Stochastic Multi-objective Multi-trip AMR Routing Problem with Time Windows

TL;DR

This paper addresses a stochastic multi-objective routing problem for autonomous mobile robots in healthcare, proposing a novel hybrid algorithm to optimize costs and service quality under uncertainty.

Contribution

It introduces a population-based tabu search algorithm combining genetic algorithms and tabu search for the first time to solve this complex stochastic routing problem.

Findings

The PTS algorithm is efficient on modified Solomon instances.

It reveals how confidence levels impact optimal solutions.

Provides insights for balancing cost and patient satisfaction.

Abstract

In recent years, with the rapidly aging population, alleviating the pressure on medical staff has become a critical issue. To improve the work efficiency of medical staff and reduce the risk of infection, we consider the multi-trip autonomous mobile robot (AMR) routing problem with the stochastic environment to find the solution to minimizing the total expected operating cost and maximizing the total service quality of patients so that each route violates the vehicle capacity and the time window with only a very small probability. The travel time of AMRs is stochastic affected by the surrounding environment, the demand for each ward is unknown until the AMR reaches the ward, and the service time is linearly related to the actual demand. We develop a population-based tabu search algorithm (PTS) that combines the genetic algorithm with the tabu search algorithm to solve the problem. Extensive numerical experiments were conducted on the modified Solomon instances to show that the PTS algorithm the efficient and reveals the impacts of the confidence level on the optimal solution, providing insights for the decision-maker to devise delivery schemes that trade-off the operating cost for patient satisfaction.