Towards Energy Efficient Scheduling and Rescheduling for Dynamic Flexible Job Shop Problem

Nouiri, Maroua; Bekrar, Abdelghani; Trentesaux, Damien

doi:10.1016/j.ifacol.2018.08.357

Cited by 49 publications

(17 citation statements)

References 16 publications

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“…The rescheduling problem is of great importance. Researchers have focused on profitability and sustainability to construct a new class of rescheduling methods namely energy-efficient rescheduling (EER) methods [42]. Other studies have focused on the rescheduling problem in a dynamic job-shop environment in which the production resources work at various rates.…”

Section: Dynamic Energy Efficient Scheduling Methodsmentioning

confidence: 99%

“…The authors also proposed a genetic algorithm-based approach for rescheduling. Nouiri et al [42] proposed a green rescheduling method (GRM), an extended version of the work in Nouiri et al [45]. The GRM takes into account energy efficiency when solving the flexible job shop rescheduling problem.…”

Section: Dynamic Energy Efficient Scheduling Methodsmentioning

confidence: 99%

“…However they do not take into account the sustainable aspect. This will also impose radical changes in the way scheduling repairing approaches are designed and implemented [42]. Before presenting the proposed rescheduling techniques, the type of perturbation considered is detailed.…”

Section: Rescheduling Methods In the Smart Sustainable Tool Of Cppsmentioning

confidence: 99%

“…In order to asses the performance of the rescheduling method, performance indicators (KPI) had be defined. In fact, in order to compare the quality of the initial schedule (predictive) and the reactive one (after rescheduling), it is necessary to define two types of performance measurements depending on the evolution of the effectiveness or the efficiency [42]. They are presented hereinafter.…”

Section: Rescheduling Performance Measuresmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Dynamic Energy Efficient Scheduling Methodsmentioning

confidence: 99%

Section: Dynamic Energy Efficient Scheduling Methodsmentioning

confidence: 99%

Section: Rescheduling Methods In the Smart Sustainable Tool Of Cppsmentioning

confidence: 99%

Section: Rescheduling Performance Measuresmentioning

confidence: 99%

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

2019

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“…Wu and Sun [50] applied a non-dominated GA for FJSP with energy-saving measures. Nouiri et al [51] considered energyefficient FJSP with machine breakdown and gave a predictive reactive method based on PSO. Wang et al [52] developed a hybrid GA and Jiang and Deng [53] designed a discrete cat swarm optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Imperialist Competitive Algorithm With the Diversified Operators for Many-Objective Scheduling in Flexible Job Shop

2019

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A number of related works have been done on multi-objective flexible job shop scheduling problem, however, many-objective flexible job shop scheduling problems (MaOFJSP) with at least four objectives are seldom considered. In this paper, an imperialist competitive algorithm with the diversified operators (DOICA) is proposed for MaOFJSP with the minimization of makespan, total tardiness, total workload, and total energy consumption. In DOICA, the diversified assimilation and revolution are used according to the features of empires, a novel imperialist competition is implemented by excluding the strongest empire and doing multiple neighborhood search of a solution in the strongest empire. The extensive experiments are conducted by using a number of benchmark instances to test the impact of strategies of DOICA on its performance and compare DOICA with other algorithms from literature finally. The computational results validate that the new strategies of DOICA are effective and DOICA can provide promising results for the considered MaOFJSP. INDEX TERMS Many-objective optimization, flexible job shop scheduling problem, imperialist competitive algorithm, diversified operators.

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Cooperation Between Smart Manufacturing Scheduling Systems and Energy Providers: A Multi-agent Perspective

Nouiri

Trentesaux

Bekrar

et al. 2018

Service Orientation in Holonic and Multi-Agent Manufacturing

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Towards Energy Efficient Scheduling and Rescheduling for Dynamic Flexible Job Shop Problem

Cited by 49 publications

References 16 publications

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

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

An Imperialist Competitive Algorithm With the Diversified Operators for Many-Objective Scheduling in Flexible Job Shop

Cooperation Between Smart Manufacturing Scheduling Systems and Energy Providers: A Multi-agent Perspective

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