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1. Evaluating on-demand warehousing via dynamic facility location models

   https://doi.org/10.1080/24725854.2021.2008066  

   Unnu, Kaan ; Pazour, Jennifer ( January 2022 , IISE Transactions)

   On-demand warehousing platforms match companies with underutilized warehouse and distribution capabilities with customers who need extra space or distribution services. These new business models have unique advantages, in terms of reduced capacity and commitment granularity, but also have different cost structures compared with traditional ways of obtaining distribution capabilities. This research is the first quantitative analysis to consider distribution network strategies given the advent of on-demand warehousing. Our multi-period facility location model – a mixed-integer linear program – simultaneously determines location-allocation decisions of three distribution center types (self-distribution, 3PL/lease, on-demand). A simulation model operationally evaluates the impact of the planned distribution strategy when various uncertainties can occur. Computational experiments for a company receiving products produced internationally to fulfil a set of regional customer demands illustrate that the power of on-demand warehousing is in creating hybrid network designs that more efficiently use self-distribution facilities through improved capacity utilization. However, the business case for on-demand warehousing is shown to be influenced by several factors, namely on-demand capacity availability, responsiveness requirements, and demand patterns. This work supports a firm’s use of on-demand warehousing if it has tight response requirements, for example for same-day delivery; however, if a firm has relaxed response requirements, then on-demand warehousing is only recommended if capacity availability of planned on-demand services is high. We also analyze capacity flexibility options leased by third-party logistics companies for a premium price and draw attention to the importance of them offering more granular solutions to stay competitive in the market. 

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2. On the Unique Features and Benefits of On-Demand Distribution Models

   Pazour, Jennifer A ; Unnu, Kaan ( January 2018 , Progress in material handling research)

   To close the gap between current distribution operations and today’s customer expectations, firms need to think differently about how resources are acquired, managed and allocated to fulfill customer requests. Rather than optimize planned resource capacity acquired through ownership or long- term partnerships, this work focuses on a specific supply-side innovation – on-demand distribution platforms. On-demand distribution systems move, store, and fulfill goods by matching autonomous suppliers' resources (warehouse space, fulfillment capacity, truck space, delivery services) to requests on-demand. On-demand warehousing systems can provide resource elasticity by allowing capacity decisions to be made at a finer granularity (at the pallet-level) and commitment (monthly versus yearly), than construct or lease options. However, such systems are inherently more complex than traditional systems, as well as have varying costs and operational structures (e.g., higher variable costs, but little or no fixed costs). New decision- supporting models are needed to capture these trade-offs. 

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3. A two-stage stochastic aggregate production planning model with renewable energy prosumers,” in Proceedings of the 2021 Institute of Industrial and Systems Engineers (IISE) Virtual Conference, May 22-25, 2021, pp. 223-228.

   Islam, S. ; Novoa, C. ; Jin, T. ( June 2021 , Proceedings of the 2021 Institute of Industrial and Systems Engineers (IISE) Conference)

   Ghate, A. ; Krishnaiyer, K. ; Paynabar, K. (Ed.)

   This study presents a two-stage stochastic aggregate production planning model to determine the optimal renewable generation capacity, production plan, workforce levels, and machine hours that minimize a production system’s operational cost. The model considers various uncertainties, including demand for final products, machine and labor hours available, and renewable power supply. The goal is to evaluate the feasibility of decarbonizing the manufacturing, transportation, and warehousing operations by adopting onsite wind turbines and solar photovoltaics coupled with battery systems assuming the facilities are energy prosumers. First-stage decisions are the siting and sizing of wind and solar generation, battery capacity, production quantities, hours of labor to keep, hire, or layoff, and regular, overtime, and idle machine hours to allocate over the planning horizon. Second-stage recourse actions include storing products in inventory, subcontracting or backorder, purchasing or selling energy to the main grid, and daily charging or discharging energy in the batteries in response to variable generation. Climate analytics performed in San Francisco and Phoenix permit to derive capacity factors for the renewable energy technologies and test their implementation feasibility. Numerical experiments are presented for three instances: island microgrid without batteries, island microgrid with batteries, and grid-tied microgrid for energy prosumer. Results show favorable levelized costs of energy that are equal to USD48.37/MWh, USD64.91/MWh, and USD36.40/MWh, respectively. The model is relevant to manufacturing companies because it can accelerate the transition towards eco-friendly operations through distributed generation. 

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4. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   null (Ed.)

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge. 

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5. Upskilling to Meet Cloud Talent Needs

   Meyer, Lawrence E. ; Billionniere, E. ( July 2021 , 2021 ASEE Virtual Annual Conference)

   null (Ed.)

   Miami Dade College, based on demands in the workforce, has focused on creating emerging technology education, particularly Cloud technology. Business Cloud migration has accelerated over the last several years, and companies around the world are investing in their future with the cloud. With the increased demand for cloud-skilled professionals the last four years, we launched a cloud literacy initiative to meet cloud talent needs. This initiative aims to provide our students in the computing/IT fields with the knowledge, abilities and skills needed to accelerate their cloud-related learning. With the support of NSF ATE, we collaborated with Amazon Web Services (AWS) to create a new pathway for the next generation of cloud computing professionals. The course sequence was designed in conjunction with an AWS Educate team assisting in the design of course sequencing and degree plans to leverage their educational experience in teaching cloud technologies. The core curriculum designed for the cloud literacy initiative leveraged an existing pathway for an associate degree in networking technology, and then partially pulled classes from the design of the bachelor’s in information systems technology degree. The classes identified, used current offerings across our programs and included a focus on the supporting infrastructure of cloud systems: Databases, Linux OS, and Networking. With these three foundational classes that were cloudified, three cloud-focused courses based on industry certifications were developed: Cloud Essentials for AWS Cloud Practitioner and Cloud Infrastructure and Services for AWS Solutions Architect certifications with a capstone class completing the academic pathway. This new curriculum includes pedagogical changes to utilize project-based learning by incorporating resources and learning from multiple sources to best mimic real-world application, data, and design attributes. In addition to the associate degree, a college credit certificate in cloud computing was created to strengthen (re-)entering students in the workforce and dual enrollment students with credentials and employability skills by using high impact educational practices. Our cloud curriculum incorporates project-based learning approach, a real-world experience using the cloud technology. This poster shares strategies and pedagogical tools for teaching a cloud-focused curriculum for broader impact and student success. 

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