Javier Puente

Prof. Javier Puente

Department of Business Administration, University of Oviedo, 33003 Oviedo, Spain
orcid

Research Focus & Metrics

Academic Career Length

26Years (2001-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 March 2002 in International Journal of Quality & Reliability Management
Verified
Journal Article
Closed access
JPJavier Puente
Javier Puente
RPRaúl Pino
Raúl Pino
PPPaolo Priore
Paolo Priore
DFDavid de la Fuente
David de la Fuente
This study describes an alternative way of applying failure mode and effects analysis (FMEA) to a wide variety of problems. It presents a methodology based on a decision system supported by qualitative rules which provides a ranking of the risks of potential causes of production system failures. By providing an illustrative example, it highlights the advantages of this flexible system over the traditional FMEA model. Finally, a fuzzy decision model is proposed, which improves the initial decision system by introducing the element of uncertainty.

1 December 2018 in Computers & Industrial Engineering
Verified
Journal Article
Closed access
PPPaolo Priore
Paolo Priore
BPBorja Ponte
Borja Ponte
JPJavier Puente
Javier Puente
AGAlberto Gomez
Alberto Gomez

1 April 2006 in Engineering Applications of Artificial Intelligence
Verified
Journal Article
Closed access
PPPaolo Priore
Paolo Priore
DFDavid de la Fuente
David de la Fuente
JPJavier Puente
Javier Puente
JPJosé Parreño
José Parreño

1 June 2001 in Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Verified
Journal Article
Closed access
PDF file
PPPaolo Priore
Paolo Priore
DFDavid DE LA Fuente
David DE LA Fuente
AGAlberto Gomez
Alberto Gomez
JPJavier Puente
Javier Puente
A common way of dynamically scheduling jobs in a flexible manufacturing system (FMS) is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no single rule exists that is better than the rest in all the possible states that the system may be in. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning can be used. Analyzing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a review of the main machine learning-based scheduling approaches described in the literature is presented.

1 May 2009 in Computers & Industrial Engineering
Verified
Journal Article
Closed access
JPJavier Puente
Javier Puente
AGAlberto Gomez
Alberto Gomez
Isabel fernandezIsabel fernandez
Isabel fernandez
PPPaolo Priore
Paolo Priore

4 May 2018 in International Journal of Production Research
Verified
Journal Article
Closed access
PVPablo Valledor
Pablo Valledor
AGAlberto Gomez
Alberto Gomez
PPPaolo Priore
Paolo Priore
JPJavier Puente
Javier Puente
In multi-objective optimisation problems, optimal decisions need to be made in the presence of trade-offs among conflicting objectives which may sometimes be expressed in different units of measure. This makes it difficult to reduce the problem to a single-objective optimisation. Furthermore, when disruptive changes emerge in manufacturing environments, such as the arrival of new jobs or machine breakdowns, the scheduling system should be adapted by responding quickly. In this paper, we propose a rescheduling architecture for solving the problem based on a predictive-reactive strategy and a new method to calculate the reactive schedule in each rescheduling period. Additionally, we developed a methodology that allows the use of multi-objective performance metrics to evaluate dispatching rules. These rules are applied at a benchmark specifically designed for this paper considering three objective functions: makespan, total weighted tardiness and stability. Three types of disruptions are also considered: arrivals of new jobs, machine breakdowns and variations in job processing times. Results showed that the RANDOM rule provides a better behaviour compared to other evaluated rules and a lower ratio of non-dominated solutions compared to ATC (apparent tardiness cost) and FIFO (first-in-first-out) rules. Moreover, the behaviour of the hypervolume metric depends on the problem dimensions.