A Metaheuristic Approach for the Problem of Motor Fuel Distribution

AbstractMotor fuel distribution problem is considered. Accepting some assumptions it can be reduced to a well-known vehicle routing problem with capacity constraints. Ant colony optimization approach is suggested for solving CVRP. Modified ant algorithms are performed. Computational results for some benchmarks are given in compare with classical ant algorithm

Article a novel algorithm for capacitated vehicle routing problem for smart cities

Smart logistics is an indispensable building block in smart cities development that requires solving the challenge of efficiently serving the demands of geographically distributed customers by a fleet of vehicles. It consists of a very well-known NP-hard complex optimization problem, which is known as the capacitated vehicle routing problem (CVRP). The CVRP has widespread real-life applications such as delivery in smart logistics, the pharmaceutical distribution of vacancies, disaster relief efforts, and others. In this work, a novel giant tour best cost crossover (GTBCX) operator is proposed which works stochastically to search for the optimal solutions of the CVRP. An NSGA-II-based routing algorithm employing GTBCX is also proposed to solve the CVRP to minimize the total distance traveled as well as to minimize the longest route length. The simulated study is performed on 88 benchmark CVRP instances to validate the success of our proposed GTBCX operator against the nearest neighbor crossover (NNX) and edge assembly crossover (EAX) operators. The rigorous simulation study shows that the GTBCX is a powerful operator and helps to find results that are superior in terms of the overall distance traveled, length of the longest route, quality, and number of Pareto solutions. This work employs a multi-objective optimization algorithm to solve the capacitated vehicle routing problem (CVRP), where the CVRP is represented in the form of a two-dimensional graph. To compute the values’ objective functions, the distance between two nodes in the graph is considered symmetric. This indicates that the genetic algorithm complex optimization algorithm is employed to solve CVRP, which is a symmetry distance-based graph

The Vehicle Routing Problem with Release and Due Dates

A novel extension of the classical vehicle routing and scheduling problems is introduced that integrates aspects of machine scheduling into vehicle routing. Associated with each customer order is a release date that defines the earliest time that the order is available to leave the depot for delivery and a due date that indicates the time by which the order should ideally be delivered to the customer. The objective is to minimize a convex combination of the operational costs and customer service level, represented by the total distance traveled and the total weighted tardiness of delivery, respectively. A path-relinking algorithm (PRA) is proposed to address the problem, and a variety of benchmark instances are generated to evaluate its performance. The PRA exploits the efficiency and aggressive improvement of neighborhood search but relies on a new path-relinking procedure and advanced population management strategies to navigate the search space effectively. To provide a comparator algorithm to the PRA, we embed the neighborhood search into a standard iterated local search algorithm (ILS). Extensive computational experiments on the benchmark instances show that the newly defined features have a significant and varied impact on the problem, and the performance of the PRA dominates that of the ILS algorithm. The online supplement is available at https://doi.org/10.1287/ijoc.2017.0756 . </jats:p

MULTI-VEHICLE ROUTE PLANNING FOR CENTRALIZED AND DECENTRALIZED SYSTEMS

Multi-vehicle route planning is the problem of determining routes for a set of vehicles to visit a set of locations of interest. In this thesis, we describe a study of a classical multi-vehicle route planning problem which compared existing solutions methods on min-sum (minimizing total distance traveled) and min-max (minimizing maximum distance traveled) cost objectives. We then extended the work in this study by adapting approaches tested to generate robust solutions to a failure-robust multi vehicle route planning problem in which a potential vehicle failure may require modifying the solution, which could increase costs. Additionally, we considered a decentralized extension to the multi-vehicle route planning problem, also known as the decentralized task allocation problem. The results of a computational study show that our novel genetic algorithm generated better solutions than existing approaches on larger instances with high communication quality

Solving the vehicle routing problem using hybrid cellular evolutionary algorithm

Problem usmjeravanja vozila (VRP) kompleksan je kombinatorički problem s kojim se svakodnevno susreću tvrtke koje obavljaju dostavu robe. Njegovim učinkovitim rješavanjem moguće je značajno smanjiti troškove dostave. Metaheurističkim metodama moguće je relativno brzo pronaći visoko kvalitetna rješenja. Stanični evolucijski algoritam metaheuristički je algoritam kod kojeg su jedinke iz populacije raspoređene unutar toroidalne mreže i mogu biti u interakciji samo sa obližnjim jedinkama. Podešavanjem selekcijskog pritiska moguće je postići odgovarajući omjer diverzifikacije i intenzifikacije koji je ključan za uspješnost algoritma. Hibridizacija postupkom pretraživanja velikog susjedstva ubrzava pronalazak visoko kvalitetnih rješenja. Razvijeni algoritam testiran je na nekoliko skupova ispitnih zadataka te na problemima dostave hrvatskih tvrtki. Rezultati ostvareni na ispitnim zadacima pokazuju da učinkovitost algoritma ne odstupa mnogo od najboljih poznatih algoritama za ovu vrstu problema, dok rezultati ostvareni na problemima hrvatskih tvrtki pokazuju da je primjenom algoritma moguće postići značajne uštede.Vehicle Routing Problem (VRP) is a complex combinatorial problem encountered daily by companies that are dealing with goods delivery. With its ecient solving it is possible to signicantly reduce the cost of delivery. Metaheuristic methods are capable of nding high-quality solutions in reasonable amount of time. The cellular evolutionary algorithm is a metaheuristic algorithm in which the individuals from the population are distributed within the toroidal grid and can interact only with nearby entities. By adjusting the selection pressure, it is possible to achieve the appropriate ratio of diversication and intensication that is crucial to the success of the algorithm. Hybridization by a large neighborhood search accelerates the nding of high quality solutions. The developed algorithm has been tested on several sets of benchmarks and on the delivery problems of Croatian companies. The results obtained on the benchmarks show that the eciency of the algorithm does not dier much from the best-known algorithms for this type of problem, while the results achieved on the problems of Croatian companies show that it is possible to achieve signicant savings by algorithm application

Solving the vehicle routing problem using hybrid cellular evolutionary algorithm

Problem usmjeravanja vozila (VRP) kompleksan je kombinatorički problem s kojim se svakodnevno susreću tvrtke koje obavljaju dostavu robe. Njegovim učinkovitim rješavanjem moguće je značajno smanjiti troškove dostave. Metaheurističkim metodama moguće je relativno brzo pronaći visoko kvalitetna rješenja. Stanični evolucijski algoritam metaheuristički je algoritam kod kojeg su jedinke iz populacije raspoređene unutar toroidalne mreže i mogu biti u interakciji samo sa obližnjim jedinkama. Podešavanjem selekcijskog pritiska moguće je postići odgovarajući omjer diverzifikacije i intenzifikacije koji je ključan za uspješnost algoritma. Hibridizacija postupkom pretraživanja velikog susjedstva ubrzava pronalazak visoko kvalitetnih rješenja. Razvijeni algoritam testiran je na nekoliko skupova ispitnih zadataka te na problemima dostave hrvatskih tvrtki. Rezultati ostvareni na ispitnim zadacima pokazuju da učinkovitost algoritma ne odstupa mnogo od najboljih poznatih algoritama za ovu vrstu problema, dok rezultati ostvareni na problemima hrvatskih tvrtki pokazuju da je primjenom algoritma moguće postići značajne uštede.Vehicle Routing Problem (VRP) is a complex combinatorial problem encountered daily by companies that are dealing with goods delivery. With its ecient solving it is possible to signicantly reduce the cost of delivery. Metaheuristic methods are capable of nding high-quality solutions in reasonable amount of time. The cellular evolutionary algorithm is a metaheuristic algorithm in which the individuals from the population are distributed within the toroidal grid and can interact only with nearby entities. By adjusting the selection pressure, it is possible to achieve the appropriate ratio of diversication and intensication that is crucial to the success of the algorithm. Hybridization by a large neighborhood search accelerates the nding of high quality solutions. The developed algorithm has been tested on several sets of benchmarks and on the delivery problems of Croatian companies. The results obtained on the benchmarks show that the eciency of the algorithm does not dier much from the best-known algorithms for this type of problem, while the results achieved on the problems of Croatian companies show that it is possible to achieve signicant savings by algorithm application

Solving the vehicle routing problem using hybrid cellular evolutionary algorithm

Problem usmjeravanja vozila (VRP) kompleksan je kombinatorički problem s kojim se svakodnevno susreću tvrtke koje obavljaju dostavu robe. Njegovim učinkovitim rješavanjem moguće je značajno smanjiti troškove dostave. Metaheurističkim metodama moguće je relativno brzo pronaći visoko kvalitetna rješenja. Stanični evolucijski algoritam metaheuristički je algoritam kod kojeg su jedinke iz populacije raspoređene unutar toroidalne mreže i mogu biti u interakciji samo sa obližnjim jedinkama. Podešavanjem selekcijskog pritiska moguće je postići odgovarajući omjer diverzifikacije i intenzifikacije koji je ključan za uspješnost algoritma. Hibridizacija postupkom pretraživanja velikog susjedstva ubrzava pronalazak visoko kvalitetnih rješenja. Razvijeni algoritam testiran je na nekoliko skupova ispitnih zadataka te na problemima dostave hrvatskih tvrtki. Rezultati ostvareni na ispitnim zadacima pokazuju da učinkovitost algoritma ne odstupa mnogo od najboljih poznatih algoritama za ovu vrstu problema, dok rezultati ostvareni na problemima hrvatskih tvrtki pokazuju da je primjenom algoritma moguće postići značajne uštede.Vehicle Routing Problem (VRP) is a complex combinatorial problem encountered daily by companies that are dealing with goods delivery. With its ecient solving it is possible to signicantly reduce the cost of delivery. Metaheuristic methods are capable of nding high-quality solutions in reasonable amount of time. The cellular evolutionary algorithm is a metaheuristic algorithm in which the individuals from the population are distributed within the toroidal grid and can interact only with nearby entities. By adjusting the selection pressure, it is possible to achieve the appropriate ratio of diversication and intensication that is crucial to the success of the algorithm. Hybridization by a large neighborhood search accelerates the nding of high quality solutions. The developed algorithm has been tested on several sets of benchmarks and on the delivery problems of Croatian companies. The results obtained on the benchmarks show that the eciency of the algorithm does not dier much from the best-known algorithms for this type of problem, while the results achieved on the problems of Croatian companies show that it is possible to achieve signicant savings by algorithm application

Meta-heuristic Solution Methods for Rich Vehicle Routing Problems

Le problème de tournées de véhicules (VRP), introduit par Dantzig and Ramser en 1959, est devenu l'un des problèmes les plus étudiés en recherche opérationnelle, et ce, en raison de son intérêt méthodologique et de ses retombées pratiques dans de nombreux domaines tels que le transport, la logistique, les télécommunications et la production. L'objectif général du VRP est d'optimiser l'utilisation des ressources de transport afin de répondre aux besoins des clients tout en respectant les contraintes découlant des exigences du contexte d’application. Les applications réelles du VRP doivent tenir compte d’une grande variété de contraintes et plus ces contraintes sont nombreuse, plus le problème est difficile à résoudre. Les VRPs qui tiennent compte de l’ensemble de ces contraintes rencontrées en pratique et qui se rapprochent des applications réelles forment la classe des problèmes ‘riches’ de tournées de véhicules. Résoudre ces problèmes de manière efficiente pose des défis considérables pour la communauté de chercheurs qui se penchent sur les VRPs. Cette thèse, composée de deux parties, explore certaines extensions du VRP vers ces problèmes. La première partie de cette thèse porte sur le VRP périodique avec des contraintes de fenêtres de temps (PVRPTW). Celui-ci est une extension du VRP classique avec fenêtres de temps (VRPTW) puisqu’il considère un horizon de planification de plusieurs jours pendant lesquels les clients n'ont généralement pas besoin d’être desservi à tous les jours, mais plutôt peuvent être visités selon un certain nombre de combinaisons possibles de jours de livraison. Cette généralisation étend l'éventail d'applications de ce problème à diverses activités de distributions commerciales, telle la collecte des déchets, le balayage des rues, la distribution de produits alimentaires, la livraison du courrier, etc. La principale contribution scientifique de la première partie de cette thèse est le développement d'une méta-heuristique hybride dans la quelle un ensemble de procédures de recherche locales et de méta-heuristiques basées sur les principes de voisinages coopèrent avec un algorithme génétique afin d’améliorer la qualité des solutions et de promouvoir la diversité de la population. Les résultats obtenus montrent que la méthode proposée est très performante et donne de nouvelles meilleures solutions pour certains grands exemplaires du problème. La deuxième partie de cette étude a pour but de présenter, modéliser et résoudre deux problèmes riches de tournées de véhicules, qui sont des extensions du VRPTW en ce sens qu'ils incluent des demandes dépendantes du temps de ramassage et de livraison avec des restrictions au niveau de la synchronization temporelle. Ces problèmes sont connus respectivement sous le nom de Time-dependent Multi-zone Multi-Trip Vehicle Routing Problem with Time Windows (TMZT-VRPTW) et de Multi-zone Mult-Trip Pickup and Delivery Problem with Time Windows and Synchronization (MZT-PDTWS). Ces deux problèmes proviennent de la planification des opérations de systèmes logistiques urbains à deux niveaux. La difficulté de ces problèmes réside dans la manipulation de deux ensembles entrelacés de décisions: la composante des tournées de véhicules qui vise à déterminer les séquences de clients visités par chaque véhicule, et la composante de planification qui vise à faciliter l'arrivée des véhicules selon des restrictions au niveau de la synchronisation temporelle. Auparavant, ces questions ont été abordées séparément. La combinaison de ces types de décisions dans une seule formulation mathématique et dans une même méthode de résolution devrait donc donner de meilleurs résultats que de considérer ces décisions séparément. Dans cette étude, nous proposons des solutions heuristiques qui tiennent compte de ces deux types de décisions simultanément, et ce, d'une manière complète et efficace. Les résultats de tests expérimentaux confirment la performance de la méthode proposée lorsqu’on la compare aux autres méthodes présentées dans la littérature. En effet, la méthode développée propose des solutions nécessitant moins de véhicules et engendrant de moindres frais de déplacement pour effectuer efficacement la même quantité de travail. Dans le contexte des systèmes logistiques urbains, nos résultats impliquent une réduction de la présence de véhicules dans les rues de la ville et, par conséquent, de leur impact négatif sur la congestion et sur l’environnement.For more than half of century, since the paper of Dantzig and Ramser (1959) was introduced, the Vehicle Routing Problem (VRP) has been one of the most extensively studied problems in operations research due to its methodological interest and practical relevance in many fields such as transportation, logistics, telecommunications, and production. The general goal of the VRP is to optimize the use of transportation resources to service customers with respect to side-constraints deriving from real-world applications. The practical applications of the VRP may have a variety of constraints, and obviously, the larger the set of constraints that need to be considered, i.e., corresponding to `richer' VRPs, the more difficult the task of problem solving. The needs to study closer representations of actual applications and methodologies producing high-quality solutions quickly to larger-sized application problems have increased steadily, providing significant challenges for the VRP research community. This dissertation explores these extensional issues of the VRP. The first part of the dissertation addresses the Periodic Vehicle Routing Problem with Time Windows (PVRPTW) which generalizes the classical Vehicle Routing Problem with Time Windows (VRPTW) by extending the planning horizon to several days where customers generally do not require delivery on every day, but rather according to one of a limited number of possible combinations of visit days. This generalization extends the scope of applications to many commercial distribution activities such as waste collection, street sweeping, grocery distribution, mail delivery, etc. The major contribution of this part is the development of a population-based hybrid meta-heuristic in which a set of local search procedures and neighborhood-based meta-heuristics cooperate with the genetic algorithm population evolution mechanism to enhance the solution quality as well as to promote diversity of the genetic algorithm population. The results show that the proposed methodology is highly competitive, providing new best solutions in some large instances. The second part of the dissertation aims to present, model and solve two rich vehicle routing problems which further extend the VRPTW with time-dependent demands of pickup and delivery, and hard time synchronization restrictions. They are called Time-dependent Multi-zone Multi-Trip Vehicle Routing Problem with Time Windows (TMZT-VRPTW), and Multi-zone Mult-Trip Pickup and Delivery Problem with Time Windows and Synchronization (MZT-PDTWS), respectively. These two problems originate from planning the operations of two-tiered City Logistics systems. The difficulty of these problems lies in handling two intertwined sets of decisions: the routing component which aims to determine the sequences of customers visited by each vehicle, and the scheduling component which consists in planning arrivals of vehicles at facilities within hard time synchronization restrictions. Previously, these issues have been addressed separately. Combining these decisions into one formulation and solution method should yield better results. In this dissertation we propose meta-heuristics that address the two decisions simultaneously, in a comprehensive and efficient way. Experiments confirm the good performance of the proposed methodology compared to the literature, providing system managers with solution requiring less vehicles and travel costs to perform efficiently the same amount of work. In the context of City Logistics systems, our results indicate a reduction in the presence of vehicles on the streets of the city and, thus, in their negative impact on congestion and environment