A New Rescheduling Heuristic for Flexible Job Shop Problem with Machine Disruption

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

doi:10.1007/978-3-319-73751-5_35

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…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%

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%

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

2019

Self Cite

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“…Nouiri et al (2018a) suggested a predictive approach based on particle swarm optimization to generate a preschedule that was able to minimize machine breakdown performance effect. They compared it with GA and show that particle swarm could provide better results in comparision with GA. Nouiri et al (2018b) presented an algorithm for rescheduling the affected operations in a flexible job shop and its performance, with respect to measures of efficiency and stability, was compared with the right shift rescheduling techniques. They tested the proposed method on different benchmark scheduling problems with various disruption scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust scheduling in two-stage assembly flow shop problem with random machine breakdowns: integrated meta-heuristic algorithms and simulation approach

Tadayonirad

Seidgar

Fazlollahtabar

et al. 2019

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Purpose In real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously. Design/methodology/approach Owing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions. Findings Finally, experimental study and analysis of variance (ANOVA) is done to investigate the effect of different proposed measures on the performance of the obtained results. ANOVA's results indicate the job and weight of makespan factors have a significant impact on the robustness of the proposed meta-heuristics algorithms. Also, it is obvious that the most effective parameter on the robustness for GA and ICA is job. Originality/value Robustness is calculated by the expected value of the relative difference between the deterministic and actual makespan.

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“…Also, disruptions can give rise to the need for rescheduling a previously planned schedule. This was treated, e.g., by Hall and Potts (2010) for the case of delayed jobs, Nouiri et al (2018) for random disruption events, and Li and Li (2020) for accidents. The recently published monograph Wang et al (2020) provides a comprehensive overview.…”

Section: Introductionmentioning

confidence: 99%

Algorithms for rescheduling jobs with a LIFO buffer to minimize the weighted number of late jobs

Pferschy

Resch

Righini

2022

J Sched

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Rescheduling can help to improve the quality of a schedule with respect to an initially given sequence. In this paper, we consider the possibility of rescheduling jobs arriving for processing at a single machine under the following limitations: (a) jobs can only be moved toward the end of the schedule and not toward the front, and (b) when a job is taken out of the sequence, it is put on a buffer of limited capacity before being reinserted in its new position closer to the end of the sequence. The buffer is organized as a stack with a last-in/first-out policy. As an objective function, we consider the minimization of the weighted number of late jobs. For this NP-hard problem, we first provide two different integer linear programming (ILP) formulations. Furthermore, we develop a branch-and-bound algorithm with a branching rule based on the movement of jobs. Then a new pseudo-polynomial dynamic programming algorithm is presented which utilizes dominance criteria and an efficient handling of states. Our computational experiments with up to 100 jobs show that this algorithm performs remarkably well and can be seen as the current method of choice.

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A New Rescheduling Heuristic for Flexible Job Shop Problem with Machine Disruption

Cited by 10 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

Robust scheduling in two-stage assembly flow shop problem with random machine breakdowns: integrated meta-heuristic algorithms and simulation approach

Algorithms for rescheduling jobs with a LIFO buffer to minimize the weighted number of late jobs

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