Structured Learning of Tree Potentials in CRF for Image Segmentation

We propose a new approach to image segmentation, which exploits the advantages of both conditional random fields (CRFs) and decision trees. In the literature, the potential functions of CRFs are mostly defined as a linear combination of some pre-defined parametric models, and then methods like structured support vector machines (SSVMs) are applied to learn those linear coefficients. We instead formulate the unary and pairwise potentials as nonparametric forests---ensembles of decision trees, and learn the ensemble parameters and the trees in a unified optimization problem within the large-margin framework. In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs. Moreover, we learn class-wise decision trees for each object that appears in the image. Due to the rich structure and flexibility of decision trees, our approach is powerful in modelling complex data likelihoods and label relationships. The resulting optimization problem is very challenging because it can have exponentially many variables and constraints. We show that this challenging optimization can be efficiently solved by combining a modified column generation and cutting-planes techniques. Experimental results on both binary (Graz-02, Weizmann horse, Oxford flower) and multi-class (MSRC-21, PASCAL VOC 2012) segmentation datasets demonstrate the power of the learned nonlinear nonparametric potentials.Comment: 10 pages. Appearing in IEEE Transactions on Neural Networks and Learning System

Comparing Different Approaches on the Door Assignment Problem in LTL-Terminals

The work at hand yields two different ways to address the assignment of inbound and outbound doors in less-than-truckload terminals. The considered optimization methods stem from two different scientific fields, which makes the comparison of the techniques a very interesting topic. The first solution approach origins from the field of discrete mathematics. For this purpose, the logistical optimization task is modeled as a time-discrete multi-commodity flow problem with side constraints. Based on this model, a decomposition approach and a modified column generation approach are developed. The second considered optimization method is an evolutionary multi-objective optimization algorithm (EMOA). This approach is able to handle different optimization goals in parallel. Both algorithms are applied to ten test scenarios yielding different numbers of tours, doors, loading areas, and affected relations

Modeling Framework and Solution Methodologies for On-Demand Mobility Services With Ridesharing and Transfer Options

The growing complexity of the urban travel pattern and its related traffic congestion, along with the extensive usage of mobile phones, invigorated On-Demand Mobility Services (ODMS) and opened the door to the emergence of Transportation Network Companies (TNC). By adopting the shared economy paradigm, TNCs enable private car owners to provide transportation services to passengers by providing user-friendly mobile phone applications that efficiently match passengers to service providers. Considering the high level of flexibility, convenience, and reliability of ODMS, compared to those offered by traditional public transportation systems, many metropolitan areas in the United States and abroad have reported rapid growth of such services. This dissertation presents a modeling framework to study the operation of on-demand mobility services (ODMS) in urban areas. The framework can analyze the operation of ODMS while representing emerging services such as ridesharing and transfer. The problem is formulated as a mixed-integer program and an efficient decomposition-based methodology is developed for its solution. This solution methodology aims at solving the offline version of the problem, in which the passengers’ demand is assumed to be known ii for the entire planning horizon. The presented approach adopts a modified column generation algorithm, which integrates iterative decomposition and network augmentation techniques to analyze networks with moderate size. Besides, a novel methodology for integrated ride-matching and vehicle routing for dynamic (online) ODMS with ridesharing and transfer options is developed to solve the problem in real-time. The methodology adopts a hybrid heuristic approach, which enables solving large problem instances in near real-time, where the passengers’ demand is not known a priori. The heuristic allows to (1) promptly respond to individual ride requests and (2) periodically re-evaluate the generated solutions and recommend modifications to enhance the overall solution quality by increasing the number of served passengers and total profit of the system. The outcomes of experiments considering hypothetical and real-world networks are presented. The results show that the modified column generation approach provides a good quality solution in less computation time than the CPLEX solver. Additionally, the heuristic approach can provide an efficient solution for large networks while satisfying the real-time execution requirements. Additionally, investigation of the results of the experiments shows that increasing the number of passengers willing to rideshare and/or transfer increases the general performance of ODMS by increasing the number of served passengers and associated revenue and reducing the number of needed vehicles

Modeling Heterogeneous Vehicle Routing Problem with Strict Time Schedule

Vehicle Routing Problem with time windows (VRPTW) is a well known combinatorial optimization problem normally to be used for obtaining the optimal set of routes used by a fleet of vehicles in logistic system. In VRPTW it is assumed that the fleet of vehicles are all homogeny. In this paper we consider a variant of the VRPTW in which the assumption of homogeny is dropped. Now the problem is called Heterogeneous VRP (HVRP). As the logistic company has so many customers, it puts a very strict restriction in time delivery for each vehicle used. Regarding to the structure of the problem we use integer programming approach to model the problem. A feasible neighbourhood method is developed to solve the model

A Column Generation for the Heterogeneous Fixed Fleet Open Vehicle Routing Problem

[EN] This paper addressed the heterogeneous fixed fleet open vehicle routing problem (HFFOVRP), in which the vehicles are not required to return to the depot after completing a service. In this new problem, the demands of customers are fulfilled by a heterogeneous fixed fleet of vehicles having various capacities, fixed costs and variable costs. This problem is an important variant of the open vehicle routing problem (OVRP) and can cover more practical situations in transportation and logistics. Since this problem belongs to NP-hard Problems, An approach based on column generation (CG) is applied to solve the HFFOVRP. A tight integer programming model is presented and the linear programming relaxation of which is solved by the CG technique. Since there have been no existing benchmarks, this study generated 19 test problems and the results of the proposed CG algorithm is compared to the results of exact algorithm. Computational experience confirms that the proposed algorithm can provide better solutions within a comparatively shorter period of time.Yousefikhoshbakht, M.; Dolatnejad, A. (2017). A Column Generation for the Heterogeneous Fixed Fleet Open Vehicle Routing Problem. International Journal of Production Management and Engineering. 5(2):55-71. doi:10.4995/ijpme.2017.5916SWORD557152Aleman, R. E., & Hill, R. R. (2010). A tabu search with vocabulary building approach for the vehicle routing problem with split demands. International Journal of Metaheuristics, 1(1), 55. doi:10.1504/ijmheur.2010.033123Anbuudayasankar, S. P., Ganesh, K., Lenny Koh, S. C., & Ducq, Y. (2012). Modified savings heuristics and genetic algorithm for bi-objective vehicle routing problem with forced backhauls. Expert Systems with Applications, 39(3), 2296-2305. doi:10.1016/j.eswa.2011.08.009Brandão, J. (2009). A deterministic tabu search algorithm for the fleet size and mix vehicle routing problem. European Journal of Operational Research, 195(3), 716-728. doi:10.1016/j.ejor.2007.05.059Çatay, B. (2010). A new saving-based ant algorithm for the Vehicle Routing Problem with Simultaneous Pickup and Delivery. Expert Systems with Applications, 37(10), 6809-6817. doi:10.1016/j.eswa.2010.03.045Dantzig, G. B., & Ramser, J. H. (1959). The Truck Dispatching Problem. Management Science, 6(1), 80-91. doi:10.1287/mnsc.6.1.80Gendreau, M., Guertin, F., Potvin, J.-Y., & Séguin, R. (2006). Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick-ups and deliveries. Transportation Research Part C: Emerging Technologies, 14(3), 157-174. doi:10.1016/j.trc.2006.03.002Gendreau, M., Laporte, G., Musaraganyi, C., & Taillard, É. D. (1999). A tabu search heuristic for the heterogeneous fleet vehicle routing problem. Computers & Operations Research, 26(12), 1153-1173. doi:10.1016/s0305-0548(98)00100-2Lei, H., Laporte, G., & Guo, B. (2011). The capacitated vehicle routing problem with stochastic demands and time windows. Computers & Operations Research, 38(12), 1775-1783. doi:10.1016/j.cor.2011.02.007Li, X., Leung, S. C. H., & Tian, P. (2012). A multistart adaptive memory-based tabu search algorithm for the heterogeneous fixed fleet open vehicle routing problem. Expert Systems with Applications, 39(1), 365-374. doi:10.1016/j.eswa.2011.07.025Li, X., Tian, P., & Aneja, Y. P. (2010). An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 46(6), 1111-1127. doi:10.1016/j.tre.2010.02.004Penna, P. H. V., Subramanian, A., & Ochi, L. S. (2011). An Iterated Local Search heuristic for the Heterogeneous Fleet Vehicle Routing Problem. Journal of Heuristics, 19(2), 201-232. doi:10.1007/s10732-011-9186-ySaadati Eskandari, Z., YousefiKhoshbakht, M. (2012). Solving the Vehicle Routing Problem by an Effective Reactive Bone Route Algorithm, Transportation Research Journal, 1(2), 51-69.Subramanian, A., Drummond, L. M. A., Bentes, C., Ochi, L. S., & Farias, R. (2010). A parallel heuristic for the Vehicle Routing Problem with Simultaneous Pickup and Delivery. Computers & Operations Research, 37(11), 1899-1911. doi:10.1016/j.cor.2009.10.011Syslo, M., Deo, N., Kowalik, J. (1983). Discrete Optimization Algorithms with Pascal Programs, Prentice Hall.Taillard, E. D. (1999). A heuristic column generation method for the heterogeneous fleet VRP, RAIRO Operations Research, 33, 1-14. https://doi.org/10.1051/ro:1999101Tarantilis, C. D., & Kiranoudis, C. T. (2007). A flexible adaptive memory-based algorithm for real-life transportation operations: Two case studies from dairy and construction sector. European Journal of Operational Research, 179(3), 806-822. doi:10.1016/j.ejor.2005.03.059Wang, H.-F., & Chen, Y.-Y. (2012). A genetic algorithm for the simultaneous delivery and pickup problems with time window. Computers & Industrial Engineering, 62(1), 84-95. doi:10.1016/j.cie.2011.08.018Yousefikhoshbakht, M., Didehvar, F., & Rahmati, F. (2013). Solving the heterogeneous fixed fleet open vehicle routing problem by a combined metaheuristic algorithm. International Journal of Production Research, 52(9), 2565-2575. doi:10.1080/00207543.2013.855337Yousefikhoshbakht, M., & Khorram, E. (2012). Solving the vehicle routing problem by a hybrid meta-heuristic algorithm. Journal of Industrial Engineering International, 8(1). doi:10.1186/2251-712x-8-1

Methods for Improving Robustness and Recovery in Aviation Planning.

In this dissertation, we develop new methods for improving robustness and recovery in aviation planning. In addition to these methods, the contributions of this dissertation include an in-depth analysis of several mathematical modeling approaches and proof of their structural equivalence. Furthermore, we analyze several decomposition approaches, the difference in their complexity and the required computation time to provide insight into selecting the most appropriate formulation for a particular problem structure. To begin, we provide an overview of the airline planning process, including the major components such as schedule planning, fleet assignment and crew planning approaches. Then, in the first part of our research, we use a recursive simulation-based approach to evaluate a flight schedule's overall robustness, i.e. its ability to withstand propagation delays. We then use this analysis as the groundwork for a new approach to improve the robustness of an airline's maintenance plan. Specifically, we improve robustness by allocating maintenance rotations to those aircraft that will most likely benefit from the assignment. To assess the effectiveness of our approach, we introduce a new metric, maintenance reachability, which measures the robustness of the rotations assigned to aircraft. Subsequently, we develop a mathematical programming approach to improve the maintenance reachability of this assignment. In the latter part of this dissertation, we transition from the planning to the recovery phase. On the day-of-operations, disruptions often take place and change aircraft rotations and their respective maintenance assignments. In recovery, we focus on creating feasible plans after such disruptions have occurred. We divide our recovery approach into two phases. In the first phase, we solve the Maintenance Recovery Problem (MRP), a computationally complex, short-term, non-recurrent recovery problem. This research lays the foundation for the second phase, in which we incorporate recurrence, i.e. the property that scheduling one maintenance event has a direct implication on the deadlines for subsequent maintenance events, into the recovery process. We recognize that scheduling the next maintenance event provides implications for all subsequent events, which further increases the problem complexity. We illustrate the effectiveness of our methods under various objective functions and mathematical programming approaches.Ph.D.Industrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91539/1/mlapp_1.pd

Cost-effective Bandwidth Provisioning in Microwave Wireless Networks under Unreliable Channel Conditions

International audienceCost-effective planning and dimensioning of backhaul microwave networks under unreliable channel conditions remains a relatively under explored area in the literature. In particular, bandwidth assignment requires special attention as the transport capacity of microwave links is prone to variations due to, e.g., weather conditions. In this paper, we formulate an optimization model that determines the minimum cost bandwidth assignment of the links in the network for which traffic requirements can be fulfilled with high probability. This model also aims to increase network reliability by adjusting dynamically traffic routes in response to variations of link capacities induced by channel conditions. Experimental results show that 45% of the bandwidth cost can be saved compared to the case where a bandwidth over-provisioning policy is uniformly applied to all links in the network planning. Comparisons with previous work also show that our solution approach, based on column generation technique, is able to solve much larger instances in significantly shorter computing times (i.e., few minutes for medium-size networks, and up to 2 hours for very large networks, unsolved so far by previous models/algorithms), with a comparable level of reliability

A Mathematical Programming Framework for Network Capacity Control in Customer Choice-Based Revenue Management

RÉSUMÉ : Cette thèse est basée sur l'étude de différentes approches pour répondre à la problématique du contrôle de capacité pour les réseaux en gestion du revenu. Elle est composée de cinq chapitres. Le premier donne une vue d’ensemble de la thèse ainsi que la méthodologie suivie pour analyser chaque approche. Les trois chapitres suivants sont à mettre en lien avec des articles que nous avons soumis dans des revues internationales. Ils proposent de nouveaux modèles et algorithmes pour le contrôle de capacité en gestion du revenu. Les cinquième et sixième chapitres contiennent la conclusion et l’ouverture de la thèse. Nous décrivons, dans la suite, chaque chapitre plus précisément. Dans le chapitre deux, nous proposons une approche de programmation mathématique avec choix de clients afin d’estimer les bid prices variant dans le temps. Notre méthode permet de prendre facilement en compte les contraintes techniques et pratiques d’un système de réservation central contrairement aux solutions actuelles proposées dans la littérature. En plus d’avoir développé un filtre vérifiant la disponibilité de combinaisons de produits sous un contrôle par bid price, nous avons mis au point un algorithme de génération de colonnes où une puissante heuristique est utilisée pour résoudre le sous-Problème fractionnel qui est NP-difficile. Encore une fois nos résultats numériques sur des données simulées montrent que notre solution est meilleure que les approches actuelles. Dans le chapitre trois, nous développons une nouvelle méthode de programmation mathématique pour obtenir une allocation optimale des ressources avec un modèle de demande à choix non paramétriques. Notre méthode est alors complétement flexible et ne souffre pas des inefficacités des modèles paramétriques actuels comme ceux de type multinomial logit. Pour cela, nous avons modifié un algorithme de génération de colonnes afin de traiter efficacement des problèmes réels de grande taille. Nos résultats numériques montrent que notre méthode est meilleure que les méthodes de la littérature actuelle à la fois en qualité de la solution qu’en temps de résolution. Dans le chapitre quatre, nous analysons un nouveau programme mathématique avec choix de clients pour estimer des booking limits qui doivent respecter une hiérarchie (nesting) ainsi que des règles commerciales imposées par le système de réservation central. De la même manière qu’au chapitre précédent, nous identifions les combinaisons de produits respectant ou non la hiérarchie (nesting) fixée par la politique de contrôle et nous développons une heuristique basée sur la décomposition. En simulant le processus stochastique d’arrivée, nous montrons encore une fois l'efficacité de notre méthode pour résoudre des problèmes complexes.----------ABSTRACT : This dissertation, composed of five chapters, studies several policies concerned with the issue of capacity control in network revenue management. The first chapter provides an overview of the thesis, together with the general methodology used to analyze the control policies. In the next three chapters, each of which corresponding to a paper submitted to an international journal, we propose new models and algorithms for addressing network revenue management. The fifth and final chapter concludes the dissertation, opening avenues for further investigation. We now describe the content of each article in more detail. In Chapter 2, we propose a customer choice-based mathematical programming approach to estimate time-dependent bid prices. In contrast with most approaches in the literature, ours can easily accommodate technical and practical constraints imposed by central reservation systems. Besides developing a filter that checks the compatibility of feasible product combinations under bid price control, we develop a column generation algorithm where a powerful heuristic is used to solve the NP-hard fractional subproblem. Again, our computational results show, based on simulated data, that the new approach outperforms alternative approaches. In Chapter 3, we develop a new mathematical programming framework to derive optimal an optimal allocation of resources under a non-parametric choice model of demand. The implemented model is completely flexible and removes the inefficiencies of current parametric models, such as those of the ubiquitous multinomial logit. We develop for its solution a modified column generation algorithm that can efficiently address large scale, real world problems. Our computational results show that the new approach outperforms alternative approaches from the current literature, both in the terms of the quality of the solution and the required processing time. In Chapter 4, we analyze a novel customer choice-based mathematical program to estimate booking limits that are required to be nested, while simultaneously satisfying the business rules imposed by most central reservation system. Similar to what was accomplished in the previous chapter, we identify product combinations that are compatible (or not) with some nested control policy, and develop a decomposition-based heuristic algorithm. By simulating the stochastic arrival process, we again illustrate the efficiency of the method to tackle complex problems

Dimensionnement et optimisation des réseaux de collecte sans fil

The main work of this thesis focuses on the wireless backhaul networks. We studied different optimization problems in such networks that represent real challenges for industrial sector.The first issue addressed focuses on the capacity allocation on the links at minimum cost. It was solved by a linear programming approach with column generation. Our method solves the problems on large size networks. We then studied the problem of network infrastructure sharing between virtual operators. The objective is to maximize the revenue of the operator of the physical infrastructure while satisfying the quality of service constraints of virtual operators customers of the network. In this context, we proposed a robust model using mixed integer linear programming. In the following problem, we proposed a robust energy-aware routing solution for the network operators to reduce their energy consumption. Our solution was formulated using a mixed integer linear program. We also proposed heuristics to find efficient solutions for large networks. The last work of this thesis focuses on cognitive radio networks and more specifi- cally on the problem of bandwidth sharing. We formalized it using a linear program with a different approach to robust optimization. We based our solution on the 2-stage linear robust method.L’essentiel des travaux de cette thèse porte sur les réseaux de collectes de données sans fil. Nous avons étudié différents problèmes d’optimisation dans ces réseaux qui représentent de vrais challenges pour les industriels du secteur. Le premier problème porte sur l’allocation de capacités sur les liens à coût minimum. Il a été résolu par une approche de programmation linéaire avec génération de colonnes. Notre modèle permet de résoudre des problèmes de grandes tailles. Nous avons ensuite étudié le problème du partage d’infrastructure réseau entre opérateurs virtuels avec comme objectif de maximiser les revenus de l’opérateur de l’infrastructure physique tout en satisfaisant les demandes et les contraintes de qualité de service des opérateurs virtuels clients du réseau. Dans ce contexte, nous avons proposé une formulation robuste du problème en programmation linéaire en nombres entiers mixte. Un autre point de dépenses dans ce type de réseau est la consommation d’énergie. Nous avons proposé une solution robuste, de routage basée sur la consommation d’énergie du réseau. Notre solution a été formulée en utilisant un programme linéaire en nombre entiers mixte. Nous avons aussi proposé des heuristiques afin de trouver assez rapidement des solutions pour de grandes instances. Le dernier travail de cette thèse porte sur les réseaux radio cognitifs et plus précisément sur le problème de partage de bande passante. Nous l’avons formalisé en utilisant un programme linéaire mais avec une autre approche d’optimisation robuste. Nous utilisons la méthode d'optimisation robuste à 2 niveaux pour le résoudre