INFORMS San Francisco Chapter In-Person Event

We will kick off at 5 pm with food, drinks and networking. The talks will start at 5:45pm, and feature a series of concise talks highlighting the diverse range of optimization use cases, with broad applications across industries, each contributing a significant impact.

Please RSVP via Meetup and register with Amazon Security via this form. You'll need to bring a photo ID. Parking is available on-site.

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Shubhangi Deshpande (Operations Research Scientist at Workday)

Title: A workforce scheduling problem modeled as a mixed integer programming problem
Abstract: During this talk, we'll begin by discussing the problem statement concerning workforce scheduling. Following, we'll delve further into the MIP formulation, covering the model variables, constraints, and objectives. Lastly, we will address existing challenges and explore potential solution approaches.
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Ed Klotz (Sr. Mathematical Optimization Specialist at Gurobi)

Title: A Brief Survey of Mathematical Optimization in the Energy Industry
Abstract: We will describe several mathematical optimization use cases in the energy industry, including renewables. We will see how mathematical optimization can address additional variability associated with renewable energy sources. Some cases involve applying classical optimization models such as facility location and supply chain logistics to fit specific operational requirements involving renewable energy. Others involve models specific to the actual energy production process.
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Luis Rodriguez Garcia (Postdoctoral Researcher at LBNL)

Title: Involving equity in the resilient planning of power distribution systems against heat waves
Abstract: Extreme heat events disrupt the operation of power systems, increasing electricity demand, causing power outages, and exposing communities to an inequitably distributed risk of overheating. This talk discusses the inclusion of the overheating risk inequity into power distribution system investment planning. This approach aims to enhance the resilience of power distribution systems to extreme heat while ensuring that new resources are equitably distributed according to community heat vulnerability.
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Phil Kaminsky (Sr. Principal Research Scientist at Amazon Middle Mile)

Title: Freight Capacity Portfolio Optimization in Amazon's Two-Sided Freight Marketplace
Abstract: Amazon acquires capacity to ship truckloads of goods in its middle mile network, and provides this capacity to external shippers. Due to Amazon’s high volumes and commitment to rapid delivery, some capacity is most efficiently acquired long before it is utilized, while other is best acquired immediately preceding its use. Amazon acquires a portion of capacity through medium and long-term contracts, while delaying the acquisition of spot capacity for highly uncertain demand. At Amazon, we developed a transportation marketplace, and a series of models and tools, that allow Amazon to use pricing levers to optimize its transportation capacity portfolio. This approach enables Amazon to effectively align capacity risk, supply risk and price, and to provide carriers and shippers with tools to effectively manage their operations.
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Larry Jin (Data Science Manager at c3.ai)

Title: Leveraging OR to tackle the challenges in production and manufacturing.
Abstract: At C3 AI, our extensive experience in deploying large-scale optimization models is evident across numerous industry sectors, encompassing production scheduling, process, and inventory optimization. This presentation will focus on a pivotal component within the C3 AI supply chain suite—production schedule optimization. We will provide an examination of how operations research, applied at scale, addresses the complexities inherent in production and manufacturing.
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Max Henrion (CEO at Lumina Decision Systems)

Title: Optimizing EV charging could reduce electric rates and increase utility profits
Abstract: As electric vehicle adoption grows, how will the increased demand for electricity affect tariffs and utility earnings? Lumina and LBNL developed a model that optimizes charging schedules and addition of new power generation. It shows that managed charging could reduce the unit cost of electricity, combining higher earnings for utilities with lower electricity rates. The Analytica model illustrates the advantages of formulating optimization problems visually using influence diagrams.