Готові списки джерел за темами / Multiagent scheduling

Добірка наукової літератури з теми "Multiagent scheduling"

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Опубліковано: 6 липня 2024

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  3. Книги
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Статті в журналах з теми "Multiagent scheduling":

1

Zhang, Jie, Gang Wang, Yafei Song, Fangzheng Zhao, and Siyuan Wang. "Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure." Mathematical Problems in Engineering 2019 (November 6, 2019): 1–14. http://dx.doi.org/10.1155/2019/4259649.

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For task planning of the command and control structure, the existing algorithms exhibit low efficiency and poor replanning quality under abnormal conditions. Given the requirements of the current accusation architecture, a distributed command and control structure model is built in this paper based on multiagents, which exploits the superiority of multiagents in achieving complex tasks. The concept of MultiAgent-HTN is proposed based on the framework. The original hierarchical task network planning algorithm is optimized, the multiagent collaboration framework is redefined, and the coordination mechanism of local conflict is developed. With the classical resource scheduling problem as the experimental background, the proposed algorithm compared with the classical HTN algorithm is drawn. According to the experimental results, the proposed algorithm exhibits higher quality and higher efficiency than the existing algorithm and the space anomaly is significant in the course of processing. The planning is more efficient and the time is more complicated and superior in solving the same problem, and the algorithm exhibits good convergence and adaptability. In the conclusion, it is proved that the distributed command and control structure proposed in this paper exhibits high practicability in relevant fields and can solve the problem of distributed command and control structure in a multiagent scenario.
2

Li, Zhipeng, Xiumei Wei, Xuesong Jiang, and Yewen Pang. "A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling." Mathematical Problems in Engineering 2021 (January 12, 2021): 1–12. http://dx.doi.org/10.1155/2021/1796296.

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It is difficult to coordinate the various processes in the process industry. We built a multiagent distributed hierarchical intelligent control model for manufacturing systems integrating multiple production units based on multiagent system technology. The model organically combines multiple intelligent agent modules and physical entities to form an intelligent control system with certain functions. The model consists of system management agent, workshop control agent, and equipment agent. For the task assignment problem with this model, we combine reinforcement learning to improve the genetic algorithm for multiagent task scheduling and use the standard task scheduling dataset in OR-Library for simulation experiment analysis. Experimental results show that the algorithm is superior.
3

Boerkoel Jr., James, and Edmund Durfee. "Decoupling the Multiagent Disjunctive Temporal Problem." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 123–29. http://dx.doi.org/10.1609/aaai.v27i1.8583.

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The Multiagent Disjunctive Temporal Problem (MaDTP) is a general constraint-based formulation for scheduling problems that involve interdependent agents. Decoupling agents' interdependent scheduling problems, so that each agent can manage its schedule independently, requires agents to adopt additional local constraints that effectively subsume their interdependencies. In this paper, we present the first algorithm for decoupling MaDTPs. Our distributed algorithm is provably sound and complete. Our experiments show that the relative efficiency of using temporal decoupling to find solution spaces for MaDTPs, compared to algorithms that find complete solution spaces, improves with the interconnectedness between agents schedules, leading to orders of magnitude relative speeedup. However, decoupling by its nature restricts agents' scheduling flexibility; we define novel flexibility metrics for MaDTPs, and show empirically how the flexibility sacrificed depends on the degree of coupling between agents' schedules.
4

Zhou, Yi, and Weili Xia. "Optimization Algorithm and Simulation of Public Resource Emergency Scheduling Based on Wireless Sensor Technology." Journal of Sensors 2021 (October 8, 2021): 1–10. http://dx.doi.org/10.1155/2021/2450346.

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Public resource scheduling refers to the rational allocation and effective use of resources, while public emergency scheduling refers to the rational allocation and effective use of resources in the context of emergencies. Its main purpose is to reduce casualties and property losses caused by emergencies. This paper mainly studies the emergency scheduling of public resources based on line sensing technology and solves the scheduling problem of public resources through algorithm optimization. Firstly, combined with the positioning algorithm of wireless sensor, this paper optimizes the positioning and detection technology of wireless sensor technology. Then, we design an improved multiagent genetic algorithm (MAGA-MTERS) using natural number coding and design a penalty function to solve the model. Then, the algorithm is compared with the traditional genetic algorithm. The results show that the accurate positioning of wireless sensor technology can improve the efficiency of public resource scheduling and save the scheduling cost. The multiagent genetic algorithm optimizes the positioning function of wireless sensor. Compared with the traditional genetic algorithm, MAGA-MTERS algorithm can obtain a better solution.
5

Boerkoel Jr., J. C., and E. H. Durfee. "Distributed Reasoning for Multiagent Simple Temporal Problems." Journal of Artificial Intelligence Research 47 (May 28, 2013): 95–156. http://dx.doi.org/10.1613/jair.3840.

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This research focuses on building foundational algorithms for scheduling agents that assist people in managing their activities in environments where tempo and complex activity interdependencies outstrip people's cognitive capacity. We address the critical challenge of reasoning over individuals' interacting schedules to efficiently answer queries about how to meet scheduling goals while respecting individual privacy and autonomy to the extent possible. We formally define the Multiagent Simple Temporal Problem for naturally capturing and reasoning over the distributed but interconnected scheduling problems of multiple individuals. Our hypothesis is that combining bottom-up and top-down approaches will lead to effective solution techniques. In our bottom-up phase, an agent externalizes constraints that compactly summarize how its local subproblem affects other agents' subproblems, whereas in our top-down phase an agent proactively constructs and internalizes new local constraints that decouple its subproblem from others'. We confirm this hypothesis by devising distributed algorithms that calculate summaries of the joint solution space for multiagent scheduling problems, without centralizing or otherwise redistributing the problems. The distributed algorithms permit concurrent execution to achieve significant speedup over the current art and also increase the level of privacy and independence in individual agent reasoning. These algorithms are most advantageous for problems where interactions between the agents are sparse compared to the complexity of agents' individual problems.
6

Montana, David, Jose Herrero, Gordon Vidaver, and Garrett Bidwell. "A multiagent society for military transportation scheduling." Journal of Scheduling 3, no. 4 (2000): 225–46. http://dx.doi.org/10.1002/1099-1425(200007/08)3:4<225::aid-jos44>3.0.co;2-r.

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7

Chien, Steve, Minh Do, Alan Fern, and Wheeler Ruml. "Preface." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 21, 2014): xi—xiii. http://dx.doi.org/10.1609/icaps.v24i1.13611.

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The papers in this proceedings present the latest advances in the field of automated planning and scheduling, ranging in scope from theoretical analyses of planning and scheduling problems and processes, to new algorithms for planning and scheduling under various constraints and assumptions, and the empirical evaluation of planning and scheduling techniques. They reflect recent research trends in subareas such as optimal planning, probabilistic and nondeterministic planning, path planning, multiagent planning, and new developments in heuristics and their analysis for planning algorithms.
8

Frankoviè, B., Labátová S., Budinská, and I. "Approach to Scheduling Problem Solution in Production Systems Using the Multiagent System." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 4 (July 20, 2000): 263–67. http://dx.doi.org/10.20965/jaciii.2000.p0263.

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This paper considers job-shop-scheduling problem in multimachine multipart manufacturing systems. The purpose of the article is to contribute to the decision on scheduling rules for job-shop problems. The paper also describes the possibility of utilization of MAS formalism to represent different parts of the production systems and their mutual relations. The interrelations in the multiagent world are examined.
9

Rabelo, Ricardo J. "Interoperating standards in multiagent agile manufacturing scheduling systems." International Journal of Computer Applications in Technology 18, no. 1/2/3/4 (2003): 146. http://dx.doi.org/10.1504/ijcat.2003.002134.

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10

Walker, S. S., R. W. Brennan, and D. H. Norrie. "Holonic Job Shop Scheduling Using a Multiagent System." IEEE Intelligent Systems 20, no. 1 (January 2005): 50–57. http://dx.doi.org/10.1109/mis.2005.9.

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Статті в журналах

Дисертації з теми "Multiagent scheduling":

1

Antoni, Vinicius de. "An asynchronous algorithm to improve scheduling quality in the multiagent simple temporal problem." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/89798.

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Ao tentar agendar uma atividade que dependa da presença de outras pessoas, geralmente acabamos desperdiçando tempo precioso avaliando os possíveis horários e verificando se os mesmos são aceitos por todos envolvidos. Embora a modelagem e a resolução do problema de agendamento multiagente pareçam estar completamente entendidas e ainda diversos algoritmos possam ser encontrados na literatura, uma questão ainda existe: Como definir horários compatíveis para uma atividade compartilhada sem que os usuários tenham que manualmente escolher horários livres de seus calendários até que todos envolvidos aceitem um horário. A principal contribuição é um algoritmo chamado Descobridor Asíncrono de Horários (ATF) baseado no Rastreamento Asíncrono (ABT) que permite que aplicações encontrem horários compatíveis para atividades compartilhadas requerendo mínima intervenção manual dos usuários. Esta dissertação revisita o Problema Temporal Simples (STP) e a sua versão multiagente (MaSTP), demonstra como eles podem ser utilizados para resolver o problema de agentamentos e ao final apresenta o ATF, a avaliação experimental e a análise de complexidade.
In order to schedule an activity that depends on other people, we very often end up wasting precious time trying to find compatible times and evaluating if they are accepted by all involved. Even though modeling and solving multiagent scheduling problems seem completely understood and several algorithms can be found in the literature, one limitation still stands up: How to find a compatible time slot for an activity shared by many users without requiring the users themselves to spend time going through their calendar and choosing time slots until everybody agrees. The main contribution of this work is an algorithm called Asynchronous Time Finder (ATF) based on the Asynchronous Backtracking (ABT) that enables applications to find compatible times when scheduling shared activities among several users while requiring minimal user interaction. This dissertation starts by revisiting the Simple Temporal Problem (STP) and its multiagent version (MaSTP), it then shows how they can be used to solve the problem of managing agendas and then finally it presents the ATF giving an experimental evaluation and the analysis of its complexity.
2

Zhang, Sicheng, and 张思成. "An enhanced ant colony optimization approach for integrating process planning and scheduling based on multi-agent system." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B49618064.

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Process planning and scheduling are two important manufacturing planning functions which are traditionally performed separately and sequentially. Usually, the process plan has to be prepared first before scheduling can be performed. However, due to the complexity of manufacturing systems and the uncertainties and dynamical changes encountered in practical production, process plans and schedules may easily become inefficient or even infeasible. The concept of integrated process planning and scheduling (IPPS) has been proposed to improve the efficiency, effectiveness as well as flexibility of the respective process plan and schedule. By combining both functions together, the process plan for producing a part could be dynamically arranged in accordance with the availability of manufacturing resources and current status of the system, and its operations’ schedule could be determined concurrently. Therefore, IPPS could provide an essential solution to the dynamic process planning and scheduling problem in the practical manufacturing environment. Nevertheless, process planning and scheduling are both complex functions that depend on many factors and flexibilities in the manufacturing system, IPPS is therefore a highly complex NP-hard problem. Ant colony optimization (ACO) is a widely applied meta-heuristics, which has been proved capable of generating feasible solutions for IPPS problem in previous research. However, due to the nature of the ACO algorithm, the performance is not that favourable compared with other heuristics. This thesis presents an enhanced ACO approach for IPPS. The weaknesses and limitations of standard ACO algorithm are identified and corresponding modifications are proposed to deal with the drawbacks and improve the performance of the algorithm. The mechanism is implemented on a specifically designed multi-agent system (MAS) framework in which ants are assigned as software agents to generate solutions. First of all, the manufacturing processes of the parts are graphically formulated as a disjunctive AND/OR graph. In applying the ACO algorithm, ants are deployed to find a path on the disjunctive graph. Such an ant route indicates a corresponding solution with associated operations scheduled by the sequence of ant visit. The ACO in this thesis is enhanced with the novel node selection heuristic and pheromone update strategy. With the node selection heuristic, pheromone is deposited on the nodes as well as edges on the ant path. This is contrast to the conventional ACO algorithm that pheromone is only deposited on edges. In addition, a more reasonable strategy based on “earliest completion time” of operations are used to determine the heuristic desirability of ants, instead of the “greedy” strategy used in standard ACO, which is based on the “shortest processing time”. The approach is evaluated by a comprehensive set of problems with a full set of flexibilities, while multiple performance measurements are considered, including makespan, mean flow time, average machine utilization and CPU time, among which makespan is the major criterion. The results are compared with other approaches and encouraging improvements on solution quality could be observed.
published_or_final_version
Industrial and Manufacturing Systems Engineering
Master
Master of Philosophy
3

Abourraja, Mohamed Nezar. "Gestion multi-agents d'un terminal à conteneurs." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMLH01/document.

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De nos jours, les plateformes portuaires cherchent à massifier leurs capacités de projection de conteneurs vers et à partir de leurs réseaux hinterland en misant sur les modes ferroviaires et fluviaux. Cela pour évacuer plus rapidement un volume quasi croissant de conteneurs livré par voie maritime et d’éviter les situations indésirables, tels que les situations d'asphyxie. De plus, les plateformes portuaires ont pris conscience que leur attractivité aux yeux des prestataires logistiques dépend non seulement de leur fiabilité et de leurs qualités nautiques mais également de leur capacité à offrir une desserte massifiée de leur hinterland. Contrairement à ce qui a pu être observé en Europe, la part du transport massifié a quasiment stagné au Havre dans les dernières années. A cet effet, le port du Havre a mis en place un terminal multimodal de conteneurs lié par rail et par voie navigable à un hinterland riche et dense en population (Bassin parisien, Marchés européens), et par des navettes ferroviaires aux autres terminaux maritimes du port Havre. L’intérêt économique et stratégique de ce nouveau terminal est de renforcer la position du Grand Port Maritime du Havre au niveau national, européen et mondial, et d’un point de vue écologique, diminuer l’utilisation excessive du routier en misant sur les modes moins polluants. Dans cette thèse, les efforts se focalisent sur la modélisation et la simulation du déroulement des opérations de manutention et d’allocation de ressources dans un terminal à conteneurs et particulièrement l’ordonnancement des portiques de manutention. Étant donné qu’un terminal à conteneurs est un système complexe, nous avons d’abord défini une démarche de modélisation qui facilite le processus de construction du modèle de simulation. Cette démarche est un processus itératif permettant de raffiner le modèle au fur et à mesure des étapes de développement réalisées. Les différentes étapes de développement sont liées par une série de diagrammes qui permet d’exprimer de façon claire les éléments et les relations formant le modèle de simulation. Ensuite, nous avons intégré dans notre modèle deux stratégies de non-croisement de portiques au niveau de la cour ferroviaire du terminal multimodal. Le but de ces stratégies est la minimisation des temps et des mouvements improductifs pour améliorer la performance et la productivité des portiques de manutention. La première stratégie est basée sur des règles de mouvement et sur la collaboration et coopération entre agents portiques. Tandis que la deuxième stratégie est basée sur une heuristique. Ces deux solutions ont été testées en utilisant l’outil de simulation AnyLogic et les résultats obtenus montrent la qualité de nos solutions. Concernant le problème d’ordonnancement des portiques de la cour fluviale, nous l’avons étudié en utilisant un couplage Optimisation-Simulation. Dans ce problème les temps de chargement et de déchargement de conteneurs et les temps de déplacement des portiques entre les baies sont considérés comme incertains. Le couplage est composé d’une méta-heuristique colonie de fourmis et d’un modèle de simulation à base d’agents. Chaque solution (une séquence de tâches) trouvée par l’algorithme d'optimisation est simulée et évaluée pour déterminer les nouvelles durées des tâches qui seront ensuite injectées comme données d’entrée de l’algorithme avant l’itération suivante
Nowadays, seaports seek to achieve a better massification share of their hinterland transport by promoting rail and river connections in order to more rapidly evacuate increasing container traffic shipped by sea and to avoid landside congestion. Furthermore, the attractiveness of a seaport to shipping enterprises depends not only on its reliability and nautical qualities but also on its massified hinterland connection capacity. Contrary to what has been observed in Europe, the massification share of Le Havre seaport has stagnated in recent years. To overcome this situation, Le Havre Port Authority is putting into service a multimodal hub terminal. This terminal is linked only with massified modes to a rich and dense geographical regions (Ile de France, Lyon), and with rail shuttles to the maritime terminals of Le Havre seaport. The aim of this new terminal is to restrict the intensive use of roads and to provide a river connection to its maritime terminals (MTs) that do not include a river connection from the beginning. In this study, we focus on the modeling and the simulation of container terminal operations (planning, scheduling, handling …) and particularly crane scheduling in operating areas. Designing multi-agents based simulation models for the operation management of a complex and dynamic system is often a laborious and tedious task, which requires the definition of a modeling approach in order to simplify the design process. In this way, we defined a top-down approach with several steps of specification, conception, implementation and verification-validation. This approach is an iterative process that allows the model to become more complex and more detailed. In this thesis, we pay more attention to crane scheduling problem in operating areas. For the rail-rail transshipment yard of the multimodal terminal, we designed two anti-collision strategies that aim to minimize unproductive times and moves to improve crane productivity and to speed up freight train processing. These strategies are tested using multi-method simulation software (Anylogic) and the simulation results reveal that our solutions are very satisfactory and outperform other existing solutions. With regard the fluvial yard, the stochastic version of crane scheduling problem is studied. The problem is solved with a mixed Optimization-Simulation approach, where the loading and unloading times of containers and travel times of cranes between bays are considered uncertain. The used approach is composed of an Ant Colony Optimization (ACO) metaheuristic coupled to an agent-based simulation model. Each solution (a tasks sequence) found by the optimization algorithm is simulated and evaluated to determine the new tasks’ periods which will then be injected as input to the ACO algorithm before the next iteration. The coupling is executed until the difference between the last iterations is too low
4

Zahout, Boukhalfa. "Algorithmes exacts et approchés pour l'ordonnancement des travaux multiressources à intervalles fixes dans des systèmes distribués : approche monocritère et multiagent." Electronic Thesis or Diss., Tours, 2021. http://www.theses.fr/2021TOUR4003.

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Les travaux de cette thèse proposent un pont entre les méthodes d'optimisation et les problèmes d'ordonnancement de travaux multiressources et à intervalle fixe sur machines parallèles identiques. Les "problèmes d'ordonnancement monocritère" et "problèmes d'ordonnancement multiagent" sont considérés. Nous développons un panel d'ordonnanceurs basés sur des méthodes exactes et approchées pour déterminer des solution réalisables où l'objectif est de maximiser la somme totale pondérée des travaux ordonnancés (ou équivalent, minimisant le coût total pondérée des travaux rejetés), durant un horizon de planification. L'application des méthodes d'optimisation exactes s'avère illusoire dans la pratique en raison du temps de calcul, plus particulièrement lorsqu'il s'agit des systèmes distribués. En revanche ces méthodes exactes serviront comme références pour évaluer les méthodes approchées.La première partie est consacrée à l'étude des problèmes d'ordonnancement monocritère. Après l'analyse de complexité, trois programmes linéaires en nombres entiers (PLNEs), un modèle basé sur la programmation par contrainte (PPC), une méthode hybride entre la PPC et un PLNE sont proposés pour résoudre à l'optimum le problème d'ordonnancement. Nous développons également une méthode exacte de type génération de colonnes basée sur la décomposition de Dantzig-Wolfe qui nous conduit à proposer un algorithme de type Branch & Price. Branch & Price offre de meilleures performances que les autres méthodes exactes sur des instances allant jusqu'à 150 travaux. Pour résoudre des instances de très grandes tailles, des heuristiques de liste basées sur des règles de priorité sont proposées. Afin d'éviter les choix myopes de ces algorithmes gloutons, nous introduisons une heuristique constructive PILOT combinant deux ou plusieurs règles d'ordonnancement et d'affectation ainsi qu'un algorithme évolutionnaire (AE). Les résultats expérimentaux mettent en évidence les performances de PILOT et AE.La seconde partie de nos travaux est dédiée à l'étude de l'ordonnancement multiagent des travaux multiressources à intervalle fixes. Ce modèle considère plusieurs agents associés à des sous-ensembles de travaux disjoints, chacun d'eux cherche à maximiser la somme totale pondérée de ses travaux ordonnancés. Les trois approches suivantes sont considérées : combinaison linéaire des critères, l'approche epsilon-contrainte et l'énumération de l'ensemble des optima de Pareto. Après une analyse de la complexité des problèmes étudiés, des programmes dynamiques polynomiaux ont été développés pour résoudre des cas particuliers. Les fronts optimaux de Pareto sont obtenus par l'approche epsilon-contrainte utilisant le PLNE, la méthode hybride PPC & PLNE ou encore la méthode Branch & Price. Les résultats des expérimentations montrent que les méthodes exactes sont beaucoup moins performantes. Pour résoudre des problèmes de grande taille, des heuristiques de liste et une méthode évolutionnaire de type NSGAII sont développées. Toutes ces méthodes ont été implémentées et testées
The work of this thesis deals with optimization methods and scheduling problems of multiresource and fixed-interval jobs on identical parallel machines. Both "monocriterion scheduling problems" and "multiagent scheduling problems" are considered. We develop a panel of schedulers based on exact and heuristic methods to determine feasible solutions where the objective is to maximize the total weighted sum of scheduled jobs (or equivalently, minimizing the total weighted cost of rejected jobs), during a scheduling horizon. The application of exact optimization methods proves to be illusory in practice due to the computational time, especially when dealing with distributed systems. On the other hand, these exact methods will be used as references to evaluate the heuristic methods.The first part is devoted to the study of monocriterion scheduling problems. After the complexity analysis, three integer linear programs (ILPs), a model based on constraint programming (CP), a hybrid method between CP and a ILP are proposed to solve optimally the scheduling problem. We also develop a column generation method based on the Dantzig-Wolfe decomposition which leads us to propose a Branch & Price algorithm. Branch & Price outperforms other exact methods (solves instances up to 150 jobs). To solve very large instances, heuristics based on priority rules are proposed. To avoid the myopic choices of these greedy algorithms, we design a constructive PILOT heuristic combining two or more scheduling and assignment rules and an evolutionary algorithm (EA). Experimental results highlight the performance of PILOT and AE.The second part of our work is dedicated to the study of multi-agent scheduling problem of fixed intervals multiresource jobs. This model considers several agents associated with disjoint subsets of jobs, each of which seeks to maximize the total weighted of its scheduled jobs. The following three approaches are considered: linear combination of criteria, the epsilon-constraint approach and the enumeration of the set of optimal Pareto solutions. After an analysis of the complexity of the studied problems, polynomial dynamic programming algorithms have been developed to solve particular cases. The optimal Pareto fronts are obtained by the epsilon-constraint approach using the ILP, the hybrid PPC & ILP or the Branch & Price method. Experimental results show that the exact methods are less efficient. To solve large problems, heuristics and an evolutionary methods (NSGA-II) are developed. All these methods have been implemented and tested

Книги з теми "Multiagent scheduling":

1

Agnetis, Alessandro, Jean-Charles Billaut, Stanisław Gawiejnowicz, Dario Pacciarelli, and Ameur Soukhal. Multiagent Scheduling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41880-8.

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2

Billaut, Jean-Charles, Stanisław Gawiejnowicz, and Alessandro Agnetis. Multiagent Scheduling: Models and Algorithms. Springer, 2014.

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3

Billaut, Jean-Charles, Alessandro Agnetis, Dario Pacciarelli, Ameur Soukhal, and Stanisław Gawiejnowicz. Multiagent Scheduling: Models and Algorithms. Springer London, Limited, 2014.

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4

Billaut, Jean-Charles, Stanisław Gawiejnowicz, Alessandro Agnetis, Dario Pacciarelli, and Ameur Soukhal. Multiagent Scheduling: Models and Algorithms. Springer, 2016.

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5

Billaut, Jean-Charles, Stanisław Gawiejnowicz, Alessandro Agnetis, Dario Pacciarelli, and Ameur Soukhal. Multiagent Scheduling: Models and Algorithms. Springer, 2014.

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6

Multiagent Based Beam Search For Realtime Production Scheduling And Control Method Software And Industrial Application. Springer, 2012.

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Частини книг з теми "Multiagent scheduling":

1

Agnetis, Alessandro, Jean-Charles Billaut, Stanisław Gawiejnowicz, Dario Pacciarelli, and Ameur Soukhal. "Multiagent Scheduling Fundamentals." In Multiagent Scheduling, 1–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41880-8_1.

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2

Agnetis, Alessandro, Jean-Charles Billaut, Stanisław Gawiejnowicz, Dario Pacciarelli, and Ameur Soukhal. "Problems, Algorithms and Complexity." In Multiagent Scheduling, 23–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41880-8_2.

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Agnetis, Alessandro, Jean-Charles Billaut, Stanisław Gawiejnowicz, Dario Pacciarelli, and Ameur Soukhal. "Single Machine Problems." In Multiagent Scheduling, 57–145. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41880-8_3.

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4

Agnetis, Alessandro, Jean-Charles Billaut, Stanisław Gawiejnowicz, Dario Pacciarelli, and Ameur Soukhal. "Batching Scheduling Problems." In Multiagent Scheduling, 147–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41880-8_4.

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Agnetis, Alessandro, Jean-Charles Billaut, Stanisław Gawiejnowicz, Dario Pacciarelli, and Ameur Soukhal. "Parallel Machine Scheduling Problems." In Multiagent Scheduling, 189–215. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41880-8_5.

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6

Agnetis, Alessandro, Jean-Charles Billaut, Stanisław Gawiejnowicz, Dario Pacciarelli, and Ameur Soukhal. "Scheduling Problems with Variable Job Processing Times." In Multiagent Scheduling, 217–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41880-8_6.

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7

Yadati, Chetan, Cees Witteveen, Yingqian Zhang, Mengxiao Wu, and Han la Poutre. "Autonomous Scheduling with Unbounded and Bounded Agents." In Multiagent System Technologies, 195–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87805-6_18.

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Rabelo, Ricardo J., Luis M. Camarinha-Matos, and Hamideh Afsarmanesh. "Multiagent Perspectives to Agile Scheduling." In Intelligent Systems for Manufacturing, 51–66. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-0-387-35390-6_5.

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Sharma, Dharmendra, and Dat Tran. "Dynamic Scheduling Using Multiagent Architecture." In Lecture Notes in Computer Science, 476–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30133-2_62.

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Mao, Xiaoyu, Adriaan ter Mors, Nico Roos, and Cees Witteveen. "Coordinating Competitive Agents in Dynamic Airport Resource Scheduling." In Multiagent System Technologies, 133–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74949-3_12.

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Тези доповідей конференцій з теми "Multiagent scheduling":

1

Dolgov, Dmitri A., Michael R. James, and Michael E. Samples. "Combinatorial resource scheduling for multiagent MDPs." In the 6th international joint conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1329125.1329369.

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"A MULTIAGENT SYSTEM FOR JOB-SHOP SCHEDULING." In 10th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001705301480153.

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3

Shibghatullah, Abdul, Tillal Eldabi, and Jasna Kuljis. "A Proposed Multiagent Model for Bus Crew Scheduling." In 2006 Winter Simulation Conference. IEEE, 2006. http://dx.doi.org/10.1109/wsc.2006.322926.

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4

"MULTIAGENT DESIGN FOR DYNAMIC JOB-SHOP SCHEDULING USING PASSI." In 4th International Conference on Web Information Systems and Technologies. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001528502880291.

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5

Medina, Gisela, and Leonardo Garrido. "Predictive Negotiation Strategy Applied to Multiagent Meeting Scheduling Problems." In 2008 Seventh Mexican International Conference on Artificial Intelligence (MICAI). IEEE, 2008. http://dx.doi.org/10.1109/micai.2008.54.

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Kamla, Vivient Corneille, Jean Etienne Ndamlabin Mboula, Jeremie Serge Wouansi Towo, and Clementin Tayou Djamegni. "Grid's Acquaintance-Based Multiagent Model of Distributed Meta-Scheduling." In 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). IEEE, 2016. http://dx.doi.org/10.1109/sitis.2016.55.

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Muneeswari, G., Dr K. L. Shunmuganathan, and A. Sobitha Ahila. "A Novel Approach to Multiagent based Scheduling for Multicore Architecture." In Annual International Conference on Advances in Distributed and Parallel Computing ADPC 2010. Global Science and Technology Forum, 2010. http://dx.doi.org/10.5176/978-981-08-7656-2_a-06.

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Mussawar, Osama, and Khaled Al-Wahedi. "Meeting scheduling using agent based modeling and multiagent decision making." In 2013 Third International Conference on Innovative Computing Technology (INTECH). IEEE, 2013. http://dx.doi.org/10.1109/intech.2013.6653655.

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9

Fazlirad, Alireza, and Robert W. Brennan. "Multiagent Manufacturing Scheduling: An Updated State of the Art Review." In 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE). IEEE, 2018. http://dx.doi.org/10.1109/coase.2018.8560576.

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Filippou, A., Dimitrios A. Karras, and I. Tsatrafyllis. "On Efficient Scheduling Schemes in Multiagent Wireless Sensor Networks Simulation Systems." In 2019 27th Telecommunications Forum (TELFOR). IEEE, 2019. http://dx.doi.org/10.1109/telfor48224.2019.8971316.

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