A genetic algorithm for optimizing space utilization in aircraft hangar shop

• DOI: http://doi.org/10.1111/itor.12642
• Date: 01 September 2019
• Published date: 01 September 2019
• Author: Xin Li,Z.X. Wang,S.H. Chung,Felix T.S. Chan

Intl. Trans. in Op. Res. 26 (2019) 1655–1675

DOI: 10.1111/itor.12642

INTERNATIONAL

TRANSACTIONS

IN OPERATIONAL

RESEARCH

A genetic algorithm for optimizing space utilization in aircraft

hangar shop

Xin Lia, Z.X. Wangb,∗, Felix T.S. Chancand S.H. Chungc

aCollege of Management, Shenzhen University, Shenzhen, Guangdong, China, 518061

bSchool of Business Administration, Dongbei University of Finance and Economics, Dalian, Liaoning, China, 116025

cDepartment of Industrial and Systems Engineering, The Hong Kong PolytechnicUniversity,

Hong Hum, Hong Kong, 999077

E-mail: xli@szu.edu.cn [Li]; wangzhengxu@dufe.edu.cn[Wang]; f.chan@polyu.edu.hk [Chan];

nick.sh.chung@polyu.edu.hk [Chung]

Received 24 January 2018; received in revised form 30 January 2019; accepted 1 February 2019

Abstract

This study considers the aircraft placement problem in aircraft hangar shops (AHS) encountered by aircraft

service companies. AHSs usually have irregular shapes, and aircraft, too, have special shapes. Moreover,

frequent operations involving moving aircraft in and out are complicated. For these reasons, aircraft place-

ment is difficult. The present study deals with operations management in AHS to optimize space utilization

by placing a greater number of aircraft, which would greatly benefit aircraft services companies. Herein, a

novel genetic algorithm (GA) based approach is applied to optimize space utilization. To exactly express

the problem, practical and operational principles, including both in AHS and in outdoor areas, are ab-

stracted based on interviews with the staff of an aircraft service company. Then, the placement space is

modeled in an x–ycoordinate system. In addition, a two-dimensional geometry model foraircraft, consisting

of seven parameters, is developed. Based on these works, a novel GA for solving the aircraft placement

problem is developed. Finally, a practical instance with eight aircraft serviced by a company is tested.

All eight aircraft are placed well by using the proposed approach. Compared to the previous scenario,

where at most seven aircraft could be placed well, the proposed approach will greatly benefit aircraft service

companies.

Keywords:aircraft hangar shops; aircraft placing; genetic algorithm; space utilization

1. Introduction

Owing to massive growthin air travel worldwide, the aviationindustry has developed and diversified

considerably,and various types of aircraft are being used, such as business flights and private aircraft.

It is challenging to operate aircrafttransportation both in the air and on the ground, that is, air traffic

∗Corresponding author.

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2019 The Authors.

International Transactionsin Operational Research C

2019 International Federation ofOperational Research Societies

Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main St, Malden, MA02148,

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1656 X. Li et al. / Intl. Trans. in Op. Res. 26 (2019) 1655–1675

management (Evans et al., 2016) and airport operations (Mirkovic et al., 2016). It is an integrated

and complicated operational system involving resource planning and arrangement (Hargaden and

Ryan, 2015).

Another type of greatly significant operation is scheduling and executing aircraft maintenance

(Tsagkas et al., 2014; Bruecker et al., 2015; Shanmugam and Robert, 2015). Moreover, owing to

space limitations in airports,maintenance works are usually performed by aircraftservice companies

near airports. The present study deals with operational problems encountered by aircraft service

companies. Optimal arrangements can certainly help these companies utilize limited sources more

effectively, thus increasing the service capacity and improving the service quality.

As for the aviation industry,extant research has focused on route scheduling (Murca and Muller,

2015; Lieder and Stolletz, 2016) and aircraft selection (Bazargan and Hartman, 2012; Sama et al.,

2013, 2014). Bazargan and Hartman (2012) proposed an aircraft replacement strategy. They devel-

oped new models and analyzed them in depth. Evans et al. (2016) focusedon air traffic management,

including models and evaluation approaches. Sama et al. (2013, 2014) worked on the integrated

aircraft scheduling and routing problem in the terminal control area of an airport. They developed

mathematical formulations. Murca and Muller (2015) investigated a similar aircraft scheduling

problem that considers alternative arrival routes. They formulated a mixed integer linear program-

ming (MILP) model to obtain the optimal scheduling results.Vancroonenburget al. (2014) proposed

an MILP model for air cargo selection and weight balancing. Lieder and Stolletz (2016) proposed

a dynamic programming approach to deal with aircraft take-off and landing to achieve the optimal

solutions in realistic problem instances. Mirkovic et al. (2016) developed a new approach to deal

with resource allocation in airports. In addition, uncertain conditions have been considered in the

literature. Hu et al. (2016) developed a GRASP-based algorithm for recovery of passengers and

aircraft in the event of any disrupt of airline operations.

After landing in an airport, a private/business aircraft requires some maintenance before it can

take off again. Shanmugam and Robert (2015) considered the influence of the human factor on

maintenance services. They applied analytical hierarchy process to prioritize the key functions.

Bruecker et al. (2015) presented a heuristic algorithm for robust roster scheduling in aircraft main-

tenance companies. Tsagkas et al. (2014) analyzed aircraft maintenance cases with the deviation

factors. The factors identified by them help aircraft service companies in making accountable de-

cisions. In addition, light maintenance work is performed in the aircraft hangar shop (AHS) of an

aircraft service company, which is close to the airport. Attention is focused on placing and packing

more aircraft in these hangar shops for maintenance (Hamzah and Adisasmita, 2015). The process

is slightly similar to dynamic facility layout (Kheirkhah et al., 2015). However, the main challenge is

that AHS employ special conditions and equipment forworking (Joseph, 2002; Pei et al., 2008). The

space in an AHS is limited and significantly valuable. Moreover, the shapes of AHSs are special.

Figure 1 shows the layout of the AHS considered in this study. The gray areas are buildings. The

light gray areas are reservedfor facilities. Neither of these areas can be used for placing aircraft, that

is, aircraft should be placed in the white areas. This makes the problem much more complicated.

The objective is to optimize space utilization in the AHS, that is, maximizing the number of

aircraft placed in the AHS. Considering the different shapes and sizes of aircraft, it is challenging

to place different types of aircraft in this irregular area. In practice, the staff of aircraft service

companies place aircraft manually based on their practical knowledge. However, the process is time

consuming, and usually optimal solutions are not achieved.

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2019 The Authors.

International Transactionsin Operational Research C

2019 International Federation ofOperational Research Societies