Alvarenga, G. B., & Mateus, G. R. (2004). A two-phase genetic and set partitioning approach for the vehicle routing problem with time windows. In *Fourth international conference on hybrid intelligent systems (HIS04)*. IEEE Computer Society Press.

Alvarenga, G. B., Mateus, G. R., & de Tomi, G. (2007). A genetic and set partitioning two-phase approach for the vehicle routing problem with time windows.

*Computers & Operations Research*,

*34*, 1561–1584.

CrossRefGoogle Scholar
Bräysy, O., Dullaert, W., & Gendreau, M. (2004). *Evolutionary algorithms for the vehicle routing problem with time windows* (Internal Report STF90 A04406). SINTEF ICT, Department of Optimization, Norway.

Bräysy, O., & Gendreau, M. (2001a). *Metaheuristics for the vehicle routing problem with time windows* (Internal Report STF42 A01025). SINTEF Applied Mathematics, Department of Optimization, Norway.

Bräysy, O., & Gendreau, M. (2001b). *Route construction and local search algorithms for the vehicle routing problem with time windows* (Internal Report STF42 A01024). SINTEF Applied Mathematics, Department of Optimisation, Norway.

Chambers, J. M., Freeny, A., & Heiberger, R. M. (1992). Analysis of variance; designed experiments. In J. M. Chambers & T. J. Hastie (Eds.), *Statistical models in *
*S*. Wadsworth & Brooks/Cole. Chap. 5.

Cordeau, J. F., Laporte, G., & Mercier, A. (2000). *A unified tabu search heuristic for vehicle routing problems with time windows* (Working Paper CRT-00-03). Centre for Research on Transportation. Montreal. Canada.

Cormen, T. H., Leiserson, C. E., & Rivest, R. L. (1999).

*Introduction to algorithms*. Cambridge: MIT Press.

Google Scholar
Dantzig, G. B., & Ramser, R. H. (1959). The truck dispatching problem.

*Management Science*,

*6*, 80.

CrossRefGoogle Scholar
De Backer, B., & Furnon, V. (1997). Meta-heuristics in constraint programming experiments with tabu search on the vehicle routing problem. In *Second international conference on metaheuristics (MIC’97)*. Sophia Antipolis, France.

de Oliveira, H. C. B., de Souza, M. M., Alvarenga, G. B., & Silva, R. M. A. (2004). An adaptation of a genetic algorithm for the vehicle routing problem with time windows. *Infocomp Journal of Computer Science*, 51–58.

de Oliveira, H. C. B., Vasconcelos, G. C., & Alvarenga, G. B. (2005). An evolutionary approach for the vehicle routing problem with time windows. In *XXXVII SBPO—Brazilian symposium on operations research*. Gramado, Brazil.

Eiben, E., & Smith, J. E. (2003).

*Introduction to evolutionary computing*.

*Natural computing series*. Berlin: MIT Press/Springer.

Google Scholar
Fraga, M. C. P. (2006). A hybrid algorithm based on ant colony and path reconnection for solving the VRPTW. In *37th Brazilian symposium on operations research*. Goiania, GO, Brazil.

Garey, M. R., & Johnson, D. S. (1990).

*Computers and intractability; A guide to the theory of NP-completeness*. New York: Freeman.

Google Scholar
Ibaraki, T., Kubo, M., Masuda, T., Uno, T., & Yagiura, M. (2001). *Effective local search algorithms for the vehicle routing problem with general time windows* (Working Paper). Department of Applied Mathematics and Physics, Kyoto University, Japan.

Kilby, P., Prosser, P., & Shaw, P. (1999). Guided local search for the vehicle routing problem with time windows. In S. Voss, S. Martello, I. H. Osman, & C. Roucairol (Eds.),

*META-HEURISTICS advances and trends in local search paradigms for optimization* (pp. 473–486). Boston: Kluwer Academic.

Google Scholar
King, G. F., & Mast, C. F. (1997). Excess travel: causes. Extent and consequences.

*Transportation Research Record*,

*1111*, 126–134.

Google Scholar
Kirkpatrick, S., Gellat, D., & Vecchi, M.P. (1983). Optimizations by simulated annealing.

*Science*,

*220*, 671–680.

CrossRefGoogle Scholar
Larsen, J. (1999). *Parallelization of the vehicle routing problem with time windows*. Phd Thesis. Department of Mathematical Modeling. Technical University of Denmark.

Lenstra, J. A., & Rinnooy, K. (1981). Complexity of vehicle routing and scheduling problems.

*Networks*,

*11*, 221–227.

CrossRefGoogle Scholar
Ombuki, B., Ross, B. J., & Hanshar, F. (2006). Multi-objective genetic algorithms for vehicle routing problem with time windows.

*Applied Intelligence*,

*24*, 17–30.

CrossRefGoogle Scholar
Papadimitriou, C. H., & Steiglitz, K. (1982).

*Combinatorial optimization—Algorithms and complexity*. New York: Dover.

Google Scholar
Riise, A., & Stølevik, M. (1999). *Implementation of guided local search for the vehicle routing problem* (SINTEF Internal report STF42 A99013). SINTEF Applied Mathematics, Norway.

Rouchat, Y., & Taillard, E. D. (1995). Probabilistic diversification and intensification in local search for vehicle routing.

*Journal of Heuristics*,

*1*, 147–167.

CrossRefGoogle Scholar
Royston, P. (1995). A remark on algorithm AS 181: The W test for normality.

*Applied Statistics*,

*44*, 547–551.

CrossRefGoogle Scholar
Russell, S. J., & Norvig, P. (2003).

*Artificial intelligence: A modern approach*. New York: Prentice Hall.

Google Scholar
Searle, S. R. (1971).

*Linear models*. New York: Wiley.

Google Scholar
Shaw, P. (1998). Using constraint programming and local search methods to solve vehicle routing problems. In M. Maher & J.-F. Puget (Eds.),

*Principles and practice of constraint programming, CP98*.

*Lecture notes in computer science* (pp. 417–431). Berlin: Springer.

CrossRefGoogle Scholar
Solomon, M. M. (1987). Algorithms for the vehicle routing problem and scheduling problem with time window constraints.

*Operational Research*,

*35*, 254–265.

Google Scholar
Thangiah, S. R., Osman, I. H., & Sun, T. (1994). *Hybrid genetic algorithm simulated annealing and tabu search methods for vehicle routing problem with time windows* (Technical Report 27). Computer Science Department. Slippery Rock University.