Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns

Nouiri, Maroua; Bekrar, Abdelghani; Jemai, Abderrazak; Trentesaux, Damien; Ammari, Ahmed Chiheb; Niar, Smaïl

doi:10.1016/j.cie.2017.03.006

Cited by 104 publications

(39 citation statements)

References 34 publications

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“…Plusieurs méthodes de ré-ordonnancement ont été proposées. Par exemple, pour le problème de type job shop classique, nous avons identifié la méthode de décalage à droite, le réordonnancement total et le ré-ordonnancement partiel (Nouiri et al, 2017). Avant de présenter la technique de ré-ordonnancement proposée, nous détaillons le type de perturbation considéré.…”

Section: Figure 4 Pseudo Code Du Comportement En Ligne De L'agent Aouunclassified

EasySched : une architecture multi-agent pour l’ordonnancement prédictif et réactif de systèmes de production de biens en fonction de l’énergie renouvelable disponible dans un contexte industrie 4.0

Nouiri¹,

Trentesaux²,

Bekrar³

2019

induProd

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L'industrie 4.0 s'accompagne de la prise en compte de contraintes de développement durable. Dans ce contexte, nous proposons une architecture multi-agent pour l'ordonnancement prédictif et réactif coordonné entre des systèmes de production de biens et des systèmes de production d'énergie renouvelable, appelée EasySched. La validation de cette architecture est originale, elle est menée de manière complètement et physiquement distribuée en utilisant des systèmes embarqués en réseau. Cette validation est menée sur une série d'instances inspirées de la littérature. Les résultats montrent que les mécanismes proposés permettent d'adapter la production selon l'énergie renouvelable disponible.

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Section: Figure 4 Pseudo Code Du Comportement En Ligne De L'agent Aouunclassified

EasySched : une architecture multi-agent pour l’ordonnancement prédictif et réactif de systèmes de production de biens en fonction de l’énergie renouvelable disponible dans un contexte industrie 4.0

Nouiri¹,

Trentesaux²,

Bekrar³

2019

induProd

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“…However, the makespan of a schedule will be affected by RMDs in practice [16][17][18]. Since RMDs postpone the completion time of operations, the actual makespan C r max of a schedule will be delayed.…”

Section: Problem Definitionmentioning

confidence: 99%

“…The values of the input variant vector x i can be determined once a schedule s i is generated based on the ith individual in the initial population P 0 . Since the EMD cannot be analytically calculated, it will be evaluated by the Monte Carlo approximation ∆ sim c as shown in Equation (17). After the training data set D c is constructed, the Multiple Linear Regression will be used to determine the coefficients of the meta-model ∆ a c for the EMD,…”

Section: Meta-model Of the Emdmentioning

confidence: 99%

“…a sudden strike, about which no a-priori information is available, while suspicions about the future arise from the intuition and experience of the human scheduler. On the other hand, known uncertainties are those events about which some information is available in advance, such as machine breakdowns [16][17][18] whose frequency and duration may be characterized by probability distributions. Under these uncertainties, a schedule will be difficult to execute as planned, and finally the actual performance of the schedule will deteriorate.…”

Section: Introductionmentioning

confidence: 99%

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A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling

2019

Mathematics

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In the real-world manufacturing system, various uncertain events can occur and disrupt the normal production activities. This paper addresses the multi-objective job shop scheduling problem with random machine breakdowns. As the key of our approach, the robustness of a schedule is considered jointly with the makespan and is defined as expected makespan delay, for which a meta-model is designed by using a data-driven response surface method. Correspondingly, a multi-objective evolutionary algorithm (MOEA) is proposed based on the meta-model to solve the multi-objective optimization problem. Extensive experiments based on the job shop benchmark problems are conducted. The results demonstrate that the Pareto solution sets of the MOEA are much better in both convergence and diversity than those of the algorithms based on the existing slack-based surrogate measures. The MOEA is also compared with the algorithm based on Monte Carlo approximation, showing that their Pareto solution sets are close to each other while the MOEA is much more computationally efficient.

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“…Predictive role: This off-line role consists of calculating a predictive schedule that minimizes makespan and energy consumption. The predictive solution is determined by a particle swarm optimization (PSO) algorithm proposed in [54] where a particle represents a potential solution to the problem. Each of these particles is characterized by a position vector, a velocity that allows the particle to move and a neighbourhood, a set of particles (neighbours), that interact directly with the particle, in particular that which has the best criterion.…”

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confidence: 99%

Towards Energy Efficient Scheduling of Manufacturing Systems through Collaboration between Cyber Physical Production and Energy Systems

2019

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Currently, enhancing sustainability, and in particular reducing energy consumption, is a huge challenge for manufacturing enterprises. The vision of the fourth industrial revolution (so-called “industry 4.0”) is not only to optimize production and minimize costs, but also to reduce energy consumption and enhance product life-cycle management. To address this challenge, a multi-agent architecture aimed at elaborating predictive and reactive energy-efficient scheduling through collaboration between cyber physical production and energy systems is proposed in this paper. Smart, sustainable decision tools for cyber physical production systems (CPPS) and cyber physical energy systems (CPES) are proposed. The decision tools are data-driven, agent-based models with dynamic interaction. The main aim of agent behaviours in the cyber part of CPPS is to find a predictive and reactive energy-efficient schedule. The role of agents in CPES is to control the energy consumption of connected factories and switch between the different renewable energy sources. Dynamic mechanisms in CPPS and CPES are proposed to adjust the energy consumption of production systems based on the availability of the renewable energy. The proposed approach was validated on a physically distributed architecture using networked embedded systems and real-time data sharing from connected sensors in each cyber physical systems. A series of instances inspired from the literature were tested to assess the performance of the proposed method. The results prove the efficiency of the proposed approach in adapting the energy consumption of connected factories based on a real-time energy threshold.

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Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns

Cited by 104 publications

References 34 publications

EasySched : une architecture multi-agent pour l’ordonnancement prédictif et réactif de systèmes de production de biens en fonction de l’énergie renouvelable disponible dans un contexte industrie 4.0

EasySched : une architecture multi-agent pour l’ordonnancement prédictif et réactif de systèmes de production de biens en fonction de l’énergie renouvelable disponible dans un contexte industrie 4.0

A Meta-Model-Based Multi-Objective Evolutionary Approach to Robust Job Shop Scheduling

Towards Energy Efficient Scheduling of Manufacturing Systems through Collaboration between Cyber Physical Production and Energy Systems

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