4,272 research outputs found
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
A dynamic approach to rebalancing bike-sharing systems
Bike-sharing services are flourishing in Smart Cities worldwide. They provide a low-cost and environment-friendly transportation alternative and help reduce traffic congestion. However, these new services are still under development, and several challenges need to be solved. A major problem is the management of rebalancing trucks in order to ensure that bikes and stalls in the docking stations are always available when needed, despite the fluctuations in the service demand. In this work, we propose a dynamic rebalancing strategy that exploits historical data to predict the network conditions and promptly act in case of necessity. We use Birth-Death Processes to model the stations' occupancy and decide when to redistribute bikes, and graph theory to select the rebalancing path and the stations involved. We validate the proposed framework on the data provided by New York City's bike-sharing system. The numerical simulations show that a dynamic strategy able to adapt to the fluctuating nature of the network outperforms rebalancing schemes based on a static schedule
Stochastic Cyclic Inventory Routing with Supply Uncertainty: A Case in Green-Hydrogen Logistics
Hydrogen can be produced from water, using electricity. The hydrogen can
subsequently be kept in inventory in large quantities, unlike the electricity
itself. This enables solar and wind energy generation to occur asynchronously
from its usage. For this reason, hydrogen is expected to be a key ingredient
for reaching a climate-neutral economy. However, the logistics for hydrogen are
complex. Inventory policies must be determined for multiple locations in the
network, and transportation of hydrogen from the production location to
customers must be scheduled. At the same time, production patterns of hydrogen
are intermittent, which affects the possibilities to realize the planned
transportation and inventory levels. To provide policies for efficient
transportation and storage of hydrogen, this paper proposes a parameterized
cost function approximation approach to the stochastic cyclic inventory routing
problem. Firstly, our approach includes a parameterized mixed integer
programming (MIP) model which yields fixed and repetitive schedules for vehicle
transportation of hydrogen. Secondly, buying and selling decisions in case of
underproduction or overproduction are optimized further via a Markov decision
process (MDP) model, taking into account the uncertainties in production and
demand quantities. To jointly optimize the parameterized MIP and the MDP model,
our approach includes an algorithm that searches the parameter space by
iteratively solving the MIP and MDP models. We conduct computational
experiments to validate our model in various problem settings and show that it
provides near-optimal solutions. Moreover, we test our approach on an
expert-reviewed case study at two hydrogen production locations in the
Netherlands. We offer insights for the stakeholders in the region and analyze
the impact of various problem elements in these case studies
Un modelo para resolver el problema dinámico de despacho de vehÃculos con incertidumbre de clientes y con tiempos de viaje en arcos
Indexación: Web of Science; ScieloIn a real world case scenario, customer demands are requested at any time of the day requiring services that are not known in advance such as delivery or repairing equipment. This is called Dynamic Vehicle Routing (DVR) with customer uncertainty environment. The link travel time for the roadway network varies with time as traffic fluctuates adding an additional component to the dynamic environment. This paper presents a model for solving the DVR problem while combining these two dynamic aspects (customer uncertainty and link travel time). The proposed model employs Greedy, Insertion, and Ant Colony Optimization algorithms. The Greedy algorithm is utilized for constructing new routes with existing customers, and the remaining two algorithms are employed for rerouting as new customer demands appear. A real world application is presented to simulate vehicle routing in a dynamic environment for the city of Taipei, Taiwan. The simulation shows that the model can successfully plan vehicle routes to satisfy all customer demands and help managers in the decision making process.En un escenario real, los pedidos de los clientes son solicitados a cualquier hora del dÃa requiriendo servicios que no han sido planificados con antelación tales como los despachos o la reparación de equipos. Esto es llamado ruteo dinámico de vehÃculos (RDV) considerando un ambiente con incertidumbre de clientes. El tiempo de viaje en una red vial varÃa con el tiempo a medida que el tráfico vehicular fluctúa agregando una componente adicional al ambiente dinámico. Este artÃculo propone un modelo para resolver el problema RDV combinando estos dos aspectos dinámicos. El modelo propuesto utiliza los algoritmos Greedy, Inserción y optimización basada en colonias de hormigas. El algoritmo Greedy es utilizado para construir nuevas rutas con los clientes existentes y los otros dos algoritmos son usados para rutear vehÃculos a medida que surjan nuevos clientes con sus respectivos pedidos. Además, se presenta una aplicación real para simular el ruteo vehicular en un ambiente dinámico para la ciudad de Taipei, Taiwán. Esta simulación muestra que el modelo es capaz de planificar exitosamente las rutas vehiculares satisfaciendo los pedidos de los clientes y de ayudar los gerentes en el proceso de toma de decisiones.http://ref.scielo.org/3ryfh
- …