Distributed scheduling based on multi-agent systems and optimization methods

The increasing relevance of complex systems in dynamic environments has received special attention during the last decade from the researchers. Such systems need to satisfy products or clients desires, which, after accomplished might change, becoming a very dynamic situation. Currently, decentralized approaches could assist in the automation of dynamic scheduling, based on the distribution of control functions over a swarm network of decision-making entities. Distributed scheduling, in an automatic manner, can be answered by a service coordination architecture of the different schedule components. However, it is necessary to introduce the control layer in the solution, encapsulating an intelligent service that merge agents with optimization methods. Multi-agent systems (MAS) can be combined with several optimization methods to extract the best of the two worlds: the intelligent control, cooperation and autonomy provided by MAS solutions and the optimum offered by optimization methods. The proposal intends to test the intelligent management of the schedule composition quality, in two case studies namely, manufacturing and home health care.FCT - Fundação para a Ciência e a Tecnologia (UID/CEC/00319/2019

Production Scheduling Requirements to Smart Manufacturing

The production scheduling has attracted a lot of researchers for many years, however most of the approaches are not targeted to deal with real manufacturing environments, and those that are, are very particular for the case study. It is crucial to consider important features related with the factories, such as products and machines characteristics and unexpected disturbances, but also information such as when the parts arrive to the factory and when should be delivered. So, the purpose of this paper is to identify some important characteristics that have been considered independently in a lot of studies and that should be considered together to develop a generic scheduling framework to be used in a real manufacturing environment.authorsversionpublishe

Optimization of prednisolone acetate-loaded chitosan microspheres using a 23 factorial design for preventing restenosis

PubMed ID: 20196705Prednisolone acetate (PA)-loaded microspheres were prepared by the spray-drying technique using different polymer (1% and 2%) and drug concentrations (10% and 20%). To obtain the optimum formulation, a three-factor two-level (23) design was employed. The independent variables were polymer molecular weight, polymer concentration, and theoretical drug loading. Responses were the particle size, percentage of encapsulation efficiency, and the t50% release. The best formulation was prepared with 20% of PA and 1% of chitosan with medium molecular weight showing relative good yield of production (48.0±6.7%) and encapsulation efficiency (45.7±0.3%), and released the drug at a constant rate in 11 days. © 2010 Informa UK Ltd.302301007This study was supported by Hacettepe University Research Fund (Project No: 0302301007). The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper. -

Hybrid system for simultaneous job shop scheduling and layout optimization based on multi-agents and genetic algorithm

A challenge is emerging in the design of scheduling support systems and facility layout planning, both for manufacturing environments where dynamic adaptation and optimization become increasingly important on the efficiency and productivity. Focusing on the interactions between these two problems, this work combines two paradigms in sequential manner, optimization techniques and multi-agent systems, to better reflect practical manufacturing scenarios. This approach, in addition to significantly improve the quality of the solutions, enables fast reaction to condition changes. In such stochastic and very volatile environments, the manufacturing industries, the fast rescheduling, or planning, are crucial to maintain the system in operation. The proposed architecture was codified in MatLab $$^{\tiny {\textregistered }}$$ and NetLogo and applied to a real-world job shop case study. The experimental results achieved optimized solutions, as well as in the responsiveness to achieve dynamic results for disruptions and simultaneously layout optimization.This work has been supported by FCT Fundação para a Ciência e Tecnologia under the Project: PEst2015-2020. This work was also supported by COMPETE:POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013

Support vector machine as tool for classifying coffee beverages

Classifiers are tools widely used nowadays to process data and obtain prediction models that are trained through supervised learning techniques; there is a wide variety of sensors that acquire the data to be processed, such as the voltammetric electronic tongue, as a device employed to analyze food compounds. This paper presents a normal and decaffeinated coffee beverage classifier using a Support Vector Machine with a linear separation function, detailing the classification function and the model optimization method; to train the model, the data measured by 4 electrodes of a voltammetric tongue that is excited by a predetermined sequence of positive pulses is used. In addition, the results graphically show the measurements obtained, the support vectors and the evaluation data, the values of the classifier parameters are also presented. Finally, the conclusions establish an acceptable error in the classification of coffee drinks according to caffeine presence at the sample analyzed. © Springer Nature Switzerland AG 2020

Dynamic dispatching priority setting in customer-oriented manufacturing environments

In today's competitive environment, customer-oriented view is essential in gaining sustainable competitive advantage. This study aims to reflect the customer-oriented view to production planning and control decisions. To this aim, a simulation optimization-based approach is developed for job shop systems with dynamic order arrivals. Product-type-based lot splitting is applied in order to improve the flow time, and machine-based dispatching rules are utilized for sublot scheduling to realize dynamic scheduling. Multiple customer segments with different importance weights and their expectations and penalties on order completion rate on due date, tardiness, and earliness are considered. A customer satisfaction-based objective function is defined. Customer-oriented dispatching rules are proposed in this study to ensure the prioritization of orders from the key customers in order fulfilling. In order to prevent customer losses by providing a balanced structure between the customer segments in terms of the satisfaction levels, weight setting functions that dynamically compute the weights in the proposed dispatching rules are proposed. It is aimed to determine the near-optimal values of the segment-based parameters of the related weight setting functions. To this aim, a differential evolution algorithm-based simulation optimization approach is proposed. To confirm its viability, the proposed approach is applied to a realistic job shop system