European driver rules in vehicle routing with time windows

As of April 2007, the European Union has new regulations concerning driver working hours. These rules force the placement of breaks and rests into vehicle routes when consecutive driving or working time exceeds certain limits. This paper proposes a large neighborhood search method for the vehicle routing problem with time windows and driver regulations. In this method, neighborhoods are explored using a column generation heuristic that relies on a tabu search algorithm for generating new columns (routes). Checking route feasibility after inserting a customer into a route in the tabu search algorithm is not an easy task. To do so, we model all feasibility rules as resource constraints, develop a label-setting algorithm to perform this check, and show how it can be used efficiently to validate multiple customer insertions into a given existing route. We test the overall solution method on modified Solomon instances and report computational results that clearly show the efficiency of our method compared to two other existing heuristics

Experimental Analysis of Pheromone-based Heuristic Column Generation using irace

Abstract. Pheromone-based heuristic column generation (ACO-HCG) is a hybrid algorithm that combines ant colony optimization and a MIP solver to tackle vehicle routing problems (VRP) with black-box feasibility. Traditionally, the experimental analysis of such a complex algorithm has been carried out manually by trial and error. Moreover, a full-factorial statistical analysis is infeasible due to the large number of parameters and the time required for each algorithm run. In this paper, we first automatically configure the algorithm parameters by using an automatic algorithm configuration tool. Then, we perform a basic sensitivity analysis of the tuned configuration in order to understand the significance of each parameter setting. In this way, we avoid wasting effort analyzing parameter settings that do not lead to a high-performing algorithm. Finally, we show that the tuned parameter settings improve the performance of ACO-HCG on the multipile VRP and the three-dimensional loading capacitated VRP.

Why to climb if one can jump: a hill jumping algorithm for the vehicle routing problem with time windows

The most common approaches to solve the variants of the well-known vehicle routing problem are based on metaheuristic hill-climbing search. The deficiency of these methods is slow local search based hill climbing that often is restricted to limited local neighborhood. In this paper we suggest a novel new two-phase metaheuristic that escapes the local minima with jumps of varying size, instead of step by step local hill climbing. The initial solution is first generated with a powerful ejection pool heuristic. The key idea of the improvement phase is to combine large neighborhood search with standard guided local search metaheuristic in a novel way, allowing improved search diversification and escape from local minima in more efficient way through jumps. The algorithm has been tested on the standard Gehring and Homberger benchmarks for the vehicle routing problem with time windows and the results indicate very competitive performance. We found 12 new and 43 matched best-known solutions and the best overall results for all problem sizes at comparable computation times