A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem

TL;DR

This paper introduces a new model for the reliable facility location problem that considers the number of facilities as an independent variable, and proposes a hybrid evolutionary algorithm to solve it efficiently, outperforming traditional methods.

Contribution

The paper presents a novel RFLP model with variable facility allocation and develops EAMLS, a hybrid evolutionary algorithm combining local search and evolutionary strategies, for improved solution quality.

Findings

EAMLS outperforms CPLEX and GA on large-scale problems.

The new model better reflects real-world scenarios.

The l3-value metric effectively analyzes convergence.

Abstract

The reliable facility location problem (RFLP) is an important research topic of operational research and plays a vital role in the decision-making and management of modern supply chain and logistics. Through solving RFLP, the decision-maker can obtain reliable location decisions under the risk of facilities' disruptions or failures. In this paper, we propose a novel model for the RFLP. Instead of assuming allocating a fixed number of facilities to each customer as in the existing works, we set the number of allocated facilities as an independent variable in our proposed model, which makes our model closer to the scenarios in real life but more difficult to be solved by traditional methods. To handle it, we propose EAMLS, a hybrid evolutionary algorithm, which combines a memorable local search (MLS) method and an evolutionary algorithm (EA). Additionally, a novel metric called l3-value is proposed to assist the analysis of the algorithm's convergence speed and exam the process of evolution. The experimental results show the effectiveness and superior performance of our EAMLS, compared to a CPLEX solver and a Genetic Algorithm (GA), on large-scale problems.