Ant Colony Optimization For Multiobjective Buffers Sizing Problems

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Hybrid Optimisation Method for the Facility Layout Problem

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Hybrid Job Shop and Parallel Machine Scheduling Problems: Minimization of Total Tardiness Criterion

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Implementing intelligent asset management systems (IAMS) within an industry 4.0 manufacturing environment

9th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2019; Berlin; Germany; 28 August 2019 through 30 August 2019. Publicado en IFAC-PapersOnLine 52(13), p. 2488-2493This paper aims to define the different considerations and results obtained in the implementation in an Intelligent Maintenance System of a laboratory designed based on basic concepts of Industry 4.0. The Intelligent Maintenance System uses asset monitoring techniques that allow, on-line digital modelling and automatic decision making. The three fundamental premises used for the development of the management system are the structuring of information, value identification and risk management

Production and Logistic Optimization: Parallel machines scheduling and total tardiness

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Parallel machines scheduling: Data Mining analysis for resolution methods

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A New technique for solution space exploration

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Approches industriels et académiques de projet de recherche : problèmes d’ordonnancement

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A Branch-and-Bound to solve the scheduling problem Pm/r_j/ΣT_j

International audienceThis paper presents the first study for the total tardiness minimization on parallel machine scheduling problem with different release dates of jobs. In this problem, a set of jobs has to be scheduled, without any preemption or splitting, on identical parallel machine. Each job has a due date and a release date. A polynomial lower bound is proposed using a relaxation procedure. A set of dominance properties are also established. A branch and bound algorithm is developed by taking into account the theoretical properties, lower and also upper bounds. This exact method has been tested on a large number of randomly generated problems. The obtained results are promising

Production and logistics optimization : Parallel machines scheduling

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