Hybrid Henry Gas Solubility Optimization: An Effective Algorithm for Fuel Consumption Vehicle Routing Problem

The depletion of non-renewable fuel reserves is the biggest problem in the logistics sector. This problem encourages the transportation sector to increase fuel efficiency in distribution activities. The fuel optimization problem in distribution routing problems is called the Fuel Consumption Vehicle Routing Problem (FCVRP). This study proposes a novel Hybrid Henry Gas Solubility Optimization (HHGSO) to solve FCVRP problems. Experiments with several parameter variants were carried out to determine the performance of HHGSO in optimizing fuel consumption. The results show that the parameters of the HHGSO algorithm affect fuel consumption and computation time. In addition, the higher the KPL, the smaller the resulting fuel consumption. The proposed algorithm is also compared with several algorithms. The comparison results show that the proposed algorithm produces better computational time and fuel consumption than the Hybrid Particle Swarm Optimization and Tabu Search algorithms

Solving the green-fuzzy vehicle routing problem using a revised hybrid intelligent algorithm

Green logistics is an emerging area in supply chain management, which has been shown to have tremendous impacts in recent years to face the serious climate changes risks. In this paper, the fuel consumption and fuzzy travel time have been delineated in developing and solving the green-fuzzy vehicle routing problem as an extension of the celebrated VRP in which routes are performed to reduce the total expenditure. Different from the existing solution manners, we transform the original fuzzy chance constrained programming model into an equivalent deterministic model, and then revise the original hybrid intelligent algorithm by replacing the embedded fuzzy simulation with analytical function calculation. Finally, a comparative study with the corresponding literature is performed, which shows that the revised algorithm can not only improve the solution accuracy but also shorten the runtime greatly

A review of recent advances in the operations research literature on the green routing problem and its variants

Since early 2010s, the Green Routing Problem (GRP) has dominated the literature of logistics and transportation. The problem itself consists of finding a set of vehicle routes for a set of customers while minimizing the detrimental effects of transportation activities. These negative externalities have been intensively tackled in the last decade. Operations research studies have particularly focused on minimizing the energy consumption and emissions. As a result, the rich literature on GRPs has already reached its peak, and several early literature reviews have been conducted on various aspects of related vehicle routing and scheduling problem variants. The major contribution of this paper is that it represents a comprehensive review of the current reviews on GRP studies. In addition to that, it is an up-to-date review based on a new chronological taxonomy of the literature. The detailed analysis provides a useful framework for understanding the research gaps for the future studies and the potential impacts for the academic community

Metodología de algoritmos meméticos para el problema de ruteo de vehículos con entregas parciales y tiempos de viaje dependientes con ventanas de tiempo

El problema de ruteo de vehículos VRP es uno de los problemas más estudiados en investigación de operaciones, dada su relevancia en los campos del transporte y la logística. En los últimos años ha aumentado el interés en minimizar la contaminación por la emisión de gases efecto invernadero a causa del consumo de combustibles fósiles. El sector transporte representa una parte importante en esas emisiones. En el transporte, situaciones como los embotellamientos en las horas pico, por ejemplo, conducen a una red vial dinámica en la que varían los tiempos de viaje y consecuentemente el consumo de combustible. Por lo anterior el problema de enrutamiento de vehículos con tiempos dependientes TDVRP es una representación más cercana la vida real que los modelos tradicionales de enrutamientos de vehículos, VRP. Por otro lado, el problema de enrutamiento de vehículos con partición de entregas, SDVRP permite asignar múltiples rutas a un mismo cliente, propiciando ahorros en las mismas. El objetivo de esta tesis es desarrollar un método para el uso de los recursos de transporte, con el fin de atender a los clientes de manera eficiente respecto al costo total de la distancia recorrida y al tiempo total de viaje requerido. El problema consiste en programar un recorrido durante un día dividido en intervalos o zonas horarias, con ventanas de tiempo para atender a cada cliente, vehículos homogéneos con capacidad fija Q y un depósito único. Para ello se propone en este trabajo un Algoritmo Memético (MA) capaz de encontrar soluciones que respetan las restricciones del problema, teniendo en cuenta la posibilidad de hacer particiones en las entregas. Mediante el Diseño de Experimentos se evaluó la calidad de las soluciones generadas respecto a un Algoritmo Genético (GA) desarrollado también para el propósito, teniendo como criterio de evaluación el porcentaje de mejores soluciones alcanzado por cada algoritmo. Los experimentos permiten afirmar que el Algoritmo Memético propuesto supera el Algoritmo Genético, resultando más robusto ante cambios en los parámetros de ambos métodos. La solución propuesta representa un modelo más cercano a la realidad de las redes viales y genera rutas tendientes a disminuir la cantidad, recorrido y tiempo de permanencia de los vehículos en la red vial, conllevando a la disminución de las emisiones de gases efecto invernadero.Abstract: The vehicle routing problem VRP is one of the most studied problems in operations research, given its relevance in the fields of transport and logistics. In recent years, interest in minimizing pollution due to the emission of greenhouse gases, as a result of the consumption of fossil fuels, has increased. The transport sector represents an important part of those emissions. In transport, situations such as traffic jams during peak hours, for example, lead to a dynamic road network in which travel times and consequently fuel consumption vary. Therefore, the time dependent vehicle routing problem, TDVRP, is a closer representation of real life than the traditional vehicle routing models, VRP. On the other hand, the split delivery vehicle routing problem, SDVRP, allows assigning multiple routes to the same client, promoting savings in them. The objective of this thesis is to develop a method for the use of transportation resources, in order to serve customers efficiently with regard to the total cost of the distance traveled and the total traveled time required. The problem consists of scheduling a trip for a day which is divided into intervals or time zones, with time windows to serve each customer, homogeneous vehicles with fixed capacity Q and a single deposit. In order to do so, a Memetic Algorithm (MA) is proposed in this work, capable of finding solutions that respect the constraints of the problem, taking into account the possibility of splitting the deliveries. By using Design of Experiments, the quality of the solutions generated by the Memetic Algorithm was evaluated with respect to a Genetic Algorithm (GA) also developed for the purpose, having as the evaluation criterion the percentage of best solutions reached by each algorithm. The experiments show that the proposed memetic algorithm surpasses the genetic algorithm, being more robust to changes in the parameters of both methods The proposed solution represents a model that is closer to the reality of road networks and generates routes that tend to reduce quantity, travel length and time spent by vehicles on the road network, leading to a reduction in greenhouse gas emissions.Maestrí