Multi-objective Evolutionary Method for Dynamic Vehicle Routing and Scheduling Problem with Customers' Satisfaction Level

Seyed Farid Ghannadpour, Mohsen Hooshfar

2015

Abstract

This paper studies the multi-objective dynamic vehicle routing and scheduling problem by using an evolutionary method. In this model, all data and information required to the routing process are not known before planning and they revealed dynamically during the routing process and the execution of the routes. Moreover, the model tries to characterize the customers’ satisfaction and the service level issues by applying the concept of fuzzy time windows. The proposed model is considered as a multi-objective problem where the overall travelling distance, fleet size and waiting time imposed on vehicles are minimized and the customers’ satisfaction or the service level of the supplier to customers is maximized. To solve this multi-objective model, an evolutionary algorithm is developed to obtain the Pareto solutions and its performance is analyzed on various test problems in the literature. The computational experiments on data sets represent the efficiency and effectiveness of the proposed approach.

References

  1. Baños, R., Ortega, J., Gil, C., Fernández, A., Toro, F., 2013. A Simulated Annealing-based parallel multiobjective approach to vehicle routing problems with time windows. Expert Systems with Applications 40: 376-383.
  2. Bent, R., W., and Van Hentenryck, P., 2004. Scenariobased planning for partially dynamic vehicle routing with stochastic customers. Operations Research 52: 977-987.
  3. Blaseiro, S.R., Loiseau, I., and Ramonet, J., 2011. An ant colony algorithm hybridized with insertion heuristics for the time dependent vehicle routing problem with time windows. Computers and Operations Research 38(6): 954-966.
  4. Blaseiro, S.R., Loiseau, I., Ramonet, J., 2011. An ant colony algorithm hybridized with insertion heuristics for the time dependent vehicle routing problem with time windows. Computers and Operations Research 38: 954-966.
  5. Chen, Z.L., and Xu, H., 2006. Dynamic column generation for dynamic vehicle routing with time windows. Transportation Science 40: 74-88.
  6. Cordeau, J.F., Maischberger, M., 2012. A parallel iterated tabu search heuristic for vehicle routing problems. Computers and Operations Research 39: 2033-2050.
  7. Deb, K., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transaction on Evolutionary Computation 6: 182-197.
  8. Garcia-Najera, A., and Bullinaria, J.A., 2011. An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows. Computers and Operations Research 38: 287-300.
  9. Ghannadpour, S.F., and Noori, S., 2012. high-level relay hybrid metaheuristic method for multi-depot vehicle routing problem with time windows. Journal of Mathematical Modelling and Algorithms 11: 159-179.
  10. Ghannadpour, S.F., Noori, S., Tavakkoli Moghaddam, R., Ghoseiri, K., 2014. A multi-objective dynamic vehicle routing problem with fuzzy time windows: Model, solution and application. Applied Soft Computing 14: 504-527.
  11. Ghoseiri, K., and Ghannadpour, S.F., 2010. Multiobjective vehicle routing problem with time windows using goal programming and genetic algorithm. Applied Soft Computing 4: 1096-1107.
  12. Haghani, A., Jung, S., 2005. A dynamic vehicle routing problem with time-dependent travel times. Computers and Operations Research 32: 2959-2986.
  13. Larsen, A., Madsen, O. B. G., Solomon, M.M., 2004. The a-priori dynamic traveling salesman problem with time windows. Transportation Science 38: 459-572.
  14. Lei, H., Laporte, G., Guo, B., 2011. The capacitated vehicle routing problem with stochastic demands and time windows. Computers and Operation Research 38: 1775-1783.
  15. Lorini, S., Potvin, J-Y., Zufferey, N., 2011. Online vehicle routing and scheduling with dynamic travel times. Computers and Operations Research 38: 1086-1090.
  16. Negata, Y., Braysy, O., Dullaret, W., 2010. A penaltybased edge assembly memetic algorithm for the vehicle routing problem with time windows. Computers and Operations Research 37: 724 - 737.
  17. Ombuki, B., Ross, B., Hanshar, F., 2006. Multi-Objective Genetic Algorithm for Vehicle Routing Problem with Time Windows. Applied Intelligence 24: 17-30.
  18. Salhi, S., Petch, R.J., 2007. A GA based heuristic for the vehicle routing problem with multiple trips. Journal of Mathematical Modelling and Algorithms 6: 591-613.
  19. Solomon, M.M., 1987. Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research 35: 254-265.
  20. Tan, K.C., Chew, Y.H., Lee, L.H., 2006. A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem with time windows. Computational Optimization and Applications 34: 115-151.
  21. Tanga, J., Pana, Zh., Fung, R.Y.K., Laus, H., 2009. vehicle routing problem with fuzzy time windows. Fuzzy Sets and Systems 160: 683-695.
Download


Paper Citation


in Harvard Style

Farid Ghannadpour S. and Hooshfar M. (2015). Multi-objective Evolutionary Method for Dynamic Vehicle Routing and Scheduling Problem with Customers' Satisfaction Level . In Proceedings of the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-075-8, pages 91-98. DOI: 10.5220/0005172600910098


in Bibtex Style

@conference{icores15,
author={Seyed Farid Ghannadpour and Mohsen Hooshfar},
title={Multi-objective Evolutionary Method for Dynamic Vehicle Routing and Scheduling Problem with Customers' Satisfaction Level},
booktitle={Proceedings of the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2015},
pages={91-98},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005172600910098},
isbn={978-989-758-075-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Multi-objective Evolutionary Method for Dynamic Vehicle Routing and Scheduling Problem with Customers' Satisfaction Level
SN - 978-989-758-075-8
AU - Farid Ghannadpour S.
AU - Hooshfar M.
PY - 2015
SP - 91
EP - 98
DO - 10.5220/0005172600910098