14 research outputs found

    Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization

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    Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems. The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness. The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntÀ ohjaa erilaisia toimitusketju- ja logistiikkasijaintipÀÀtöksiÀ, ja agenttipohjainen mallinnusmenetelmÀ (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. TÀmÀ vÀitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmÀÀn ja verkon optimointiin tÀllaisten ongelmien ratkaisemiseksi, ja sisÀltÀÀ kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin. EnsimmÀinen tapaustutkimus esittelee kuinka kÀyttÀmÀllÀ vÀestötiheyksiÀ Norjassa, Suomessa ja Ruotsissa voidaan mÀÀrittÀÀ strategioita jakelukeskusten (DC) sijaintiin kÀyttÀmÀllÀ matkan etÀisyyden minimoimista. Kullekin skenaariolle kehitetÀÀn matka-aikakartat. LisÀksi analysoidaan nÀistÀ kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetÀÀn viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerÀtÀÀn useilta alueilta ja kuljetetaan lÀhimpÀÀn kerÀyspisteeseen. TÀmÀ tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistÀÀ kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenÀÀn kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetÀÀn erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekÀ monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan kÀytössÀ olevat kuorma-autot, sekÀ varastomÀÀrÀt ja ajetut matkat. LisÀksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumÀÀrÀstÀ sekÀ tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen mÀÀrÀ. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa kÀytetÀÀn ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. RatkaisumenetelmÀÀ kÀytetÀÀn dataan, joka on perÀisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiÀ suorituskykyindikaattoreita. ABM-menetelmÀ, toisin kuin monet muut mallintamismenetelmÀt, on tÀysin rÀÀtÀlöitÀvissÀ oleva menetelmÀ, joka voi sisÀltÀÀ laajasti erilaisia prosesseja ja elementtejÀ. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan jÀrjestelmÀn toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed

    New Formulations and Solution Methods for the Dial-a-ride Problem

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    The classic Dial-A-Ride Problem (DARP) aims at designing the minimum-cost routing solution that accommodates a set of user requests under constraints at the operations planning level. It is a highly constrained combinatorial optimization problem initially designed for providing door-to-door transportation for people with limited mobility (e.g. the elderly or disabled). It consists of routing and scheduling a fleet of capacitated vehicles to service a set of requests with specified pickup and drop-off locations and time windows. With the details of requests obtained either beforehand (static DARP) or en-route (dynamic DARP), dial-a-ride operators strive to deliver efficient and yet high-quality transport services that satisfy each passenger's individual travel needs. The goal of this thesis is threefold: (1) to propose rich DARP formulations where users' preferences are taken into account, in order to improve service quality of Demand-Responsive Transport (DRT) services and promote ridership strategically; (2) to develop novel and efficient solution methods where local search, column generation, metaheuristics and machine learning techniques are integrated to solve large-scale DARPs; and (3) to conduct real-life DARP case studies (using data extracted from NYC Yellow Taxi trip records) to test the practicality of proposed models and solution methods, as well as to emphasise the importance of connecting algorithms with real-world datasets. These aims are achieved and presented in the three core chapters of this thesis. In the first core chapter (Chapter 3), two Mixed Integer Programming (MIP) formulations (link-based and path-based) of DARP are presented, alongside with their objective functions and standard solution methods. This chapter builds the foundation of the thesis by elaborating the base models and algorithms that this thesis is based on, and by running benchmark experiments and reporting numerical results as the base line of the whole thesis. In the second core chapter (Chapter 4), two DARP models (one deterministic, one stochastic) integrated with users' preferences from dial-a-ride service operators' perspective are proposed, facilitating them to optimise their overall profit while maintaining service quality. In these models, users' utility users' preferences are considered within a dial-a-ride problem. A customized local search based heuristic and a matheuristic are developed to solve the proposed Chance-Constrained DARP (CC-DARP). Numerical results are reported for both DARP benchmark instances and a realistic case study based on New York City yellow taxi trip data. This chapter also explores the design of revenue/fleet management and pricing differentiation. The proposed chance-constrained DARP formulation provides a new decision-support tool to inform on revenue and fleet management, including fleet sizing, for DRT systems at a strategic planning level. In the last core chapter (Chapter 5), three hybrid metaheuristic algorithms integrated with Reinforcement Learning (RL) techniques are proposed and implemented, aiming to increase the scale-up capability of existing DARP solution methods. Machine learning techniques and/or a branching scheme are incorporated with various metaheuristic algorithms including VNS and LNS, providing innovative methodologies to solve large-instance DARPs in a more efficient manner. Thompson Sampling (TS) is applied to model dual values of requests under a column generation setting to negate the effect of dual oscillation (i.e. promote faster converging). The performance of proposed algorithms is tested benchmark datasets, and strengths and weaknesses across different algorithms are reported

    Shared Mobility Optimization in Large Scale Transportation Networks: Methodology and Applications

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    abstract: Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). Previous research has made a number of important contributions to the challenging pickup and delivery problem along different formulation or solution approaches. However, there are a number of modeling and algorithmic challenges for a large-scale deployment of a vehicle routing and scheduling algorithm, especially for regional networks with various road capacity and traffic delay constraints on freeway bottlenecks and signal timing on urban streets. The main thrust of this research is constructing hyper-networks to implicitly impose complicated constraints of a vehicle routing problem (VRP) into the model within the network construction. This research introduces a new methodology based on hyper-networks to solve the very important vehicle routing problem for the case of generic ride-sharing problem. Then, the idea of hyper-networks is applied for (1) solving the pickup and delivery problem with synchronized transfers, (2) computing resource hyper-prisms for sustainable transportation planning in the field of time-geography, and (3) providing an integrated framework that fully captures the interactions between supply and demand dimensions of travel to model the implications of advanced technologies and mobility services on traveler behavior.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

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

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    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

    An overview of fuzzy techniques in supply chain management: bibliometrics, methodologies, applications and future directions

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    Every practice in supply chain management (SCM) requires decision making. However, due to the complexity of evaluated objects and the cognitive limitations of individuals, the decision information given by experts is often fuzzy, which may make it difficult to make decisions. In this regard, many scholars applied fuzzy techniques to solve decision making problems in SCM. Although there were review papers about either fuzzy methods or SCM, most of them did not use bibliometrics methods or did not consider fuzzy sets theory-based techniques comprehensively in SCM. In this paper, for the purpose of analyzing the advances of fuzzy techniques in SCM, we review 301 relevant papers from 1998 to 2020. By the analyses in terms of bibliometrics, methodologies and applications, publication trends, popular methods such as fuzzy MCDM methods, and hot applications such as supplier selection, are found. Finally, we propose future directions regarding fuzzy techniques in SCM. It is hoped that this paper would be helpful for scholars and practitioners in the field of fuzzy decision making and SCM

    13th International Conference on Modeling, Optimization and Simulation - MOSIM 2020

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    ComitĂ© d’organisation: UniversitĂ© Internationale d’Agadir – Agadir (Maroc) Laboratoire Conception Fabrication Commande – Metz (France)Session RS-1 “Simulation et Optimisation” / “Simulation and Optimization” Session RS-2 “Planification des Besoins MatiĂšres PilotĂ©e par la Demande” / ”Demand-Driven Material Requirements Planning” Session RS-3 “IngĂ©nierie de SystĂšmes BasĂ©es sur les ModĂšles” / “Model-Based System Engineering” Session RS-4 “Recherche OpĂ©rationnelle en Gestion de Production” / "Operations Research in Production Management" Session RS-5 "Planification des MatiĂšres et des Ressources / Planification de la Production” / “Material and Resource Planning / Production Planning" Session RS-6 “Maintenance Industrielle” / “Industrial Maintenance” Session RS-7 "Etudes de Cas Industriels” / “Industrial Case Studies" Session RS-8 "DonnĂ©es de Masse / Analyse de DonnĂ©es” / “Big Data / Data Analytics" Session RS-9 "Gestion des SystĂšmes de Transport” / “Transportation System Management" Session RS-10 "Economie Circulaire / DĂ©veloppement Durable" / "Circular Economie / Sustainable Development" Session RS-11 "Conception et Gestion des ChaĂźnes Logistiques” / “Supply Chain Design and Management" Session SP-1 “Intelligence Artificielle & Analyse de DonnĂ©es pour la Production 4.0” / “Artificial Intelligence & Data Analytics in Manufacturing 4.0” Session SP-2 “Gestion des Risques en Logistique” / “Risk Management in Logistics” Session SP-3 “Gestion des Risques et Evaluation de Performance” / “Risk Management and Performance Assessment” Session SP-4 "Indicateurs ClĂ©s de Performance 4.0 et Dynamique de Prise de DĂ©cision” / ”4.0 Key Performance Indicators and Decision-Making Dynamics" Session SP-5 "Logistique Maritime” / “Marine Logistics" Session SP-6 “Territoire et Logistique : Un SystĂšme Complexe” / “Territory and Logistics: A Complex System” Session SP-7 "Nouvelles AvancĂ©es et Applications de la Logique Floue en Production Durable et en Logistique” / “Recent Advances and Fuzzy-Logic Applications in Sustainable Manufacturing and Logistics" Session SP-8 “Gestion des Soins de SantĂ©â€ / ”Health Care Management” Session SP-9 “IngĂ©nierie Organisationnelle et Gestion de la ContinuitĂ© de Service des SystĂšmes de SantĂ© dans l’Ere de la Transformation NumĂ©rique de la SociĂ©tĂ©â€ / “Organizational Engineering and Management of Business Continuity of Healthcare Systems in the Era of Numerical Society Transformation” Session SP-10 “Planification et Commande de la Production pour l’Industrie 4.0” / “Production Planning and Control for Industry 4.0” Session SP-11 “Optimisation des SystĂšmes de Production dans le Contexte 4.0 Utilisant l’AmĂ©lioration Continue” / “Production System Optimization in 4.0 Context Using Continuous Improvement” Session SP-12 “DĂ©fis pour la Conception des SystĂšmes de Production Cyber-Physiques” / “Challenges for the Design of Cyber Physical Production Systems” Session SP-13 “Production AvisĂ©e et DĂ©veloppement Durable” / “Smart Manufacturing and Sustainable Development” Session SP-14 “L’Humain dans l’Usine du Futur” / “Human in the Factory of the Future” Session SP-15 “Ordonnancement et PrĂ©vision de ChaĂźnes Logistiques RĂ©silientes” / “Scheduling and Forecasting for Resilient Supply Chains

    Current Topics on Risk Analysis: ICRA6 and RISK2015 Conference

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    Peer ReviewedPostprint (published version

    Current Topics on Risk Analysis: ICRA6 and RISK2015 Conference

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    ArtĂ­culos presentados en la International Conference on Risk Analysis ICRA 6/RISK 2015, celebrada en Barcelona del 26 al 29 de mayo de 2015.Peer ReviewedPostprint (published version
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