Dedicated algorithm and modelling improvements continue to advance the state-of-the-art with respect to solving vehicle routing problems (VRPs). Despite these academic achievements, solving large VRP instances sufficiently fast for real-life applicability remains challenging. This paper therefore advocates the employment of a fast optimization heuristic which exploits a VRP solution's characteristics in an effective manner. This works primary contributions are threefold: a ruin method, a recreate method and a fleet minimization procedure. The ruin method functions via adjacent string removal, introducing with it a novel property regarding vehicle routing problems which we term spatial slack, while the recreate method is categorised as greedy insertion with blinks. Combining these results in SISRs: a powerful ruin & recreate approach. The fleet minimization procedure, meanwhile, introduces an absences-based acceptance criterion which serves as a complementary optimization component for when minimizing the number of vehicles constitutes the primary VRP objective. Together, these three elements provide a suite of simple, powerful and easily-reproducible algorithmic methods which are not only successfully applied to the CVRP but also for a wide range of related problems such as pickup and delivery problems as well as others which include time windows. SISRs serves to strip back the layers of complexity and specialisation synonymous with the trend of algorithmic development throughout recent decades. Moreover, such simplicity and reproducibility are shown to not necessarily come at the expense of solution quality, with SISRs consistently outperforming alternative general approaches as well as dedicated single-purpose methods, obtaining highly competitive results and new best results, particularly for large-scale instances. Finally, aside from performance-related criteria, SISRs also serves to showcase a fresh perspective with respect to vehicle routing problems more generally, introducing a range of new terminology and procedures which it is hoped will invigorate further research and innovation.
This work presents the stochastic local search method for the Swap-Body Vehicle Routing Problem (SB-VRP) that won the First VeRoLog Solver Challenge. The SB-VRP, proposed on the occasion of the challenge, is a generalization of the classical Vehicle Routing Problem (VRP) in which customers are served by vehicles whose sizes may be enlarged via the addition of a swap body (trailer). The inclusion of a swap body doubles vehicle capacity while also increasing its operational cost. However, not all customers may be served by vehicles consisting of two bodies. Therefore swap locations are present where one of the bodies may be temporarily parked, enabling double body vehicles to serve customers requiring a single body. Both total travel time and distance incur costs that should be minimized, while the number of customers visited by a single vehicle is limited both by its capacity and by a maximum travel time. State of the art VRP approaches do not accommodate SB-VRP generalizations well. Thus, dedicated approaches taking advantage of the swap body characteristic are desired. The present paper proposes a stochastic local search algorithm with both general and dedicated heuristic components, a subproblem optimization scheme and a learning automaton. The algorithm improves the best known solution for the majority of the instances proposed during the challenge. Results are also presented for a new set of instances with the aim of stimulating further research concerning the SB-VRP.
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