45 research outputs found
Métaheuristiques de recherche avec tabous pour le problÚme de synthÚse de réseau multiproduits avec capacités
ThÚse numérisée par la Direction des bibliothÚques de l'Université de Montréal
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A cycle-based evolutionary algorithm for the fixed-charge capacitated multi-commodity network design problem
This paper presents an evolutionary algorithm for the fixed-charge multicommodity network design problem (MCNDP), which concerns routing multiple commodities from origins to destinations by designing a network through selecting arcs, with an objective of minimizing the fixed costs of the selected arcs plus the variable costs of the flows on each arc. The proposed algorithm evolves a pool of solutions using principles of scatter search, interlinked with an iterated local search as an improvement method. New cycle-based neighborhood operators are presented which enable complete or partial re-routing of multiple commodities. An efficient perturbation strategy, inspired by ejection chains, is introduced to perform local compound cycle-based moves to explore different parts of the solution space. The algorithm also allows infeasible solutions violating arc capacities while performing the "ejection cycles", and subsequently restores feasibility by systematically applying correction moves. Computational experiments on benchmark MCNDP instances show that the proposed solution method consistently produces high-quality solutions in reasonable computational times
Hybrid Statistical Data Mining Framework for Multi-Commodity Fixed Charge Network Flow Problem
This paper presents a new approach to analyze the network structure in multi-commodity fixed charge network flow problems (MCFCNF). This methodology uses historical data produced from repeatedly solving the traditional MCFCNF mathematical model as input for the machine-learning framework. Further, we reshape the problem as a binary classification problem and employ machine-learning algorithms to predict network structure. This predicted network structure is further used as an initial solution for our mathematical model. The quality of the initial solution generated is judged on the basis of predictive accuracy, feasibility and reduction in solving time
TS2PACK: A Two-Level Tabu Search for the Three-dimensional Bin Packing Problem
Three-dimensional orthogonal bin packing is a problem NP-hard in the strong sense where a set of boxes must be orthogonally packed into the minimum number of three-dimensional bins. We present a two-level tabu search for this problem. The first-level aims to reduce the number of bins. The second optimizes the packing of the bins. This latter procedure is based on the Interval Graph representation of the packing, proposed by Fekete and Schepers, which reduces the size of the search space. We also introduce a general method to increase the size of the associated neighborhoods, and thus the quality of the search, without increasing the overall complexity of the algorithm. Extensive computational results on benchmark problem instances show the effectiveness of the proposed approach, obtaining better results compared to the existing one
Optimisation of transportation service network using Îș-node large neighbourhood search
The Service Network Design Problem (SNDP) is generally considered as a fundamental problem in transportation logistics and involves the determination of an efficient transportation network and corresponding schedules. The problem is extremely challenging due to the complexity of the constraints and the scale of real-world applications. Therefore, efficient solution methods for this problem are one of the most important research issues in this field. However, current research has mainly focused on various sophisticated high-level search strategies in the form of different local search metaheuristics and their hybrids. Little attention has been paid to novel neighbourhood structures which also play a crucial role in the performance of the algorithm. In this research, we propose a new efficient neighbourhood structure that uses the SNDP constraints to its advantage and more importantly appears to have better reachability than the current ones. The effectiveness of this new neighbourhood is evaluated in a basic Tabu Search (TS) metaheuristic and a basic Guided Local Search (GLS) method. Experimental results based on a set of well-known benchmark instances show that the new neighbourhood performs better than the previous arc-flipping neighbourhood. The performance of the TS metaheuristic based on the proposed neighbourhood is further enhanced through fast neighbourhood search heuristics and hybridisation with other approaches
Solution Methods for Service Network Design with Resource Management Consideration
La gestion des ressources, Ă©quipements, Ă©quipes de travail, et autres, devrait ĂȘtre prise en compte lors de la conception de tout plan rĂ©alisable pour le problĂšme de conception de rĂ©seaux de services. Cependant, les travaux de recherche portant sur la gestion des ressources et la conception de rĂ©seaux de services restent limitĂ©s. La prĂ©sente thĂšse a pour objectif de combler cette lacune en faisant lâexamen de problĂšmes de conception de rĂ©seaux de services prenant en compte la gestion des ressources. Pour ce faire, cette thĂšse se dĂ©cline en trois Ă©tudes portant sur la conception de rĂ©seaux.
La premiĂšre Ă©tude considĂšre le problĂšme de capacitated multi-commodity fixed cost network design with design-balance constraints(DBCMND). La structure multi-produits avec capacitĂ© sur les arcs du DBCMND, de mĂȘme que ses contraintes design-balance, font quâil apparaĂźt comme sous-problĂšme dans de nombreux problĂšmes reliĂ©s Ă la conception de rĂ©seaux de services, dâoĂč lâintĂ©rĂȘt dâĂ©tudier le DBCMND dans le contexte de cette thĂšse. Nous proposons une nouvelle approche pour rĂ©soudre ce problĂšme combinant la recherche tabou, la recomposition de chemin, et une procĂ©dure dâintensification de la recherche dans une rĂ©gion particuliĂšre de lâespace de solutions. Dans un premier temps la recherche tabou identifie de bonnes solutions rĂ©alisables. Ensuite la recomposition de chemin est utilisĂ©e pour augmenter le nombre de solutions rĂ©alisables. Les solutions trouvĂ©es par ces deux mĂ©ta-heuristiques permettent dâidentifier un sous-ensemble dâarcs qui ont de bonnes chances dâavoir un statut ouvert ou fermĂ© dans une solution optimale. Le statut de ces arcs est alors fixĂ© selon la valeur qui prĂ©domine dans les solutions trouvĂ©es prĂ©alablement. Enfin, nous utilisons la puissance dâun solveur de programmation mixte en nombres entiers pour intensifier la recherche sur le problĂšme restreint par le statut fixĂ© ouvert/fermĂ© de certains arcs. Les tests montrent que cette approche est capable de trouver de bonnes solutions aux problĂšmes de grandes tailles dans des temps raisonnables. Cette recherche est publiĂ©e dans la revue scientifique Journal of heuristics.
La deuxiĂšme Ă©tude introduit la gestion des ressources au niveau de la conception de rĂ©seaux de services en prenant en compte explicitement le nombre fini de vĂ©hicules utilisĂ©s Ă chaque terminal pour le transport de produits. Une approche de solution faisant appel au slope-scaling, la gĂ©nĂ©ration de colonnes et des heuristiques basĂ©es sur une formulation en cycles est ainsi proposĂ©e. La gĂ©nĂ©ration de colonnes rĂ©sout une relaxation linĂ©aire du problĂšme de conception de rĂ©seaux, gĂ©nĂ©rant des colonnes qui sont ensuite utilisĂ©es par le slope-scaling. Le slope-scaling rĂ©sout une approximation linĂ©aire du problĂšme de conception de rĂ©seaux, dâoĂč lâutilisation dâune heuristique pour convertir les solutions obtenues par le slope-scaling en solutions rĂ©alisables pour le problĂšme original. Lâalgorithme se termine avec une procĂ©dure de perturbation
qui amĂ©liore les solutions rĂ©alisables. Les tests montrent que lâalgorithme proposĂ© est capable de trouver de bonnes solutions au problĂšme de conception de rĂ©seaux de services avec un nombre fixe des ressources Ă chaque terminal. Les rĂ©sultats de cette recherche seront publiĂ©s dans la revue scientifique Transportation Science. La troisiĂšme Ă©tude Ă©largie nos considĂ©rations sur la gestion des ressources en prenant en compte lâachat ou la location de nouvelles ressources de mĂȘme que le repositionnement de ressources existantes. Nous faisons les hypothĂšses suivantes: une unitĂ© de ressource est nĂ©cessaire pour faire fonctionner un service, chaque ressource doit retourner Ă son terminal dâorigine, il existe un nombre fixe de ressources Ă chaque terminal, et la longueur du circuit des ressources est limitĂ©e. Nous considĂ©rons les alternatives suivantes dans la gestion des ressources: 1)
repositionnement de ressources entre les terminaux pour tenir compte des changements de la demande, 2) achat et/ou location de nouvelles ressources et leur distribution à différents terminaux, 3) externalisation de certains services. Nous présentons une formulation intégrée combinant les décisions reliées à la gestion des ressources avec les décisions reliées à la conception
des réseaux de services. Nous présentons également une méthode de résolution matheuristique
combinant le slope-scaling et la génération de colonnes. Nous discutons des performances de
cette mĂ©thode de rĂ©solution, et nous faisons une analyse de lâimpact de diffĂ©rentes dĂ©cisions de gestion des ressources dans le contexte de la conception de rĂ©seaux de services. Cette Ă©tude sera prĂ©sentĂ©e au XII International Symposium On Locational Decision, en conjonction avec XXI Meeting of EURO Working Group on Locational Analysis, Naples/Capri (Italy), 2014. En rĂ©sumĂ©, trois Ă©tudes diffĂ©rentes sont considĂ©rĂ©es dans la prĂ©sente thĂšse. La premiĂšre porte sur une nouvelle mĂ©thode de solution pour le "capacitated multi-commodity fixed cost network design with design-balance constraints". Nous y proposons une matheuristique comprenant la recherche tabou, la recomposition de chemin, et lâoptimisation exacte. Dans la deuxiĂšme Ă©tude, nous prĂ©sentons un nouveau modĂšle de conception de rĂ©seaux de services prenant en compte un nombre fini de ressources Ă chaque terminal. Nous y proposons une matheuristique avancĂ©e
basĂ©e sur la formulation en cycles comprenant le slope-scaling, la gĂ©nĂ©ration de colonnes, des heuristiques et lâoptimisation exacte. Enfin, nous Ă©tudions lâallocation des ressources dans la conception de rĂ©seaux de services en introduisant des formulations qui modĂšlent le repositionnement, lâacquisition et la location de ressources, et lâexternalisation de certains services. Ă cet Ă©gard, un cadre de solution slope-scaling dĂ©veloppĂ© Ă partir dâune formulation en cycles est proposĂ©. Ce dernier comporte la gĂ©nĂ©ration de colonnes et une heuristique. Les mĂ©thodes proposĂ©es dans ces trois Ă©tudes ont montrĂ© leur capacitĂ© Ă trouver de bonnes solutions.Resource management in freight transportation service network design is an important issue
that has been studied extensively in recent years. Resources such as vehicles, crews, etc. are
factors that can not be ignored when designing a feasible plan for any service network design
problem. However, contributions related to resource management issues and service network
design are still limited. The goal of the thesis is to fill this gap by taking into account service
network design problems with resource management issues. In this thesis, we propose and
address three service network design problems that consider resource management.
In the first study, we consider the capacitated multi-commodity fixed cost network design
with design-balance constraints which is a basic sub-problem for many service design problems
because of the capacitated multi-commodity structure as well as its design-balance property.
We propose a three-phase matheuristic that combines tabu-search, path-relinking and an exactbased intensification procedure to find high quality solutions. Tabu-search identifies feasible
solutions while path-relinking extends the set of feasible solutions. The solutions found by
these two meta-heuristics are used to fix arcs as open or close. An exact solver intensifies
the search on a restricted problem derived from fixing arcs. The experiments on benchmark
instances show that the solution approach finds good solutions to large-scale problems in a
reasonable amount of time. The contribution with regard to this study has been accepted in the
Journal of Heuristics.
In the second study, together with the consideration of the design of routes to transport a
set of commodities by vehicles, we extend resources management by explicitly taking account
of the number of available vehicles at each terminal. We introduce a matheuristic solution
framework based on a cycle-based formulation that includes column generation, slope-scaling,
heuristic and exact optimization techniques. As far as we know, this is the first matheuristic
procedure developed for a cycle-based formulation. The column generation solves the linear
relaxation model and provides a set of cycles to define the approximation model used in slopescaling loop. A heuristic is used to convert each solution to the approximation problem into a
feasible solution. Memory-based perturbation procedure is used to enhance the performance
of the algorithm. Experiments show that the proposed algorithm is able to find good feasible
solutions for the problem. The contribution with regard to this study has been accepted for
publication in Transportation Science. In the third study, we examine resources allocation issues in service network design. We
aim to address a number of fleet utilization issues which usually appear at the beginning of the
season because of the change of demand patterns: 1) reposition resources among terminals to
account for shifts in demand patterns; 2) acquire (buy or long-term rent) new resources and as
sign them to terminals; 3) outsource particular services. We present an integrated formulation
combining these selection-location and scheduled service design decisions. The mixed-integer
formulation is defined over a time-space network, the initial period modeling the location de
cisions on resource acquisition and positioning, while the decisions on service selection and
scheduling, resource assignment and cycling routing, and demand satisfaction being modeled
on the rest of the network. We also present a matheuristic solution method combining slope
scaling and column generation, discuss its algorithmic performance, and explore the impact
of combining the location and design decisions in the context of consolidation carrier service
design. This study will be presented at XII International Symposium On Locational Deci
sion, in conjunction with the XXI Meeting of EURO Working Group on Locational Analysis,
Naples/Capri (Italy), 2014.
In summary, three studies are considered in this thesis. The first one considers the capaciated
multi-commodity fixed cost network design with design-balance constraints, a basic problem
in many service network design problems with design-balance constraints. We propose an ef
ficient three-phase matheuristic solution method that includes tabu search, path relinking and
exact optimization. In the second study, we propose a new service network design model
that takes into account resources limitations at each terminal. We also propose an advanced
matheuristic framework solution method based on a cycle-based formulation which includes
slope-scaling, column generation, heuristics and exact optimization for this problem. The last
study addresses resources allocation issues in service network design. We introduce formula
tions that model the reposition, acquisition/renting of resources and outsourcing of services. A
solution framework based on the slope-scaling approach on cycle-based formulations is pro
posed. Tests indicate that these proposed algorithms are able to find good feasible solutions for
each of threse problems
Lagrangian-based methods for single and multi-layer multicommodity capacitated network design
Le problÚme de conception de réseau avec coûts fixes et capacités (MCFND) et le problÚme
de conception de réseau multicouches (MLND) sont parmi les problÚmes de
conception de réseau les plus importants. Dans le problÚme MCFND monocouche, plusieurs
produits doivent ĂȘtre acheminĂ©s entre des paires origine-destination diffĂ©rentes
dâun rĂ©seau potentiel donnĂ©. Des liaisons doivent ĂȘtre ouvertes pour acheminer les produits,
chaque liaison ayant une capacité donnée. Le problÚme est de trouver la conception
du réseau à coût minimum de sorte que les demandes soient satisfaites et que les capacités
soient respectées. Dans le problÚme MLND, il existe plusieurs réseaux potentiels,
chacun correspondant à une couche donnée. Dans chaque couche, les demandes pour un
ensemble de produits doivent ĂȘtre satisfaites. Pour ouvrir un lien dans une couche particuliĂšre,
une chaĂźne de liens de support dans une autre couche doit ĂȘtre ouverte. Nous
abordons le problÚme de conception de réseau multiproduits multicouches à flot unique
avec coĂ»ts fixes et capacitĂ©s (MSMCFND), oĂč les produits doivent ĂȘtre acheminĂ©s uniquement
dans lâune des couches.
Les algorithmes basĂ©s sur la relaxation lagrangienne sont lâune des mĂ©thodes de rĂ©solution
les plus efficaces pour résoudre les problÚmes de conception de réseau. Nous
prĂ©sentons de nouvelles relaxations Ă base de noeuds, oĂč le sous-problĂšme rĂ©sultant se
décompose par noeud. Nous montrons que la décomposition lagrangienne améliore significativement
les limites des relaxations traditionnelles.
Les problÚmes de conception du réseau ont été étudiés dans la littérature. Cependant,
ces derniÚres années, des applications intéressantes des problÚmes MLND sont apparues,
qui ne sont pas couvertes dans ces études. Nous présentons un examen des problÚmes de
MLND et proposons une formulation générale pour le MLND. Nous proposons également
une formulation générale et une méthodologie de relaxation lagrangienne efficace
pour le problÚme MMCFND. La méthode est compétitive avec un logiciel commercial
de programmation en nombres entiers, et donne généralement de meilleurs résultats.The multicommodity capacitated fixed-charge network design problem (MCFND) and
the multilayer network design problem (MLND) are among the most important network
design problems. In the single-layer MCFND problem, several commodities have to
be routed between different origin-destination pairs of a given potential network. Appropriate
capacitated links have to be opened to route the commodities. The problem
is to find the minimum cost design and routing such that the demands are satisfied and
the capacities are respected. In the MLND, there are several potential networks, each
at a given layer. In each network, the flow requirements for a set of commodities must
be satisfied. However, the selection of the links is interdependent. To open a link in a
particular layer, a chain of supporting links in another layer has to be opened. We address
the multilayer single flow-type multicommodity capacitated fixed-charge network
design problem (MSMCFND), where commodities are routed only in one of the layers.
Lagrangian-based algorithms are one of the most effective solution methods to solve
network design problems. The traditional Lagrangian relaxations for the MCFND problem
are the flow and knapsack relaxations, where the resulting Lagrangian subproblems
decompose by commodity and by arc, respectively. We present new node-based
relaxations, where the resulting subproblem decomposes by node. We show that the
Lagrangian dual bound improves significantly upon the bounds of the traditional relaxations.
We also propose a Lagrangian-based algorithm to obtain upper bounds.
Network design problems have been the object of extensive literature reviews. However,
in recent years, interesting applications of multilayer problems have appeared that
are not covered in these surveys. We present a review of multilayer problems and propose
a general formulation for the MLND. We also propose a general formulation and
an efficient Lagrangian-based solution methodology for the MMCFND problem. The
method is competitive with (and often significantly better than) a state-of-the-art mixedinteger
programming solver on a large set of randomly generated instances
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The congested multicommodity network design problem
This paper studies a version of the fixed-charge multicommodity network design problem where in addition to the traditional costs of flow and design, congestion at nodes is explicitly considered. The problem is initially modeled as a nonlinear integer programming formulation and two solution approaches are proposed: (i) a reformulation of the problem as a mixed integer second order cone program to optimally solve the problem for small to medium scale problem instances, and (ii) an evolutionary algorithm using elements of iterated local search and scatter search to provide upper bounds. Extensive computational results on new benchmark problem instances and on real case data are presented
Parallel metaheuristics for stochastic capacitated multicommodity network design
Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal
Learning-Based Matheuristic Solution Methods for Stochastic Network Design
Cette dissertation consiste en trois Ă©tudes, chacune constituant un article de recherche.
Dans tous les trois articles, nous considérons le problÚme 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 premiÚre étape avant que la demande réelle ne soit réalisée, tandis
que les décisions de flux de la deuxiÚme étape ajustent la solution de la premiÚre étape
Ă la rĂ©alisation de la demande observĂ©e. Nous considĂ©rons lâincertitude de la demande
comme un nombre fini de scénarios discrets, ce qui est une approche courante dans la
littĂ©rature. En utilisant lâensemble de scĂ©narios, le problĂšme mixte en nombre entier
(MIP) rĂ©sultant, appelĂ© formulation Ă©tendue (FE), est extrĂȘmement difficile Ă rĂ©soudre,
sauf dans des cas triviaux. Cette thĂšse vise Ă faire progresser le corpus de connaissances
en dĂ©veloppant des algorithmes efficaces intĂ©grant des mĂ©canismes dâapprentissage en
matheuristique, capables de traiter efficacement des problĂšmes stochastiques de conception
pour des réseaux de grande taille.
Le premier article, sâintitulĂ© "A Learning-Based Matheuristc for Stochastic Multicommodity
Network Design". Nous introduisons et décrivons formellement un nouveau
mĂ©canisme dâapprentissage basĂ© sur lâoptimisation pour extraire des informations
concernant la structure de la solution du problĂšme stochastique Ă partir de solutions
obtenues avec des combinaisons particuliÚres de scénarios. Nous proposons ensuite
une matheuristique "Learn&Optimize", qui utilise les mĂ©thodes dâapprentissage pour
déduire un ensemble de variables de conception prometteuses, en conjonction avec un
solveur MIP de pointe pour résoudre un problÚme réduit.
Le deuxiĂšme article, sâintitulĂ© "A Reduced-Cost-Based Restriction and Refinement
Matheuristic for Stochastic Network Design". Nous Ă©tudions comment concevoir efficacement
des mĂ©canismes dâapprentissage basĂ©s sur lâinformation 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 modÚle restreint. Nous proposons
ensuite une approche matheuristique utilisant des techniques itératives de réduction
des problĂšmes.
Le troisiĂšme article, sâintitulĂ© "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 lâalgorithme Progressive Hedging (PHA), ou les scĂ©narios sont regroupĂ©s
en sous-problĂšmes. Nous intĂ©grons des methodes dâapprentissage au sein de
PHA pour traiter une grand nombre de scénarios. Dans notre approche, les mécanismes
dâapprentissage developpĂ©s dans le premier article de cette thĂšse sont adaptĂ©s pour rĂ©soudre
les sous-problÚmes multi-scénarios. Nous introduisons une nouvelle solution
de rĂ©fĂ©rence Ă chaque Ă©tape dâagrĂ©gation de notre ILPH en exploitant les informations
collectĂ©es Ă partir des sous problĂšmes et nous utilisons ces informations pour mettre Ă
jour les pénalités dans PHA. Par conséquent, PHA est guidé par les informations locales
fournies par la procĂ©dure dâapprentissage, rĂ©sultant en une approche intĂ©grĂ©e capable de
traiter des instances complexes et de grande taille.
Dans les trois articles, nous montrons, au moyen de campagnes expérimentales approfondies,
lâintĂ©rĂȘt des approches proposĂ©es en termes de temps de calcul et de qualitĂ©
des solutions produites, en particulier pour traiter des cas trĂšs 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