Optimizing Service Level Agreements for Efficiency and Equity in City Government Operations: A Queuing-Based Approach

Temel Kavramlar

Balancing efficiency and equity in city service allocation, particularly for time-sensitive incidents, can be achieved by optimizing Service Level Agreements (SLAs) using a queuing-based model that considers incident risk, arrival rates, and budget constraints.

Özet

• Bibliographic Information: Liu, Z., & Garg, N. (2024). Redesigning Service Level Agreements: Equity and Efficiency in City Government Operations. Proceedings of the 25th ACM Conference on Economics and Computation, 1-22.
• Research Objective: This paper investigates the optimization of Service Level Agreements (SLAs) in city government operations, aiming to balance efficiency (prioritizing urgent incidents) and equity (ensuring fair service distribution across neighborhoods) in resource allocation.
• Methodology: The authors develop a queuing network model to represent the inspection scheduling process, incorporating incident arrival rates, risk levels, and budget constraints. They formulate an optimization problem to determine optimal SLAs, budget allocations, and incident prioritization policies, considering both efficiency and equity objectives. The authors further propose a simulation-optimization framework to handle real-world complexities, such as non-Poisson arrival rates and capacity limitations.
• Key Findings: The theoretical analysis reveals that the "price of equity"—the efficiency loss from prioritizing equity—is often small, particularly when risk distributions are similar across neighborhoods. Empirical application of the framework to New York City's Department of Parks and Recreation data demonstrates that optimal budget allocations differ significantly from the status quo, leading to substantial improvements in both efficiency and equity. The study also finds that centralizing response operations offers only a modest benefit over optimized borough-specific allocations.
• Main Conclusions: The research highlights the feasibility and benefits of optimizing SLAs to enhance both efficiency and equity in city service delivery. The findings suggest that inefficient policies are often inequitable, implying a potential "win-win" scenario where improvements in efficiency also promote equity.
• Significance: This work provides a practical and data-driven approach for city agencies to design and implement more effective and equitable service allocation policies, particularly for time-sensitive incidents.
• Limitations and Future Research: The study acknowledges limitations in capturing the full complexity of real-world agency operations and suggests exploring more sophisticated models and policies in future research.

İstatistikler

From 2019 to 2023, around 30% of inspected incidents were inspected later than another incident from the same category which got reported later.
Historically, only around 60% of all service requests are responded to.

Alıntılar

"We consider government service allocation – how the government allocates resources (e.g., maintenance of public infrastructure) over time. It is important to make these decisions efficiently and equitably – though these desiderata may conflict."
"Service Level Agreements have the following desirable properties: (a) They are commonly used to characterize and communicate system performance... (b) If met, they can translate to equity and efficiency desiderata"
"We further find that the empirical price of equity is indeed small: inefficient policies are also inequitable, and vice-versa."

Önemli Bilgiler Şuradan Elde Edildi

Redesigning Service Level Agreements: Equity and Efficiency in City Government Operations

by Zhi Liu, Nik... : arxiv.org 10-22-2024

https://arxiv.org/pdf/2410.14825.pdf

Daha Derin Sorular

How can this framework be adapted to address situations with limited or unreliable data on incident risk or arrival rates?

The framework's reliance on accurate data for incident risk (rk,b) and arrival rates (λk,b) poses a significant challenge when such data is limited or unreliable. Here's how the framework can be adapted:
1. Data Imputation and Estimation:

Expert Elicitation: In the absence of reliable historical data, domain experts can be consulted to provide estimates for incident risk and arrival rates. Structured elicitation techniques can be employed to gather informed judgments and quantify uncertainty in these estimates.
Proxy Data: Explore the use of proxy data sources that might correlate with incident risk or arrival rates. For instance, socioeconomic indicators, population density, or environmental factors could be used as proxies, especially when stratified by geographic units like Boroughs.
Statistical Imputation: Statistical methods can be used to impute missing values or adjust for unreliable data. Techniques like k-nearest neighbors, regression imputation, or model-based imputation can leverage existing data points to estimate missing or unreliable values.
2. Robust Optimization:

Uncertainty Sets: Instead of point estimates, define uncertainty sets around the estimated risk and arrival rates. This acknowledges the inherent uncertainty in the data and seeks solutions that perform well under a range of possible scenarios.
Minimax Optimization: Formulate the optimization problem to minimize the worst-case cost (in terms of efficiency and equity) over the defined uncertainty sets. This approach prioritizes robustness to data limitations.
3. Iterative Approach with Data Collection:

Pilot Studies: Implement the framework in a limited scope, focusing on areas or incident types with more reliable data. Use the insights gained to refine the model and data collection strategies.
Active Learning: Identify areas where data is most uncertain and prioritize data collection efforts in those areas. This iterative approach allows for continuous improvement of the model as more data becomes available.
4. Qualitative Considerations:

Community Engagement: Engage with community members and organizations to gather qualitative insights about service needs and disparities. This can help compensate for data limitations and ensure that the model reflects local priorities.
Transparency and Accountability: Clearly communicate the limitations of the data and the assumptions made in the model. Establish mechanisms for feedback and accountability to ensure that the policy outcomes are equitable and responsive to community needs.
By incorporating these adaptations, the framework can be made more resilient to data limitations and provide valuable guidance for service allocation even in data-scarce environments.

Could focusing solely on optimizing SLAs lead to unintended consequences, such as neglecting other important aspects of service quality or exacerbating existing inequalities in service access?

Yes, solely focusing on optimizing SLAs, while seemingly objective and quantifiable, can lead to unintended consequences. Here's why:
1. Narrow Definition of Service Quality:

Overemphasis on Speed: SLAs primarily focus on response times, potentially neglecting other crucial aspects of service quality like effectiveness of resolution, communication with residents, or professionalism of service delivery.
Gaming the System: Agencies might prioritize "easy" incidents with faster resolution times to meet SLAs, neglecting more complex or time-consuming issues that might be equally important to residents.
2. Exacerbating Existing Inequalities:

Ignoring Underlying Needs: Optimizing for city-wide average SLAs might mask disparities in service needs across different neighborhoods. Areas with historically lower service levels or higher incident rates might continue to experience disadvantages.
Reinforcing Spatial Bias: If incident risk ratings are based on historical data that reflects existing biases (e.g., under-reporting in certain areas), optimizing SLAs based on these ratings could perpetuate those biases.
3. Lack of Adaptability and Responsiveness:

Static Targets: SLAs are often set as static targets, failing to adapt to changing needs, seasonal variations, or unforeseen events. This lack of flexibility can lead to inefficiencies and inequities.
Limited Community Input: Focusing solely on SLAs might prioritize internal efficiency metrics over community priorities and feedback. This can lead to a disconnect between service delivery and actual resident needs.
To mitigate these risks, consider the following:

Holistic Service Quality Metrics: Incorporate a broader range of metrics beyond response times, capturing aspects like resolution effectiveness, resident satisfaction, and communication quality.
Equity-Aware Optimization: Instead of city-wide averages, optimize for SLAs that explicitly consider the needs and historical disadvantages of different neighborhoods.
Dynamic SLA Adjustments: Implement mechanisms to adjust SLAs dynamically based on factors like seasonal variations, changing needs, or feedback from residents.
Community Engagement and Transparency: Actively engage with communities to understand their priorities and concerns. Communicate clearly about service allocation policies and performance metrics.
By adopting a more holistic and equity-aware approach, city governments can leverage the benefits of SLA optimization while mitigating potential unintended consequences and ensuring equitable service delivery for all residents.

How might the increasing use of AI and automation in city government operations impact the design and implementation of equitable and efficient service allocation policies?

The increasing use of AI and automation presents both opportunities and challenges for designing and implementing equitable and efficient service allocation policies:
Opportunities:

Enhanced Predictive Capabilities: AI can analyze vast datasets to predict incident occurrence, risk levels, and service demand with greater accuracy. This enables proactive resource allocation and more effective SLA target setting.
Data-Driven Insights for Equity: AI can uncover hidden patterns of inequality in service access and identify areas requiring targeted interventions. This facilitates data-driven decision-making to address historical disadvantages.
Automated Workflow Optimization: AI can optimize workforce scheduling, routing, and task assignment in real-time, improving efficiency and responsiveness to dynamic service demands.
Personalized Service Delivery: AI can personalize service delivery based on individual needs and preferences, ensuring that residents receive tailored support.
Challenges:

Bias Amplification: If AI models are trained on biased historical data, they can perpetuate and even amplify existing inequalities in service allocation. Careful data selection, bias mitigation techniques, and ongoing monitoring are crucial.
Lack of Transparency and Explainability: Complex AI models can be opaque, making it difficult to understand the rationale behind service allocation decisions. This lack of transparency can erode trust and hinder accountability.
Job Displacement and Workforce Impacts: Automation of certain tasks might lead to job displacement, requiring reskilling programs and workforce adjustments to ensure a just transition.
Exacerbating Digital Divide: Reliance on AI-powered services could disadvantage residents without access to technology or digital literacy skills, further widening the digital divide.
Recommendations for Equitable and Efficient Implementation:

Prioritize Fairness and Equity: Embed fairness considerations into every stage of AI development and deployment, from data collection and model training to performance evaluation and policy decisions.
Ensure Transparency and Explainability: Utilize explainable AI techniques to provide clear justifications for service allocation decisions, fostering trust and accountability.
Human-in-the-Loop Systems: Design systems that leverage both human expertise and AI capabilities, ensuring that human oversight and ethical considerations remain central to decision-making.
Address Digital Equity: Invest in digital inclusion initiatives to bridge the digital divide and ensure that all residents can benefit from AI-powered services.
Continuous Monitoring and Evaluation: Regularly monitor AI systems for bias, unintended consequences, and effectiveness in achieving equity and efficiency goals.
By carefully navigating these opportunities and challenges, city governments can harness the power of AI and automation to create more equitable, efficient, and responsive service allocation policies that benefit all residents.