A Hybrid Heuristic for a Broad Class of Vehicle Routing Problems with Heterogeneous Fleet

We consider a family of Rich Vehicle Routing Problems (RVRP) which have the particularity to combine a heterogeneous fleet with other attributes, such as backhauls, multiple depots, split deliveries, site dependency, open routes, duration limits, and time windows. To efficiently solve these problems, we propose a hybrid metaheuristic which combines an iterated local search with variable neighborhood descent, for solution improvement, and a set partitioning formulation, to exploit the memory of the past search. Moreover, we investigate a class of combined neighborhoods which jointly modify the sequences of visits and perform either heuristic or optimal reassignments of vehicles to routes. To the best of our knowledge, this is the first unified approach for a large class of heterogeneous fleet RVRPs, capable of solving more than 12 problem variants. The efficiency of the algorithm is evaluated on 643 well-known benchmark instances, and 71.70\% of the best known solutions are either retrieved or improved. Moreover, the proposed metaheuristic, which can be considered as a matheuristic, produces high quality solutions with low standard deviation in comparison with previous methods. Finally, we observe that the use of combined neighborhoods does not lead to significant quality gains. Contrary to intuition, the computational effort seems better spent on more intensive route optimization rather than on more intelligent and frequent fleet re-assignments

Um algoritmo heurístico híbrido para minimizar os custos com a antecipação e o atraso da produção em ambientes com janelas de entrega e tempos de preparação dependentes da sequência.

Este trabalho de dissertação tem seu foco no problema de sequenciamento em uma máquina com penalidades por antecipação e atraso da produção. São considerados tempos de preparação da máquina dependentes da sequência de produção, bem como a existência de janelas de entrega distintas. Para resolução do problema, desenvolveu-se um algoritmo heurístico de três fases. A primeira fase baseada em GRASP e Descida em Vizinhança Variável para a geração da solução inicial, a segunda fase baseada em Busca Tabu para re namento da solução, e por m, a Reconexão por Caminhos como estratégia de pós-otimização, na terceira fase. Para cada sequência gerada pela heurística é utilizado um algoritmo de tempo polinomial para determinar a data ótima de início de processamento de cada tarefa. Os re- sultados computacionais mostraram que houve melhoria em relação a um algoritmo da literatura, tanto com relação à qualidade da solução nal quanto em relação ao desvio médio.This work deals with the single machine scheduling problem with earliness and tar- diness penalties. Sequence dependent setup times and distinct due windows are con- sidered. To solve this problem, a three-phase heuristic approach was developed. The rst phase is based on GRASP and Variable Neighborhood Descent to generate an initial solution; the second phase is based on a Tabu Search for solution re ning, and nally Path Relinking is used as a mechanism of post-optimization as a third pha- se. For each job sequence generated by the heuristic, an optimal timing algorithm is used to determine the completion time for each job in the job sequence. Computatio- nal experiments carried out show that previous algorithms found in related literature have been improved, regarding the quality of the nal solution and the average gap

A hybrid heuristic for a broad class of vehicle routing problems with heterogeneous ?eet

Abstract We consider a family of rich vehicle routing problems (RVRP) which have the particularitytocombineaheterogeneous?eetwithotherattributes,suchasbackhauls,multiple depots, split deliveries, site dependency, open routes, duration limits, and time windows. To ef?ciently solve these problems, we propose a hybrid metaheuristic which combines an iteratedlocalsearchwithvariableneighborhooddescent,forsolutionimprovement,andaset partitioning formulation, to exploit the memory of the past search. Moreover, we investigate aclassofcombinedneighborhoodswhichjointlymodifythesequencesofvisitsandperform eitherheuristicoroptimalreassignmentsofvehiclestoroutes.Tothebestofourknowledge, this is the ?rst uni?ed approach for a large class of heterogeneous ?eet RVRPs, capabl

Solving last-mile distribution problems after major earthquakes

International audienc

A multi-depot heterogeneous vehicle routing problem in cities affected by a large scale disaster

International audienceThis study takes place in a national project on disaster logistics and concerns the last mile distribution phase after a large scale disaster, taking the Haiti earthquake as case study. From supply depots in the suburbs, the goal is to design vehicle routes with multiples trips to reach camps of refugees, knowing that the streets can be more or less obstructed, which implies several types of vehicles. The goal is to minimize the total distribution time and the cost of vehicles used. The proposed solution method is a matheuristic derived from Penna et al. (2013), a multi-start iterated local search. The improvement procedure is a variable neighborhood search (VND). A set-partitioning problem (SPP) is solved periodically, using a pool of good routes found by the heuristic, but the communication is bidirectional: the heuristic is also called each time a new solution is obtained during the SPP resolution. The algorithm competes with published metaheuristics on multi-depot VRP instances from the literature. On data from Haiti earthquake (16,000 nodes, 19,000 edges, 12 depots, 62 camps, 3 vehicle types), the algorithm returns in one minute on a PC solutions which are considered as very good by the decision maker

Heuristics for last-mile distribution in case of large-scale earthquakes

International audienc

Solving last-mile distribution problems after major earthquakes

International audienc

A multi-depot heterogeneous vehicle routing problem in cities affected by a large scale disaster

International audienceThis study takes place in a national project on disaster logistics and concerns the last mile distribution phase after a large scale disaster, taking the Haiti earthquake as case study. From supply depots in the suburbs, the goal is to design vehicle routes with multiples trips to reach camps of refugees, knowing that the streets can be more or less obstructed, which implies several types of vehicles. The goal is to minimize the total distribution time and the cost of vehicles used. The proposed solution method is a matheuristic derived from Penna et al. (2013), a multi-start iterated local search. The improvement procedure is a variable neighborhood search (VND). A set-partitioning problem (SPP) is solved periodically, using a pool of good routes found by the heuristic, but the communication is bidirectional: the heuristic is also called each time a new solution is obtained during the SPP resolution. The algorithm competes with published metaheuristics on multi-depot VRP instances from the literature. On data from Haiti earthquake (16,000 nodes, 19,000 edges, 12 depots, 62 camps, 3 vehicle types), the algorithm returns in one minute on a PC solutions which are considered as very good by the decision maker

Uma heurística para o problema de roteamento de veículos com frota heterogênea e múltiplos depósitos aplicada à distribuição pós-catástrofes

International audienc