Mix-opt: A new route operator for optimal coverage path planning for a fleet in an agricultural environment

Some of the best known route optimization operators are combined to form a new operator called mix-optMix-opt is intended to hasten route planning for vehicle fleets in agricultural contexts.Mix-opt is configured and tested for both classical CVRP problems and agricultural routing problems.Mix-opt outperforms the operator approaches from which it was developed. To accomplish agriculture tasks, a field is usually divided into tracks based on the implement width. The order in which the crop tracks are covered during this process is critical because it directly affects the distances travelled by the agricultural machines while completing the task and, consequently, soil compaction and inputs. Identifying the best tracks for a set of vehicles to completely cover a field can be formulated as a capacitated vehicle routing problem (CVRP), in which tracks can be viewed as the customers of the CVRP problem. In other words, given a set of n tracks and m vehicles, the objective is to determine a set of routes such that each track is covered exactly once by any of the involved vehicles while minimizing the total cost of covering all the tracks. There are many metaheuristic optimisation methods that address the CVRP problem by using operators to iteratively improve the routes. Most of these operators consist of easy elementary operations such as relocations, swaps or inversions of the order in which customers in the route are visited. In this paper, a new operator, named Mix-opt, is proposed with the aim of accelerating the convergence of metaheuristic optimisation methods and make them less dependent on the operator chosen on routing problems. The proposed operator combines and extends some of the features of the most commonly used route operators by integrating the best-performing elementary operations on which they are based. Further variants of those elementary operations were tested, such as the use of different numbers of elements in the relocations or swaps or reverse orders as well as combining the operations with local searches. The best variants were selected for integration into the proposed operator. Furthermore, Mix-opt was compared against well-established operators by integrating each of them into a Simulated Annealing algorithm and solving well-known CVRP benchmarks and a typical and complex agricultural routing problem. Finally, the proposed operator was applied to be integrated into an agricultural route planner to identify the best routes in some illustrative agricultural problems.All tests demonstrated that Mix-opt, on average, outperforms existing approaches for solving general routing problems as well as a broad spectrum of agricultural routing situations. This helps to better route plan in agricultural contexts, even better than other approaches in a very short time, which is interesting to route plan in real time, for example, because one vehicle may fail during the execution and then it is necessary to route the plan again and very fast to distribute the remaining part of the global task among the rest of the vehicles in the fleet.