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Optimization of a Predictive Aircraft Maintenance Routing Model Using Mutated Constrained Particle Swarm Optimization

Optimization of a Predictive Aircraft Maintenance Routing Model Using Mutated Constrained Particle Swarm Optimization
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Author(s): Abdellatif El Afia (ENSIAS, Mohammed V University, Morocco) and Malek Sarhani (ENSIAS, Mohammed V University, Morocco)
Copyright: 2018
Pages: 17
Source title: Contemporary Approaches and Strategies for Applied Logistics
Source Author(s)/Editor(s): Lincoln C. Wood (University of Otago, New Zealand)
DOI: 10.4018/978-1-5225-5273-4.ch015

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Abstract

Aircraft maintenance routing (AMR) is one of the most studied problems in the airline industry and has gained much attention. The aim of this chapter is to solve a mathematical formulation of the daily AMR problem, which aims to minimize the routing cost while incorporating the risk of unscheduled maintenance. This predictive model requires the optimization algorithm to both assure the feasibility of the solution and to continuously track unscheduled maintenance events. To address these issues, the authors propose a hybrid solution approach with two main contributions: it examines the use of a binary version of particle swarm optimization (PSO) adapted to this constrained optimization problem, and it consists of using an adaptive mutation operator designed to deal with unscheduled maintenance.

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