By Tonci Caric and Hrvoje Gold
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Additional resources for Vehicle Routing Problem
Example text
Similar to the VVH, the “best accept” approach is used in which the move which reduces the value returned by the objective function O by the largest amount will be executed. StoppingCondition() checks whether the predetermined stopping criterion to terminate the search is satisfied in each iteration. In many real-life applications, the stopping criterion can be defined in terms of the maximum number of iterations or improving moves. Choose Penalty Features (S, p) takes the current solution S and the penalty vector p, and then returns the set of features fi to be penalised.
Distribution of customers and vehicle routes are shown in Fig. 7. The set of standard VRP problems found in the literature and used to validate the performance of VRP solving algorithms use the Euclidian metric of distances. In contrast, solving the real-world VRP problems, due to the traffic rules and transport network topology requires the use of the traffic matrix. In the bidirectional traffic matrix the distances between the pairs of points stored in the transport layer of the Geographic Information System are not necessarily symmetric.
Section 3 will present a more detailed discussion on the initialization heuristics. After the initial routes for the VRPs are generated, we can apply many possible heuristic methods to improve on the current solution until a better delivery plan with lower operational cost is obtained. The Tabu search (TS) [1] is a well-known meta-heuristic possibly used for such improvement, which has also been successfully applied to solve many other combinatorial optimization problems [3]. In solving VRPs, given any initial route(s), there can be many possible moves [1] such as the 2-opt operation, which replaces any two links in a route with two different links to reduce the operational cost, to generate other possible route(s).