The mixed capacitated general routing problem with turn penalties

In this paper we deal with the mixed capacitated general routing problem with turn penalties. This problem generalizes many important arc and node routing problems, and it takes into account turn penalties and forbidden turns, which are crucial in many real-life applications, such as mail delivery, waste collection and street maintenance operations. Through a polynomial transformation of the considered problem into a Generalized Vehicle routing problem, we suggest a new approach for solving this new problem by transforming it into an Asymmetric Capacitated Vehicle routing problem. In this way, we can solve the new problem both optimally and heuristically using existing algorithms. A powerful memetic algorithm and a set of 336 new benchmark instances are also suggested. The experimental results show that the average deviation of the suggested solution method is less than 0.05% with respect to optimum. © 2010 Elsevier Ltd. All rights reserved.This work has been partially supported by the Ministerio de Educacion y Ciencia of Spain (project TIN2008-06441-C02-01).Braysy, O.; Martínez Molada, E.; Nagata, Y.; Soler Fernández, D. (2011). The mixed capacitated general routing problem with turn penalties. Expert Systems with Applications. 38(10):12954-12966. https://doi.org/10.1016/j.eswa.2011.04.092S1295412966381

The Vehicle Routing Problem with Service Level Constraints

We consider a vehicle routing problem which seeks to minimize cost subject to service level constraints on several groups of deliveries. This problem captures some essential challenges faced by a logistics provider which operates transportation services for a limited number of partners and should respect contractual obligations on service levels. The problem also generalizes several important classes of vehicle routing problems with profits. To solve it, we propose a compact mathematical formulation, a branch-and-price algorithm, and a hybrid genetic algorithm with population management, which relies on problem-tailored solution representation, crossover and local search operators, as well as an adaptive penalization mechanism establishing a good balance between service levels and costs. Our computational experiments show that the proposed heuristic returns very high-quality solutions for this difficult problem, matches all optimal solutions found for small and medium-scale benchmark instances, and improves upon existing algorithms for two important special cases: the vehicle routing problem with private fleet and common carrier, and the capacitated profitable tour problem. The branch-and-price algorithm also produces new optimal solutions for all three problems

Hybrid Metaheuristics for the Clustered Vehicle Routing Problem

The Clustered Vehicle Routing Problem (CluVRP) is a variant of the Capacitated Vehicle Routing Problem in which customers are grouped into clusters. Each cluster has to be visited once, and a vehicle entering a cluster cannot leave it until all customers have been visited. This article presents two alternative hybrid metaheuristic algorithms for the CluVRP. The first algorithm is based on an Iterated Local Search algorithm, in which only feasible solutions are explored and problem-specific local search moves are utilized. The second algorithm is a Hybrid Genetic Search, for which the shortest Hamiltonian path between each pair of vertices within each cluster should be precomputed. Using this information, a sequence of clusters can be used as a solution representation and large neighborhoods can be efficiently explored by means of bi-directional dynamic programming, sequence concatenations, by using appropriate data structures. Extensive computational experiments are performed on benchmark instances from the literature, as well as new large scale ones. Recommendations on promising algorithm choices are provided relatively to average cluster size.Comment: Working Paper, MIT -- 22 page

Profitable mixed capacitated arc routing and related problems

Mixed Capacitated Arc Routing Problems (MCARP) aim to identify a set of vehicle trips that, starting and ending at a depot node, serve a given number of links, regarding the vehicles capacity, and minimizing a cost function. If both profits and costs on arcs are considered, the Profitable Mixed Capacitated Arc Routing Problem (PMCARP) may be defined. We present compact flow based models for the PMCARP, where two types of services are tackled, mandatory and optional. Adaptations of the models to fit into some other related problems are also proposed. The models are evaluated, according to their bounds quality as well as the CPU times, over large sets of test instances. New instances have been created from benchmark ones in order to solve variants that have been introduced here for the first time. Results show the new models performance within CPLEX and compare, whenever available, the proposed models against other resolution methods.info:eu-repo/semantics/publishedVersio

An artificial bee colony algorithm for the capacitated vehicle routing problem

Session MF-03: Population-based metaheuristics for routing problems - Stream: Metaheuristics - Invited session no. 3This paper introduces an artificial bee colony heuristic for the capacitated vehicle routing problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. The performance of the heuristic is evaluated on two sets of benchmark instances. A new scheme is also developed to improve the performance of the artificial bee colony heuristic. Computational results show that the heuristic with the new scheme produces good solutions.postprintThe 24th European Conference on Operational Research (EURO 24), Lisbon, Portual, 11-14 July 2010. In Abstract Book of EURO 24, 2010, p. 89, MF-03-

A GRASP Algorithm Based on New Randomized Heuristic for Vehicle Routing Problem

This paper presents a novel GRASP algorithm based on a new randomized heuristic for solving the capacitated vehicle routing problem, which characterized by using a fleet of homogenous vehicle capacity that will start from one depot, to serve a number of customers with demands that are less than the vehicle capacity. The proposed method is based on a new constructive heuristic and a simulated annealing procedure as an improvement phase. The new constructive heuristic uses four steps to generate feasible initial solutions, and the simulated annealing enhances these solutions found to reach the optimal one. We tested our algorithm on two sets of benchmark instances and the obtained results are very encouraging

A large neighbourhood based heuristic for two-echelon routing problems

In this paper, we address two optimisation problems arising in the context of city logistics and two-level transportation systems. The two-echelon vehicle routing problem and the two-echelon location routing problem seek to produce vehicle itineraries to deliver goods to customers, with transits through intermediate facilities. To efficiently solve these problems, we propose a hybrid metaheuristic which combines enumerative local searches with destroy-and-repair principles, as well as some tailored operators to optimise the selections of intermediate facilities. We conduct extensive computational experiments to investigate the contribution of these operators to the search performance, and measure the performance of the method on both problem classes. The proposed algorithm finds the current best known solutions, or better ones, for 95% of the two-echelon vehicle routing problem benchmark instances. Overall, for both problems, it achieves high-quality solutions within short computing times. Finally, for future reference, we resolve inconsistencies between different versions of benchmark instances, document their differences, and provide them all online in a unified format