Strategic Infrastructure Design via Multi-Agent Congestion Games with Joint Placement and Pricing

TL;DR

This paper introduces a multi-agent framework for joint infrastructure placement and pricing, optimizing resource allocation in congestion-prone environments like EV charging, by modeling strategic agent interactions with a bi-level approach.

Contribution

It presents a novel bi-level optimization model and a scalable approximation algorithm for joint placement and pricing in congestion games, addressing a complex NP-hard problem.

Findings

Reduces social cost by up to 40% in benchmark tests

Effectively models strategic agent responses in infrastructure planning

Generalizes to various multi-agent system domains

Abstract

Real-world infrastructure planning increasingly involves strategic interactions among autonomous agents competing over congestible, limited resources. Applications such as Electric Vehicle (EV) charging, emergency response, and intelligent transportation require coordinated resource placement and pricing decisions, while anticipating the adaptive behaviour of decentralised, self-interested agents. We propose a novel multi-agent framework for joint placement and pricing under such interactions, formalised as a bi-level optimisation model. The upper level represents a central planner, while the lower level captures agent responses via coupled non-atomic congestion games. Motivated by the EV charging domain, we study a setting where a central planner provisions chargers and road capacity under budget and profitability constraints. The agent population includes both EV drivers and non-charging drivers (NCDs), who respond to congestion, delays, and costs. To solve the resulting NP-hard problem, we introduce ABO-MPN, a double-layer approximation framework that decouples agent types, applies integer adjustment and rounding, and targets high-impact placement and pricing decisions. Experiments on benchmark networks show that our model reduces social cost by up to 40% compared to placement- or pricing-only baselines, and generalises to other MAS-relevant domains.