Abstract :
With the rapid development of e-commerce, logistic industry has also experienced a new reform. Intelligent logistics is an important part of it and plays a key role to realize highly effective logistics. In the process of logistics, there are abundant operational and decision-making problems that need to be solved, and logistic vehicle routing problem is one of which. However, the vehicle routing problem with time windows (VRPTM) is a combination optimization problem and is a NP-complete problem, so we can´t get satisfying results when we use exact approaches and normal heuristic ones. In this paper, an improved genetic algorithm based on RC operator which is an improvement of Route Crossover (RC) is developed to solve the VRPTM. Computational experiments show that this improved algorithm can obtain a general optimality for all evaluated indexes on the premise of satisfying every customer´s demand and its performance is superior to the genetic algorithm based on RC or partially mapped crossover (PMX).
Keywords :
electronic commerce; genetic algorithms; logistics; transportation; decision making problems; e-business; e-commerce; improved genetic algorithm; intelligent logistics; logistic industry; logistic vehicle routing problem; logistics delivery; partially mapped crossover; route crossover; vehicle routing problem with time windows; Computer industry; Decision making; Evolution (biology); Evolutionary computation; Genetic algorithms; Logistics; Mathematics; NP-complete problem; Routing; Vehicles; VRPTM; crossover; genetic algorithm; logistics delivery; operator; route crossover (RC);
