236 research outputs found
The double traveling salesman problem with partial last-in-first-out loading constraints
In this paper, we introduce the double traveling salesman problem with partial last-in-first-out loading constraints (DTSPPL). It is a pickup-and-delivery single-vehicle routing problem, where all pickup operations must be performed before any delivery operation because the pickup-and-delivery areas are geographically separated. The vehicle collects items in the pickup area and loads them into its container, a horizontal stack. After performing all pickup operations, the vehicle begins delivering the items in the delivery area. Loading and unloading operations must obey a partial last-in-first-out (LIFO) policy, that is, a version of the LIFO policy that may be violated within a given reloading depth. The objective of the DTSPPL is to minimize the total cost, which involves the total distance traveled by the vehicle and the number of items that are unloaded and then reloaded due to violations of the standard LIFO policy. We formally describe the DTSPPL through two integer linear programming (ILP) formulations and propose a heuristic algorithm based on the biased random-key genetic algorithm (BRKGA) to find high-quality solutions. The performance of the proposed solution approaches is assessed over a broad set of instances. Computational results have shown that both ILP formulations have been able to solve only the smaller instances, whereas the BRKGA obtained good-quality solutions for almost all instances, requiring short computational times
Mathematical models and heuristic algorithms for routing problems with multiple interacting components.
Programa de P?s-Gradua??o em Ci?ncia da Computa??o. Departamento de Ci?ncia da Computa??o, Instituto de Ci?ncias Exatas e Biol?gicas, Universidade Federal de Ouro Preto.Muitos problemas de otimiza??o com aplica??es reais t?m v?rios componentes de intera??o. Cada um deles pode ser um problema pertencente ? classe N P-dif?cil, e eles podem estar em conflito um com o outro, ou seja, a solu??o ?tima para um componente n?o representa necessariamente uma solu??o ?tima para os outros componentes. Isso pode ser um desafio devido ? influ?ncia que cada componente tem na qualidade geral da solu??o. Neste trabalho, foram abordados quatro problemas de roteamento complexos com v?rios componentes de intera??o: o Double Vehicle Routing Problem with Multiple Stacks (DVRPMS), o Double Traveling Salesman Problem with Partial Last-InFirst-Out Loading Constraints (DTSPPL), o Traveling Thief Problem (TTP) e Thief Orienteering Problem (ThOP). Enquanto os DVRPMS e TTP j? s?o bem conhecidos na literatura, os DTSPPL e ThOP foram recentemente propostos a fim de introduzir e estudar variantes mais realistas dos DVRPMS e TTP, respectivamente. O DTSPPL foi proposto a partir deste trabalho, enquanto o ThOP foi proposto de forma independente. Neste trabalho s?o propostos modelos matem?ticos e/ou algoritmos heur?sticos para a solu??o desses problemas. Dentre os resultados alcan?ados, ? poss?vel destacar que o modelo matem?tico proposto para o DVRPMS foi capaz de encontrar inconsist?ncias nos resultados dos algoritmos exatos previamente propostos na literatura. Al?m disso, conquistamos o primeiro e o segundo lugares em duas recentes competi??es de otimiza??o combinat?ria que tinha como objetivo a solu??o de uma vers?o bi-objetiva do TTP. Em geral, os resultados alcan?ados por nossos m?todos de solu??es mostraram-se melhores do que os apresentados anteriormente na literatura considerando cada problema investigado neste trabalho.I would like to express my greatest thanks to my parents, Jo?o Batista and Adelma, and my sister, Jaqueline, for their wise counsel. They have always supported me and given me the strength to continue towards my goals. To Bruna Vilela, I am grateful for her fondness, for always listening to my complaints, and for celebrating with me my personal and academic achievements. I love you all demais da conta1 ! Throughout the writing of this thesis, I have received great assistance. I would like to acknowledge my advisors, Prof. Ph.D. Marcone J. F. Souza, and Prof. Ph.D. Andr? G. Santos, for their support and guidance over these years. I would also like to thank all the authors who have contributed to the research papers produced from this work, in particular, to Prof. Ph.D. Markus Wagner for his great collaboration in some of my projects. I would like to thank Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES), and Universidade Federal de Ouro Preto (UFOP) for funding this project. I thank the Universidade Federal de Vi?osa (UFV) for receiving me as a collaborating researcher over these last two years. I could not but offer up my thanks to the HassoPlattner-Institut (HPI) Future SOC Lab, the Divis?o de Suporte ao Desenvolvimento Cient?fico e Tecnol?gico (DCT/UFV), and the Programa de P?s-gradua??o em Ci?ncia da Computa??o (PPGCC/UFOP) for enabling this research by providing access to their computing infrastructure
Thirty years of heterogeneous vehicle routing
It has been around thirty years since the heterogeneous vehicle routing problem was introduced, and significant progress has since been made on this problem and its variants. The aim of this survey paper is to classify and review the literature on heterogeneous vehicle routing problems. The paper also presents a comparative analysis of the metaheuristic algorithms that have been proposed for these problems
Le problĂšme de tournĂ©es de vĂ©hicules avec cueillettes, livraisons, fenĂȘtres de temps et contraintes de manutention
RĂSUMĂ : Les problĂšmes de tournĂ©es de vĂ©hicules avec cueillettes et livraisons consistent Ă trouver des
tournĂ©es rĂ©alisables minimisant le nombre de vĂ©hicules utilisĂ©s et la distante totale parcourue, et permettant de complĂ©ter toutes les requĂȘtes. Une requĂȘte est dĂ©finie par un point de cueillette et un point de livraison, et une quantitĂ© de marchandise Ă transporter du point de cueillette au point de livraison. Ce faisant, une tournĂ©e est dite rĂ©alisable si la charge du vĂ©hicule ne dĂ©passe pas sa capacitĂ© et si, pour chaque requĂȘte, on visite le point de cueillette avant le point de livraison avec le mĂȘme vĂ©hicule. Dans la derniĂšre dĂ©cennie, la communautĂ© de recherche opĂ©rationnelle sâest attaquĂ©e Ă des problĂšmes de plus en plus complexes qui tiennent compte de contraintes opĂ©rationnelles difficiles Ă traiter. Cette thĂšse sâinsĂšre dans cette tendance. Cette thĂšse propose des modĂšles et des algorithmes pour rĂ©soudre deux variantes du problĂšme de tournĂ©es de vĂ©hicules avec cueillettes et livraisons : le problĂšme de tournĂ©es de vĂ©hicules avec cueillettes, livraisons, fenĂȘtres de temps et contrainte de chargement dernier entrĂ© premier sorti (last-in-first-out â LIFO) (pickup and delivery problem with time Windows and LIFO loading â PDPTWL) et le problĂšme de tournĂ©es de vĂ©hicules avec fenĂȘtres de temps et plusieurs piles (pickup and delivery problem with time windows and multiple stacks
â PDPTWMS). Dans le PDPTWL, la contrainte de chargement dernier entrĂ© premier sorti stipule quâaucune manutention non nĂ©cessaire nâest faite lors de la livraison dâun item : un item peut seulement ĂȘtre livrĂ© sâil est situĂ© sur le dessus de la pile. Dans le PDPTWMS, chaque vĂ©hicule contient plusieurs piles qui sont gĂ©rĂ©es selon une politique de chargement dernier entrĂ© premier sorti.
Afin de résoudre le PDPTWL, trois algorithmes de génération de colonnes avec plans coupants et un algorithme heuristique sont proposés. Le premier algorithme de génération de colonnes incorpore la contrainte de chargement dans le problÚme maßtre, alors que le second
lâincorpore dans le sous-problĂšme. Pour ce faire, un algorithme dâĂ©tiquetage et un critĂšre de dominance spĂ©cialisĂ©s sont proposĂ©s. Le troisiĂšme algorithme de gĂ©nĂ©ration de colonnes est une combinaison des deux premiers algorithmes. Des inĂ©galitĂ©s valides connues sont adaptĂ©es pour le PDPTWL. Des instances ayant jusquâĂ 75 requĂȘtes sont rĂ©solues par ces trois algorithmes exacts en une heure de temps de calcul. Lâalgorithme heuristique, quant Ă lui, permet de traiter plus rapidement des instances de plus grande taille. Dâabord, un ensemble de solutions initiales est construit avec un algorithme
glouton. Puis, pour chaque solution, un algorithme de recherche locale est utilisé afin de diminuer en priorité le nombre de véhicules et ensuite la distance totale parcourue. Puis, deux stratégies sont utilisées pour créer des solutions enfants. La premiÚre choisit aléatoirement des
tournĂ©es de lâensemble de solutions alors que la deuxiĂšme utilise un opĂ©rateur de croisement. Pour les deux stratĂ©gies, un algorithme de recherche locale est ensuite utilisĂ©. Finalement, les enfants sont ajoutĂ©s Ă lâensemble de solutions et les meilleurs survivants sont conservĂ©s.
Lâensemble de solutions est gĂ©rĂ© afin de garder uniquement les solutions variĂ©es de meilleure qualitĂ© par rapport au coĂ»t total. Des instances ayant jusquâĂ 300 requĂȘtes sont rĂ©solues par cette heuristique en deux heures de temps de calcul. Afin de rĂ©soudre le PDPTWMS, deux algorithmes de gĂ©nĂ©ration de colonnes avec plans coupants sont proposĂ©s. Le premier algorithme de gĂ©nĂ©ration de colonnes incorpore la contrainte de chargement avec plusieurs piles dans le sous-problĂšme. Pour ce faire, un algorithme dâĂ©tiquetage
et un critÚre de dominance spécialisés sont proposés. Le deuxiÚme algorithme incorpore partiellement la contrainte de chargement avec plusieurs piles dans le sous-problÚme et
ajoute, au besoin, des contraintes au problĂšme maĂźtre lorsque la solution trouvĂ©e ne respecte pas la contrainte de chargement avec plusieurs piles. Des instances avec une, deux et trois piles et ayant jusquâĂ 75 requĂȘtes sont rĂ©solues par ces deux algorithmes exacts en deux heures de temps de calcul.----------ABSTRACT : In the pickup and delivery problem, vehicles based at a depot are used to satisfy a set of requests which consists of transporting goods (or items) from a specific pickup location to a
specific delivery location. We consider an unlimited fleet of identical vehicles with multiple homogeneous compartments of limited capacity. A vehicle route is feasible if the load in each compartment of the vehicle does not exceed its capacity and each completed request is first picked up at its pickup location and then delivered at its corresponding delivery location. The pickup and delivery problem consists of determining a set of least-cost feasible routes in which the number of vehicles is first minimized. In the last decade, the operations research
community has tackled more complex problems that consider real-life constraints. This thesis follows this trend.
This thesis proposes models and algorithms for two variants of the pickup and delivery problem: the pickup and delivery problem with time windows and last-in-first-out (LIFO)
loading constraints (PDPTWL) and the pickup and delivery problem with time windows and multiple stacks (PDPTWMS). In the first problem, the LIFO loading rule ensures that no
handling is required prior to unloading an item from a vehicle: an item can only be delivered if it is the last one in the stack. In the second problem, each vehicle contains multiple stacks that are operated in a LIFO fashion. To solve the PDPTWL, three exact branch-price-and-cut algorithms and one metaheuristic algorithm are developed. The first branch-price-and-cut algorithm incorporates the LIFO constraints in the master problem. The second branch-price-and-cut algorithm handles the
LIFO constraints directly in the shortest path pricing problem and applies a dynamic programming algorithm relying on an ad hoc dominance criterion. The third branch-price-andcut algorithm is a hybrid between the first two. Known valid inequalities are adapted to the PDPTWL. Instances with up to 75 requests are solved within one hour of computational time. The metaheuristic is capable of handling larger instances much faster. First, a set of initial solutions is generated with a greedy randomized adaptive search procedure. For each of these solutions, local search is applied in order to first decrease the total number of vehicles and then the total traveled distance. Two different strategies are used to create offspring. The first selects vehicle routes from the solution pool. The second selects two parents to create
an offspring with a crossover operator. For both strategies, local search is then performed on the child solution. Finally, the offspring is added to the population and the best survivors are kept. The population is managed so as to maintain good quality solutions with respect
to total cost and population diversity. Instances with up to 300 requests are solved within two hours of computational time. To solve the PDPTWMS, two exact branch-price-and-cut algorithms are proposed. The first
branch-price-and-cut algorithm handles the multiple stacks policy in the shortest path pricing problem and applies a dynamic programming algorithm relying on an ad hoc dominance
criterion. The second branch-price-and-cut algorithm incorporates the multiple stacks Policy partly in the shortest path pricing problem and adds additional inequalities to the master problem when infeasible LIFO multiple stacks are encountered. Instances with one, two and
three stacks involving up to 75 requests are solved within two hours of computational time
ProbleÌmes de tourneÌes de veÌhicules avec contraintes de chargement
Cette theÌse sâinteÌresse aux probleÌmes de tourneÌes de veÌhicules ouÌ lâon retrouve des contraintes de chargement ayant un impact sur les seÌquences de livraisons permises. Plus particulieÌrement, les items placeÌs dans lâespace de chargement dâun veÌhicule doivent eÌtre directement accessibles lors de leur livraison sans quâil soit neÌcessaire de deÌplacer dâautres items. Ces probleÌmes sont rencontreÌs dans plusieurs entreprises de transport qui livrent de gros objets (meubles, eÌlectromeÌnagers).
Le premier article de cette theÌse porte sur une meÌthode exacte pour un probleÌme de confection dâune seule tourneÌe ouÌ un veÌhicule, dont lâaire de chargement est diviseÌe en un certain nombre de piles, doit effectuer des cueillettes et des livraisons respectant une contrainte de type dernier entreÌ, premier sorti. Lors dâune collecte, les items recueillis doivent neÌcessairement eÌtre deÌposeÌs sur le dessus de lâune des piles. Par ailleurs, lors dâune livraison, les items doivent neÌcessairement se trouver sur le dessus de lâune des piles. Une meÌthode de seÌparation et eÌvaluation avec plans seÌcants est proposeÌe pour reÌsoudre ce probleÌme.
Le second article preÌsente une meÌthode de reÌsolution exacte, eÌgalement de type seÌparation et eÌvaluation avec plans seÌcants, pour un probleÌme de tourneÌes de veÌhicules avec chargement dâitems rectangulaires en deux dimensions. Lâaire de chargement des veÌhicules correspond aussi aÌ un espace rectangulaire avec une orientation, puisque les items doivent eÌtre chargeÌs et deÌchargeÌs par lâun des coÌteÌs. Une contrainte impose que les items dâun client soient directement accessibles au moment de leur livraison.
Le dernier article aborde une probleÌme de tourneÌes de veÌhicules avec chargement dâitems rectangulaires, mais ouÌ les dimensions de certains items ne sont pas connus avec certitude lors de la planification des tourneÌes. Il est toutefois possible dâassocier une distribution de probabiliteÌs discreÌte sur les dimensions possibles de ces items. Le probleÌme est reÌsolu de manieÌre exacte avec la meÌthode L-Shape en nombres entiers.In this thesis, we study mixed vehicle routing and loading problems where a constraint is imposed on delivery sequences. More precisely, the items in the loading area of a vehicle must be directly accessible, without moving any other item, at delivery time. These problems are often found in the transportation of large objects (furniture, appliances).
The first paper proposes a branch-and-cut algorithm for a variant of the single vehicle pickup and delivery problem, where the loading area of the vehicle is divided into several stacks. When an item is picked up, it must be placed on the top of one of these stacks. Conversely, an item must be on the top of one of these stacks to be delivered. This requirement is called âLast In First Outâ or LIFO constraint.
The second paper presents another branch-and-cut algorithm for a vehicle routing and loading problem with two-dimensional rectangular items. The loading area of the vehicles is also a rectangular area where the items are taken out from one side. A constraint states that the items of a given customer must be directly accessible at delivery time.
The last paper considers a stochastic vehicle routing and loading problem with two- dimensional rectangular items where the dimensions of some items are unknown when the routes are planned. However, it is possible to associate a discrete probability distribution on the dimensions of these items. The problem is solved with the Integer L-Shaped method
Optimization for Decision Making II
In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled âOptimization for Decision Making IIâ. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner
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