412 research outputs found
The bid construction problem for truckload transportation services procurement in combinatorial auctions : new formulations and solution methods
De nos jours, l'Ă©volution du commerce Ă©lectronique ainsi que des niveaux de la consommation requiĂšrent des acteurs de la chaine logistique et en particulier les transporteurs de gĂ©rer efficacement leurs opĂ©rations. Afin de rester concurrentiels et maximiser leurs profits, ils doivent optimiser leurs opĂ©rations de transport. Dans cette thĂšse de doctorat, nous nous focalisons sur les enchĂšres combinatoires en tant que mĂ©canisme de nĂ©gociation pour les marchĂ©s d'approvisionnement des services de transport routier par camions permettant Ă un expĂ©diteur d'externaliser ses opĂ©rations de transport et aux transporteurs d'acquĂ©rir des contrats de transport. Les mises combinatoires permettent Ă un transporteur participant Ă l'enchĂšre d'exprimer ses intĂ©rĂȘts pour une combinaison de contrats mis Ă l'enchĂšre dans une mĂȘme mise. Si la mise gagne, tous les contrats qui la forment seront allouĂ©s au transporteur au tarif exigĂ©. Les dĂ©fis majeurs pour le transporteur sont de dĂ©terminer les contrats de transport sur lesquels miser, les regrouper dans plusieurs mises combinatoires, s'il y a lieu, et dĂ©cider des prix Ă soumettre pour chaque mise gĂ©nĂ©rĂ©e. Ces dĂ©fis dĂ©cisionnels dĂ©finissent le problĂšme de construction de mises combinatoires (BCP pour Bid Construction Problem). Chaque transporteur doit rĂ©soudre le BCP tout en respectant ses engagements prĂ©existants et ses capacitĂ©s de transport et en tenant compte des offres des compĂ©titeurs, ce qui rend le problĂšme difficile Ă rĂ©soudre. Dans la pratique, la majoritĂ© des transporteurs se basent sur leur connaissance du marchĂ© et leur historique pour fixer leurs prix des mises. Dans la littĂ©rature, la majoritĂ© des travaux sur le BCP considĂšrent des modĂšles dĂ©terministes oĂč les paramĂštres sont connus et se limitent Ă un contexte de flotte homogĂšne. En plus, nous notons qu'un seul travail Ă considĂ©rer une variante stochastique du BCP. Dans cette thĂšse de doctorat, nous visons Ă faire avancer les connaissances dans ce domaine en introduisant de nouvelles formulations et mĂ©thodes de rĂ©solution pour le BCP Le premier chapitre de cette thĂšse introduit une nouvelle variante du BCP avec une flotte hĂ©tĂ©rogĂšne. En partant d'une comparaison des similitudes et des diffĂ©rences entre le BCP et les problĂšmes classiques de de tournĂ©es de vĂ©hicules, nous proposons une nouvelle formulation basĂ©e sur les arcs avec de nouvelles contraintes de bris de symĂ©trie pour accĂ©lĂ©rer la rĂ©solution. Ensuite, nous proposons une approche heuristique et une autre exacte pour rĂ©soudre ce problĂšme. L'heuristique dĂ©veloppĂ©e est une recherche adaptative Ă grands voisinages (ALNS pour Adaptive Large Neighborhood Search) et se base sur le principe de destruction puis rĂ©paration de la solution Ă l'aide d'opĂ©rateurs conçus spĂ©cifiquement pour le BCP traitĂ©. La mĂ©thode exacte utilise la meilleure solution heuristique pour rĂ©soudre notre modĂšle mathĂ©matique avec le solveur CPLEX. Les rĂ©sultats obtenus montrent la pertinence de nos mĂ©thodes en termes de qualitĂ©s des solutions et des temps de calculs et ce pour des instances de grande taille. Dans le deuxiĂšme chapitre, nous nous attaquons Ă un cas particulier du BCP oĂč le transporteur n'a pas d'engagements existants et vise Ă dĂ©terminer un ensemble de contrats mis Ă l'enchĂšre profitables Ă miser dessus. Cette problĂ©matique correspond Ă un problĂšme de tournĂ©es de vĂ©hicules avec profits (TOP pour Team Orienteering Problem). Nous proposons pour le TOP une heuristique ALNS hybride avec de nouveaux opĂ©rateurs ainsi que de nouvelles fonctionnalitĂ©s tenant compte de la nature du problĂšme. Ensuite, nous comparons les performances de notre mĂ©thode avec toutes les mĂ©thodes dĂ©jĂ publiĂ©es dans la littĂ©rature traitant du TOP. Les rĂ©sultats montrent que notre mĂ©thode surpasse gĂ©nĂ©ralement toutes les approches existantes en termes de qualitĂ© des solutions et/ou temps de calculs quand elle est testĂ©e sur toutes les instances de la littĂ©rature. Notre mĂ©thode amĂ©liore la solution d'une instance de grande taille, ce qui surligne sa performance. Dans le troisiĂšme chapitre, nous nous focalisons sur l'incertitude associĂ©e aux prix de cessions des contrats mis Ă l'enchĂšre 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 gĂ©nĂ©rer des mises multiples. Ainsi, nous proposons une nouvelle formulation pour le BCP avec des prix stochastiques permettant de gĂ©nĂ©rer des mises combinatoires et disjointes. Nous prĂ©sentons deux mĂ©thodes pour rĂ©soudre ce problĂšme. La premiĂšre mĂ©thode est hybride et Ă deux Ă©tapes. Dans un premier temps, elle rĂ©sout un problĂšme de sĂ©lection pour dĂ©terminer un ensemble de contrats profitables. Dans un second temps, elle rĂ©sout simultanĂ©ment un problĂšme de sĂ©lection de contrats et de dĂ©termination de prix des mises (CSPP pour Contracts Selection and Pricing Problem) en ne considĂ©rant que les contrats sĂ©lectionnĂ©s dans la premiĂšre Ă©tape. Notre mĂ©thode exacte rĂ©sout, avec l'algorithme de branch-and-cut, le CSPP sans prĂ©sĂ©lectionner des contrats. Les rĂ©sultats expĂ©rimentaux et de simulations que nous rapportons soulignent la performance de nos deux mĂ©thodes et Ă©valuent l'impact de certains paramĂštres sur le profit rĂ©el du transporteur. Dans le quatriĂšme chapitre, nous nous focalisons sur l'incertitude liĂ©e au succĂšs des mises et Ă la non-matĂ©rialisation des contrats. GĂ©nĂ©ralement, le transporteur souhaite avoir la garantie que si certaines des mises ne sont pas gagnĂ©es ou un contrat ne se matĂ©rialise pas, il n'encourra pas de perte en servant le sous-ensemble de contrats gagnĂ©s. Dans cette recherche, nous adressons le BCP avec prix stochastiques et dĂ©veloppons une mĂ©thode exacte qui garantit un profit non nĂ©gatif pour le transporteur peu importe le rĂ©sultat des enchĂšres. Nos simulations des solutions optimales dĂ©montrent, qu'en moyenne, notre approche permet au transporteur d'augmenter son profit en plus de garantir qu'il reste non-nĂ©gatif peu importe les mises gagnĂ©es ou la matĂ©rialisation des contrats suivant l'enchĂšre.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
Combinatorial-Based Auction For The Transportation Procurement: An Optimization-Oriented Review
This paper conducts a literature review on freight transport service procurements (FTSP) and explores the application of combinatorial auctions (CAs) mechanism and the mathematical modeling approach of the associated problems. It provides an overview of modeling the problems and their solution strategies. The results demonstrate that there has been limited scholarly attention to sustainable issues, risk mitigation and the stochastic nature of parameters. Finally, several promising future directions for FTSP research have been proposed, including FTSP for green orientation in the context of carbon reduction, shipperâs reputation, carrier collaboration for bid generation, etc
Multi-agent System Models for Distributed Services Scheduling
This thesis investigates the computational and modeling issues involved with developing solutions for distributed service scheduling problems. Compared with traditional manufacturing scheduling, service scheduling poses additional challenges due to the significant customer involvement in service processes. The first challenge is that the service scheduling environment is a distributed environment in which scheduling-related information is scattered among individual identities, such as service providers and customers. The second challenge is that the service scheduling environment is a dynamic environment. Uncertainty in customer demand, customer cancellations and no-shows make the scheduling of services a complex dynamic process. Service scheduling has to be robust and prepared to accommodate any contingencies caused by customer involvement in service production. The third challenge concerns customersâ private information. To compute optimal schedules, ideally, the scheduler should know the complete customer availability and preference information within the scheduling horizon. However, customers may act strategically to protect their private information. Therefore, service scheduling systems should be designed so that they are able to elicit enough of a customerâs private information that will make it possible to compute high quality schedules. The fourth challenge is that in a service scheduling environment, the objectives are complicated and they may even be in opposition. The distributed service scheduling environment enables each agent to have their own scheduling objectives. The objectives of these agents can vary from one to another. In addition to multiple objectives, since agents are self-interested, they are likely to behave strategically to achieve their own objectives without considering the global objectives of the system. Existing approaches usually deal with only a part of the challenges in a specific service domain. There is a need for general problem formulations and solutions that address service scheduling challenges in a comprehensive framework.
In this thesis, I propose an integrated service scheduling framework for the general service scheduling problem. The proposed framework uses iterative auction as the base mechanism to tackle service scheduling challenges in distributed and dynamic environments. It accommodates customerâs private information by providing appropriate incentives to customers and it has the potential to accommodate dynamic events. This framework integrates customersâ preferences with the allocation of a providerâs capacity through multilateral negotiation between the provider and its customers. The framework can accommodate both price-based commercial settings and non-commercial service settings. Theoretical and experimental results are developed to verify the effectiveness of the proposed framework. The application of the framework to the mass customization of services and to appointment scheduling are developed to demonstrate the applicability of the general framework to specific service domains. A web-based prototype is designed and implemented to evaluate the scalability of the approach in a distributed environment
Decentralized and Dynamic Home Health Care Resource Scheduling Using an Agent-Based Model
The purpose of this thesis is to design an agent-based scheduling system, simulated in a dynamic environment that will reduce home healthcare service costs. The study focuses on situations where a health care agency needs to assign home visits among a group of independent healthcare practitioners. Each practitioner has different skill sets, time constraints, and cost structures, given the nature, time and location of each home visit. Each expects reasonable payment commensurate with their skill levels as well as the costs incurred. The healthcare agency in turn needs all planned visits performed by qualified practitioners while minimizing overall service costs. Decisions about scheduling are made both before and during the scheduling period, requiring the health care agency to respond to unexpected situations based on the latest scheduling information.
This problem is examined in a multi-agent system environment where practitioners are modeled as self-interested agents. The study first analyzes the problem for insights into the combinatorial nature of such a problem occurring in a centralized environment, then discusses the decentralized and dynamic challenges. An iterated bidding mechanism is designed as the negotiation protocol for the system. The effectiveness of this system is evaluated through a computational study, with results showing the proposed multi-agent scheduling system is able to compute high quality schedules in the decentralized home healthcare environment. Following this, the system is also implemented in a simulation model that can accommodate unexpected situations. We presents different simulation scenarios which illustrate the process of how the system dynamically schedules incoming visits, and cost reduction can be observed from the results
Recommended from our members
Nature inspired computational intelligence for financial contagion modelling
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the âtransmissionâ of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Tradersâ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial marketâs parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market
The Pickup and Multiple Delivery Problem
This thesis presents my work on the pickup and multiple delivery problem, a real-world vehicle routing and scheduling problem with soft time windows, working time and last-in-first-out constraints, developed in collaboration with Transfaction Ltd., who conduct logistics analysis for several large retailers in the UK. A summary of relevant background literature is presented highlighting where my research fits into and contributes to the broader academic landscape. I present a detailed model of the problem and thoroughly analyse a case-study data set, obtaining distributions used for further research. A new variable neighbourhood descent with memory hyper-heuristic is presented and shown to be an effective technique for solving instances of the real-world problem. I analyse strategies for cooperation and competition amongst haulage companies and quantify their effectiveness. The value of time and timely information for planning pickup and delivery requests is investigated. The insights gained are of real industrial relevance, highlighting how a variety of business decisions can produce significant cost savings
Evolutionary Computation
This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field
- âŠ