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The evaluation of performance of public transportation, such as bus lines for example, is a major issue for operators. To be able to integrate specific and local behaviors, microscopic simulations of the lines, modelling each buses on a daily basis, brings an actual added value in terms of precision and quality. A scientific deadlock then appears regarding the parameterization of the simulation model. In order to be able to gather relevant performance indicators on a potential evolution of the configuration of the line, validated and modifiable simulation models need to be developed. This study aims at proposing a model development methodology based on a multi-agent simulation framework and data inputs extracted by a hybrid approach combining machine learning (ML) trained on actual bus data to predict travel times and probabilistic distributions to accurately estimate travel time variability. It also aims to propose a two-step validation framework that exhibits the performance of the obtained model on a case study based on actual data. The results of the proposed approach are validated by a real case study of three bus lines, including a number of simulation scenarios, to study the impacts of bus recovery time and bus control strategies on bus punctuality. The results obtained show that proposed hybrid approach combining ML with probabilistic distributions outperforms probabilistic distributions on average. Overall, the results show a good fit with the actual Key Performance Indicator (KPI) used by bus operators. [ABSTRACT FROM AUTHOR]

Any manufacturing activity creates product and energy flows whose management provides an overview of how a manufacturing system interacts with its environment. Recently, simultaneous management of these flows using discrete event simulation is increasingly being discussed in literature. However, the reliability of such projects basically depends on the method used to model energy consumption and quality of input data. Mindful of these challenges, this work reviews some existing energy related discrete-event simulation projects and discuss the issue of energy related input data management. Against this background, it proposes an operation-based concept to model the energy consumption of a manufacturing system and put an emphasis on how the load profiles could be acquired, processed and introduced into the simulation model, depending on its variabilities and the complexity of the associated energy-using equipment. The main proposal of this article is to introduce the possibility to model complex energy consumption behaviours of various pieces of equipment using stochastic distributions. The case of an injection moulding system is presented to validate the proposed method and to evaluate its accuracy. A comparison with actual measurements of energy consumption shows a deviation of less than 3% of the discrete-event simulation model. [ABSTRACT FROM AUTHOR]

Holonic and Service-Oriented Architectures have been proposed as solutions for the conception of flexible and reactive systems. The combination of both architectures has been recognized as an attractive solution for the conception of more flexible and reactive systems. Services, originally conceived for web applications, need new models that better adapt to the needs of manufacturing systems namely, the specification and planning of processes. This paper proposes a methodology for designing customizable product-process specifications based on manufacturing-services that are suitable for product driven applications. A framework is proposed for modeling manufacturing-services designed for its application in Holonic Manufacturing Systems, preserving the fractal character found in products and processes. Three types of manufacturing processes are identified based on the relations among its composing operations and a model is proposed for each type. Application service-ontologies are created to describe operations and resource capabilities rather than complex semantic descriptions to facilitate process design and reduce the computational load of service discovery for planning. The framework leverages service reusability among process families in the same way as physical features are reused among product families. An illustrative example describes the design of a product's process specification based on the manufacturing service framework in a Holonic Manufacturing System, giving rise to Service-oriented Holonic Manufacturing Systems. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the use of online simulation on Holonic manufacturing systems. Concepts needed for the use of online simulation in a classical hierarchical system were already defined, the observer being the central one. The behavior''s differences between both classes of systems are studied to determine the best way to adapt these concepts to this new environment. In the product resource order staff approach (PROSA) reference architecture, staff holons were chosen to welcome the simulation models and the observer. An application on an industrial sized Holonic manufacturing system is described to demonstrate the validity of the approach. [Copyright &y& Elsevier]