Transfer-robot task scheduling in flexible job shop

Ham, Andy

doi:10.1007/s10845-020-01537-6

Cited by 40 publications

(11 citation statements)

References 22 publications

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“…To prove the effectiveness and efficiency of the proposed IGA, it is compared with state-of-the-art algorithms, namely, the MILP model [3], CP2 [29], LAHC [3], BRKGA [11], GATS [36], and PGA [35]. The comparison results of the five sets of benchmark data sets are shown in Tables 2-7.…”

Section: Resultsmentioning

confidence: 99%

“…Lacomme et al [28] designed a disjunctive graph-based framework for modeling JSP-AGV and an improved memetic algorithm. Ham [29] first developed a constraint programming model and a new set of benchmark instances.…”

Section: Literature Review Of Fjsp-agvmentioning

confidence: 99%

“…Moreover, the maximum number of jobs, the limited input/output buffer capacities, and the no-move-ahead trips were taken into consideration simultaneously. With regard to FJSP-AGV on minimizing makespan, Ham [29] extended the constraint programming model of JSP-AGV and proved the optimality of ten benchmark instances [34]. Homayouni and Fontes [3] proposed the first MILP model for solving smallsized instances to optimality and a local search-based heuristic for solving small to largesized instances.…”

Section: Literature Review Of Fjsp-agvmentioning

confidence: 99%

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An Improved Genetic Algorithm for Solving the Multi-AGV Flexible Job Shop Scheduling Problem

Meng

Cheng

Zhang

et al. 2023

Sensors

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In real manufacturing environments, the number of automatic guided vehicles (AGV) is limited. Therefore, the scheduling problem that considers a limited number of AGVs is much nearer to real production and very important. In this paper, we studied the flexible job shop scheduling problem with a limited number of AGVs (FJSP-AGV) and propose an improved genetic algorithm (IGA) to minimize makespan. Compared with the classical genetic algorithm, a population diversity check method was specifically designed in IGA. To evaluate the effectiveness and efficiency of IGA, it was compared with the state-of-the-art algorithms for solving five sets of benchmark instances. Experimental results show that the proposed IGA outperforms the state-of-the-art algorithms. More importantly, the current best solutions of 34 benchmark instances of four data sets were updated.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Review Of Fjsp-agvmentioning

confidence: 99%

Section: Literature Review Of Fjsp-agvmentioning

confidence: 99%

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An Improved Genetic Algorithm for Solving the Multi-AGV Flexible Job Shop Scheduling Problem

Meng

Cheng

Zhang

et al. 2023

Sensors

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“…Of course, it would be of great interest to study the calibration engineer scheduling problem in distributed multiple‐machine settings where several calibration engineers are available. It is also worth considering the coordination of machine calibrations in some stages or machines of a flow shop (Ferone et al., 2020; de Siqueira et al., 2020; Hakeem‐Ur‐Rehman et al., 2021), a job shop (Benttaleb et al., 2019; Abedi et al., 2020; Ham, 2020), or a parallel machine setting where a server is in charge of setups (Arnaout, 2017; Bektur and Saraç, 2019; Krim et al., 2020) such that the production runs smoothly from an overall perspective.…”

Section: Discussionmentioning

confidence: 99%

Short‐term scheduling with machine calibration

Zhao

et al. 2021

Int Trans Operational Res

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In most of the past research on scheduling, it is conventional to model machine deterioration by decreasing production speed over time. However, as is observed, there is a fair number of scenarios in practice where deterioration has a little impact on machine speed but a great impact on production accuracy and a machine is unusable unless it has been calibrated recently. In this paper, we consider a single-machine short-term scheduling issue in distributed production systems where the engineer calibrates the machines in a round-robin manner. The objective is to minimize total job completion time, which is a popular criterion indicating total holding or inventory cost. We consider two cases of the problem and show that one of them can be solved optimally by providing an extended shortest processing time first rule. As for the other case, which is intractable, we propose a polynomial-time approximation scheme based on the extended rule and an exchange procedure. Computational experiments show that the proposed polynomial-time approximation scheme is very promising even if limited number of exchangeable jobs is allowed.

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“…The time period mentioned in the table is taken using the setup mentioned in Figure 19, and this operation does not involve holding_bay or error zone selection. There is literature published on the scheduler presented in Reference [38][39][40][41], but, to the best of authors' knowledge, there is no published literature on the timing details of the same scheduler which provides an integration of a MES and AIV. So, a fair comparison of timing is not possible.…”

Section: Testingmentioning

confidence: 99%

Integration of an MES and AIV Using a LabVIEW Middleware Scheduler Suitable for Use in Industry 4.0 Applications

et al. 2020

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Industry 4.0 uses the analysis of real-time data, artificial intelligence, automation, and the interconnection of components of the production lines to improve manufacturing efficiency and quality. Manufacturing Execution Systems (MESs) and Autonomous Intelligent Vehicles (AIVs) are key elements of Industry 4.0 implementations. An MES connects, monitors, and controls data flows on the factory floor, while automation is achieved by using AIVs. The Robot Operating System (ROS) built AIVs are targeted here. To facilitate MES and AIV interactions, there is a need to integrate the MES and the AIVs to help in building an automated and interconnected manufacturing environment. This integration needs middleware, which understands both MES and AIVs. To address this issue, a LabVIEW-based scheduler is proposed here as the middleware. LabVIEW communicates with the MES through webservices and has support for ROS. The main task of the scheduler is to control the AIV based on MES requests. The scheduler developed was tested in a real factory environment using the SAP MES and a Robotnik ‘RB-1′ robot. The scheduler interface provides real-time information about the current status of the MES, AIV, and the current stage of scheduler processing. The proposed scheduler provides an efficient automated product delivery system that transports the product from process cell to process cell using the AIV, based on the production sequences defined by the MES. In addition, using the proposed scheduler, integration of an MES is possible with any low-cost ROS-built AIV.

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Transfer-robot task scheduling in flexible job shop

Cited by 40 publications

References 22 publications

An Improved Genetic Algorithm for Solving the Multi-AGV Flexible Job Shop Scheduling Problem

An Improved Genetic Algorithm for Solving the Multi-AGV Flexible Job Shop Scheduling Problem

Short‐term scheduling with machine calibration

Integration of an MES and AIV Using a LabVIEW Middleware Scheduler Suitable for Use in Industry 4.0 Applications

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