Winner Determination in Combinatorial Auctions using Hybrid Ant Colony Optimization and Multi-Neighborhood Local Search

A combinatorial auction is an auction where the bidders have the choice to bid on bundles of items. The WDP in combinatorial auctions is the problem of finding winning bids that maximize the auctioneer’s revenue under the constraint that each item can be allocated to at most one bidder. The WDP is known as an NP-hard problem with practical applications like electronic commerce, production management, games theory, and resources allocation in multi-agent systems. This has motivated the quest for efficient approximate algorithms both in terms of solution quality and computational time. This paper proposes a hybrid Ant Colony Optimization with a novel Multi-Neighborhood Local Search (ACO-MNLS) algorithm for solving Winner Determination Problem (WDP) in combinatorial auctions. Our proposed MNLS algorithm uses the fact that using various neighborhoods in local search can generate different local optima for WDP and that the global optima of WDP is a local optima for a given its neighborhood. Therefore, proposed MNLS algorithm simultaneously explores a set of three different neighborhoods to get different local optima and to escape from local optima. The comparisons between ACO-MNLS, Genetic Algorithm (GA), Memetic Algorithm (MA), Stochastic Local Search (SLS), and Tabu Search (TS) on various benchmark problems confirm the efficiency of ACO-MNLS in the terms of solution quality and computational time

The bid construction problem for truckload transportation services procurement in combinatorial auctions : new formulations and solution methods

De nos jours, l'évolution du commerce électronique ainsi que des niveaux de la consommation requièrent des acteurs de la chaine logistique et en particulier les transporteurs de gérer efficacement leurs opérations. Afin de rester concurrentiels et maximiser leurs profits, ils doivent optimiser leurs opérations de transport. Dans cette thèse de doctorat, nous nous focalisons sur les enchères combinatoires en tant que mécanisme de négociation pour les marchés d'approvisionnement des services de transport routier par camions permettant à un expéditeur d'externaliser ses opérations de transport et aux transporteurs d'acquérir des contrats de transport. Les mises combinatoires permettent à un transporteur participant à l'enchère d'exprimer ses intérêts pour une combinaison de contrats mis à l'enchère dans une même mise. Si la mise gagne, tous les contrats qui la forment seront alloués au transporteur au tarif exigé. Les défis majeurs pour le transporteur sont de déterminer les contrats de transport sur lesquels miser, les regrouper dans plusieurs mises combinatoires, s'il y a lieu, et décider des prix à soumettre pour chaque mise générée. Ces défis décisionnels définissent le problème de construction de mises combinatoires (BCP pour Bid Construction Problem). Chaque transporteur doit résoudre le BCP tout en respectant ses engagements préexistants et ses capacités de transport et en tenant compte des offres des compétiteurs, ce qui rend le problème difficile à résoudre. Dans la pratique, la majorité des transporteurs se basent sur leur connaissance du marché et leur historique pour fixer leurs prix des mises. Dans la littérature, la majorité des travaux sur le BCP considèrent des modèles déterministes où les paramètres sont connus et se limitent à un contexte de flotte homogène. En plus, nous notons qu'un seul travail à considérer une variante stochastique du BCP. Dans cette thèse de doctorat, nous visons à faire avancer les connaissances dans ce domaine en introduisant de nouvelles formulations et méthodes de résolution pour le BCP Le premier chapitre de cette thèse introduit une nouvelle variante du BCP avec une flotte hétérogène. En partant d'une comparaison des similitudes et des différences entre le BCP et les problèmes classiques de de tournées de véhicules, nous proposons une nouvelle formulation basée sur les arcs avec de nouvelles contraintes de bris de symétrie pour accélérer la résolution. Ensuite, nous proposons une approche heuristique et une autre exacte pour résoudre ce problème. L'heuristique développée est une recherche adaptative à grands voisinages (ALNS pour Adaptive Large Neighborhood Search) et se base sur le principe de destruction puis réparation de la solution à l'aide d'opérateurs conçus spécifiquement pour le BCP traité. La méthode exacte utilise la meilleure solution heuristique pour résoudre notre modèle mathématique avec le solveur CPLEX. Les résultats obtenus montrent la pertinence de nos méthodes en termes de qualités des solutions et des temps de calculs et ce pour des instances de grande taille. Dans le deuxième chapitre, nous nous attaquons à un cas particulier du BCP où le transporteur n'a pas d'engagements existants et vise à déterminer un ensemble de contrats mis à l'enchère profitables à miser dessus. Cette problématique correspond à un problème de tournées de véhicules avec profits (TOP pour Team Orienteering Problem). Nous proposons pour le TOP une heuristique ALNS hybride avec de nouveaux opérateurs ainsi que de nouvelles fonctionnalités tenant compte de la nature du problème. Ensuite, nous comparons les performances de notre méthode avec toutes les méthodes déjà publiées dans la littérature traitant du TOP. Les résultats montrent que notre méthode surpasse généralement toutes les approches existantes en termes de qualité des solutions et/ou temps de calculs quand elle est testée sur toutes les instances de la littérature. Notre méthode améliore la solution d'une instance de grande taille, ce qui surligne sa performance. Dans le troisième chapitre, nous nous focalisons sur l'incertitude associée aux prix de cessions des contrats mis à l'enchère et sur les offres des transporteurs concurrents. Il n'existe qu'un seul article qui traite de l'incertitude dans le BCP cependant il ne permet pas de générer des mises multiples. Ainsi, nous proposons une nouvelle formulation pour le BCP avec des prix stochastiques permettant de générer des mises combinatoires et disjointes. Nous présentons deux méthodes pour résoudre ce problème. La première méthode est hybride et à deux étapes. Dans un premier temps, elle résout un problème de sélection pour déterminer un ensemble de contrats profitables. Dans un second temps, elle résout simultanément un problème de sélection de contrats et de détermination de prix des mises (CSPP pour Contracts Selection and Pricing Problem) en ne considérant que les contrats sélectionnés dans la première étape. Notre méthode exacte résout, avec l'algorithme de branch-and-cut, le CSPP sans présélectionner des contrats. Les résultats expérimentaux et de simulations que nous rapportons soulignent la performance de nos deux méthodes et évaluent l'impact de certains paramètres sur le profit réel du transporteur. Dans le quatrième chapitre, nous nous focalisons sur l'incertitude liée au succès des mises et à la non-matérialisation des contrats. Généralement, le transporteur souhaite avoir la garantie que si certaines des mises ne sont pas gagnées ou un contrat ne se matérialise pas, il n'encourra pas de perte en servant le sous-ensemble de contrats gagnés. Dans cette recherche, nous adressons le BCP avec prix stochastiques et développons une méthode exacte qui garantit un profit non négatif pour le transporteur peu importe le résultat des enchères. Nos simulations des solutions optimales démontrent, qu'en moyenne, notre approche permet au transporteur d'augmenter son profit en plus de garantir qu'il reste non-négatif peu importe les mises gagnées ou la matérialisation des contrats suivant l'enchère.Nowadays, the evolution of e-commerce and consumption levels require supply chain actors, in particular carriers, to efficiently manage their operations. In order to remain competitive and to maximize their profits, they must optimize their transport operations. In this doctoral thesis, we focus on Combinatorial Auctions (CA) as a negotiation mechanism for truckload (TL) transportation services procurement allowing a shipper to outsource its transportation operations and for a carrier to serve new transportation contracts. Combinatorial bids offer a carrier the possibility to express his valuation for a combination of contracts simultaneously. If the bid is successful, all the contracts forming it will be allocated to the carrier at the submitted price. The major challenges for a carrier are to select the transportation contracts to bid on, formulate combinatorial bids and associated prices. These decision-making challenges define the Bid Construction Problem (BCP). Each carrier must solve a BCP while respecting its pre-existing commitments and transportation capacity and considering unknown competitors' offers, which makes the problem difficult to solve. In practice, the majority of carriers rely on their historical data and market knowledge to set their prices. In the literature, the majority of works on the BCP propose deterministic models with known parameters and are limited to the problem with a homogeneous fleet. In addition, we found a single work addressing a stochastic BCP. In this thesis, we aim to advance knowledge in this field by introducing new formulations and solution methods for the BCP. The first chapter of this thesis introduces the BCP with a heterogeneous fleet. Starting from a comparison between the BCP and classical Vehicle Routing Problems (VRPs), we propose a new arc-based formulation with new symmetry-breaking constraints for the BCP. Next, we propose exact and heuristic approaches to solve this problem. Our Adaptive Large Neighborhood Search (ALNS) heuristic is based on a destroy-repair principle using operators designed for this problem. Our exact method starts from the heuristic solution and solves our mathematical model with CPLEX. The results we obtained revealed the relevance of our methods in terms of solutions quality and computational times for large instances with up to 500 contracts and 50 vehicles. In the second chapter, we tackle a particular case of the BCP where the carrier has no pre-existing commitments and aims to select a set of profitable auctioned contracts to bid on. This problem corresponds to a Team Orienteering Problem (TOP). We propose a hybrid ALNS heuristic for the TOP with new operators as well as new features taking into account the nature of the problem. Then, we compare the performance of our algorithm against the best solutions from the literature. The results show that our method generally outperforms all the existing ones in terms of solutions quality and/or computational times on benchmark instances. Our method improves one large instance solution, which highlights its performance. In the third chapter, we focus on the uncertainty associated with the auctioned contracts clearing prices and competing carriers offers. Only one article dealing with uncertainty in the BCP existed but it does not allow to generate multiple bids. Thus, we propose a new formulation for the BCP with stochastic prices allowing to generate non-overlapping combinatorial bids. We present two methods to solve this problem. The first one is a two-step hybrid heuristic. First, it solves a Contracts Selection Problem to determine a set of profitable contracts to bid on. Secondly, it simultaneously solves a Contracts Selection and Pricing Problem (CSPP) by considering only the set of auctioned contracts selected in the first stage. Our exact method solves a CSPP by branch-and-cut without pre-selecting contracts. The experimental and simulation results underline the performance of our two methods and evaluate the impact of certain parameters on the carrier's real profit. In the fourth chapter, we focus on the uncertainty associated with bids success and contracts non-materialization. Generally, the carrier seeks to be assured that if some of the submitted bids are not won or a contract does not materialize, it will not incur a loss by serving the remaining contracts. In this research, we address the BCP with stochastic prices and develop an exact method that ensures a non-negative profit for the carrier regardless of the auction outcomes and contracts materialization. Our simulations of the optimal solutions show that, on average, our approach increases the carrier's profit in addition to guaranteeing its non-negativity regardless of the bids won or the contracts materialization

Three local search meta-heuristics for the Minimum Interference Frequency Assignment Problem (MI-FAP) in cellular networks,

International audienc

Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space

Existing work in multi-agent collision prediction and avoidance typically assumes discrete-time trajectories with Gaussian uncertainty or that are completely deterministic. We propose an approach that allows detection of collisions even between continuous, stochastic trajectories with the only restriction that means and variances can be computed. To this end, we employ probabilistic bounds to derive criterion functions whose negative sign provably is indicative of probable collisions. For criterion functions that are Lipschitz, an algorithm is provided to rapidly find negative values or prove their absence. We propose an iterative policy-search approach that avoids prior discretisations and yields collision-free trajectories with adjustably high certainty. We test our method with both fixed-priority and auction-based protocols for coordinating the iterative planning process. Results are provided in collision-avoidance simulations of feedback controlled plants.Comment: This preprint is an extended version of a conference paper that is to appear in \textit{Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014)

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

Addressing stability issues in mediated complex contract negotiations for constraint-based, non-monotonic utility spaces

Negotiating contracts with multiple interdependent issues may yield non- monotonic, highly uncorrelated preference spaces for the participating agents. These scenarios are specially challenging because the complexity of the agents’ utility functions makes traditional negotiation mechanisms not applicable. There is a number of recent research lines addressing complex negotiations in uncorrelated utility spaces. However, most of them focus on overcoming the problems imposed by the complexity of the scenario, without analyzing the potential consequences of the strategic behavior of the negotiating agents in the models they propose. Analyzing the dynamics of the negotiation process when agents with different strategies interact is necessary to apply these models to real, competitive environments. Specially problematic are high price of anarchy situations, which imply that individual rationality drives the agents towards strategies which yield low individual and social welfares. In scenarios involving highly uncorrelated utility spaces, “low social welfare” usually means that the negotiations fail, and therefore high price of anarchy situations should be avoided in the negotiation mechanisms. In our previous work, we proposed an auction-based negotiation model designed for negotiations about complex contracts when highly uncorrelated, constraint-based utility spaces are involved. This paper performs a strategy analysis of this model, revealing that the approach raises stability concerns, leading to situations with a high (or even infinite) price of anarchy. In addition, a set of techniques to solve this problem are proposed, and an experimental evaluation is performed to validate the adequacy of the proposed approaches to improve the strategic stability of the negotiation process. Finally, incentive-compatibility of the model is studied.Spain. Ministerio de Educación y Ciencia (grant TIN2008-06739-C04-04

Resource Management In Cloud And Big Data Systems

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field