Organizers

Michalis Mavrovouniotis:
Cyprus University of Technology,
Limassol, Cyprus
email: m.mavrovouniotis@hotmail.com

Important Dates

Submission Deadline:
June 5, 2020

Notification of Results:
19 July, 2020

Sponsors

IEEE Task Force on Evolutionary Computation in Dynamic and Uncertain Environments

 

 .

Call for Papers

 

  CEC-12 Competition on Electric Vehicle Routing Problem
 
  
July 19 - 24, 2020, Glasgow, Scotland, U.K.

Overview

Transportation has been the main contributor to CO_2 emissions. Due to the global warming, pollution and climate changes, logistic companies such as FedEx, UPS, DHL and TNT have become more sensitive to the environment and they are investing in ways to reduce the CO_2 emissions that result as part of their daily operations. There is no doubt that using electric vehicles (EVs) instead of conventional vehicles will significantly contribute to the reduction of CO_2 emissions. With the growing interest of these logistic companies, a problem of routing a fleet of EVs has recently emerged, namely the electric vehicle routing (EVRP), which is a challenging NP-hard combinatorial optimization problem. The EVRP can be described as follows: given a fleet of EVs, we need to find the best possible routes within the battery charge level limits of the EVs, starting from the central depot and returning to it, to serve a set of customers.

The aim of the competition is to promote the research in the field of evolutionary computation (EC) methods, including nature-inspired computational models, heuristic and meta-heuristic techniques, and other intelligent methods, for the solution of the EVRP. However, non-EC methods or other traditional optimization methods are also welcome. Since the EVRP is a relatively new combinatorial optimization problem, a library of the source code of all the methods submitted to the competition will be maintained on the website that will be useful to the research community for future comparisons. For this competition we will use a set of newly generated benchmark problem instances generated specifically for the described EVRP.

Source Code and Benchmark Set

  • Download the benchmark set
  • Download the technical report of the benchmark set (Cite this report as: M. Mavrovouniotis, C. Menelaou, S. Timotheou, Christos Panayiotou, G. Ellinas, M. Polycarpou. Benchmark Set for the IEEE WCCI-2020 Competition on Evolutionary Computation for the Electric Vehicle Routing Problem, Technical Report 2020, KIOS CoE, University of Cyprus, Cyprus, March 2020)
  • Download the sample code (written in C++) to start your implemention. Implementations in any other programming language are permitted.

Submission Instructions

  • Submit the output files (as a zip file) of the source code above, via email. One output file per instance.
  • If a paper is to be submitted to the conference, please submit it to the Main Track. Then send via email the paper that contains details of the solver.
  • If a paper is NOT submitted to the conference, please send details of the solver (including parameter settings) via email.
  • Submit the source code (as a zip file) of the implemented solver
  • All the required files (1. ZIP file with the output files, 2. PDF with the details of the solver, and 3. ZIP file that contains the source code of the solver) must be submitted via email
  • The submission deadline is on the 5th of June 2020

For further assistance and details contact us at: m.mavrovouniotis@hotmail.com

Competition Results

We had five proposals:
  • Sequence-based hyper heuristic by A. Kheiri
  • Simulated Annealing by V. Mak-Hau, B. Hill
  • Max-Min Ant System by M. R. C. O. Leite, H. S. Bernardino, L. B. Goncalves
  • Variable Neighbourhood Search by D. Woller, V. Vavra, V. Kozak, M. Kulich
  • Genetic Algorithm by V. Q. Hien, T. C. Dao, T. B. Thang, H. T. T, Binh
The winning proposals are from the following teams:
  1. (Team 1) Variable Neighbourhood Search by D. Woller, V. Vavra, V. Kozak, M. Kulich
  2. (Team 2) Simulated Annealing by V. Mak-Hau, B. Hill
  3. (Team 3) Genetic Algorithm by V. Q. Hien, T. C. Dao, T. B. Thang, H. T. T, Binh
Results
instances(Team1) VNS (Team2) SA (Team3) GA
minmaxmeanstdevminmaxmeanstdevminmaxmeanstdev
E-n22-k4.evrp384.67 384.67 384.67 0.0 384.67 384.67 384.67 0.00 384.67 384.67 384.67 0.0
E-n23-k3.evrp571.94 571.94 571.940.0 571.94 571.94 571.94 0.00 571.94 571.94 571.94 0.0
E-n30-k3.evrp509.47 509.47 509.470.0 509.47 509.47 509.47 0.00 509.47 509.47 509.47 0.0
E-n33-k4.evrp840.14 840.46840.43 1.18 840.57 873.33 854.07 12.80 844.25 846.21 845.62 0.92
E-n51-k5.evrp529.90 548.98543.26 3.52 533.66 533.66 533.66 0.00 529.90 553.23 542.08 8.57
E-n76-k7.evrp692.64707.49 697.89 3.09 701.03 716.77 712.17 5.78 697.27 730.92 717.30 9.58
E-n101-k8.evrp839.29 853.34 853.34 4.73 845.84 856.74 852.48 3.44852.69 887.14 872.69 9.58
X-n143-k7.evrp16028.05 16883.3816459.31 242.59 16610.37 17396.06 17188.90 170.44 16488.60 17478.86 16911.50 282.30
X-n214-k11.evrp11323.56 11660.70 11482.20 76.14 11404.44 11881.73 11680.35 116.47 11762.07 12309.38 12007.06 156.69
X-n352-k40.evrp27064.8827418.38 27217.77 86.20 27222.96 27796.69 27498.03 155.6228008.09 28792.66 28336.07 205.29
X-n459-k26.evrp25370.80 25774.62 25582.27 106.89 25464.84 26038.65 25809.47157.97 26048.21 26742.11 26345.12 185.14
X-n573-k30.evrp52181.51 51929.24 52548.09 278.85 51929.2453534.01 52793.66 577.24 54189.62 56327.62 55327.62 548.05
X-n685-k75.evrp71345.40 72187.75 71770.57 197.08 72549.90 73693.49 73124.98 320.07 73925.56 75535.99 74508.03409.43
X-n749-k98.evrp81002.0181634.06 81327.39 176.19 81392.78 82414.80 81848.13 275.26 84034.73 85549.36 84759.79 376.10
X-n819-k171.evrp164289.95 165571.48 164926.41 318.62 165069.77 166640.37165895.78 403.70170965.68 173371.76 172410.12568.58
X-n916-k207.evrp341649.91343338.01 342460.70 510.66 342796.88 344521.64 343533.85 556.98 357391.57 362422.52 360269.94 1192.57
X-n1001-k43.evrp77476.3678464.68 77920.52 234.73 78053.86 79226.81 78593.50 306.27 78832.90 79567.00 79163.34229.19