Abstract
A multi-objective methodology was proposed for solving the green vehicle routing problem with a private fleet and common carrier considering workload equity. The iterated local search metaheuristic, which is adapted to the solution of the problem with three objectives, was proposed as a solution method. A solution algorithm was divided into three stages. In the first, initial solutions were identified based on the savings heuristic. The second and third act together using the random variable neighbourhood search algorithm, which allows performing an intensification process and perturbance processes, giving the possibility of exploring new regions in the search space, which are proposed within the framework of optimizing the three objectives. According to the previous review of the state of the art, there is little related literature; through discussions with the productive sector, this problem is frequent due to increases in demand in certain seasons or a part of the maintenance vehicle fleet departing from service. The proposed methodology was verified using case studies from the literature, which were adapted to the problem of three objectives, obtaining consistent solutions. Where cases were not reported in the literature, these could be used as a reference in future research.
Highlights
The solution of the routing problem is of great interest to the productive sectors of a country since the costs associated with transporting goods or services is a significant part of their final value, which implies that it has a direct macroeconomic effect
In the search methodology for problems with several objectives, the factor that indicates whether one solution is better than another is indicated by dominance but this could not be used in the local search because, by the definition given for dominance, it accepts solutions that are not strictly better; the search might not converge towards the optimal solutions and get caught in search loops
The value of k corresponds to the number of private fleet vehicles, calculated as k = 0.8q/Q, where q is the value of total customer demand and Q is the capacity of the vehicles
Summary
The solution of the routing problem is of great interest to the productive sectors of a country since the costs associated with transporting goods or services is a significant part of their final value, which implies that it has a direct macroeconomic effect. The solution to this problem implies a positive impact on the economic variables of organizations. Dantzing et al (1959) proposed a variation on the TPS problem that considers using “m” vehicles (replacing agents) with capacity restrictions This problem is known as a capacitated vehicle routing problem (CVRP). When other optimization criteria are considered, such as the impact on the environment due to operating vehicle fleets and workload balance, the problem is defined as a green vehicle route problem with private fleet and common carrier considering workload equity (GVRPPCWE)
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