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 more close 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.

Highlights

  • The facility location problem aims at finding the optimal locations for facilities from a set of candidate location nodes in order to minimize the cost such as the fixed facility cost and the transposition cost, or to maximize the total revenue

  • M−1 r=0 hi cij pr Constraint (14) makes the number of facilities allocated to each customer (i.e., m) a variable and its value is related to location decision variables (i.e., X)

  • This paper proposes a new reliable facility location problem (RFLP) formulation in which the number of facilities allocated to each customer (i.e., m) is not fixed but varies with decision variables X

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Summary

Introduction

The facility location problem aims at finding the optimal locations for facilities from a set of candidate location nodes in order to minimize the cost such as the fixed facility cost and the transposition cost, or to maximize the total revenue. One or more of them may not work from time to time because of disruptions, examples include natural disasters, inclement weather, destruction of facilities by fire or flood, expiration of the contract, and any other force majeure factors. In such a situation, these are facility “failures”. Based on the work of [5,6], we propose a new reliable facility locationallocation problem (RFLP) formulation, which does not fix the number of allocated facilities to each customer as a constant and is more close to reality. EAMLS combines a memorable local search method with an evolutionary algorithm, which has a good performance on both small-scale and large-scale problems considered in this paper.

Related Work
Problem Formulation
A Hybrid Evolutionary Algorithm
16: Return bestSol
Operator Design of GA and EAMLS
Convergence Metric l3-Value
Computational Studies
Experimental Design
Experiments on the m = 2 and m = j∈J Xj Models
Analyses and Discussions
Conclusion
Full Text
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