A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem
This work addresses facility location decisions under disruption risks for supply chain and logistics management, representing an incremental improvement with a novel model and algorithm.
The authors tackled the reliable facility location problem by proposing a new model that treats the number of allocated facilities as a variable, making it more realistic but harder to solve, and developed a hybrid evolutionary algorithm (EAMLS) that outperformed CPLEX and a genetic algorithm on large-scale problems.
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.