Enhancing Urban Sustainability Infrastructure: Mathematical Approaches for Optimizing Investments Proceedings of a Workshop (2023) / Chapter Skim
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4 Decision Making for Infrastructure Investments
4 Decision Making for Infrastructure Investments
Pages 35-53
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From page 35... ...
A panel of experienced experts highlighting the challenges and opportunities in multi-objective decision making preceded speakers who addressed the practical barriers and processes for bringing optimal decisions to fruition. 4.1 DECARBONIZING INFRASTRUCTURE REQUIRES SYSTEMS THINKING AND SYSTEMS MODELING Costa Samaras, White House Office of Science and Technology Policy (OSTP)
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From page 36... ...
Part of this optimization involves deploying cyberphysical controls and managing smart devices while maintaining cybersecurity: remote sensing, artificial intelligence, and forecasting are critical. Samaras highlighted three critical and interconnected challenges related to electrification and decarbonization: ensuring a net-zero power grid, confirming that electrified end uses work together, and enhancing climate-induced reliability and resilience.
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From page 37... ...
He urged the optimization and analysis community to focus on multiple criteria to ensure a successful energy transition, including reducing household energy burdens, increasing energy security, addressing historical equity and justice concerns, reducing GHG emissions, building secure supply chains for critical materials for the 21st century economy, and creating new well-paying jobs and industries. He stressed that innovation in 21st century cyberphysical systems enables decarbonization, reliability, and resilience in an increasingly complex energy system.
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From page 38... ...
For example, a recent flexibility analysis revealed that a flexible plan generated a ~20 percent increase in cost effectiveness and performance of a system.2 De Neufville stressed that the decision-making process includes a sequence of choices made over time in the context of changing situations, where each choice is part of a strategy to win and to avoid loss. Effective decision making demands careful consideration of possible developments over time, consequences of possible decisions, and evaluation of the overall results from a combination of possible developments and decisions.
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From page 39... ...
Reflecting on Latanya Sweeney's work,3 Noble explained that it is no longer difficult to reconstitute data and identify these individuals. Another emerging issue is the use of last-mile public transportation infrastructure for redlining -- for example, neighborhoods that experience surge pricing from or are avoided by mobility service providers.
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An emerging area of optimization is non-convex, non-smooth optimization, which is driven by the need to solve models relevant in training deep neural nets and in reinforcement learning. Thinking about an optimization model in terms of 5 For more information about the Distributed Artificial Intelligence Research Institute, see https://www.dair-institute.org/about, accessed August 28, 2022.
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From page 41... ...
process to optimize investments to enhance urban sustainability infrastructure: develop a suite of metrics that capture system performance, optimize operations for a given system design with respect to these metrics, optimize the system design repeatedly, and integrate the impact of human interaction. He shared a case study of using optimization to guide infrastructure design for bikesharing.
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From page 42... ...
Therefore, using the same mathematical program, they embedded preparedness actions and modeled reduction in recovery costs based on those preparedness actions. When these were added to the formulation, the structure of the mathematical program that supports decision making changed to a two-stage stochastic program, which is solved with an integer L-shaped decomposition approach.
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4.6 COOPERATIVE AND ADAPTIVE INFRASTRUCTURE INVESTMENT PATHWAYS FOR URBAN WATER SUPPLIES Patrick Reed, Cornell University, described challenges to urban water supply planning, including a growing population and a changing climate. The American Society of Civil Engineers estimates that the U.S.
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From page 44... ...
or longer term, with consideration of the individual and collective supply capacity through architectural design and prioritization. Using this model of cooperative, adaptive infrastructure investment pathways, it might be possible to wait 25 years before adding specific infrastructure based on expected future conditions; if the future is more challenging, investment might be needed 5 years earlier, and if the future is mild, no investments would be needed.
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From page 45... ...
NOTE: DCR, drought crisis robustness; DFSR, drought crisis and long-term financial stability robustness; EDF, expected drought performance and financial stability; Inf NPC, infrastructure net present cost; MEI, minimum expected investment. PFC, peak financial cost; Rel, reliability objective; RF, restriction frequency; UC, unit cost of infrastructure investment; WCC, worst case cost.
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From page 46... ...
For example, Banks suggested the use of free individual spreadsheets based on town size that explain respective decision theories and priorities. Lempert asked for an example of an institution that has used this analytical decision-making process, and Banks said that the Department of Transportation has allocated money more thoughtfully to railroads, airports, and roadways by using optimization techniques introduced in the 2000s.
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From page 47... ...
plastic waste currently goes to landfills. Yet, he asserted that relying on landfills for the disposal of plastic waste is not sustainable owing to limited space, and alternative approaches are needed.
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The level of food insecurity varies across the United States; in North Carolina, for example, some rural areas have food insecurity rates that are higher than the national average. Barriers to equitable food access include transportation, poverty, and physical locations of food distributors (i.e., the food landscape)
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From page 49... ...
This project combined siloed data from the North Carolina Department of Commerce, the Census Bureau, and the Durham Neighborhood Compass and considered food vendor names and locations; community health, transportation, and housing; and census tract boundaries to understand food access. For example, the visualization of the food landscape by business category showed fewer grocery stores in more heavily populated areas of the county; more grocery and convenience stores and restaurants were located in the center of downtown Durham (see Figure 4-2)
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From page 50... ...
He noted that project risk analysis has many facets: statutes, regulations, and ordinances provide project constraints; existing standards and guidance influence design criteria; prioritization adjustments are made based on fiscal and financial availability, political preferences, leadership changes, community preferences, and resources to maintain projected benefits; and engineering and contractor inputs are incorporated in projects.
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From page 51... ...
Combining several data sources with the use of geographic information systems and statistical software, a spatial data architecture was created to determine the weights of particular influences -- age of housing, owning versus renting, assessed tax values, median housing income, and race -- on lead exposure and reactions to lead. This information was leveraged to develop an intervention model that could be used to protect children (see Figure 4-3)
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From page 52... ...
She described an ongoing project to identify culturally relevant food, because matching food supply to people's specific needs is an important component of equity. Miranda noted that adding grocery stores and eliminating food deserts alone does not solve food access problems; people might not have information about what is healthy for their family or the money to purchase healthy food, so data on how people decide what type of food to eat would also be useful.
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From page 53... ...
Liban highlighted the human factor in decision making: "the model is the model and the data are the data, but the science is an art." Miranda directed workshop participants to study work by Kathy Ensor, Mercedes Bravo, and Dan Kowal on spatial analysis.8 Miranda and Davis championed building relationships with affected communities who will use the models to make decisions; the mathematics cannot substitute for understanding people and the full scope of their problems. 8For more information about current work on spatial analysis, see http://ensor.rice.edu/, https://globalhealth.duke.edu/people/bravo-mercedes, and http://www.danielrkowal.
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