Scheduling for airport baggage transport vehicles based on diversity enhancement genetic algorithm

The scheduling problems for airport baggage transport vehicles are much influential to the quality of airport service. An optimal scheduling is very helpful to provide an efficient and safe airport operation and improve customers’ experience. To address the issue, in this paper, a novel genetic algorithm (GA) is proposed for the vehicles scheduling. To enhance the exploitation ability of GA, the algorithm is improved by considering both population diversity and population fitness simultaneously. In the proposed GA, a cooperative mechanism is employed to design the selection operation for genetic algorithm where both exploitation ability and exploration ability can be considered. Numerical experiments are conducted on widely used benchmarks, and several peer meta-heuristic algorithms are also used in performance comparison. To address the airport baggage transport vehicle scheduling problem, real data is adopted in the proposed algorithm for simulation. According to simulation results, the proposed algorithm is feasible and effective to obtain competitive performance and the airport baggage transport vehicles scheduling problem in is well addressed.

[1]  Dario Ambrosini,et al.  Multi-year consumption analysis and innovative energy perspectives: The case study of Leonardo da Vinci International Airport of Rome , 2016 .

[2]  Mario Vanhoucke,et al.  A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem , 2010, Eur. J. Oper. Res..

[3]  Sou-Sen Leu,et al.  Metaheuristics for project and construction management – A state-of-the-art review , 2011 .

[4]  Michael Schmidt,et al.  Challenges for ground operations arising from aircraft concepts using alternative energy , 2016 .

[5]  Yong Wang,et al.  A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization , 2006, IEEE Transactions on Evolutionary Computation.

[6]  Huan Liu,et al.  An Approach for QoS-Aware Web Service Composition Based on Improved Genetic Algorithm , 2010, 2010 International Conference on Web Information Systems and Mining.

[7]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[8]  Ian C. Parmee,et al.  Evolutionary and adaptive computing in engineering design , 2001 .

[9]  Thomas Stützle,et al.  Guest editorial: special section on ant colony optimization , 2002, IEEE Trans. Evol. Comput..

[10]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[11]  M. Clerc,et al.  Particle Swarm Optimization , 2006 .

[12]  Di Yuan,et al.  Scheduling de-icing vehicles within airport logistics: a heuristic algorithm and performance evaluation , 2012, J. Oper. Res. Soc..

[13]  Bo Wu,et al.  Virtual Simulation-Based Evaluation of Ground Handling for Future Aircraft Concepts , 2013, J. Aerosp. Inf. Syst..

[14]  Thomas Stützle,et al.  Special Section on Ant Colony Optimization , 2002 .

[15]  Maurizio Bevilacqua,et al.  The impact of business growth in the operation activities: a case study of aircraft ground handling operations , 2015 .

[16]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[17]  Maolin Tang,et al.  A Hybrid Genetic Algorithm for the Energy-Efficient Virtual Machine Placement Problem in Data Centers , 2014, Neural Processing Letters.

[18]  Andrés García Higuera,et al.  Agent-Based Distributed Control for Improving Complex Resource Scheduling: Application to Airport Ground Handling Operations , 2014, IEEE Systems Journal.

[19]  Wang Lihong,et al.  A hybrid genetic algorithm for Job-Shop scheduling problem , 2015, 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE).

[20]  Shuzhi Sam Ge,et al.  Drift analysis of mutation operations for biogeography-based optimization , 2015, Soft Comput..

[21]  Ching-Chih Tsai,et al.  Parallel Elite Genetic Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation , 2011, IEEE Transactions on Industrial Electronics.

[22]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[23]  Usama A. Badawi,et al.  Real-time aircraft turnaround operations manager , 2014 .

[24]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[25]  A. E. Eiben,et al.  From evolutionary computation to the evolution of things , 2015, Nature.

[26]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[27]  Thomas Stützle,et al.  Ant Colony Optimization Theory , 2004 .

[28]  D. J. Smith,et al.  A Study of Permutation Crossover Operators on the Traveling Salesman Problem , 1987, ICGA.

[29]  Zbigniew Michalewicz,et al.  GAVaPS-a genetic algorithm with varying population size , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[30]  Godfrey C. Onwubolu,et al.  New optimization techniques in engineering , 2004, Studies in Fuzziness and Soft Computing.

[31]  Zbigniew Michalewicz,et al.  Inver-over Operator for the TSP , 1998, PPSN.

[32]  Gennadiy Yun,et al.  Resource allocation improvement in the tasks of airport ground handling operations , 2015 .

[33]  Michel Gendreau,et al.  A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows , 2013, Comput. Oper. Res..

[34]  Liang Gao,et al.  An effective genetic algorithm for the flexible job-shop scheduling problem , 2011, Expert Syst. Appl..

[35]  Nei Yoshihiro Soma,et al.  A Constructive Hybrid Genetic Algorithm for the Flowshop Scheduling Problem , 2008 .

[36]  Salma Fitouri-Trabelsi,et al.  A bi-objective approach for scheduling ground-handling vehicles in airports , 2016, Comput. Oper. Res..