Greedy randomized adaptive search procedure for simultaneous scheduling of production and preventive maintenance activities in dynamic flexible job shops

Baykasoğlu, Adil; Madenoğlu, Fatma Selen

doi:10.1007/s00500-021-06053-0

Cited by 18 publications

(11 citation statements)

References 73 publications

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“…2 illustrate the various simulation results of job shop scheduling efficiency by considering the 150 jobs taken from datasets. The scheduling efficiency is measured using four methods namely MESHS and existing GRASP [1], MOMA [2], and PSOSA [3]. Among the three methods, the above-reported results noticeably confirm that the job shop scheduling efficiency of the MESHS technique is higher than the GRASP [1], MOMA [2], and PSOSA [3].…”

Section: 𝐽𝑆𝑆𝐸 = ( 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑗𝑜𝑏𝑠 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝑠𝑐ℎ𝑒𝑑𝑢𝑙𝑒𝑑 𝑛mentioning

confidence: 67%

“…The scheduling efficiency is measured using four methods namely MESHS and existing GRASP [1], MOMA [2], and PSOSA [3]. Among the three methods, the above-reported results noticeably confirm that the job shop scheduling efficiency of the MESHS technique is higher than the GRASP [1], MOMA [2], and PSOSA [3]. This is owing to MESHS initially identifying the optimal machine for processing the high-priority operations of the given job.…”

Section: 𝐽𝑆𝑆𝐸 = ( 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑗𝑜𝑏𝑠 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝑠𝑐ℎ𝑒𝑑𝑢𝑙𝑒𝑑 𝑛mentioning

confidence: 75%

“…In this section, the performance of the proposed MESHS and existing GRASP [1], MOMA [2], PSOSA [3] are discussed with different metrics such as Job shop scheduling efficiency, makespan, and energy consumption. The performances of the three methods are discussed with a table or graphical representation.…”

Section: Resultsmentioning

confidence: 99%

“…In this section, experimental evaluation of proposed MESHS and existing GRASP [1] MOMA [2] PSOSA [3] are implemented using MATLAB simulator and run on an Intel Core i7-3520M 2.9 GHz processor with 8 GB of RAM. The benchmark OR-Library dataset is used for dual resourceconstrained processes such as optimal machine selection and sequencing.…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Multi-objective elitist spotted hyena resource optimized flexible job shop scheduling

Senthilvel,

Hemamalini,

Geetha

2024

Int. J. Ind. Optim.

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The job shop scheduling problem (JSSP) has drained a lot of consideration since it is one of the most important optimization problems in the manufacturing domain. The scheduling method is crucial for optimizing the objective of minimizing makespan among thousands of jobs, but evaluating machine capacity for achieving this goal remains challenging despite the development of various population-based optimization algorithms for job shop scheduling problems. To improve the efficiency of Job shop scheduling, a novel Multi-objective Elitist Spotted Hyena Monotonic Scheduling (MESHS) technique is introduced. The proposed MESHS technique includes two major processes: machine selection and operation sequences. The number of jobs is considered for solving the scheduling problem. First, the machine selection is performed by applying the Multi-objective Elitist Spotted Hyena optimization technique. The optimization technique selects the optimal machines parallelly based on multiple objective functions such as energy consumption, CPU utilization, and job completion time. The fitness of every machine is calculated based on these multiple objective functions using Levenberg–Marquardt method. Then the Elitist strategy is applied to select the optimal machine based on fitness. After the machine selection, the rate-monotonic preemptive scheduling is modeled to provide a robust operation sequence by assigning high-priority jobs to the optimal machines. As a result, efficient job scheduling is achieved with minimum time. Finally, the experimental valuation is carried out using a benchmark OR-Library dataset with different factors such as job shop scheduling efficiency, job scheduling time, makespan, and memory consumption concerning a number of jobs.

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Section: 𝐽𝑆𝑆𝐸 = ( 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑗𝑜𝑏𝑠 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝑠𝑐ℎ𝑒𝑑𝑢𝑙𝑒𝑑 𝑛mentioning

confidence: 67%

Section: 𝐽𝑆𝑆𝐸 = ( 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑗𝑜𝑏𝑠 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝑠𝑐ℎ𝑒𝑑𝑢𝑙𝑒𝑑 𝑛mentioning

confidence: 75%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Multi-objective elitist spotted hyena resource optimized flexible job shop scheduling

Senthilvel,

Hemamalini,

Geetha

2024

Int. J. Ind. Optim.

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show abstract

“…Although many techniques can be used to schedule such operations (Lohmer and Lasch, 2021), the ones often used in the industry are based on "greedy approaches", which have extremely short-term economic horizons (Khalaf et al, 2010;Hosseini et al, 2020;Baykasoğlu and Madenoglu, 2021;Fadlallah et al, 2021;Zhou et al, 2021;Hong et al, 2022). Maintenance is mostly invariably disruptive to the production process, ranging from reduced capacity operation during maintenance to complete shutdown.…”

Section: Optimization Of Maintenance Schedulingmentioning

confidence: 99%

Big data and machine learning: A roadmap towards smart plants

et al. 2022

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Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management.

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A self-learning discrete salp swarm algorithm based on deep reinforcement learning for dynamic job shop scheduling problem

Chen

Wang

2023

Appl Intell

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Greedy randomized adaptive search procedure for simultaneous scheduling of production and preventive maintenance activities in dynamic flexible job shops

Cited by 18 publications

References 73 publications

Multi-objective elitist spotted hyena resource optimized flexible job shop scheduling

Multi-objective elitist spotted hyena resource optimized flexible job shop scheduling

Big data and machine learning: A roadmap towards smart plants

A self-learning discrete salp swarm algorithm based on deep reinforcement learning for dynamic job shop scheduling problem

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