Dynamic vehicle routing using hybrid genetic algorithms

Abstract

This paper presents a novel approach to solving the single-vehicle pickup and delivery problem with time windows and capacity constraints (or single-vehicle P D P T W ) . While dynamic programming has been used to find the optimal routing to a given problem, it requires time exponential in the number of tasks. Therefore, at often fails to find the solutions under real-time conditions in an automated factory. This research explores anytime problem solving using genetic algorithms. By utilizing optimal but possibly partial solutions from dynamic programming, the hybrid genetic algorithms can produce near-optimal solutions for problems of sizes up to 25 percent bigger than what can be solved previously. This paper reports the experimental results of the proposed hybrid approach with four different crossover operators as well as three mutation operators. The experiments demonstrated the advantages of the hybrid approach with respect to dynamic task requests.

A hybrid evolutionary computation algorithm for global optimization

“…Genetic algorithms have proven to provide a heuristic means of solving complex optimization problems that require a robust solution method. Recently, they have been successfully applied in the areas of computing and industrial engineering such as vehicle routing [21], scheduling and sequencing [22], network design and synthesis [23,24], reliability design [25], facility layout and location [26], to mention a few.…”

“…Nevertheless, many problem dependent crossover operators are still proposed [73,64,21] and applied on various problem domains. Man et al [22] argued that conventional crossover operators do not perform well on complex optimization problems because they lack problemspecific knowledge in their encoding.…”

A New Hybrid Optimization Method, Application to a Single Objective Active Flow Control Test Case

“…An example of this kind of hybridization can be found at Kelly Jr and Davis [6], which proposes a combination of a Genetic Algorithm and a k-nearest neighbors classification algorithm. Another example of hybridization with multiple algorithms is proposed at Jih and Hsu [7]. In this case, a Genetic Algorithm and dynamic programming is used to address vehicle routing optimization problems.…”