Dynamic vehicle routing by means of a genetic algorithm

Purpose – To propose and to evaluate a new genetic algorithm (GA) for solving the dynamic pickup and delivery problem with time windows (DPDPTW). Design/methodology/approach – First, a grouping genetic algorithm (GGA) for the (static) PDPTW is described. In order to solve the dynamic problem, the GGA then is embedded in a rolling horizon framework. Special updating mechanisms are provided which assure that reusable solution knowledge is preserved over the plan revisions. The approach is evaluated using a large number of test instances with varying degrees of dynamism. Findings – The experimental results have demonstrated that the proposed approach is able to find high-quality solutions when compared with two comparative heuristics. Research limitations/implications – Future research will be dedicated to the following issues: testing the proposed method using larger problem instances, using more sophisticated objective functions in order to further improve and evaluate the approach, integrating fast local search techniques into the genetic search, speeding up the algorithm by optimizing its implementation. Practical implications – In order to meet the increasing demands on the flexibility and the promptness of transportation services, algorithms are needed for dispatching transportation requests that arrive dynamically during the planning period. The findings of this contribution justify the employment of GAs in such dynamic transportation planning environments. Originality/value – Although the application of GAs in dynamic environments attracts growing attention, up to now no such algorithm has been published for the DPDPTW. To the best of the author's knowledge, this is the first time a GA has been applied to the DPDPTW.