135 research outputs found
A Survey on Environmentally Friendly Vehicle Routing Problem and a Proposal of Its Classification
The growth of environmental awareness and more robust enforcement of numerous regulations to reduce greenhouse gas (GHG) emissions have directed efforts towards addressing
current environmental challenges. Considering the Vehicle Routing Problem (VRP), one of the effective strategies to control greenhouse gas emissions is to convert the fossil fuel-powered fleet
into Environmentally Friendly Vehicles (EFVs). Given the multitude of constraints and assumptions defined for different types of VRPs, as well as assumptions and operational constraints specific to each
type of EFV, many variants of environmentally friendly VRPs (EF-VRP) have been introduced. In this paper, studies conducted on the subject of EF-VRP are reviewed, considering all the road transport
EFV types and problem variants, and classifying and discussing with a single holistic vision. The aim of this paper is twofold. First, it determines a classification of EF-VRP studies based on different
types of EFVs, i.e., Alternative-Fuel Vehicles (AFVs), Electric Vehicles (EVs) and Hybrid Vehicles (HVs). Second, it presents a comprehensive survey by considering each variant of the classification,
technical constraints and solution methods arising in the literature. The results of this paper show that studies on EF-VRP are relatively novel and there is still room for large improvements in several
areas. So, to determine future insights, for each classification of EF-VRP studies, the paper provides the literature gaps and future research needs
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
Combining statistical learning with metaheuristics for the multi-depot vehicle routing problem with market segmentation
In real-life logistics and distribution activities it is usual to face situations in
which the distribution of goods has to be made from multiple warehouses or
depots to the nal customers. This problem is known as the Multi-Depot Vehicle
Routing Problem (MDVRP), and it typically includes two sequential and
correlated stages: (a) the assignment map of customers to depots, and (b) the
corresponding design of the distribution routes. Most of the existing work in the
literature has focused on minimizing distance-based distribution costs while satisfying
a number of capacity constraints. However, no attention has been given
so far to potential variations in demands due to the tness of the customerdepot
mapping in the case of heterogeneous depots. In this paper, we consider
this realistic version of the problem in which the depots are heterogeneous in
terms of their commercial o er and customers show di erent willingness to consume
depending on how well the assigned depot ts their preferences. Thus,
we assume that di erent customer-depot assignment maps will lead to di erent
customer-expenditure levels. As a consequence, market-segmentation strategies
need to be considered in order to increase sales and total income while accounting
for the distribution costs. To solve this extension of the MDVRP, we
propose a hybrid approach that combines statistical learning techniques with
a metaheuristic framework. First, a set of predictive models is generated from
historical data. These statistical models allow estimating the demand of any
customer depending on the assigned depot. Then, the estimated expenditure of
each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A
set of computational experiments contribute to illustrate our approach and how
the extended MDVRP considered here diré in terms of the proposed solutions
from the traditional one.Peer ReviewedPreprin
Internet of Things in urban waste collection
Nowadays, the waste collection management has an important role in urban areas. This paper faces this issue and proposes the application of a metaheuristic for the optimization of a weekly schedule and routing of the waste collection activities in an urban area. Differently to several contributions in literature, fixed periodic routes are not imposed. The results significantly improve the performance of the company involved, both in terms of resources used and costs saving
Optimizing transportation systems and logistics network configurations : From biased-randomized algorithms to fuzzy simheuristics
242 páginasTransportation and logistics (T&L) are currently highly relevant functions in any competitive industry. Locating facilities or distributing goods to hundreds or thousands of customers are activities with a high degree of complexity, regardless of whether facilities and customers are placed all over the globe or in the same city. A countless number of alternative strategic, tactical, and operational decisions can be made in T&L systems; hence, reaching an optimal solution –e.g., a solution with the minimum cost or the maximum profit– is a really difficult challenge, even by the most powerful existing computers. Approximate methods, such as heuristics, metaheuristics, and simheuristics, are then proposed to solve T&L problems. They do not guarantee optimal results, but they yield good solutions in short computational times. These characteristics become even more important when considering uncertainty conditions, since they increase T&L problems’ complexity. Modeling uncertainty implies to introduce complex mathematical formulas and procedures, however, the model realism increases and, therefore, also its reliability to represent real world situations. Stochastic approaches, which require the use of probability distributions, are one of the most employed approaches to model uncertain parameters. Alternatively, if the real world does not provide enough information to reliably estimate a probability distribution, then fuzzy logic approaches become an alternative to model uncertainty. Hence, the main objective of this thesis is to design hybrid algorithms that combine fuzzy and stochastic simulation with approximate and exact methods to solve T&L problems considering operational, tactical, and strategic decision levels. This thesis is organized following a layered structure, in which each introduced layer enriches the previous one.El transporte y la logística (T&L) son actualmente funciones de gran relevancia en cual quier industria competitiva. La localización de instalaciones o la distribución de mercancías
a cientos o miles de clientes son actividades con un alto grado de complejidad, indepen dientemente de si las instalaciones y los clientes se encuentran en todo el mundo o en la
misma ciudad. En los sistemas de T&L se pueden tomar un sinnúmero de decisiones al ternativas estratégicas, tácticas y operativas; por lo tanto, llegar a una solución óptima –por
ejemplo, una solución con el mínimo costo o la máxima utilidad– es un desafío realmente di fícil, incluso para las computadoras más potentes que existen hoy en día. Así pues, métodos
aproximados, tales como heurísticas, metaheurísticas y simheurísticas, son propuestos para
resolver problemas de T&L. Estos métodos no garantizan resultados óptimos, pero ofrecen
buenas soluciones en tiempos computacionales cortos. Estas características se vuelven aún
más importantes cuando se consideran condiciones de incertidumbre, ya que estas aumen tan la complejidad de los problemas de T&L. Modelar la incertidumbre implica introducir
fórmulas y procedimientos matemáticos complejos, sin embargo, el realismo del modelo
aumenta y, por lo tanto, también su confiabilidad para representar situaciones del mundo
real. Los enfoques estocásticos, que requieren el uso de distribuciones de probabilidad, son
uno de los enfoques más empleados para modelar parámetros inciertos. Alternativamente,
si el mundo real no proporciona suficiente información para estimar de manera confiable
una distribución de probabilidad, los enfoques que hacen uso de lógica difusa se convier ten en una alternativa para modelar la incertidumbre. Así pues, el objetivo principal de
esta tesis es diseñar algoritmos híbridos que combinen simulación difusa y estocástica con
métodos aproximados y exactos para resolver problemas de T&L considerando niveles de
decisión operativos, tácticos y estratégicos. Esta tesis se organiza siguiendo una estructura
por capas, en la que cada capa introducida enriquece a la anterior. Por lo tanto, en primer
lugar se exponen heurísticas y metaheurísticas sesgadas-aleatorizadas para resolver proble mas de T&L que solo incluyen parámetros determinísticos. Posteriormente, la simulación
Monte Carlo se agrega a estos enfoques para modelar parámetros estocásticos. Por último,
se emplean simheurísticas difusas para abordar simultáneamente la incertidumbre difusa
y estocástica. Una serie de experimentos numéricos es diseñada para probar los algoritmos
propuestos, utilizando instancias de referencia, instancias nuevas e instancias del mundo
real. Los resultados obtenidos demuestran la eficiencia de los algoritmos diseñados, tanto
en costo como en tiempo, así como su confiabilidad para resolver problemas realistas que
incluyen incertidumbre y múltiples restricciones y condiciones que enriquecen todos los
problemas abordados.Doctorado en Logística y Gestión de Cadenas de SuministrosDoctor en Logística y Gestión de Cadenas de Suministro
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