A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem
- URL: http://arxiv.org/abs/2007.04769v1
- Date: Sat, 27 Jun 2020 11:31:55 GMT
- Title: A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem
- Authors: Han Zhang, Jialin Liu, and Xin Yao
- Abstract summary: The reliable facility location problem (RFLP) plays a vital role in the decision-making and management of modern supply chain and logistics.
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.
To handle it, we propose EAMLS, a hybrid evolutionary algorithm, which combines a memorable local search (MLS) method and an evolutionary algorithm (EA)
- Score: 10.668347198815438
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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