Computational and Applied Mathematics

, Volume 36, Issue 2, pp 1023–1041 | Cite as

Design of a public bicycle-sharing system with safety

Article

Abstract

In this paper, the bike-sharing problem was extended by considering safety in addition to system costs. Moreover, we determined the cost and safety levels for different kinds of stations. For solving the problem of conflicting objective functions, we used the NSGA-II and MOPSO algorithms and compared them. The results confirmed that the NSGA-II algorithm performs better than MOPSO for considering different solutions to the bike-sharing system with safety design problem. In the second stage, a multi-objective model was transformed to a linear single-objective model to find a preferred solution. A genetic algorithm (GA) was developed to solve the proposed large-scale bike-sharing model, and the results were compared with the solution obtained by commercial software. The results showed that the proposed GA outperforms the commercial software solution approach in large-scale instances.

Keywords

Bicycle sharing system Evolutionary algorithm Multi-objective  Multi-type station System safety 

Mathematics Subject Classification

90-XX 

References

  1. Aghaei J, Amjady N, Shayanfar HA (2011) Multi-objective electricity market clearing considering dynamic security by lexicographic optimization and augmented epsilon constraint method. Appl Soft Comput 11(4):3846–3858CrossRefGoogle Scholar
  2. Bashiri M, AliAskari E (2014) A permutation decision making method with multiple weighting vectors of criteria using NSGA-II and MOPSO. Decis Sci Lett 3(2):197–208CrossRefGoogle Scholar
  3. Bordagaray M, Ibeas A, dell’Olio L (2012) Modeling user perception of public bicycle services. Procedia Soc Behav Sci 54:1308–1316Google Scholar
  4. Brake J, Mulley C, Nelson JD, Wright S (2007) Key lessons learned from recent experience with flexible transport services. Transport Policy 14(6):458–466CrossRefGoogle Scholar
  5. Chang Y-C, Lee N (2010) A multi-objective goal programming airport selection model for low-cost carriers’ networks. Transport Res E Logist Transport Rev 46(5):709–718CrossRefGoogle Scholar
  6. Chemla D, Meunier F, Wolfler Calvo R (2013) Bike sharing systems: solving the static rebalancing problem. Discrete Optim 10(2):120–146MathSciNetCrossRefMATHGoogle Scholar
  7. Chen K-H, Su C-T (2010) Activity assigning of fourth party logistics by particle swarm optimization-based preemptive fuzzy integer goal programming. Expert Syst Appl 37(5):3630–3637CrossRefGoogle Scholar
  8. Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279CrossRefGoogle Scholar
  9. Demirci M, Bettinger P (2015) Using mixed integer multi-objective goal programming for stand tending block designation: a case study from Turkey. For Policy Econ 55:28–36CrossRefGoogle Scholar
  10. Díaz-Alvarado FA (2015) An example of Pareto dominance for dimensionality reduction in multi-objective optimization. Comput Chem Eng 79:135–136CrossRefGoogle Scholar
  11. Dikas G, Minis I (2014) Scheduled paratransit transport systems. Transport Res B Methodol 67:18–34CrossRefGoogle Scholar
  12. Dorneich MC, Sahinidis NV (1995) Global optimization algorithms for chip layout and compaction. Eng Optim+ A35 25(2):131–154Google Scholar
  13. Du Y, Xie L, Liu J, Wang Y, Xu Y, Wang S (2014) Multi-objective optimization of reverse osmosis networks by lexicographic optimization and augmented epsilon constraint method. Desalination 333(1):66–81CrossRefGoogle Scholar
  14. Eberhart, R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, 1995. MHS ’95Google Scholar
  15. Fu L (2002) A simulation model for evaluating advanced dial-a-ride paratransit systems. Transport Res A Policy Pract 36(4):291–307CrossRefGoogle Scholar
  16. García-Palomares JC, Gutiérrez J, Latorre M (2012) Optimizing the location of stations in bike-sharing programs: a GIS approach. Appl Geogr 35(1–2):235–246CrossRefGoogle Scholar
  17. George DK, Xia CH (2011) Fleet-sizing and service availability for a vehicle rental system via closed queueing networks. Eur J Oper Res 211(1):198–207MathSciNetCrossRefMATHGoogle Scholar
  18. Gomes dos Santos W, Rocco E, Boge T (2015) Design of a linear time-invariant control system based on a multiobjective optimization approach. Comput Appl Math 1–13. doi: 10.1007/s40314-015-0243-2
  19. Gitizadeh M, Kalantar M (2009) A novel approach for optimum allocation of FACTS devices using multi-objective function. Energy Convers Manag 50(3):682–690CrossRefMATHGoogle Scholar
  20. Homburg C (1998) Hierarchical multi-objective decision making. Eur J Oper Res 105(1):155–161MathSciNetCrossRefMATHGoogle Scholar
  21. Jadidi O, Zolfaghari S, Cavalieri S (2014) A new normalized goal programming model for multi-objective problems: a case of supplier selection and order allocation. Int J Prod Econ 148:158–165CrossRefGoogle Scholar
  22. Kuriger GW, Hank Grant F (2011) A lexicographic Nelde—Mead simulation optimization method to solve multi-criteria problems. Comput Ind Eng 60(4):555–565CrossRefGoogle Scholar
  23. Lin J-R, Yang T-H (2011) Strategic design of public bicycle sharing systems with service level constraints. Transport Res E Logist Transport Rev 47(2):284–294CrossRefGoogle Scholar
  24. Lin J-R, Yang T-H, Chang Y-C (2013) A hub location inventory model for bicycle sharing system design: formulation and solution. Comput Ind Eng 65(1):77–86CrossRefGoogle Scholar
  25. Lindsey C, Mahmassani HS, Mullarkey M, Nash T, Rothberg S (2014) Regional logistics hubs, freight activity and industrial space demand: econometric analysis. Res Transport Bus Manag 11:98–104CrossRefGoogle Scholar
  26. Martinez LM, Caetano L, Eiró T, Cruz F (2012) An optimisation algorithm to establish the location of stations of a mixed fleet biking system: an application to the city of Lisbon. Procedia Soc Behav Sci 54:513–524CrossRefGoogle Scholar
  27. Moghaddam KS (2013) Multi-objective preventive maintenance and replacement scheduling in a manufacturing system using goal programming. Int J Prod Econ 146(2):704–716CrossRefGoogle Scholar
  28. Mortazavi SM, Soltani MR, Motieyan H (2015) A Pareto optimal multi-objective optimization for a horizontal axis wind turbine blade airfoil sections utilizing exergy analysis and neural networks. J Wind Eng Ind Aerodyn 136:62–72CrossRefGoogle Scholar
  29. Okafor EG, Sun Y-C (2012) Multi-objective optimization of a series-parallel system using GPSIA. Reliab Eng Syst Saf 103:61–71CrossRefGoogle Scholar
  30. Parkhurst G (2000) Influence of bus-based park and ride facilities on users’ car traffic. Transport Policy 7(2):159–172CrossRefGoogle Scholar
  31. Romero JP, Ibeas A, Moura JL, Benavente J, Alonso B (2012) A simulation-optimization approach to design efficient systems of bike-sharing. Procedia Soc Behav Sci 54:646–655CrossRefGoogle Scholar
  32. Sahinidis NV (1996) BARON: a general purpose global optimization software package. J Glob Optim 8(2):201–205MathSciNetCrossRefMATHGoogle Scholar
  33. Salazar FJT, Macau EEN, Winter OC (2015) Pareto Frontier for the time-energy cost vector to an Earth-Moon transfer orbit using the patched-conic approximation. Comput Appl Math 34(2):461–475MathSciNetCrossRefMATHGoogle Scholar
  34. Sayarshad H, Tavassoli S, Zhao F (2012) A multi-periodic optimization formulation for bike planning and bike utilization. Appl Math Model 36(10):4944–4951CrossRefGoogle Scholar
  35. Schalekamp H, Behrens R (2013) Engaging the paratransit sector in Cape Town on public transport reform: progress, process and risks. Res Transport Econ 39(1):185–190CrossRefGoogle Scholar
  36. Sedighizadeh M, Faramarzi H, Mahmoodi MM, Sarvi M (2014) Hybrid approach to FACTS devices allocation using multi-objective function with NSPSO and NSGA-II algorithms in fuzzy framework. Int J Electr Power Energy Syst 62:586–598CrossRefGoogle Scholar
  37. Shu J, Chou M, Liu Q, Teo C-P, Wang I-L (2010) Bicycle-sharing system: deployment, utilization and the value of re-distribution. National University of Singapore-NUS Business School, SingaporeGoogle Scholar
  38. Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248CrossRefGoogle Scholar
  39. Üstün AK, Anagün AS (2015) Multi-objective mitigation budget allocation problem and solution approaches: the case of İstanbul. Comput Ind Eng 81:118–129CrossRefGoogle Scholar
  40. Vogel P, Greiser T, Mattfeld DC (2011) Understanding bike-sharing systems using data mining: exploring activity patterns. Procedia Soc Behav Sci 20:514–523CrossRefGoogle Scholar
  41. Xiong P (2010) Park and ride behaviors for non-local private car travelers in big events. J Transport Sys Eng Inf Technol 10(5):188–193Google Scholar

Copyright information

© SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional 2015

Authors and Affiliations

  1. 1.Department of Industrial Engineering, Faculty of EngineeringShahed UniversityTehranIran

Personalised recommendations