Skip to main content
SpringerLink
Log in

Recurrent Neural Network and Genetic Algorithm Approaches for a Dual Route Optimization Problem: A Real Case Study

  • Conference paper
  • First Online:
Proceedings of the Sixth International Conference on Management Science and Engineering Management

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 185))

Abstract

This paper describes a real case study has been considered. It presents a dual optimization problem that consists in finding the optimal routes in the called principal and capillary routes. The problem has been considered as a travel salesman problem with time windows (TSPTW). The restrictions of Miller et al. have been used in order to reduce the computational cost [56]. A recurrent neural network approach is employed, which involves not just unsupervised learning to train neurons, but an integrated approach where Genetic Algorithm is utilized for training neurons so as to obtain a model with the least error.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Aleksander I, Morton H (1990) An introduction to neural computing. Chapman & Hall, London

    Google Scholar 

  2. DBK (2009) Transporte de mercanc´ıas por carretera, DBK

    Google Scholar 

  3. Nilsson NJ (1982) Principles of artificial intelligence. Editing Birkhauser, New York

    Google Scholar 

  4. Junger M, Reinelt G, Rinaldi G (1997) The travelling salesman problem. Handbooks in Operations Research and Management Science 7:225–330

    Google Scholar 

  5. Blanco A, Delgado M, Pegalajar MC (2000) A genetic algorithm to obtain the optimal recurrent neural network. International Journal of Approximate Reasoning 23:67–83

    Google Scholar 

  6. Bourlard H, Wellekens C (1989) Speech pattern discrimination and multi-layered perceptrons. Computer Speech and Language 3:1–19

    Google Scholar 

  7. Le Cun Y, Boser B, Denker J et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Computation 1:541–551

    Google Scholar 

  8. Sejnowski T, Rosenberg C (1987) Parallel networks that learn to pronounce English text. Complex Systems 1:145–168

    Google Scholar 

  9. Waibel A, Hanazawa T, Hinton G et al (1989) Phoneme recognition using time delay neural networks. IEEE Transactions on Acoustics, Speech, and Signal Processing 37:328–339

    Google Scholar 

  10. Bellmore M, Nemhauser GL (1968) The traveling salesman problem: A survey. Operations Research 16:538–588

    Google Scholar 

  11. Bodin L (1975) A toxonomic structure for vehicle routing and scheduling problems. Computers and Urban Society 1:11–29

    Google Scholar 

  12. Gillett B, Miller L (1974) A heuristic algorithm for the vehicle dispatch problem. Operations Research 22:340–340

    Google Scholar 

  13. Turner WC, Fouids L, Ghare PM (1974) Transportation routing problem — A survey. AIIE Transactions 6:288–301

    Google Scholar 

  14. Lawler EL, Lenstra JK, Rinnooy AHG et al (1985) The traveling salesman problem. Wiley, New York

    Google Scholar 

  15. Gutin G, Punnen A (2002) The traveling salesman problem and its variations. Kluwer Academic Publishers

    Google Scholar 

  16. Ahr D and Reinelt G (2006) A tabu search algorithm for the minmax k-chinese postman problem. Computers & Operations Research 33:3403–3422

    Google Scholar 

  17. Augerat P, Belenguer E, Benavent JM et al (1998) Separating capacity constraints in the CVRP using tabu search. European Journal of Operational Research 106:546–557

    Google Scholar 

  18. Badeau P, Gendreau M, Guertin F et al (1997) A parallel tabu search heuristic for the vehicle routing problem with time windows. Transportation Research 5:109–122

    Google Scholar 

  19. Barbarosoglu G, Ozgur D (1999) A tabu search algorithm for the vehicle routing problem. Computers and Operations Research 26:255–270

    Google Scholar 

  20. Bianchessi N, Righini G (2007) Heuristic algorithms for the vehicle routing problem with simultaneous pick-up and delivery. Computers & Operations Research 34:578–594

    Google Scholar 

  21. Brandao J, Mercer A (1997) A tabu search algorithm for the multi-trip vehicle routing and scheduling problem. European Journal of Operational Research 100:180–191

    Google Scholar 

  22. Breedam AV (2001) Comparing descent heuristics and metaheuristics for the vehicle routing problem. Computers & Operations Research 24:289–315

    Google Scholar 

  23. Chao IM (2002) A tabu search method for the truck and trailer routing problem. Computers & Operations Research 29:33–51

    Google Scholar 

  24. Daniels RL, Rummel JL, Schantz R (1998) A model for warehouse order picking. European Journal of Operational Research 105:1–17

    Google Scholar 

  25. Garcia BL, Potvin JY, Rousseau JM (1994) A parallel implementation of the tabu search heuristic for vehicle routing problems with time window constraints. Computers&Operations Research 21:1025–1033

    Google Scholar 

  26. Gendreau M, Laporte G, Sguin R (1996) A tabu search heuristic for the vehicle routing problem with stochastic demands and customers. Operations Research 44:469–477

    Google Scholar 

  27. Gendreau M, Laporte G, Vigo D (1999). Heuristics for the traveling salesman problem with pickup and delivery. Computers & Operations Research 26:699–714

    Google Scholar 

  28. Hertz A, Laporte G, Mittaz M (2000) A tabu search heuristic for the capacitated arc routing problem. Operations Research 48:129–135

    Google Scholar 

  29. Lau HC, Sim M, Teo KM (2003) Vehicle routing problem with time windows and a limited number of vehicles. European Journal of Operational Research 148:559–569

    Google Scholar 

  30. Malek M, Guruswamy M, Pandya M et al (1989) Serial and parallel simulated annealing and tabu search algorithms for the traveling salesman problem. Annals of Operations Research 21:59–84

    Google Scholar 

  31. Montane FAT, Galvao RD (2006) A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service. Computers & Operations Research 33:595–619

    Google Scholar 

  32. Nanry WP, Barnes JW (2000) Solving the pickup and delivery problem with time windows using reactive tabu search. Transportation Research Part B: Methodological 34:107–121

    Google Scholar 

  33. Osman IH (1993) Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem. Annals of Operations Research 41:421–451

    Google Scholar 

  34. Potvin JY, Kervahut T, Garcia BL et al (1996) The vehicle routing problem with time windows — part I: Tabu search. INFORMS Journal of Computing 8:158–164

    Google Scholar 

  35. Scheuerer S (2006) A tabu search heuristic for the truck and trailer routing problem. Computers & Operations Research 33:894–909

    Google Scholar 

  36. Semet F, Taillard E (1993) Solving real-life vehicle routing problems efficiently using taboo search. Annals of Operations Research 41:469–488

    Google Scholar 

  37. Tarantilis CD (2005) Solving the vehicle routing problem with adaptive memory programming methodology. Computers & Operations Research 32:2309–2327

    Google Scholar 

  38. Tarantilis CD, Kiranoudis CT (2007) A flexible adaptive memory-based algorithm for reallife transportation operations: Two case studies from dairy and construction sector. European Journal of Operational Research 179:806–822

    Google Scholar 

  39. Carpaneto G, Toth P (1980) Some new branching and bounding criteria for the asymmetric traveling salesman problem. Management Science 26:736–743

    Google Scholar 

  40. Gen M, Cheng R (1997) Genetic algorithms and engineering design. Wiley, New York

    Google Scholar 

  41. Fischetti M, Toth P (1989) An additive bounding procedure for combinatorial optimization problems. Operations Research 37:319–328

    Google Scholar 

  42. Finke G, Claus A, Gunn E (1984) A two-commodity network flow approach to the traveling salesman problem. Congressus Numerantium 41:167–178

    Google Scholar 

  43. Glover F (1990) Artificial intelligence, heuristic frameworks and tabu search. Managerial & Decision Economics 11:365–378

    Google Scholar 

  44. Gouveia L, Pires JM (1999) The asymmetric travelling salesman problem and a reformulation of the Miller-Tucker-Zemlin constraints. European Journal of Operational Research 112:134–146

    Google Scholar 

  45. Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1985) Configuration space analysis of travelling salesman problem. Journal Physique 46:1277–1292

    Google Scholar 

  46. Lin S, Kernighan BW (1973) An effective heuristic algorithm for travelling salesman problem. Operations Research 21:498–516

    Google Scholar 

  47. Lysgaard J (1999) Cluster based branching for the asymmetric travelling salesman problem. European Journal of Operational Research 119:314–325

    Google Scholar 

  48. Potvin JY (1996) Genetic algorithms for the travelling salesman problem. Annals of Operations Research 63:339–370

    Google Scholar 

  49. Shirrish B, Nigel J, Kabuka MR (1993) A boolean neural network approach for the travelling salesman problem. IEEE Transactions on Computers 42:1271–1278

    Google Scholar 

  50. Wong R (1980) Integer programming formulations of the travelling salesman problem. In: Proceedings of the IEEE International Conference of Circuits and Computers 149–152

    Google Scholar 

  51. Moon C, Kim J, Choi G et al (2002) An efficient genetic algorithm for the travelling salesman problem with precedence constraints. European Journal of Operational Research 140:606–617

    Google Scholar 

  52. Little J, Murty K, Sweeney D et al (1963) An algorithm for the traveling salesman problem. Operations Research 11:972–89

    Google Scholar 

  53. Balas E, Toth P (1985) Branch and bound methods. In: Lawler EL, Lenstra JK, Rinnooy Kan AHG, Shmoys DB (Eds.), The traveling salesman problem. Wiley, New York

    Google Scholar 

  54. Verhoeven MGA, Aarts EHL, Swinkels PCJ (1995) A parallel 2-opt algorithm for the travelling salesman problem. Future Generation Computer Systems 11:175–182

    Google Scholar 

  55. Burke LI (1994) Neural methods for the traveling salesman problem: Insights from operations research. Neural Networks 7:681-690

    Google Scholar 

  56. Miller CE, Tucker AW, Zemlin RA (1960) Integer programming formulation of travelling salesman problems. Journal of the ACM 3:326–329

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Ingenium Research Group, Universidad Castilla-La Mancha, 13005, Ciudad Real, Spain

    Fausto Pedro Garc’ıa M’arquez & Marta Ramos Mart’ın Nieto

Authors
  1. Fausto Pedro Garc’ıa M’arquez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marta Ramos Mart’ın Nieto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fausto Pedro Garc’ıa M’arquez .

Editor information

Editors and Affiliations

  1. Sichuan University, Chengdu, China, People's Republic

    Jiuping Xu

  2. Quaid-e-Azam University, Islamabad, Pakistan

    Masoom Yasinzai

  3. , LeBow College of Business, Drexel University, Peck PSR Center 204, Philadelphia, 19104, Pennsylvania, USA

    Benjamin Lev

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this paper

Cite this paper

M’arquez, F.P.G., Nieto, M.R.M. (2013). Recurrent Neural Network and Genetic Algorithm Approaches for a Dual Route Optimization Problem: A Real Case Study. In: Xu, J., Yasinzai, M., Lev, B. (eds) Proceedings of the Sixth International Conference on Management Science and Engineering Management. Lecture Notes in Electrical Engineering, vol 185. Springer, London. https://doi.org/10.1007/978-1-4471-4600-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-4600-1_2

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4599-8

  • Online ISBN: 978-1-4471-4600-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation