Two-phase genetic-annealing algorithm for vehicle routing problem with multiple constraints

A novel two-phase genetic-annealing algorithm is proposed to solve the vehicle routing problem with time window (MDVRPTW) and multi-constraint in multiple dispatching centers. In the first phase, users are partitioned into fuzzy regions according to quantity supplied and the length of paths using genetic algorithm; in the second phase the global optimization is carried out by the hybrid genetic algorithm with 2D variable-length chromosomes and corresponding genetic operators. The random greedy algorithm is used in generating of initial population and crossover and mutation operator to avoid invalid solution, then the simulated annealing algorithm is employed to enhance the diversity of population. The experimental results show that compared with the traditional genetic algorithm the search speed of the proposed algorithm is 3-10 times faster, the convergence is speed up, and search efficiency is increased.