An auction for collaborative vehicle routing: Models and algorithms

Increasing competition and expectations from customers pressures carriers to further improve efficiency. Forming collaborations is essential for carriers to reach their targeted efficiency levels. In this study, we investigate an auction mechanism to facilitate collaboration amongst carriers while maintaining autonomy for the individual carriers. Multiple auction implementations are evaluated. As the underlying decision problem (which is a traditional vehicle routing problem) is known to be NP-hard, this auction mechanism has an important inherent complexity. Therefore, we use fast and efficient algorithms for the vehicle routing problem to ensure that the auction can be used in operational decision making. Numerical results are presented, indicating that the auction achieves a savings potential better than the thus far reported approaches in the literature. Managerial insights are discussed, particularly related to the properties of the auction and value of the information

Iterative Combinatorial Auction for Carrier Collaboration in Logistic Services

In collaborative logistics, multiple carriers form a network to share their transportation capacities. Collaboration among carriers results in improved resource utilization and, therefore, reduced costs. In this thesis, we propose an auction-based model for carrier collaboration in transportation services. The model achieves carrier collaboration through facilitating the negotiation among carriers over a group of shipping orders required by one or a group of shippers. The negotiation is conducted through a combinatorial iterative auction mechanism with the objective of minimizing the carriers’ overall costs. We first present a centralized carrier collaboration problem model in which a central entity has all required information to compute an optimal solution. We then consider a more realistic game theoretic setting where auction-based mechanism is applied to deal with self-interests of carriers. Compared with one-shot auctions, the proposed iterative bidding framework has the properties of reducing carriers’ information revelation and accommodating dynamic changes during the bidding process. Experimental results show that the procurement cost performance and the quality of solutions computed using the proposed iterative auction model is close to that of the optimal solutions

Interaction between intelligent agent strategies for real-time transportation planning

In this paper we study the real-time scheduling of time-sensitive full truckload pickup-and-delivery jobs. The problem involves the allocation of jobs to a fixed set of vehicles which might belong to different collaborating transportation agencies. A recently proposed solution methodology for this problem is the use of a multi-agent system where shipper agents offer jobs through sequential auctions and vehicle agents bid on these jobs. In this paper we consider such a system where both the vehicle agents and the shipper agents are using profit maximizing look-ahead strategies. Our main contribution is that we study the interrelation of these strategies and their impact on the system-wide logistical costs. From our simulation results, we conclude that the system-wide logistical costs (i) are always reduced by using the look-ahead strategies instead of a myopic strategy (10–20%) and (ii) the joint effect of two look-ahead strategies is larger than the effect of an individual strategy. To provide an indication of the savings that might be realized under centralized decision making, we benchmark our results against an integer programming approach

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

Simulation and optimization of a multi-agent system on physical internet enabled interconnected urban logistics.

An urban logistics system is composed of multiple agents, e.g., shippers, carriers, and distribution centers, etc., and multi-modal networks. The structure of Physical Internet (PI) transportation network is different from current logistics practices, and simulation can effectively model a series of PI-approach scenarios. In addition to the baseline model, three more scenarios are enacted based on different characteristics: shared trucks, shared hubs, and shared flows with other less-than-truckload shipments passing through the urban area. Five performance measures, i.e., truck distance per container, mean truck time per container, lead time, CO2 emissions, and transport mean fill rate, are included in the proposed procedures using real data in an urban logistics case. The results show that PI enables a significant improvement of urban transportation efficiency and sustainability. Specifically, truck time per container reduces 26 percent from that of the Private Direct scenario. A 42 percent reduction of CO2 emissions is made from the current logistics practice. The fill rate of truckload is increased by almost 33 percent, whereas the relevant longer distance per container and the lead time has been increased by an acceptable range. Next, the dissertation applies an auction mechanism in the PI network. Within the auction-based transportation planning approach, a model is developed to match the requests and the transport services in transport marketplaces and maximize the carriers’ revenue. In such transportation planning under the protocol of PI, it is a critical system design problem for decision makers to understand how various parameters through interactions affect this multi-agent system. This study provides a comprehensive three-layer structure model, i.e. agent-based simulation, auction mechanism, and optimization via simulation. In term of simulation, a multi-agent model simulates a complex PI transportation network in the context of sharing economy. Then, an auction mechanism structure is developed to demonstrate a transport selection scheme. With regard of an optimization via simulation approach and sensitivity analysis, it has been provided with insights on effects of combination of decision variables (i.e. truck number and truck capacity) and parameters settings, where results can be drawn by using a case study in an urban freight transportation network. In the end, conclusions and discussions of the studies have been summarized. Additionally, some relevant areas are required for further elaborate research, e.g., operational research on airport gate assignment problems and the simulation modelling of air cargo transportation networks. Due to the complexity of integration with models, I relegate those for future independent research

Algorithms for bundling and pricing trucking services: Deterministic and stochastic approaches

Bundling and pricing trucking services is an important strategic decision for carriers. This is helpful when they consider the incorporation of new businesses to their networks, look for economic and optimal operations, and develop revenue management strategies. Reverse combinatorial auctions for trucking services are real-world examples that illustrate the necessity of such strategies. In these auctions, a shipper asks carriers for quotes to serve combinations of lanes and the carriers have to bundle demand and price it properly. This dissertation explores several dimensions of the problem employing state-of-the-art analytical tools. These dimensions include: Truckload (TL) and less-than-truckload (LTL) operations, behavioral attributes driving the selection of trucking services, and consideration of deterministic and stochastic demand. Analytical tools include: advanced econometrics, network modeling, statistical network analysis, combinatorial optimization, and stochastic optimization. The dissertation is organized as follows. Chapter 1 introduces the problem and related concepts. Chapter 2 studies the attributes driving the selection of trucking services and proposes an econometric model to quantify the shipper willingness to pay using data from a discrete choice experiment. Chapter 3 proposes an algorithm for demand clustering in freight logistics networks using historical data from TL carriers. Chapter 4 develops an algorithmic approach for pricing and demand segmentation of bundles in TL combinatorial auctions. Chapter 5 expands the latter framework to consider stochastic demand. Chapter 6 uses an analytical approach to demonstrate the benefits of in-vehicle consolidation for LTL carriers. Finally, Chapter 7 proposes an algorithm for pricing and demand segmentation of bundles in LTL combinatorial auctions that accounts for stochastic demand. This research provides meaningful negotiation guidance for shippers and carriers, which is supported by quantitative methods. Likewise, numerical experiments demonstrate the benefits and efficiencies of the proposed algorithms, which are transportation modeling contributions

Combinatorial auction for transportation matching service: Formulation and adaptive large neighborhood search heuristic

National Research Foundation (NRF) Singapore under Corp Lab @ Universit