2777. Discrete-choice Multi-agent Optimization: Decentralized Hard Constraint Satisfaction for Smart Cities
Invited abstract in session WD-3: Operational Research Frontiers: the past & the future, stream Celebrating 50 Years of EURO.
Wednesday, 14:30-16:00Room: Esther Simpson 1.01
Authors (first author is the speaker)
| 1. | SRIJONI MAJUMDAR
|
Abstract
Making Smart Cities more sustainable, resilient and democratic
is emerging as an endeavor of satisfying hard constraints, for instance meeting net-zero targets. Decentralized multi-agent methods for socio-technical optimization of large-scale complex infrastructures such as energy and transport networks are scalable and
more privacy-preserving by design. However, they mainly focus
on satisfying soft constraints to remain cost-effective. This paper
introduces a new model for decentralized hard constraint satisfaction in discrete-choice combinatorial optimization problems. The
model solves the cold start problem of partial information for coordination during initialization that can violate hard constraints. It
also preserves a low-cost satisfaction of hard constraints in subsequent coordinated choices during which soft constraints optimization is performed. Strikingly, experimental results in real-world
Smart City application scenarios such as energy, bike sharing and UAVs swarm sensing demonstrate the required behavioral shift to preserve optimality to a large extent when hard constraints are satisfied. The optimality sacrifice as moving from soft to hard constraints is measured in terms of the required behavioural shift to preserve performance, i.e. restoring altruism
deficit. These findings are significant for policymakers, system operators, designers and architects to create the missing social capital of running cities in more viable trajectories.
Keywords
- OR in Sustainability
- Multi-Objective Decision Making
- Economic Modeling
Status: accepted
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