Robust Branch-Cut-and-Price for the Capacitated Minimum Spanning Tree Problem over a Large Extended Formulation

This paper presents a robust branch-cut-and-price algorithm for the Capacitated Minimum Spanning Tree Problem (CMST). The variables are associated to q-arbs, a structure that arises from a relaxation of the capacitated prize-collecting arbores- cence problem in order to make it solvable in pseudo-polynomial time. Traditional inequalities over the arc formulation, like Capacity Cuts, are also used. Moreover, a novel feature is introduced in such kind of algorithms. Powerful new cuts expressed over a very large set of variables could be added, without increasing the complexity of the pricing subproblem or the size of the LPs that are actually solved. Computational results on benchmark instances from the OR-Library show very signi¯cant improvements over previous algorithms. Several open instances could be solved to optimalityNo keywords;

Industrial and Tramp Ship Routing Problems: Closing the Gap for Real-Scale Instances

Recent studies in maritime logistics have introduced a general ship routing problem and a benchmark suite based on real shipping segments, considering pickups and deliveries, cargo selection, ship-dependent starting locations, travel times and costs, time windows, and incompatibility constraints, among other features. Together, these characteristics pose considerable challenges for exact and heuristic methods, and some cases with as few as 18 cargoes remain unsolved. To face this challenge, we propose an exact branch-and-price (B&P) algorithm and a hybrid metaheuristic. Our exact method generates elementary routes, but exploits decremental state-space relaxation to speed up column generation, heuristic strong branching, as well as advanced preprocessing and route enumeration techniques. Our metaheuristic is a sophisticated extension of the unified hybrid genetic search. It exploits a set-partitioning phase and uses problem-tailored variation operators to efficiently handle all the problem characteristics. As shown in our experimental analyses, the B&P optimally solves 239/240 existing instances within one hour. Scalability experiments on even larger problems demonstrate that it can optimally solve problems with around 60 ships and 200 cargoes (i.e., 400 pickup and delivery services) and find optimality gaps below 1.04% on the largest cases with up to 260 cargoes. The hybrid metaheuristic outperforms all previous heuristics and produces near-optimal solutions within minutes. These results are noteworthy, since these instances are comparable in size with the largest problems routinely solved by shipping companies

A Branch-Price-and-Cut Algorithm for the Capacitated Multiple Vehicle Traveling Purchaser Problem with Unitary Demand

The multiple vehicle traveling purchaser problem (MVTPP) consists of simultaneously selecting suppliers and routing a fleet of homogeneous vehicles to purchase different products at the selected suppliers so that all product demands are fulfilled and traveling and purchasing costs are minimized. We consider variants of the MVTPP in which the capacity of the vehicles can become binding and the demand for each product is one unit. Corresponding solution algorithms from the literature are either branch-and-cut or branch-and-price algorithms, where in the latter case the route-generation subproblem is solved on an expanded graph by applying standard dynamic-programming techniques. Our branch-price-and-cut algorithm employs a novel labeling algorithm that works directly on the original network and postpones the purchasing decisions until the route has been completely defined. Moreover, we define a new branching rule generally applicable in case of unitary product demands, introduce a new family of valid inequalities to apply when suppliers can be visited at most once, and show how product incompatibilities can be handled without considering additional resources in the pricing problem. In comprehensive computational experiments with standard benchmark sets we prove that the new branch-price-and-cut approach is highly competitive

A Branch-and-Cut based Pricer for the Capacitated Vehicle Routing Problem

openIl Capacitated Vehicle Routing Problem, abbreviato come CVRP, è un problema di ottimizzazione combinatoria d'instradamento nel quale, un insieme geograficamente sparso di clienti con richieste note deve essere servito da una flotta di veicoli stazionati in una struttura centrale. Negli ultimi due decenni, tecniche di Column generation incorporate all'interno di frameworks branch-price-and-cut sono state infatti l'approccio stato dell'arte dominante per la costruzione di algoritmi esatti per il CVRP. Il pricer, un componente critico nella column generation, deve risolvere il Pricing Problem (PP) che richiede la risoluzione di un Elementary Shortest Path Problem with Resource Constraints (ESPPRC) in una rete di costo ridotto. Pochi sforzi scientifici sono stati dedicati allo studio di approcci branch-and-cut per affrontare il PP. L'ESPPRC è stato tradizionalmente rilassato e risolto attraverso algoritmi di programmazione dinamica. Questo approccio, tuttavia, ha due principali svantaggi. Per cominciare, peggiora i dual bounds ottenuti. Inoltre, il tempo di esecuzione diminuisce all'aumentare della lunghezza dei percorsi generati. Per valutare la performance dei loro contributi, la comunità di ricerca operativa ha tradizionalmente utilizzato una serie d'istanze di test storiche e artificiali. Tuttavia, queste istanze di benchmark non catturano le caratteristiche chiave dei moderni problemi di distribuzione del mondo reale, che sono tipicamente caratterizzati da lunghi percorsi. In questa tesi sviluppiamo uno schema basato su un approccio branch-and-cut per risolvere il pricing problem. Studiamo il comportamento e l'efficacia della nostra implementazione nel produrre percorsi più lunghi comparandola con soluzioni all'avanguardia basate su programmazione dinamica. I nostri risultati suggeriscono che gli approcci branch-and-cut possono supplementare il tradizionale algoritmo di etichettatura, indicando che ulteriore ricerca in quest'area possa portare benefici ai risolutori CVRP.The Capacitated Vehicle Routing Problem, CVRP for short, is a combinatorial optimization routing problem in which, a geographically dispersed set of customers with known demands must be served by a fleet of vehicles stationed at a central facility. Column generation techniques embedded within branch-price-and-cut frameworks have been the de facto state-of-the-art dominant approach for building exact algorithms for the CVRP over the last two decades. The pricer, a critical component in column generation, must solve the Pricing Problem (PP), which asks for an Elementary Shortest Path Problem with Resource Constraints (ESPPRC) in a reduced-cost network. Little scientific efforts have been dedicated to studying branch-and-cut based approaches for tackling the PP. The ESPPRC has been traditionally relaxed and solved through dynamic programming algorithms. This approach, however, has two major drawbacks. For starters, it worsens the obtained dual bounds. Furthermore, the running time degrades as the length of the generated routes increases. To evaluate the performance of their contributions, the operations research community has traditionally used a set of historical and artificial test instances. However, these benchmark instances do not capture the key characteristics of modern real-world distribution problems, which are usually characterized by longer routes. In this thesis, we develop a scheme based on a branch-and-cut approach for solving the pricing problem. We study the behavior and effectiveness of our implementation in producing longer routes by comparing it with state-of-the-art solutions based on dynamic programming. Our results suggest that branch-and-cut approaches may supplement the traditional labeling algorithm, indicating that further research in this area may bring benefits to CVRP solvers

A Column Generation Based Heuristic for the Multicommodity-ring Vehicle Routing Problem

AbstractWe study a new routing problem arising in City Logistics. Given a ring connecting a set of urban distribution centers (UDCs) in the outskirts of a city, the problem consists in delivering goods from virtual gates located outside the city to the customers inside of it. Goods are transported from a gate to a UDC, then either go to another UDC before being delivered to customers or are directly shipped from the first UDC. The reverse process occurs for pick-up. Routes are performed by electric vans and may be open. The objective is to find a set of routes that visit each customer and to determine ring and gates-UDC flows so that the total transportation and routing cost is minimized. We solve this problem using a column generation-based heuristic, which is tested over a set of benchmark instances issued from a more strategic location-routing problem

Tutorial: Modern Branch-and-Cut-and-Price for Vehicle Routing Problems Plan of the talk

International audienc

Reformulation and decomposition of integer programs

In this survey we examine ways to reformulate integer and mixed integer programs. Typically, but not exclusively, one reformulates so as to obtain stronger linear programming relaxations, and hence better bounds for use in a branch-and-bound based algorithm. First we cover in detail reformulations based on decomposition, such as Lagrangean relaxation, Dantzig-Wolfe column generation and the resulting branch-and-price algorithms. This is followed by an examination of Benders’ type algorithms based on projection. Finally we discuss in detail extended formulations involving additional variables that are based on problem structure. These can often be used to provide strengthened a priori formulations. Reformulations obtained by adding cutting planes in the original variables are not treated here.Integer program, Lagrangean relaxation, column generation, branch-and-price, extended formulation, Benders' algorithm

Improvements on Column-Generation-Based Algorithms for Vehicle Routing and Other Combinatorial Problems

RÉSUMÉ : Plusieurs applications dans le contexte de la logistique et de la planification de la production peuvent être modélisées comme des problèmes d’optimisation combinatoire (POC). En particulier,l’un des problèmes les plus étudiés dans ce domaine est le problème de tournées de véhicules (PTV). Le PTV consiste à trouver des tournées de véhicules qui minimisent le coût total de transport pour visiter un ensemble de clients, de telle sorte que leur demande soit complètement satisfaite en une seule visite, et que la capacité des véhicules ne soit jamais dépassée. Présentement, la principale méthode de résolution exacte pour les PTVs est la génération de colonnes. Dans cette thèse, nous nous intéressons à l’étude des algorithmes de génération de colonnes et proposons de nouvelles idées pour améliorer leur efficacité. Dans le Chapitre 4, nous présentons une revue de littérature très exhaustive dans laquelle nous mettons en évidence les principales contributions algorithmiques et de modélisation apportées au cours des dernières années dans la cadre du développent des algorithmes de génération de colonnes et de plans coupants intégrés à des méthodes d’énumération implicite pour le PTV. Notre étude est divisée en deux parties principales. Dans la première partie, nous présentons des aspects qui peuvent s’appliquer à la plupart des variantes de PTV, à savoir : des algorithmes de résolution du sous-problème de la génération de colonnes, la séparation de plans coupants, les stratégies de branchement et la stabilisation des variables duales dans le problème-maître. La deuxième partie est dédiée à la résolution de problèmes spécifiques. Dans cette partie, nous discutons comment les spécificités de chaque problème peuvent êtres traitées lors du développement des algorithmes d’énumération implicite combinant génération de colonnes et plans coupants. On étude les attributs suivants : l’existence d’une flotte hétérogène et des dépôts multiples, la considération de fenêtres de temps souples chez les clients, la possibilité d’effectuer des livraisons fractionnées, les coûts dépendant du temps, la réalisation de cueillettes et livraisons, la présence d’incertitude dans les données et des aspects environnementaux. Dans le Chapitre 5, nous proposons un algorithme sélectif pour résoudre des sous-problèmes de la génération de colonnes afin de générer des routes relaxées de type arc-ng. Notre méthode considère une généralisation de la dominance par ensemble proposée par Bulhões et al. [1]. Les résultats numériques obtenus sur des instances du PTV avec fenêtres de temps montrent que le nouveau mécanisme aide à réduire le nombre d’étiquettes non-dominées dans l’algorithme d’étiquetage utilisé pour résoudre le sous-problème et, par conséquent, le temps de calcul. Enfin, dans le Chapitre 6, nous présentons une nouvelle méthode de stabilisation pour des POCs avec des structures qui favorisent l’parution de dégénérescence. Le nouvel algorithme de stabilisation, appelé dyn-SAR, est basé sur la séparation dynamique de contraintes agrégées, qui sont obtenues en additionnant des contraintes du problème maître de génération de colonnes. L’effet de stabilisation induit par dyn-SAR provient des fortes interactions qui surviennent entre les variables duales, ce qui n’est pas observé lors de la résolution explicite d’une formulation de partition d’ensemble (recouvrement / empaquetage). L’intérêt principal pour l’utilisation du dyn-SAR est dû à sa simplicité et généralité. Ce dernier aspect est confirmé dans nos expériences, où nous considérons des problèmes dont la fonction objectif et le sous-problème de génération de colonnes sont considérablement différents. Les résultats numériques montrent un avantage important du dyn-SAR par rapport à une méthode de génération de colonnes standard en termes de nombre d’itérations et de temps de calcul.----------ABSTRACT : Several applications arising in the context of logistics and production planning can be modeled as combinatorial optimization problems (COPs). In particular, one of the most studied problems in this field is the vehicle routing problem (VRP). The VRP is the problem of finding least-cost routes to visit a set of customers in such a way that their demand is completely satisfied in a single visit, and the capacity of vehicles is not exceeded. Nowadays, the leading exact method to cope with different classes of VRPs is column generation (CG). In this thesis, we are interested in studying CG algorithms and propose new ideas to enhance their efficiency. In Chapter 4, we present a methodological survey in which we highlight and discuss the main algorithmic and modeling contributions made over the years in the context of branch-priceand-cut methods for VRPs. Our study is divided into two main parts. In the first part, we discuss topics that may apply to most VRPs variants, namely: pricing algorithms, cut separation, branching strategies, and dual variable stabilization. The second part is more problem-oriented and describes how aspects such as heterogeneous fleet, multi-depots, soft time windows, split deliveries, time dependency, pickups and deliveries, uncertainty, and environmental aspects can be handled in devising branch-price-and-cut algorithms. In Chapter 5, we propose a selective pricing algorithm to solve pricing subproblems defined in terms of arc-ng-route relaxations. Our method extends the set-based dominance rule proposed by Bulhões et al. [1], making it more general and stronger. Computational experiments performed over instances of the VRP with time windows show that the proposed mechanism helps in reducing the number of non-dominated labels kept by the labeling algorithm and, as a consequence, the CPU time. Finally, in Chapter 6, we develop a new stabilization framework to tackle COPs with degenerate structures. The new stabilization method, called dyn-SAR, relies on the dynamic separation of aggregated constraints, which are obtained by summing up constraints from the CG master problem. The stabilization effect induced by dyn-SAR is due to strong interactions that arise from dual variables, which is not observed when solving explicitly a set-partitioning (covering/packing) formulation. The main interests in using the dyn-SAR method are its simplicity and generality. The latter aspect is confirmed in our experiments, where we solve instances from problems differing considerably in their objective function and pricing subproblem. Numerical results show a clear advantage of dyn-SAR over a standard CG method in terms of both the number of iterations and running time