Genetic Programming Hyper-Heuristics with Vehicle Collaboration for Uncertain Capacitated Arc Routing Problems

Abstract

Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem. Stochastic models are critical to study as they more accurately represent the real-world than their deterministic counterparts. Although there have been extensive studies in solving routing problems under uncertainty, very few have considered UCARP, and none consider collaboration between vehicles to handle the negative effects of uncertainty. This paper proposes a novel Solution Construction Procedure (SCP) that generates solutions to UCARP within a collaborative, multivehicle framework. It consists of two types of collaborative activities: one when a vehicle unexpectedly expends capacity (route failure), and the other during the refill process. Then, we propose a Genetic Programming Hyper-Heuristic (GPHH) algorithm to evolve the routing policy used within the collaborative framework. The experimental studies show that the new heuristic with vehicle collaboration and GP-evolved routing policy significantly outperforms the compared state-of-the-art algorithms on commonly studied test problems. This is shown to be especially true on instances with larger numbers of tasks and vehicles. This clearly shows the advantage of vehicle collaboration in handling the uncertain environment, and the effectiveness of the newly proposed algorithm.

Robust Optimization in Uncertain Capacitated Arc Routing Problems: Progresses and Perspectives [Review Article]

“…Chen et al [20] studied the average run time of solving some CARP-SSTT instances optimally, while the run time of finding e-optimal solutions is studied in [7]. Instead of the run time of (approximately) solving UCARP instances, Maclachlan et al [28] reported the computational time for training routing policies.…”

“…This assumption is not always true in reality. To the best of our knowledge, MacLachlan et al [28] were the first to split deliveries in UCARP and proposed an enhanced GPHH with vehicle collaboration (GPHH-C). The vehicle collaboration was proved to be effective compared to the GPHH without collaboration [25] on the ugdb, uval, uegl benchmarks, and also to EDASLS [17] on most of the tested instances.…”

“…For instance, some work assumed that a vehicle can have at most one extra trip, while some of them do not. Only [28] considered collaboration between vehicles. In all the other work, it is assumed that when a route failure occurs, no other vehicle is able to help, and then a recourse is mandatory.…”

“…Current recourse strategies (except [28]) assume that assigning another vehicle is impossible or inefficient, hence only the actual capacity-violated vehicle is replanned. In reality, it is possible to allocate an additional vehicle as an alternative or ask a nearby vehicle to help.…”

Arc routing problems: A review of the past, present, and future

“…Both profits and travel times are considered to be stochastic. The CARP with uncertain demands is also addressed by MacLachlan et al [151], but in this case vehicles can collaborate to handle the negative effects of uncertainty. It seems that, even if uncertainty has been extensively studied for the CARP, only some recent papers have taken into account this feature in other ARPs, so this is something that could be addressed in future works.…”