Case-based reasoning for meta-heuristics self-parameterization in a multi-agent scheduling system

A novel agent-based approach to Meta-Heuristics self-configuration is proposed in this work. Meta-heuristics are examples of algorithms where parameters need to be set up as efficient as possible in order to unsure its performance. This paper presents a learning module for self-parameterization of Meta-heuristics (MHs) in a Multi-Agent System (MAS) for resolution of scheduling problems. The learning is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. In the end, some conclusions are reached and future work outlined.

Meta-heuristics self-parameterization in a multi-agent scheduling system using case-based reasoning

This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-Agent System (MAS) for resolution of scheduling problems is proposed in this work. The learning module is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. Finally, some conclusions are reached and future work outlined.

Examination timetabling automation using hybrid meta-heuristics

Trabalho de projeto realizado para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Meta-heuristics self-configuration for scheduling

Scheduling resolution requires the intervention of highly skilled human problemsolvers. This is a very hard and challenging domain because current systems are becoming more and more complex, distributed, interconnected and subject to rapidly changing. A natural Autonomic Computing evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference. This paper addresses the resolution of complex scheduling problems using cooperative negotiation. A Multi-Agent Autonomic and Meta-heuristics based framework with self-configuring capabilities is proposed.

Multi-apprentice learning for meta-heuristics parameter tuning in a multi agent scheduling system

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

Meta-heuristics tunning using CBR for dynamic scheduling

This paper addresses the problem of Biological Inspired Optimization Techniques (BIT) parameterization, considering the importance of this issue in the design of BIT especially when considering real world situations, subject to external perturbations. A learning module with the objective to permit a Multi-Agent Scheduling System to automatically select a Meta-heuristic and its parameterization to use in the optimization process is proposed. For the learning process, Casebased Reasoning was used, allowing the system to learn from experience, in the resolution of similar problems. Analyzing the obtained results we conclude about the advantages of its use.

Cooperation mechanism for team-work based multi-agent system in dynamic scheduling through meta-heuristics

This paper describes a Multi-agent Scheduling System that assumes the existence of several Machines Agents (which are decision-making entities) distributed inside the Manufacturing System that interact and cooperate with other agents in order to obtain optimal or near-optimal global performances. Agents have to manage their internal behaviors and their relationships with other agents via cooperative negotiation in accordance with business policies defined by the user manager. Some Multi Agent Systems (MAS) organizational aspects are considered. An original Cooperation Mechanism for a Team-work based Architecture is proposed to address dynamic scheduling using Meta-Heuristics.

Simulation model using meta heuristic algorithms for achieving optimal arrangement of storage bins in a sawmill yard

Bin planning (arrangements) is a key factor in the timber industry. Improper planning of the storage bins may lead to inefficient transportation of resources, which threaten the overall efficiency and thereby limit the profit margins of sawmills. To address this challenge, a simulation model has been developed. However, as numerous alternatives are available for arranging bins, simulating all possibilities will take an enormous amount of time and it is computationally infeasible. A discrete-event simulation model incorporating meta-heuristic algorithms has therefore been investigated in this study. Preliminary investigations indicate that the results achieved by GA based simulation model are promising and better than the other meta-heuristic algorithm. Further, a sensitivity analysis has been done on the GA based optimal arrangement which contributes to gaining insights and knowledge about the real system that ultimately leads to improved and enhanced efficiency in sawmill yards. It is expected that the results achieved in the work will support timber industries in making optimal decisions with respect to arrangement of storage bins in a sawmill yard.

Heuristics and meta-heuristics for lot sizing and scheduling in the soft drinks industry: A comparison study

This chapter studies a two-level production planning problem where, on each level, a lot sizing and scheduling problem with parallel machines, capacity constraints and sequence-dependent setup costs and times must be solved. The problem can be found in soft drink companies where the production process involves two interdependent levels with decisions concerning raw material storage and soft drink bottling. Models and solution approaches proposed so far are surveyed and conceptually compared. Two different approaches have been selected to perform a series of computational comparisons: an evolutionary technique comprising a genetic algorithm and its memetic version, and a decomposition and relaxation approach. © 2008 Springer-Verlag Berlin Heidelberg.

Gestion adaptative des ressources dans les réseaux maillés sans fil à multiples-radios multiples-canaux

Depuis quelques années, la recherche dans le domaine des réseaux maillés sans fil ("Wireless Mesh Network (WMN)" en anglais) suscite un grand intérêt auprès de la communauté des chercheurs en télécommunications. Ceci est dû aux nombreux avantages que la technologie WMN offre, telles que l'installation facile et peu coûteuse, la connectivité fiable et l'interopérabilité flexible avec d'autres réseaux existants (réseaux Wi-Fi, réseaux WiMax, réseaux cellulaires, réseaux de capteurs, etc.). Cependant, plusieurs problèmes restent encore à résoudre comme le passage à l'échelle, la sécurité, la qualité de service (QdS), la gestion des ressources, etc. Ces problèmes persistent pour les WMNs, d'autant plus que le nombre des utilisateurs va en se multipliant. Il faut donc penser à améliorer les protocoles existants ou à en concevoir de nouveaux. L'objectif de notre recherche est de résoudre certaines des limitations rencontrées à l'heure actuelle dans les WMNs et d'améliorer la QdS des applications multimédia temps-réel (par exemple, la voix). Le travail de recherche de cette thèse sera divisé essentiellement en trois principaux volets: le contrôle d‟admission du trafic, la différentiation du trafic et la réaffectation adaptative des canaux lors de la présence du trafic en relève ("handoff" en anglais). Dans le premier volet, nous proposons un mécanisme distribué de contrôle d'admission se basant sur le concept des cliques (une clique correspond à un sous-ensemble de liens logiques qui interfèrent les uns avec les autres) dans un réseau à multiples-sauts, multiples-radios et multiples-canaux, appelé RCAC. Nous proposons en particulier un modèle analytique qui calcule le ratio approprié d'admission du trafic et qui garantit une probabilité de perte de paquets dans le réseau n'excédant pas un seuil prédéfini. Le mécanisme RCAC permet d‟assurer la QdS requise pour les flux entrants, sans dégrader la QdS des flux existants. Il permet aussi d‟assurer la QdS en termes de longueur du délai de bout en bout pour les divers flux. Le deuxième volet traite de la différentiation de services dans le protocole IEEE 802.11s afin de permettre une meilleure QdS, notamment pour les applications avec des contraintes temporelles (par exemple, voix, visioconférence). À cet égard, nous proposons un mécanisme d'ajustement de tranches de temps ("time-slots"), selon la classe de service, ED-MDA (Enhanced Differentiated-Mesh Deterministic Access), combiné à un algorithme efficace de contrôle d'admission EAC (Efficient Admission Control), afin de permettre une utilisation élevée et efficace des ressources. Le mécanisme EAC prend en compte le trafic en relève et lui attribue une priorité supérieure par rapport au nouveau trafic pour minimiser les interruptions de communications en cours. Dans le troisième volet, nous nous intéressons à minimiser le surcoût et le délai de re-routage des utilisateurs mobiles et/ou des applications multimédia en réaffectant les canaux dans les WMNs à Multiples-Radios (MR-WMNs). En premier lieu, nous proposons un modèle d'optimisation qui maximise le débit, améliore l'équité entre utilisateurs et minimise le surcoût dû à la relève des appels. Ce modèle a été résolu par le logiciel CPLEX pour un nombre limité de noeuds. En second lieu, nous élaborons des heuristiques/méta-heuristiques centralisées pour permettre de résoudre ce modèle pour des réseaux de taille réelle. Finalement, nous proposons un algorithme pour réaffecter en temps-réel et de façon prudente les canaux aux interfaces. Cet algorithme a pour objectif de minimiser le surcoût et le délai du re-routage spécialement du trafic dynamique généré par les appels en relève. Ensuite, ce mécanisme est amélioré en prenant en compte l‟équilibrage de la charge entre cliques.

Evolutionary multi-move path-relinking for transmission network expansion planning

This paper presents the application of a new metaheuristic algorithm to solve the transmission expansion planning problem. A simple heuristic, using a relaxed network model associated with cost perturbation, is applied to generate a set of high quality initial solutions with different topologies. The population is evolved using a multi-move path-relinking with the objective of finding minimum investment cost for the transmission expansion planning problem employing the DC representation. The algorithm is tested on the southern Brazilian system, obtaining the optimal solution for the system with better performance than similar metaheuristics algorithms applied to the same problem. ©2010 IEEE.

Hybrid Algorithms for Routing and Assignment Wavelengths in Optical Networks

This paper presents a strategy for the solution of the WDM optical networks planning. Specifically, the problem of Routing and Wavelength Allocation (RWA) in order to minimize the amount of wavelengths used. In this case, the problem is known as the Min-RWA. Two meta-heuristics (Tabu Search and Simulated Annealing) are applied to take solutions of good quality and high performance. The key point is the degradation of the maximum load on the virtual links in favor of minimization of number of wavelengths used; the objective is to find a good compromise between the metrics of virtual topology (load in Gb/s) and of the physical topology (quantity of wavelengths). The simulations suggest good results when compared to some existing in the literature.

Otimização de problemas multimodais usando meta-heurísticas evolutivas

Pós-graduação em Engenharia Elétrica - FEIS

Meta-heurísticas para o problema de planejamento de expansão da rede de transmissão de energia elétrica considerando restrições de segurança

Pós-graduação em Engenharia Elétrica - FEIS

MASDScheGATS: a prototype system for dynamic scheduling

A manufacturing system has a natural dynamic nature observed through several kinds of random occurrences and perturbations on working conditions and requirements over time. For this kind of environment it is important the ability to efficient and effectively adapt, on a continuous basis, existing schedules according to the referred disturbances, keeping performance levels. The application of Meta-Heuristics and Multi-Agent Systems to the resolution of this class of real world scheduling problems seems really promising. This paper presents a prototype for MASDScheGATS (Multi-Agent System for Distributed Manufacturing Scheduling with Genetic Algorithms and Tabu Search).