paper, “Tensor Decomposition to Compress
Convolutional Layers in Deep Learning,” doctoral student
Yinan Wang, assistant professor Xiaowei Yue from Virginia
Tech and assistant professor Grace Guo from Rutgers Uni-
versity show that the convolutional layers in deep learning
can be reformulated and compressed by using tensor de-
composition to reduce model complexity while preserving
its accuracy.
The authors describe how the tensor decomposition is
incorporated into designing a novel convolutional layer in
deep learning. The newly proposed layer is highly modu-
larized and ready to be used with other layers. Both the
theoretical analysis and experimental results demonstrate
that the newly proposed layer uses fewer parameters, has
lower time complexity and maintains comparable perfor-
mance in image-based surface defect classification. It also
has the flexibility to adjust the number of parameters for
balancing the model complexity and its accuracy.
Furthermore, the proposed compressed layer can be gen-
erally used as a low-rank alternative to the convolutional
layers in broader applications. The idea of this work is also
extendable to compress other types of deep learning.
CONTACT: Xiaowei Yue; xwy@vt.edu; Grado Department of Industrial
and Systems Engineering, Virginia Tech, 1145 Perry Street, Blacksburg,
VA 24061
How articial intelligence could
help pulmonologists and radiologists
diagnose lung cancer
Lung cancer is not only the most prevalent form of cancer,
but it also has a high mortality rate. To diagnose lung can-
cer at its early stages, computed tomography (CT) is the
most common way for pulmonologists and radiologists to
identify and analyze lung nodules by scanning the chest
area of patients. However, identifying malignant nodules,
which are rounded or irregular opacity at the lung region,
is very challenging because of nodule heterogeneity and the
lack of annotated lung nodule images.
Different pulmonologists and radiologists may have differ-
ent opinions and diagnostic results for a single lung nodule
because lung nodule analysis is highly dependent on subjective
interpretations and the radiologists’ preceding experiences. In
recent years, the development of computer-aided diagnosis
(CAD) has promoted lung nodule diagnosis by providing reli-
able diagnosis results and enhancing analysis effectiveness.
In their paper, “A Novel Self-Adaptive Convolutional
Neural Network Model Using Spatial Pyramid Pooling for
3D Lung Nodule Computer-Aided Diagnosis,
This month we highlight two articles from IISE
Transactions on Healthcare Systems Engineering
(Volume 12, No. 1). In the first paper, researchers
studied lung nodule heterogeneity in diagnosing
malignancies from CT scans. In an effort to design
an effective nodule classification framework for
CAD systems, they proposed a deep learning
algorithm that identifies malignant and benign
lung nodules from 3D CT images in more effective
ways. In the second paper, the authors develop
an advanced epidemiological formulation in
combination with vaccine and treatment resources
allocation, namely epidemic-vaccination-logistics,
to support decision-making surrounding the
management of epidemic diseases. They present
a multistage stochastic programming framework
that evaluates various disease growth scenarios to
optimize distribution of treatment centers, medical
resources and vaccines.
Yinan Wang Xiaowei Yue
Grace Guo
54 ISE Magazine | www.iise.org/ISEmagazine
Qianqian Zhang and Sang Won Yoon of the State Univer-
sity of New York at Binghamton studied lung nodule hetero-
geneity and designed an effective nodule classication frame-
work for CAD systems. They proposed a novel deep learning
algorithm that identifies malignant and benign lung nodules
from 3D CT images in more effective ways.
3D lung CT images are collected by sorting 2D CT im-
ages based on their transverse positions on the craniocaudal
axis. It has been tested with 3D CT images from Lung Image
Database Consortium and Image Database Resource Initia-
tive (LIDC-IDRI), which is one of the largest lung datasets
publicly available for lung nodule screening images and nodule
The experimental results show that the proposed method
improves lung nodule diagnosis accuracy up to 12.12%, with
fewer than 10% of the parameters that are involved in other
recent lung nodule diagnosis studies. This is a notable perfor-
mance achievement in terms of diagnostic sensitivity, showing
the algorithms capacity of recognizing malignant lung nod-
ules. In practice, the artificial intelligence and machine learn-
ing algorithms can offer complementary opinions in CAD
systems as a supportive tool for radiologists and physicians in
medical image interpretation, analysis and diagnosis.
CONTACT: Sang Won Yoon, Professor, Systems Science and Industrial
Engineering, Thomas J. Watson College of Engineering and Applied Science,
State University of New York at Binghamton, Binghamton, NY 13902
How healthcare decision-makers
can effectively respond to epidemic
under uncertainty and risk
Making resource-allocation decisions under a public health
emergency, such as the COVID-19 pandemic and Ebola virus
disease, is always challenging, especially given the uncertainty
of disease transmission among the human population. Health-
care professionals and government officials often have to make
difficult decisions before knowing the trajectory of the disease
progression and its impacts on their people. Since the evolu-
tion of an epidemic disease is quite dynamic and uncertain,
every decision should be made at the risk of experiencing di-
sastrous scenarios with many infections and deaths due to the
infectious disease.
In their paper, “Risk-Averse Multi-Stage Stochastic Pro-
gramming to Optimizing Vaccine Allocation and Treatment
Logistics for Effective Epidemic Response,” Xuecheng Yin,
a postdoctoral research associate at Yale University School
of Public Health and Esra Büyüktahtakın Toy, an associate
professor at New Jersey Institute of Technology, develop an
advanced epidemiological formulation in combination with
vaccine and treatment resources allocation, namely epidemic-
vaccination-logistics, to support decision-making surrounding
the management of epidemic diseases.
The authors present a multistage stochastic programming
framework that evaluates various disease growth scenarios
under the conditional value-at-risk (CVaR) to optimize the
distribution of treatment centers, medical resources and vac-
cines while minimizing the total expected number and risk of
infections, deaths and close contacts of infected people under
a limited budget.
The authors integrate ring vaccination strategy into an epi-
demics-logistics model in the paper. The model is formulated
under the uncertainty of vaccine supply and disease transmis-
sion rates and the potential risk of experiencing highly adverse
disease growth scenarios.
The authors incorporate human mobility into the model and
develop a new method to estimate the migration rate between
each spatial region while data on migration rates is not avail-
able. They demonstrate the mathematical optimization results
on the case of controlling the 2018-2020 Ebola virus disease in
the Democratic Republic of the Congo using real data.
The research shows that isolating and treating infected in-
dividuals are the most efficient ways to slow the transmission
Qianqian Zhang
Esra Büyüktahtakın Toy Xuecheng Yin
April 2022 | ISE Magazine 55
of the Ebola Virus Disease, which transmits through physi-
cal contact. While vaccination is supplementary to primary
interventions, such as treatment and isolation, its delay could
cause an exponential increase in the number of infections and
deaths. Our findings also suggest using the limited budget for
vaccination in regions where the disease has just started, while
the model gives priority to building new Ebola treatment cen-
ters (ETCs) and treating infected people over vaccination in
regions with high initial infections. The results further show
that as the risk-averseness level increases, the budget allocated
to areas with the highest initial infection level is decreased by
moving the ETCs and treatment budget to neighboring lo-
cations under the risk of getting infections. In addition, the
results indicate that vaccine acceptance rates affect the optimal
vaccine-allocation policy only at the initial stages of the vac-
cine rollout when the vaccine supply is tight.
CONTACT: Esra Büyüktahtakın Toy, Mechanical and Industrial Engineering,
NJIT; esratoy@njit.edu; web.njit.edu/~esratoy; University Heights, Mechani-
cal and Industrial Engineering Center, Suite 204, Newark, NJ 07102; Xuech-
eng Yin, xuecheng.yin@yale.edu; School of Public Health, Yale University,
350 George St., New Haven, CT 06511
Yu Ding is the Mike and Sugar Barnes Professor of Industrial
and Systems Engineering at Texas A&M University and Associ-
ate 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.
Oguzhan Alagoz is a professor in the Department of Industrial and
Systems Engineering at the University of Wisconsin-Madison. He
is editor-in-chief of IISE Transactions on Healthcare Systems
Engineering 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 Healthcare Systems Engineering (link.iise.org/
iisetransactions_healthcare) is a quarterly, refereed journal that
publishes papers about the application of industrial engineering tools
and techniques to healthcare systems.
To subscribe, call (800) 494-0460 or (770) 449-0460.
About the journals
Advanced Excel Tools for ISEs ● Honing Your FMEA Skills ● The Basics of Simulation
Regression Refresher ● Systems Engineering Tools for Process Improvement ● and more …
The quickest, most cost-efficient CEUs around!