297 research outputs found
Arc routing problems: A review of the past, present, and future
[EN] Arc routing problems (ARPs) are defined and introduced. Following a brief history of developments in this area of research, different types of ARPs are described that are currently relevant for study. In addition, particular features of ARPs that are important from a theoretical or practical point of view are discussed. A section on applications describes some of the changes that have occurred from early applications of ARP models to the present day and points the way to emerging topics for study. A final section provides information on libraries and instance repositories for ARPs. The review concludes with some perspectives on future research developments and opportunities for emerging applicationsThis research was supported by the Ministerio de Economia y Competitividad and Fondo Europeo de Desarrollo Regional, Grant/Award Number: PGC2018-099428-B-I00. The Research Council of Norway, Grant/Award Numbers: 246825/O70 (DynamITe), 263031/O70 (AXIOM).Corberán, Á.; Eglese, R.; Hasle, G.; Plana, I.; Sanchís Llopis, JM. (2021). Arc routing problems: A review of the past, present, and future. Networks. 77(1):88-115. https://doi.org/10.1002/net.21965S8811577
Arc Routing with Time-Dependent Travel Times and Paths
Vehicle routing algorithms usually reformulate the road network into a
complete graph in which each arc represents the shortest path between two
locations. Studies on time-dependent routing followed this model and therefore
defined the speed functions on the complete graph. We argue that this model is
often inadequate, in particular for arc routing problems involving services on
edges of a road network. To fill this gap, we formally define the
time-dependent capacitated arc routing problem (TDCARP), with travel and
service speed functions given directly at the network level. Under these
assumptions, the quickest path between locations can change over time, leading
to a complex problem that challenges the capabilities of current solution
methods. We introduce effective algorithms for preprocessing quickest paths in
a closed form, efficient data structures for travel time queries during routing
optimization, as well as heuristic and exact solution approaches for the
TDCARP. Our heuristic uses the hybrid genetic search principle with tailored
solution-decoding algorithms and lower bounds for filtering moves. Our
branch-and-price algorithm exploits dedicated pricing routines, heuristic
dominance rules and completion bounds to find optimal solutions for problem
counting up to 75 services. Based on these algorithms, we measure the benefits
of time-dependent routing optimization for different levels of travel-speed
data accuracy
Two-Echelon Vehicle and UAV Routing for Post-Disaster Humanitarian Operations with Uncertain Demand
Humanitarian logistics service providers have two major responsibilities
immediately after a disaster: locating trapped people and routing aid to them.
These difficult operations are further hindered by failures in the
transportation and telecommunications networks, which are often rendered
unusable by the disaster at hand. In this work, we propose two-echelon vehicle
routing frameworks for performing these operations using aerial uncrewed
autonomous vehicles (UAVs or drones) to address the issues associated with
these failures. In our proposed frameworks, we assume that ground vehicles
cannot reach the trapped population directly, but they can only transport
drones from a depot to some intermediate locations. The drones launched from
these locations serve to both identify demands for medical and other aids
(e.g., epi-pens, medical supplies, dry food, water) and make deliveries to
satisfy them. Specifically, we present two decision frameworks, in which the
resulting optimization problem is formulated as a two-echelon vehicle routing
problem. The first framework addresses the problem in two stages: providing
telecommunications capabilities in the first stage and satisfying the resulting
demands in the second. To that end, two types of drones are considered. Hotspot
drones have the capability of providing cell phone and internet reception, and
hence are used to capture demands. Delivery drones are subsequently employed to
satisfy the observed demand. The second framework, on the other hand, addresses
the problem as a stochastic emergency aid delivery problem, which uses a
two-stage robust optimization model to handle demand uncertainty. To solve the
resulting models, we propose efficient and novel solution approaches
Tutorial: Modern Branch-and-Cut-and-Price for Vehicle Routing Problems Plan of the talk
International audienc
An updated annotated bibliography on arc routing problems
The number of arc routing publications has increased significantly in the last decade. Such an increase justifies a second annotated bibliography, a sequel to Corberán and Prins (Networks 56 (2010), 50–69), discussing arc routing studies from 2010 onwards. These studies are grouped into three main sections: single vehicle problems, multiple vehicle problems and applications. Each main section catalogs problems according to their specifics. Section 2 is therefore composed of four subsections, namely: the Chinese Postman Problem, the Rural Postman Problem, the General Routing Problem (GRP) and Arc Routing Problems (ARPs) with profits. Section 3, devoted to the multiple vehicle case, begins with three subsections on the Capacitated Arc Routing Problem (CARP) and then delves into several variants of multiple ARPs, ending with GRPs and problems with profits. Section 4 is devoted to applications, including distribution and collection routes, outdoor activities, post-disaster operations, road cleaning and marking. As new applications emerge and existing applications continue to be used and adapted, the future of arc routing research looks promising.info:eu-repo/semantics/publishedVersio
Learning-Based Matheuristic Solution Methods for Stochastic Network Design
Cette dissertation consiste en trois études, chacune constituant un article de recherche.
Dans tous les trois articles, nous considérons le problème de conception de réseaux
multiproduits, avec coût fixe, capacité et des demandes stochastiques en tant que programmes
stochastiques en deux étapes. Dans un tel contexte, les décisions de conception
sont prises dans la première étape avant que la demande réelle ne soit réalisée, tandis
que les décisions de flux de la deuxième étape ajustent la solution de la première étape
à la réalisation de la demande observée. Nous considérons l’incertitude de la demande
comme un nombre fini de scénarios discrets, ce qui est une approche courante dans la
littérature. En utilisant l’ensemble de scénarios, le problème mixte en nombre entier
(MIP) résultant, appelé formulation étendue (FE), est extrêmement difficile à résoudre,
sauf dans des cas triviaux. Cette thèse vise à faire progresser le corpus de connaissances
en développant des algorithmes efficaces intégrant des mécanismes d’apprentissage en
matheuristique, capables de traiter efficacement des problèmes stochastiques de conception
pour des réseaux de grande taille.
Le premier article, s’intitulé "A Learning-Based Matheuristc for Stochastic Multicommodity
Network Design". Nous introduisons et décrivons formellement un nouveau
mécanisme d’apprentissage basé sur l’optimisation pour extraire des informations
concernant la structure de la solution du problème stochastique à partir de solutions
obtenues avec des combinaisons particulières de scénarios. Nous proposons ensuite
une matheuristique "Learn&Optimize", qui utilise les méthodes d’apprentissage pour
déduire un ensemble de variables de conception prometteuses, en conjonction avec un
solveur MIP de pointe pour résoudre un problème réduit.
Le deuxième article, s’intitulé "A Reduced-Cost-Based Restriction and Refinement
Matheuristic for Stochastic Network Design". Nous étudions comment concevoir efficacement
des mécanismes d’apprentissage basés sur l’information duale afin de guider la
détermination des variables dans le contexte de la conception de réseaux stochastiques.
Ce travail examine les coûts réduits associés aux variables hors base dans les solutions
déterministes pour guider la sélection des variables dans la formulation stochastique.
Nous proposons plusieurs stratégies pour extraire des informations sur les coûts réduits
afin de fixer un ensemble approprié de variables dans le modèle restreint. Nous proposons
ensuite une approche matheuristique utilisant des techniques itératives de réduction
des problèmes.
Le troisième article, s’intitulé "An Integrated Learning and Progressive Hedging
Method to Solve Stochastic Network Design". Ici, notre objectif principal est de concevoir
une méthode de résolution capable de gérer un grand nombre de scénarios. Nous
nous appuyons sur l’algorithme Progressive Hedging (PHA), ou les scénarios sont regroupés
en sous-problèmes. Nous intégrons des methodes d’apprentissage au sein de
PHA pour traiter une grand nombre de scénarios. Dans notre approche, les mécanismes
d’apprentissage developpés dans le premier article de cette thèse sont adaptés pour résoudre
les sous-problèmes multi-scénarios. Nous introduisons une nouvelle solution
de référence à chaque étape d’agrégation de notre ILPH en exploitant les informations
collectées à partir des sous problèmes et nous utilisons ces informations pour mettre à
jour les pénalités dans PHA. Par conséquent, PHA est guidé par les informations locales
fournies par la procédure d’apprentissage, résultant en une approche intégrée capable de
traiter des instances complexes et de grande taille.
Dans les trois articles, nous montrons, au moyen de campagnes expérimentales approfondies,
l’intérêt des approches proposées en termes de temps de calcul et de qualité
des solutions produites, en particulier pour traiter des cas très difficiles avec un grand
nombre de scénarios.This dissertation consists of three studies, each of which constitutes a self-contained
research article. In all of the three articles, we consider the multi-commodity capacitated
fixed-charge network design problem with uncertain demands as a two-stage stochastic
program. In such setting, design decisions are made in the first stage before the actual
demand is realized, while second-stage flow-routing decisions adjust the first-stage solution
to the observed demand realization. We consider the demand uncertainty as a finite
number of discrete scenarios, which is a common approach in the literature.
By using the scenario set, the resulting large-scale mixed integer program (MIP)
problem, referred to as the extensive form (EF), is extremely hard to solve exactly in
all but trivial cases. This dissertation is aimed at advancing the body of knowledge
by developing efficient algorithms incorporating learning mechanisms in matheuristics,
which are able to handle large scale instances of stochastic network design problems
efficiently.
In the first article, we propose a novel Learning-Based Matheuristic for Stochastic
Network Design Problems. We introduce and formally describe a new optimizationbased
learning mechanism to extract information regarding the solution structure of a
stochastic problem out of the solutions of particular combinations of scenarios. We subsequently
propose the Learn&Optimize matheuristic, which makes use of the learning
methods in inferring a set of promising design variables, in conjunction with a state-ofthe-
art MIP solver to address a reduced problem.
In the second article, we introduce a Reduced-Cost-Based Restriction and Refinement
Matheuristic. We study on how to efficiently design learning mechanisms based on dual
information as a means of guiding variable fixing in the context of stochastic network
design. The present work investigates how the reduced cost associated with non-basic
variables in deterministic solutions can be leveraged to guide variable selection within
stochastic formulations. We specifically propose several strategies to extract reduced
cost information so as to effectively identify an appropriate set of fixed variables within
a restricted model. We then propose a matheuristic approach using problem reduction techniques iteratively (i.e., defining and exploring restricted region of global solutions,
as guided by applicable dual information).
Finally, in the third article, our main goal is to design a solution method that is able
to manage a large number of scenarios. We rely on the progressive hedging algorithm
(PHA) where the scenarios are grouped in subproblems. We propose a two phase integrated
learning and progressive hedging (ILPH) approach to deal with a large number of
scenarios. Within our proposed approach, the learning mechanisms from the first study
of this dissertation have been adapted as an efficient heuristic method to address the
multi-scenario subproblems within each iteration of PHA.We introduce a new reference
point within each aggregation step of our proposed ILPH by exploiting the information
garnered from subproblems, and using this information to update the penalties. Consequently,
the ILPH is governed and guided by the local information provided by the
learning procedure, resulting in an integrated approach capable of handling very large
and complex instances.
In all of the three mentioned articles, we show, by means of extensive experimental
campaigns, the interest of the proposed approaches in terms of computation time and
solution quality, especially in dealing with very difficult instances with a large number
of scenarios
Solving the time capacitated arc routing problem under fuzzy and stochastic travel and service times
Stochastic, as well as fuzzy uncertainty, can be found in most real-world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper we propose a fuzzy simheuristic to solve the Time Capacitated Arc Routing Problem (TCARP) when the nature of the travel time can either be deterministic, stochastic or fuzzy. The main goal is to find a solution (vehicle routes) that minimizes the total time spent in servicing the required arcs. However, due to uncertainty, other characteristics of the solution are also considered. In particular, we illustrate how reliability concepts can enrich the probabilistic information given to decision-makers. In order to solve the aforementioned optimization problem, we extend the concept of simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated into the CARP. In order to test our approach, classical CARP instances have been adapted and extended so that customers' demands become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones. In particular, our fuzzy simheuristic is capable of generating new best-known solutions for the stochastic versions of some instances belonging to the tegl, tcarp, val, and rural benchmarks.This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/01100011033), as well as by the Barcelona Council and the “laCaixa” Foundation under the framework of the Barcelona Science Plan 2020-2023 (grant21S09355-01) and Generalitat Valenciana (PROMETEO/2021/065).Peer ReviewedPostprint (published version
Solving the time capacitated arc routing problem under fuzzy and stochastic travel and service times
[EN] Stochastic, as well as fuzzy uncertainty, can be found in most real-world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper we propose a fuzzy simheuristic to solve the Time Capacitated Arc Routing Problem (TCARP) when the nature of the travel time can either be deterministic, stochastic or fuzzy. The main goal is to find a solution (vehicle routes) that minimizes the total time spent in servicing the required arcs. However, due to uncertainty, other characteristics of the solution are also considered. In particular, we illustrate how reliability concepts can enrich the probabilistic information given to decision-makers. In order to solve the aforementioned optimization problem, we extend the concept of simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated into the CARP. In order to test our approach, classical CARP instances have been adapted and extended so that customers' demands become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones. In particular, our fuzzy simheuristic is capable of generating new best-known solutions for the stochastic versions of some instances belonging to the tegl, tcarp, val, and rural benchmarks.Spanish Ministry of Science, Grant/Award Number: PID2019-111100RB-C21/AEI/10.13039/501100011033; Barcelona Council and the "la Caixa" Foundation under the framework of the Barcelona Science Plan 2020-2023, Grant/Award Number: 21S09355-001; Generalitat Valenciana,Grant/Award Number: PROMETEO/2021/065Martín, XA.; Panadero, J.; Peidro Payá, D.; Pérez Bernabeu, E.; Juan-Pérez, ÁA. (2023). Solving the time capacitated arc routing problem under fuzzy and stochastic travel and service times. Networks. 82(4):318-335. https://doi.org/10.1002/net.2215931833582
OPTIMIZATION OF RAILWAY TRANSPORTATION HAZMATS AND REGULAR COMMODITIES
Transportation of dangerous goods has been receiving more attention in the realm of academic and scientific research during the last few decades as countries have been increasingly becoming industrialized throughout the world, thereby making Hazmats an integral part of our life style. However, the number of scholarly articles in this field is not as many as those of other areas in SCM. Considering the low-probability-and-high-consequence (LPHC) essence of transportation of Hazmats, on the one hand, and immense volume of shipments accounting for more than hundred tons in North America and Europe, on the other, we can safely state that the number of scholarly articles and dissertations have not been proportional to the significance of the subject of interest. On this ground, we conducted our research to contribute towards further developing the domain of Hazmats transportation, and sustainable supply chain management (SSCM), in general terms.
Transportation of Hazmats, from logistical standpoint, may include all modes of transport via air, marine, road and rail, as well as intermodal transportation systems. Although road shipment is predominant in most of the literature, railway transportation of Hazmats has proven to be a potentially significant means of transporting dangerous goods with respect to both economies of scale and risk of transportation; these factors, have not just given rise to more thoroughly investigation of intermodal transportation of Hazmats using road and rail networks, but has encouraged the competition between rail and road companies which may indeed have some inherent advantages compared to the other medium due to their infrastructural and technological backgrounds. Truck shipment has ostensibly proven to be providing more flexibility; trains, per contra, provide more reliability in terms of transport risk for conveying Hazmats in bulks.
In this thesis, in consonance with the aforementioned motivation, we provide an introduction into the hazardous commodities shipment through rail network in the first chapter of the thesis. Providing relevant statistics on the volume of Hazmat goods, number of accidents, rate of incidents, and rate of fatalities and injuries due to the incidents involving Hazmats, will shed light onto the significance of the topic under study. As well, we review the most pertinent articles while putting more emphasis on the state-of-the-art papers, in chapter two. Following the discussion in chapter 3 and looking at the problem from carrier company’s perspective, a mixed integer quadratically constraint problem (MIQCP) is developed which seeks for the minimization of transportation cost under a set of constraints including those associating with Hazmats. Due to the complexity of the problem, the risk function has been piecewise linearized using a set of auxiliary variables, thereby resulting in an MIP problem. Further, considering the interests of both carrier companies and regulatory agencies, which are minimization of cost and risk, respectively, a multiobjective MINLP model is developed, which has been reduced to an MILP through piecewise linearization of the risk term in the objective function. For both single-objective and multiobjective formulations, model variants with bifurcated and nonbifurcated flows have been presented. Then, in chapter 4, we carry out experiments considering two main cases where the first case presents smaller instances of the problem and the second case focuses on a larger instance of the problem.
Eventually, in chapter five, we conclude the dissertation with a summary of the overall discussion as well as presenting some comments on avenues of future work
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