Restless Multi-armed Bandits under Frequency and Window Constraints for Public Service Inspections
Municipal inspections are an important part of maintaining the quality of goods and services. In this paper, we approach the problem of intelligently scheduling service inspections to maximize their impact, using the case of food establishment inspections in Chicago as a case study. The Chicago Department of Public Health (CDPH) inspects thousands of establishments each year, with a substantial fail rate (over 3,000 failed inspection reports in 2023). To balance the objectives of ensuring adherence to guidelines, minimizing disruption to establishments, and minimizing inspection costs, CDPH assigns each establishment an inspection window every year and guarantees that they will be inspected exactly once during that window. These constraints create a challenge for a restless multi-armed bandit (RMAB) approach, for which there are no existing methods. We develop an extension to Whittle index-based systems for RMABs that can guarantee action window constraints and frequencies, and furthermore can be leveraged to optimize action window assignments themselves. Briefly, we combine MDP reformulation and integer programming-based lookahead to maximize the impact of inspections subject to constraints. A neural network-based supervised learning model is developed to model state transitions of real Chicago establishments using public CDPH inspection records, which demonstrates 10\% AUC improvements compared with directly predicting establishments' failures. Our experiments not only show up to 24\% (in simulation) or 33\% (on real data) reward improvements resulting from our approach but also give insight into the impact of scheduling constraints.
Code (0)
등록된 구현이 없습니다.
Tasks
Multi-Armed BanditsSchedulingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Indexability of Finite State Restless Multi-Armed Bandit and Rollout Policy
We consider finite state restless multi-armed bandit problem. The decision maker can act on M bandits out of N bandits in each time step. The play of arm (active arm) yields state dependent rewards based on action and wh…
Global Rewards in Restless Multi-Armed Bandits
Restless multi-armed bandits (RMAB) extend multi-armed bandits so pulling an arm impacts future states. Despite the success of RMABs, a key limiting assumption is the separability of rewards into a sum across arms. We ad…
Multi-Armed BanditsMARBLE: Multi-Armed Restless Bandits in Latent Markovian Environment
Restless Multi-Armed Bandits (RMABs) are powerful models for decision-making under uncertainty, yet classical formulations typically assume fixed dynamics, an assumption often violated in nonstationary environments. We i…
Multi-Armed BanditsNetworked Restless Bandits with Positive Externalities
Restless multi-armed bandits are often used to model budget-constrained resource allocation tasks where receipt of the resource is associated with an increased probability of a favorable state transition. Prior work assu…
Multi-Armed BanditsContextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making
This paper introduces a novel multi-armed bandits framework, termed Contextual Restless Bandits (CRB), for complex online decision-making. This CRB framework incorporates the core features of contextual bandits and restl…
Decision MakingMulti-Armed Bandits