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 team’s research findings, projects, and insights could be valuable to the academic and industry community.
  2. Engaging — If anything you see on this site is of interest to you, please contact me through email.  I am constantly looking for interesting research projects motivated by industry problems, as well as new team members and research collaborators.
  3. Promoting —  Industrial & Systems Engineering (ISE) is a profession I am lucky to have discovered and one I find extremely valuable, practical, and rewarding.  The world needs more ISE’s, who can think systematically and analytically about complex problems.  Unfortunately, it is also a field too few people know about.  This blog, and this Intro to ISE video, is my small way to communicate the power of industrial and systems engineering.

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Congrats to Rosemonde Ausseil

Somehow September has arrived; summer has flown. A professional highlight of my summer was the PhD Defense by Rosemonde Ausseil. Her dissertation “Dynamic Methods for Matching Requests to Suppliers in Transportation Platforms” was co-advised by Marlin Ulmer and me. A central challenge in peer-to-peer transportation platforms is how to make matching decisions that can accommodate the different needs of all three stakeholders (platform, suppliers, requests) and in environments where both demand requests and suppliers spontaneously arrive to the platform over time. Further, because suppliers are not employed nor controlled by the platform, the platform cannot be certain that a supplier will accept an offered request. To mitigate this selection uncertainty, her dissertation models and creates new optimization approaches for two strategies that provide more autonomy to suppliers while also providing
low request match times for requests and high match rates by the platform. A good source for her contributions on Supplier Menus for Dynamic Matching are in her Transportation Science publication. Other work is under review. In August, Rosemonde joined Clarkson University’s Engineering and Management program. Her students are lucky to have her insights, knowledge and care, and she’s happy to enjoy long runs in cold weather.

Everyone should have students like Rosemonde. She is extremely talented and capable, and so very generous; while at RPI Rosemonde was often found helping others with their research (she is especially good at debugging code), or helping a student in any class (not just the ones she was assigned as a TA). We had great discussions of how to connect important societal issues into classes and how to design courses to have students engaging with the materials and supported in their learning. She was a super star TA, as illustrated by an email I received (unprompted) from an undergrad last year. “I just wanted to give a shout-out to Rosemonde who is an awesome TA!  Although her office hours were 6-7:30 today, she extended it an extra 2 hours for us to ask her questions about the OR homework.  I know she has a research presentation due tomorrow with you so please relay this thanks for me.” FYI she rocked her research presentation too!

In August, we both made new friends and had a great time on the Maid of the Mist and during the educational sessions held at the Material Handling Teaching Institute hosted by the University at Buffalo and CICMHE. Photo credit: Rosemarie Santa Gonzalez

Congrats to Dr. Hannah Horner

Congrats to Dr. Hannah Horner on successfully defending her PhD dissertation in July 2021 entitled, “Optimizing Personalized Menus and Incentives to Increase Driver Autonomy In Ridesharing and Crowdsourced Delivery Platforms With Stochastic Driver Behavior.” She earned her Ph.D. from the Department of Mathematics at Rensselaer Polytechnic Institute.

Her work presents a progression of optimization approaches to increase driver autonomy in ridesharing/crowdsourced delivery platforms, including providing drivers with menus of requests to choose from and personalized incentives. She makes a number of modeling and solution approach contributions by capturing the hierarchical structure between the platform’s recommendations and the drivers’ selection behaviors, which are not fully observable to the platform when decisions need to be made.

Dissertation Abstract

Peer-to-peer logistics platforms have become increasingly popular in recent years for performing last mile delivery, ridesharing, and more. In general, current platforms have the suppliers i.e. the drivers either sift through and select from a large number of requests or are assigned a single request that they may or may not be able to reject. In this dissertation we offer an alternative framework, that provides drivers with a small but personalized menu of requests to choose from. This creates a Stackelberg game, in which the platform leads by deciding what menu of requests to send to each driver, and the drivers follow by selecting which request(s) to accept from their received menus. Determining optimal menus, menu size, and request overlaps is complex as the platform has limited knowledge of drivers’ request preferences. Exploiting the problem structure when drivers signal willingness to fulfill each request, we reformulate our problem as an equivalent single-level Mixed Integer Linear Program (MILP) and apply the Sample Average Approximation (SAA) method. Computational tests recommend a training sample size for inputted SAA scenarios and a test sample size for completing performance analysis. Our stochastic optimization approach performs better than current approaches, as well as deterministic optimization alternatives. A simplified formulation ignoring `unhappy drivers’ who accept requests but are not matched is shown to produce similar objective values with a fraction of the runtime. A ridesharing case study of the Chicago Regional transportation network provides insights for a platform wanting to provide driver autonomy via menu creation. The proposed methods achieved high demand performance as long as the drivers are well compensated (e.g., even when drivers are allowed to reject requests, on average over 90% of requests are fulfilled when 80% of the fare goes to drivers; this drops to below 60% when only 40% of the fare goes to drivers). Thus, neither the platform nor the drivers benefit from low driver compensation due to its resulting low driver participation and thus low request fulfillment. Finally, for the cases tested, a maximum menu size of 5 is recommended as it produces good quality platform solutions without requiring much driver selection time.

Stochastic driver responses, independently accepting or rejecting each request in their menus, endogenously depend on the offered compensation for each request and the driver’s effort required to fulfill the request (e.g. extra driving time). Therefore, in this dissertation, we also create and solve an optimization model to simultaneously determine personalized menus and incentives to offer drivers. We exploit variable properties to circumvent nonlinear variable relationships, formulating the model as a linear integer program. Stochastic driver responses are modeled as a sample of variable and fixed scenarios. An imposed premium counterbalances solution overfitting. Solution methods decompose and iterate, improving performance of computational experiments that use request/driver trip information from the Chicago Regional transportation network. Our approach outperforms alternative methods and our first approach that has no incentives by strategically using personalized incentives to prioritize promising matches and to increase drivers’ willingness to accept requests. This benefits both customers and drivers: the average driver income is increased by 4.1% compared to the menu-only model, and 96.6% of requests are matched (4.1% higher than the menu-only method). Higher incentives are offered when drivers are more likely to accept, while fewer incentives and menu slots are reserved for driver-request pairs less likely to be accepted.

We design a third approach to examine the tradeoffs between the potential performance gains with the inclusion of incentives, and ensuring a fair experience for drivers. We use the same methodology and experiment data as our second framework producing menus and incentives, with added constraints that enforce one of three fairness types. These constraints require that nearby drivers receive the same compensation for the same request (driver proximity fairness), that drivers closer to the request receive higher compensation as they incur a shorter customer wait time (closer higher fairness), or that all compensation offers for a request are the same (all equal fairness). Computational experiments illustrate that personalized incentives even under such fairness constraints can still benefit the platform. Several of the properties of the fairness-constrained solutions, namely, match rate, profit, and driver income, are in between those of the two extremes found in the no-incentives solutions and the incentives-without-fairness-constraints solutions, while also providing a certain level of incentive fairness. Compared to the remaining fairness settings, solutions from driver proximity fairness with a low distance threshold value (the method with the fewest fairness constraints) and from our method with no fairness constraints have fewer compensation offers that have a nonzero incentive, but have the highest incentive offer average.

Her contribution on optimizing menus under stochastic driver selection was recently accepted for publication.

Horner, Hannah, Pazour, Jennifer A., and Mitchell, John, 2021 “Optimizing Driver Menus Under Stochastic Selection Behavior for Ridesharing and Crowdsourced Delivery.” Transportation Research Part E: Logistics and Transportation Review: [Preprint] [https://doi.org/10.1016/j.tre.2021.102419]  [Free access until Sept 19, 2021]

Her contributions on menus and personalized incentives is currently under review. The preprint version of the paper can be found here. Further, her INFORMS 2020 presentation, posted below, gives a great overview of the work.

Her dissertation was co-advised by Prof. John Mitchell and me. She has been a joy to work with, is fiercely intelligent and by far the most chill PhD student I have advised. She’s lucky #7 of my PhD advised family; I’m biased, but it’s a fantastic group of independent thinkers and kind, helpful humans. Congrats Hannah!

New Team Members Join

Our research group welcomed two new PhD students to our team today, Joyjit and Milan.

Joyjit Bhowmick is an Industrial Engineering graduate with over four years of industry experience as a Supply Chain professional. During his industry years, he was involved in research projects with a focus on operations research implementations. Currently, his research interest lies in the application of optimization techniques in the area of supply chain management.

Milan Preet Kaur has completed her Undergraduate Studies in Management Engineering from the University of Waterloo, Canada. She has over 2 years of industry experience as a data and process engineering analyst. During her undergraduate studies, she worked on various process optimization projects, one of which went on to win first prize in the Undergraduate Research Paper competitions in both CORS and INFORMS conferences. As a graduate student at RPI, she is interested in working on the application of supply chain management and optimization techniques to Volunteer Management in non-profit organizations.

I’m excited to work with them and have their talents in our group.

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.