Random Forest Assisted Differential Evolution for Multi-server Congested p-median Problem

This paper addresses the facility location problem in the context of multiple-server facilities subject to congestion. The objective is to select a subset of facilities from a pool of candidate locations in order to meet customers’ demands. Additionally, the number of servers allocated to each facility is treated as a decision variable, and the service time for each server follows an exponential distribution. As network location problems are known to be NP-hard, this study introduces a random forest as a surrogate model with differential evaluation to minimize the aggregate expected traveling times and aggregate expected waiting times of customers. The proposed algorithm is implemented and evaluated on a set of test problems with different sizes and specifications, demonstrating its high efficiency compared to differential evaluation.