Welcome to my blog that is intended to keep interested parties up to date on my latest research and teaching endeavors.  Specifically, I view this blog as a way to engage the online community by:

  1. Sharing — As we live in a society where the majority of us spend a great proportion of our days “staring at glowing rectangles” – I thought a digital presence to share my current research findings, projects, and insights would be valuable to the academic and industry community.
  2. Engaging — If anything you see on my blog is of interest to you, please contact me through email.  I am constantly looking for interesting research projects that are motivated by industry problems, as well as research collaborators throughout the world.
  3. Promoting —  Industrial & Systems Engineering is a profession I am very lucky to have discovered and is a profession I find extremely valuable, practical, and rewarding.  The world needs more ISE’s, who can think systematically and analytically about complex problems, but unfortunately, is also a field with a marketing problem.  When I give presentations to prospective students, I constantly get comments such as “I had no idea an industrial engineer did that.”  This blog, and this Intro to ISE video, is my way to try and get the word out about industrial and systems engineering.

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Amazing Students and Industrial Hemp


I’ve had the great joy of working with many talented undergraduate students.  Ana Gabriela Duque, who graduated with a degree in industrial and management engineering in May, is one of them.  Featured as a Class of 2020 Changemaker, Rensselaer wrote up a nice article about her achievements here.

Harnessing information to improve the lives of others is Industrial and Systems Engineering 101 and Ana embraces this plus more.  She started a nonprofit, volunteered substantially during college, and is the first author of the following peer review publication on industrial hemp’s agricultural supply chain:

  • Schumacher, A. G. D., Pequito, S., & Pazour, J. (2020). Industrial hemp fiber: A sustainable and economical alternative to cotton. Journal of Cleaner Production, 122180. Link to article:  https://authors.elsevier.com/a/1b8w-3QCo9YiNb

Here’s a video explaining her work:

This was all before graduating with her undergraduate degree.

Ana was the well-deserved recipient of the 2020 Class of 1957 Spectrum Award, established in 2017 to honor an Undergraduate Student in School of Engineering with high academic achievement in engineering, coupled with generous service to RPI and the greater Rensselaer community.

She also was the 2020 recipient of the Ray Palmer Bake Prize, which is awarded to a senior in management engineering who has demonstrated outstanding ability in academic work and gives promise of outstanding professional success.  

Congrats Dr. Unnu

Today Kaanu Unnu defended his PhD dissertation on “Optimization Models and Frameworks for On-demand Warehousing Systems.”  Congrats Dr. Unnu!

As illustrated by Kaan’s slide, his research is timely, with on-demand warehousing starting in 2013 with Flexe and has grown to become a global business model.

The main purpose of this research is to develop decision-support models and frameworks given the advent of on-demand distribution systems. On-demand distribution platforms match companies with underutilized warehouse and distribution center (DC) capacities with customers who need extra space or fulfillment services. These systems provide unique advantages, but also have different cost structures and risks. Therefore, a company interested in adopting these new systems must consider new dynamics, which requires new knowledge and methods for the design and operations of distribution networks.

In this dissertation, first, different options to acquire warehouse and distribution capacities, which have varying benefits but also have varying cost structures, are analyzed. Next, new dynamic facility location models are developed to simultaneously consider acquiring three distribution center types (self-distribution, lease, on-demand) which have different capacity granularity, commitment granularity, and access to scale properties. Then, a heuristic approach is designed to solve such dynamic facility location models for large-scale and national level distribution networks. Finally, from a warehouse owner perspective, new decisions for deploying on-demand distribution are explored, and a framework summarizing its impact on facility design and operations is created.

If you are interested in the on-demand warehousing business model, I would encourage you to check out his research, available at his Research Gate page.  Specifically, he has a cost calculator that industry may find useful.  His paper, currently under review and available here  answers “Is there a business case to be made for the use of on-demand systems, and if so, in what environments?”  There’s a lot of great insights in the paper, into when on-demand warehousing models make sense.  The short answer, is it depends (sometimes it’s useful, sometimes not as much).  Three interesting findings:

  1. On-demand warehousing is a good supplement to more traditional forms of acquiring fulfillment services.  Specifically, incorporating on-demand warehousing into a company’s network design strategy improves the capacity utilization of the build and lease DCs a company uses.
  2. If on-demand availability is not guaranteed and response requirements are relaxed (e.g., a 250-mile maximum range), lower distribution costs can be achieved with designs not incorporating on-demand warehousing. However, if the response requirement is tight, like for same day delivery, regardless of the on-demand alternative’s capacity availability, on-demand warehousing is cost effective.
  3. The flexibility provided by 3PL/lease capacity expansion with a premium cost performs worse than using the on-demand alternatives. Thus, 3PLs should offer more granular solutions to their customers – without the overcapacity penalty – to be competitive in the market.

His committee was impressed by his use of a diverse set of tools, combined with  data, to provide insights.  As a proud advisor, I echo these comments and would like to commend Dr. Unnu on a dissertation I am immensely proud of.  It’s been fun to work with him, learn from him, and see him grow.

I wish we could be in person to celebrate his tremendous accomplishments.  Be assured, when we are allowed to do so, we’ll host a party in his honor. Until then, congrats!

What’s your stORy?


New Research: When is it beneficial to provide freelance suppliers with choice?

Think about the things you or your business own.  Are those things highly utilized?  When you’re at a stoplight, count the empty seats in the vehicles around you.  When you’re in a parking lot, think about the fact that most of the surrounding cars get an hour of use a day.  Or consider the duplication of effort when both you and your neighbor make individual grocery trips.  Now think of resources your company owns.  Is your lab equipment used all the time?  What about your warehouse – is it full to capacity 12 months a year?  How many empty miles does your fleet of trucks accumulate each month?  These example all represent underutilized capacity, and with the right systems, data, and technology, individuals and companies could start accessing these underutilized resources.

Attempts to build these systems are all the rage these days (platform companies such as Uber, Lyft, Flexe all claim to increase underutilized resources).  Existing systems to match supply and demand in platforms can be organized broadly into two categories.

The first is a centralized model, which prioritizes meeting demand commitments and enabling a quick time to match.  However, centralized approaches do not allow for decision making by the resource owners, so it dampens their participation. Examples of a centralized approach are the default settings in ride sharing.  For example, did you know that Uber drivers don’t know where they’re taking you until you get in the car?  The driver instead has 15 seconds to accept a recommended request (again without knowing its destination). This means drivers are not provided with enough information to make decisions based on their needs and schedule.  This limits who can participate as a driver and utilizing underutilized capacity is not prioritized.  If you’re interested in learning more about drivers’ experiences in ride sharing applications – I highly recommend a recent book on the topic: Uberland by Alex Rosenblat

The other approach is a “jobs board” system, where all requests are displayed to all suppliers.  This can lead to myopic decision making: no one supplier has the whole picture of the marketplace, which results in reduced system performance because some requests receive multiple selections and others are left unfulfilled.  Also, this is a passive system – in which individuals must take the initiative (and invest the time) to find preferable demand requests. Recently, a number of interesting empirical work captures reduced systematic performance in such systems, see Cullen and Farronato (2018); Fradkin (2017) and Horton (2014).

Our research is interested in a new way to match underutilized capacity owned by freelance suppliers to demand requests that combines the advantages of the two approaches. This is a hard problem, because we have two different sets of stake-holders tugging us in different directions.  On the one hand, we want to entice the owners of these resources to provide access, this means creating pro-supplier policies, taking into account individual preferences (for example, that these freelance suppliers may have planned tasks that we want to tag along for).  However, on the other, we need to ensure high levels of service for the demand-side.

Millennial Millie

Our innovation is an approach enabling “Supplier Choice”.  The platform – without control nor perfect knowledge of suppliers’ preferences  – uses choices estimated from suppliers’ past behavior to increase participation and resource utilization.  To illustrate, meet Millennial Millie. Millennial Millie has gone through training and signed up to be an on-demand volunteer. She gets a notification on her phone asking if she’d like to deliver groceries to shut-in residents. Millie clicks yes. Two requests appear. She chooses the one that fits with her plans that day.

Before this example can become a reality, research is needed to discover new ways to provide choices and quantify the impact of those choices on suppliers and demand requests. Our research, led by Shahab Mofidi, recently published in Transportation Research Part B: Methodological is an important starting point for platforms that must coordinate demand requests with decentralized owned resources. For all the details, please check out our paper:

Mofidi, Shahab, and Pazour, Jennifer A., “When is it Beneficial to Provide Freelance Suppliers with Choice? A Hierarchical Approach for Peer-to-Peer Logistics Platforms,” to appear in Transportation Research Part B: Methodological. [PrePrint]  https://doi.org/10.1016/j.trb.2019.05.008

This link provides free access until July 23, 2019: https://authors.elsevier.com/a/1Z9wHhVEAswhb

In what follows, I walk you through why I am excited about this research, and why I believe it can start to improve crowdsourced delivery and rider sharing, but also impact volunteer management and other applications.

As displayed in the above figure, our research recasts the platform’s role as one providing personalized recommendations (i.e., a menu of requests) to a set of suppliers.  This means that to match supply and demand, two decisions processes take place.

  1. First, the platform must decide how multiple, simultaneous recommendations are made.  The same request may be recommended to multiple suppliers to decrease the time to match and to hedge against suppliers’ autonomy to decline recommendations.
  2. Second, suppliers have autonomy to select requests (if any) from the personalized menus, resulting in some requests not selected and others selected by more than one supplier.

This hierarchical approach enables a quick time to match, and does not require suppliers to explicitly provide preference information for all requests. Suppliers retain autonomy to select requests that can be interleaved with their planned activities.  And as the number of choices increases, suppliers have a higher chance to be recommended a request they are willing to select (increasing participation). However, due to misalignment between the suppliers and the platform’s preferences, a larger number of choices may lead to suppliers selecting a request with lower platform benefit. Also, as the number of choices increases, less systematic coordination occurs, and the chance for rejected requests increases. Therefore, we are interested in understanding when is it beneficial for a platform to use personalized recommendations to a set of freelance suppliers to coordinate demand requests? We also want to quantify this benefit to the platform, the suppliers, and the demand requests under different environmental factors.

To be able to answer these questions, we develop a new bi-level optimization framework to explicitly model both the platform’s decisions and the suppliers’ selection decisions.  What’s challenging about the model is that the platform’s objective function is influenced by both the platform’s recommendation, but also the suppliers’ interdependent selection outcomes. Our model assumes the platform uses expected values of agents’ utilities to make recommendations. We have a single period model and assume the model is re-solved iteratively with a given set of available requests and available suppliers. Because bi-level optimization problems are computationally hard to solve, we exploit the problem’s structure to reformulate as an equivalent single-level optimization problem.  For mathematical and computational details, please see our journal article.

Choice is only useful if the platform does not fully understand what the freelance suppliers want to do.  This is typically the case – because eliciting utility values of all requests is time consuming and tedious for suppliers.  Instead, in practice a platform is able to only partially estimate suppliers’ selection outcomes. Therefore, we develop a computational study – based on ride-sharing – to investigate whether personalized recommendation sets can be used as a coordination mechanism for distributed resources when the platform has neither perfect knowledge nor control over suppliers. The computational experiments have four phases (see Figure below), which generates 1140 instances and solves 10,260 optimization problems (see journal paper for details).

The goals of our computational experiments are to:

(1) compare the performance of our proposed hierarchical model with existing platform matching methods, namely, centralized, decentralized, and many-to-many stable matching mechanisms.

(2) quantify the platform’s value of providing choices in on-demand environments;

(3) determine under what scenarios it is better to recommend only one alternative, a few alternatives, or all alternatives, and to investigate when it is useful for the recommendations to contain overlapping alternatives.


First, we find that providing choices and recommending alternatives to more than one supplier simultaneously provide value, on average, to the platform, suppliers, and demand requests. When the platform is only partially able to estimate suppliers’ utilities, the hierarchical approach creates recommendation sets with higher average platform performance than centralized, decentralized, and many-to-many stable matching approaches. A centralized approach does not perform well due to a higher chance of suppliers electing not to participate in the platform’s assignment. However, too much choice can be problematic even for the suppliers themselves. As excess discretion creates collisions, the decentralized approach had the worst angry driver rate and lowest serviced ride rate. Thus, in uncertain environments, the proposed hierarchical approach can benefit the platform, drivers, and ride requests.

Second, a platform with uncertainty about suppliers’ selection outcomes can sometimes improve its performance by offering choices to suppliers, but not always. In platforms where uncertainty over suppliers’ selection exists, choices help the platform when (1) suppliers are inflexible with a high no-choice utility; and (2) suppliers’ utility values have higher variance than the platform’s utility values. Choices increase the chance of enticing supplier participation, and in both cases, the increase in participation likelihood outweighs the match’s reduced platform benefit likelihood. However, when (1) the platform’s benefit values have higher variance than the suppliers’ expected utilities or (2) suppliers are flexible, offering additional choices, on average, can reduce the platform’s benefit.

We view the current paper as a “proof of concept” that providing freelance suppliers with choices can sometimes be beneficial.  However, this is really just a jumping off point, and much more research is needed.  Thankfully I have smart students and funding support to keep researching. Specifically, Hannah Horner (a math PhD student), John Mitchell (Professor in Mathematics at RPI) and I are explicitly capturing the stochastic nature of suppliers’ utilities directly in the optimization formulation, which allows us to extend our analysis beyond answering if choice is beneficial, and to determine optimal menu characteristics (i.e., menu sizes and request overlaps).  Rosemonde Ausseil (PhD student in ISE) and I are working to extend the optimization model from a single-period model to one that considers the dynamic properties of the problem directly in the optimization formulation. Safron Smith (MS student at RPI) is interested in application specific formulations for volunteer management. Talented undergrads are also contributing to this work, including Tina Nazario, Brooke Ramlakhan, Wenlin Gong, Parker Shawver, and Karthik Dusi.

We greatly appreciate funding of this work from the National Science Foundation Award# 1751801:  CAREER: Distribution Resource Elasticity: A New Hierarchical Approach for On-Demand Distribution Platforms. I would like to acknowledge Dr. Shahab Mofidi – who is the lead author on this work.  This was part of his dissertation, which was awarded the 2019 Del and Ruth Karger Dissertation Prize. Also, thank you to John Backman, who wrote up some of this work for the RPI ISE department newsletter

We would appreciate feedback on our work, send questions and inquiries to pazouj@rpi.edu

A busy couple of days: RPI graduation and IISE Annual Conference

Academically, it’s summer in Troy.  While classes ended in early May, the first official day of summer (to me anyways) starts after the Institute of Industrial and Systems Engineers (IISE) Annual Conference.  This year RPI’s graduation and the IISE conference coincided, making for a very long (but also rewarding) Saturday, May 18th.

The day started off bright and early with the PhD hooding ceremony, which began promptly at 7am.  I had the privilege of hooding Dr. Shahab Mofidi.  He received the 2019 Del and Ruth Karger Dissertation Prize, given to the top dissertation out of RPI’s Industrial and Systems engineering department.  We miss him terribly – as he is a fantastic researcher, and just a fun person to work with.  Honeywell-Intelligrated in Atlanta is lucky to have him as an Operations Research Scientist.  Shahab’s dissertation focused on mathematical models for modern distribution.  We recently received fantastic news that his paper, When is it Beneficial to Provide Freelance Suppliers with Choice? A Hierarchical Approach for Peer-to-Peer Logistics Platforms, was accepted to the special issue on Innovative Shared Transportation in Transportation Research Part B: Methodological.  It’s always fun to see your hard work in print, and this one is especially special as it’s my favorite research paper I’ve written yet. (I plan to write up a blog post shortly explaining why, but in the meantime here’s the Preprint version).  A previous contribution of Shahab’s dissertation was on sea-based logistics, published in Transportation Research Part E: Logistics and Transportation Review.

Congrats 2019 Graduates!Then it was off to carry the Engineering banner at RPI’s 213th Commencement Ceremony.  Congrats to the class of 2019!  We are confidence you’ll set the world on fire in whatever endeavors you seek.  Thanks to the many dedicated volunteer faculty and staff who helped with the big day, and to the graduates’ family and friends, who provide important support and encouragement.

After a quick nap, I was boarded onto a flight to Orlando.  Four graduate students and one undergraduate student presented research at the IISE Annual Conference.  Everyone did a great job, covering the following topics:

  1. Kaan Unnu, Analyzing varying cost structures of alternative warehouse strategies (conference proceedings PDF).
  2. Hannah Horner, A stochastic bilevel approach to fulfill on-demand requests (Joint work with Professor John Mitchell)
  3. Safron Smith, On-demand volunteer platforms
  4. Rosemonde Ausseil, Multi-period recommendation model with non-compliant suppliers
  5. Ning Zhang, Expected Travel Models for Retail Store Order Fulfillment
  6. Kaan Unnu, Blockchain Enabled Supply Chains & Directions for Future Research  (joint work with Aly Megahed and Chandra Narayanaswami, IBM Research).
  7. Jen Pazour, On-Demand Distribution Platforms

A highlight of the IISE conference was Ning Zhang receiving first place in the Undergraduate Student Research Dissemination competition given by IISE’s Operations Research division.  The award recognizes undergraduate researchers for their contributions to the field of industrial engineering and operations research, as well as their ability to communicate results effectively.  The award evaluation was based on both a written conference paper and an oral research presentation. Ning graduated with his BS in Industrial and Management Engineering at RPI’s  graduation (so he also had a busy couple of days).  His conference paper and presentation were entitled “Expected Travel Distance Models for Retail Store Order Fulfillment. Here’s a link to his conference paper, which focuses on order-online-pickup-in-store policies, which are a new option for customers to order items online but pick them up at a brick-and-mortar store. This provides convenience to customers but requires store employees to conduct order fulfillment operations at retail stores. Although many retailers have implemented pick-up in stores policies, challenges exist in estimating labor requirements and evaluating where to place the pick-up and backroom locations. Reviewing previous literature on order fulfillment and layout designs in warehouses and distribution centers, quantitative models for order fulfillment processes in retail stores are lacking. To fill this research gap, we combine ideas from omni-channel retailing and warehouse expected travel models to derive new travel distance models for retail store order fulfillment. Capturing different placements of pick-up locations and backrooms, multiple models compute the expected efforts employees spend picking single-line orders. We quantify the influences on the sales clerks’ expected travel efforts due to different placements of items, the backroom, and the pick-up location, and varying item demand skewness.

The best part of the conference is seeing old friends, especially graduate student buddies – many who are now tenured-associate professors.  I caught up with research collaborators, mentors, and people I admire in the field.  It was a fun-filled and knowledge-packed couple of days; the introvert in me was glad for a three-day weekend and the unofficial official start of summer.

Happy National Engineers Week

Happy National Engineers Week!

Ana-Duque-GreenA highlight of the last year has been getting to work with Ana Gabriela Duque and Sergio Pequito on research understanding how industrial hemp fiber can be used in the fashion industry.  Ana  – interested in sustainability and fashion  – identified this timely research topic.  She is a superstar undergraduate researcher, whom I have learned a ton about the supply chains of industrial hemp fiber.  In honor of National Engineers Week, RPI’s Every Day Matters blog featured Ana and her research “Towards Green Fashion Design: A Systems Engineer’s Perspective”.

Another highlight of this year has been working with undergraduates Anand Gandhi and Fiona Flynn to create hands-on presentations about our on-demand resource allocation research.  As part of RPI’s Engineering Ambassadors, they’ve put their creative juices to work making an engaging presentation and hands-on activity to motivate our research to middle and high school students.   This is my second group of Engineering Ambassadors I’ve been privileged to work with.  Last year, Fiona Flynn and Brook Rulewich created an Introduction to Industrial and Systems Engineering, all through the eyes of optimizing traffic.

Finally, the world needs more industrial and systems engineers.  Check out this video to find out why.

Happy National Engineers Week!


Post-Doctoral Research Position Available in Our Research Lab

A post-doctoral position is available in Rensselaer Polytechnic Institute’s Industrial and Systems Engineering Department.  In this appointment, you will work under the supervision of Dr. Jen Pazour to research new ways to meet the demands of modern distribution.  The ideal candidate will have a strong background in optimization methodologies, ideally (but not required) with some exposure to bi-level optimization and Stackelberg games.  The initial appointment is one year, with potential for renewable based on satisfactory performance and funding availability.  The position is available immediately.    The ideal candidate would start by September 1st, 2018; while this starting date is flexible, priority will be given to candidates with earlier start dates.

If interested, please apply to the open post-doc position at this link: https://rpijobs.rpi.edu/postings/6639 and send your CV to Dr. Jen Pazour at pazouj@rpi.edu.

More information about our group’s research can be found here: https://jenpazour.wordpress.com/.  This specific post provides details about the types of skills ideal for this open position: https://jenpazour.wordpress.com/2017/12/11/looking-for-new-ph-d-students-to-join-my-research-lab/   Feel free to contact Jen (pazouj@rpi.edu) if you have questions or to request a SKYPE appointment to learn more.

Efficiency on Demand

Article written by John Backman for Rensselaer’s ISE Spring 2018 newsletter

Jennifer Pazour is finding ways to streamline supply chains in the new collaborative economy

Jennifer Pazour started early on her quest for optimal efficiency.

“I really enjoyed organizing my room as a kid,” said the ISE assistant professor. “I have just always liked efficiency.”

The lifelong fascination recently led to two of engineering’s most prestigious awards—and perhaps a better life for Uber drivers, Meals on Wheels volunteers, and others who inhabit on-demand supply chains.

Early this year, the National Science Foundation awarded Pazour a Faculty Early Career Development (CAREER) grant. More recently, Johnson & Johnson honored her with its Manufacturing Scholar Award, given as part of the WiSTEM2D Scholar Program (Women in Science, Technology, Engineering, Mathematics, Manufacturing, and Design).

The research behind the accolades has to do with Pazour’s signature focus: the seismic shift from centralized to on-demand and collaborative distribution.

“Modern distribution systems need to fulfill a wide variety of requests quickly with little warning in small units to many dispersed locations at low costs,” she wrote in her research summaries.  “This is fundamentally different than yesterday’s demand, which aggregated at fixed (store) locations.  Thus, today’s supply chains are optimized for yesterday’s customers.”

In the face of this disconnect, Pazour’s team is rethinking supply chain design to meet the demands of modern distribution.  That involves researching new ways to move supply chain networks from fixed and static to collaborative, dynamic and agile.

One aspect of this research involves underutilized resources and how organizations might obtain them.  To understand it, consider cars and what they do – or don’t do – all day.

“The tings we own have extremely low utilization rates, spending the majority of their useful lives idle,” Pazour explained.  “When you’re at a stoplight, count the empty seats in the vehicles around you.  When you’re in a parking lot, think about the fact that most of the surrounding cars get an hour of use a day.  Or consider the duplication of effort when both you and your neightbor make invividual grocery trips.  These example all represent underutilized capacity, and with the right algorithms and models, companies could start accessing these underutilized resources.”

But there’s a challenge in the way.  “We need to entice the owners of these resources to provide access,” she continued.  “On the one hand, the centralized model doesn’t allow for decision making by the owners, so it dampens their participation.  On the other, a fully decentralized system leads to myopic decision making: no one supplier has the whole picture of the marketplace, which results in reduced system performance because some requests receive multiple selections and others are left unfulfilled.”

The CAREER award-winning research aims to combine the two in what Pazour calls a hierarchical approach, which gives suppliers “recommendations” to help them make efficient decisions. She used Meals on Wheels volunteers as an example.

“Millennial Millie gets a notification on her phone asking if she’d like to deliver groceries to shut-in residents,” she recounted. “Millie clicks yes. Two requests appear. She chooses the one that fits with her plans that day. What the platform did—without control or knowledge of Millie’s plans—was to provide choices estimated from her past behavior.  Before this example can become a reality, research is needed to discover new ways to provide choices and quantify the impact of those choices on suppliers and demand requests.”

Another part of the Johnson & Johnson and CAREER research dovetails with Pazour’s dedication to her profession. Working with Rensselaer’s Engineering Ambassadors program, she will mentor undergraduates to create active learning modules, inspired by her research, and use them with K-12 students.

“I want to gain more exposure for the field, and inspire more young people to get involved,” she said. “The world need more industrial and systems engineers. We are wired to think systematically about complex problems, which exist in all sectors of society. Yet too few incoming Rensselaer students even know what we do. That needs to change.”



Zach Shearin, Presenting his Research at the 2017 IISE Regional Conference

Zach Shearin, an undergraduate Industrial and Management Engineering student at Rensselaer Polytechnic Institute and a die-hard Carolina Hurricanes fan, used analytics to analyze the National Hockey League’s point system through an operations research and statistical analysis lens.  This work started as Zach’s project for my Operations Research Methods (ISYE 4600) course.  He then continued research and analysis as an undergraduate researcher.  The NHL’s current point system awards more total points to games ending in overtime than games ending in regular time.  In this work, we evaluate if this a fair system, and if it influences style of game.  We find that the current point system results in statistically more passive play in the last five minutes of regulation for an even-score game than the first fifty-five because both teams want to ensure at least one point. The “3-2-1-0” point system minimizes discrepancies with win-loss record, and does not compromise the competitiveness and entertainment of the game and is recommended.  Zach presented this research at the 2017 Regional Institute of Industrial and Systems Engineering Conference, which earned him a second place finish.  We have submitted this work to the 2018 IISE Conference; our submitted conference paper can be downloaded here: [Shearin and Pazour, NHL Point System Fairness Optimization and Game Play Stat Analysis].


Click here for the full analysis and post



2017 Recap

Welcome to our 2017 Recap!  These Year-in-Review posts, an annual tradition, catalog our team’s progress while encouraging reflection and preparation for the voyage into the new year.

Quote: Rainer Maria Rilke #riflepaperco

Quote by Rainer Maria Rilke, Artwork by Rifle Paper Co. 

2017 culminated in two success stories. Dr. Uzma Mushtaque defended her PhD Dissertation entitled Context Dependent Discrete Choice Models and Assortment Optimization for Online Retail. Joan Climes defended her MS Thesis on Analytical Models for Retrieving Items in Dense Storage Systems and Optimizing the Location of an Open Square.

Uzma developed descriptive mathematical models to capture context-effects associated with individual user selection behavior.  Her models are novel as they capture the influence of assortment properties (specifically the assortment size), in addition to user and item attributes (as are commonly captured in existing research).   Using her new class of random utility models as inputs to optimization problems, she proves insights and creates new algorithms to determine “what to recommend” and “how many to recommend” in online settings.  She validated her approaches using data from online movie recommender systems and online retail. More details are available in this paper under review.  This research, partially supported by the National Science Foundation, earned Uzma an honorable mention at the 2017 IISE Doctoral Colloquium and a trip to Amazon’s Graduate Research Symposium. Uzma is currently serving as a Post-Doctoral Researcher in my lab and an instructor for the Core-Engineering course Modeling and Analysis of Uncertainty.

Joan’s research focused on dense storage systems, which allow for highly effective use of space, at the expense of requiring the repositioning of stored items to retrieve other more densely desired items. These dense storage systems are found in warehouses and distribution centers, and aboard US Navy ships used for sea-based logistics (this work was partially supported by the Office of Naval Research).  Her research creates mathematical models to determine the value of an empty space in a specific dense storage environment, the double-inverted T configuration (discovered by the always innovative Kevin Gue). Retrieval and repositioning distance equations are derived for each item in a layout.  An optimization problem is presented to select which location should be left open. Best locations for an open square are along the aisle and close to the vertical walls if h > k, or close to the horizontal wall if h = k. Due to the symmetry in repositioning distances, multiple optimal solutions exist.  Joan has accepted a position with Deloitte starting in February. In the meantime, we hope to work together to submit this work for peer-review.  Joan conducted this independent research as an undergraduate student, leading the modeling and coding development, all while taking challenging PhD-level optimization and statistics classes. She happens to be a runner on the RPI track team too. She has a standing offer to rejoin our team and pursue a Ph.D., as do a number of talented undergraduate researchers I’ve been honored to work with in the past.

Due to Uzma and Joan’s graduations, my research group has openings. I am looking for curious, talented people to join my research team.  If this year doesn’t work, keep us in mind in the future.

Kaan Unnu made great research progress in 2017 for his dissertation “Optimization Models for On-Demand Supply Chain Collaboration.”  On-demand systems provide resource elasticity: enabling finer granularity capacity and commitment decisions, and access to scale.  Kaan has chosen on-demand warehousing as a focus. Novel mixed integer linear programming models and efficient solution algorithms decide location-allocation in a dynamic network, capturing build, lease, and on-demand distribution simultaneously.  Computational experiments, utilizing the mathematical models, identify significant factors impacting performance and codify policy recommendations.  We’ve also partnered with IBM Research to start exploring the potential for blockchain technology to improve trust and facilitate physical movement/storage of goods’ data into a distributed ledger system.

Shahab Mofidi defended his PhD candidacy this summer, which focuses on “agile resource allocation decisions in modern supply chains with on-demand suppliers”.  Most recently, he has been developing new models and algorithms for online platforms.  To understand the trade-offs of providing choices to drivers through simultaneous personalized recommendations, we propose a hierarchical decision-making framework where the platform decides a recommendation set for each driver. Drivers then have discretion to choose the riders that best fit with their preferences or planned travel from this set. We model this framework as a bilevel optimization problem with a profit maximizing objective for the platform in the upper level (leader) problem and a utilitarian social welfare objective for the lower level (follower) problem. This results in a computationally expensive mixed integer linear bilevel problem. Since the platform needs to make instant recommendation for a relatively large problem size, we transform the formulation into a single level problem through proposing logical expressions. This research provided preliminary results for a NSF research proposal I submitted this summer. Shahab is a crucial resource.  He’s an excellent collaborator, and he continues to win national awards and scholarships.


I was humbled to be awarded the 2017 IISE Dr. Hamed K. Eldin Outstanding Early Career IE In Academia Award.  This award is especially exciting to me because so many of the past recipients are human beings whose careers I admire and hope to emulate.


I was awarded the 2017 SDSM&T Outstanding Recent Graduate.  As a blast from the past, I dug out my undergraduate graduation speech, which included the lines below…with a few inside jokes.

You might be a Tech grad if…

You can name the flavor of the day at Armadillos for the whole next week.

When asked to take a picture, you count off 1, 3, 5

You’ve ever applied probability and statistics in Deadwood or fluid dynamics to a night on the town.

Your student ID is only important one day of the week and that’s Wednesday.

You actually know your professors and they know you.

In 2017, I enjoyed giving back to my graduate school Alma mater – the University of Arkansas – by serving on their IE Liaison Board.  The IE department is conducting innovative research, while keeping students the focus.

We wrapped up our work on Sea-Based Delivery systems, funded through the Young Investigator program by the Office of Naval Research.  2017 saw two papers accepted for publication out of this research, with a couple more in the pipeline.  I joined the editorial board of IISE Transactions.  Having handled my first paper as an associate editor of the Emerging Applications and Analytics Department, I was reminded of the flip-side of the peer-review process.

2017 provided plenty of evidence the future is bright.  This included undergraduate ISE majors from across the Northeast giving up their weekend to attend the 2017 IISE Regional Conference, hosted at RPI.  Zach Shearin did a great job presenting his research on Analytics for the NHL Point System, earning him 2nd place in the undergraduate technical paper competition.  The winning Rutgers team did a fantastic job, and earned 2nd place at the National Competition. I was inspired by research conducted by undergraduates across the globe as I served on INFORMS Undergraduate Operations Research Prize committee.  I am the chair for the prize committee in 2018, and look forward to receiving inspiring applications.   Serving as RPI’s IME Class of 2020 adviser, our students’ focus, self-awareness, and vision, which are much beyond what I was thinking about as a freshman/sophomore in college, are encouraging.

Teaching brings me great joy.  I enjoyed polishing my course materials for two courses in 2017: Design and Analysis of Supply Chains and Operations Research Methods.  I am a big fan of clicker questions to keep students engaged and participating in the materials.  I’ve found these can work for quantitative materials too.  Please click in:

D3 Inventory Management Uncertain Demand.jpg

I spent a lot of time in the Fall preparing to give a RED (Research, Education, and Discovery) Talks – A Transformative Rensselaer Confronts the Global Challenges.  My presentation with Professor Jim Hendler was about “The Data Challenge.”  I presented my vision of the future of supply chains and how researchers across RPI are addressing the need for tomorrow’s supply chains to be resilient and agile.

Our research was featured in a 2017 Supply Chain Dive article “How retail supply chains are adapting to the Amazon effect.”  In particular, we were quoted, “A wide variety of our [order] requests are made with very little warning and are expected to be fulfilled quickly, in small units, to a number of different locations,” Jennifer Pazour, Assistant Professor of Industrial and Systems Engineering for Rensselaer Polytechnic Institute said at a WERC conference panel. This is the Amazon effect. This is the idea that I want my stuff now and fast, and oh by the way I don’t want to pay much for it.”  This is fundamentally different than yesterday’s demand, which aggregated at fixed (store) locations.  Thus, today’s supply chains are optimized for yesterday’s customers. To close the gap between current supply chain operations and customer expectations, our team rethinks supply chain design.   Our research was well-received by industry groups, having presented in 2017 at three separate APICS events, at WERC’s annual meeting and through WERC’s webinar series.  Such interactions have led to connections and research projects with supply chain and logistics companies and start-ups.  I’ve also continued involvement with a number of programs to encourage youth to pursue a career in engineering and logistics.  A highlight was the presentation given by undergraduates Brook Rulewich and Fiona Flynn, created as part of RPI’s Engineering Ambassador’s program.  Their presentation, geared toward innovative ways to deal with traffic, motivates middle and high school students to think about careers in Industrial and Systems Engineering, and Civil Engineering.

Collaborations created through the Gulf Research Program Fellowship program have led to new friends, new problems, and new data.  I am specifically excited about my collaboration with Diego Figueroa, from the School of Earth Environmental and Marine Sciences, University of Texas Rio Grande Valley, to explore Optimal Marine Protected Area Design for Mesophotic Reef Conservation in the Gulf of Mexico.

To avoid the trap of social media only showing a curated life of success, I should discuss some disappointments in 2017 as well. I received the gut-wrenching email that my proposal has been rejected 4 times, along with several similar emails for paper submissions.  As I mentioned in a presentation to the IISE Doctoral Colloquium, a research career is not a monotonically increasing function: feedback and criticism lead to better end products and growth.

Pazour Doctoral Colloquium

But 2017 wasn’t all research and papers! In 2017, I marched for science, equality, and facts.  We become first-time homeowners, saw 90% of the solar eclipse, visited the Math Museum, celebrated the successful return of Crash Bandicoot (and his sister Coco), enjoyed the consistency of meeting up with friends at Troy’s Farmer Market and Wine Wednesdays, enjoyed get-aways to MASS MoCA, hosted family in Troy, read a few great books…and a few ok books.


Cheers to 2018!