Dispatching algorithm for production programming of flexible job-shop systems in the smart factory industry

Ortíz, Miguel A; Betancourt, Leidy E.; Negrete, Kevin Parra; Felice, Fabio De; Petrillo, Antonella

doi:10.1007/s10479-017-2678-x

Cited by 23 publications

(12 citation statements)

References 32 publications

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“…Ortiz et al [28] analyzed a flexible job-shop problem and proposed a new model for the solution. They formulated a real-world production-scheduling problem and also provided an efficient tool to solve it.…”

Section: Related Workmentioning

confidence: 99%

A Decision Support System for Dynamic Job-Shop Scheduling Using Real-Time Data with Simulation

et al. 2019

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The wide usage of information technologies in production has led to the Fourth Industrial Revolution, which has enabled real data collection from production tools that are capable of communicating with each other through the Internet of Things (IoT). Real time data improves production control especially in dynamic production environments. This study proposes a decision support system (DSS) designed to increase the performance of dispatching rules in dynamic scheduling using real time data, hence an increase in the overall performance of the job-shop. The DSS can work with all dispatching rules. To analyze its effects, it is run with popular dispatching rules selected from the literature on a simulation model created in Arena®. When the number of jobs waiting in the queue of any workstation in the job-shop falls to a critical value, the DSS can change the order of schedules in its preceding workstations to feed the workstation as soon as possible. For this purpose, it first determines the jobs in the preceding workstations to be sent to the current workstation, then finds the job with the highest priority number according to the active dispatching rule, and lastly puts this job in the first position in its queue. The DSS is tested under low, normal, and high demand rate scenarios with respect to six performance criteria. It is observed that the DSS improves the system performance by increasing workstation utilization and decreasing both the number of tardy jobs and the amount of waiting time regardless of the employed dispatching rule.

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Section: Related Workmentioning

confidence: 99%

A Decision Support System for Dynamic Job-Shop Scheduling Using Real-Time Data with Simulation

et al. 2019

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“…Productivity. Industry 4.0 has a potentially positive impact on a series of activities, such as employee's optimization (Kim 2018;Ortíz et al 2018), unit cost reduction (Kumar et al 2018;Brad et al 2018), unit time reduction (Moeuf et al 2018;Jung et al 2017), and inventory and warehouse management (Buer et al 2018;Qu et al 2017).…”

Section: Goalsmentioning

confidence: 99%

“…Productivity; Decision-making. Brad et al 2018;Ortíz et al 2018;Putman et al 2017;Theorin et al 2017. Taking the first line of Table 2 as an example, productivity can be obtained if production line technologies are adopted, provided that some internal and external barriers (e.g.…”

Section: The Industry 40 Frameworkmentioning

confidence: 99%

Industry’s 4.0 transformation process: how to start, where to aim, what to be aware of

Calabrese

Dora

Ghiron

et al. 2020

Production Planning & Control

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Industry 4.0 has fused digitalisation with traditional industrial processes bridging the physical and virtual worlds and opening unimagined possibilities for 21st century business growth. Research is still evolving toward the development of frameworks linking Industry's 4.0 enabling technologies to specific goals and to their impact on the manufacturers' businesses. A systematic review of all peer-reviewed managerial research is performed to extract Industry's 4.0 enabling technologies, barriers and goals. A framework linking technologies, barriers, and goals is then developed together with a web application based on its contents. The use of the framework is demonstrated through its application to two specific empirical case studies. The review shows that there are 9 classes of Industry's 4.0 enabling technologies, whose different arrangements lead to the achievement of up to 15 business goals that can be thwarted by 21 barriers. The pursuit of these goals leads to three different destinations, i.e., three diverse digital transformations of the manufacturing firms. This paper provides a holistic framework that analyses the relationships among Industry's 4.0 enabling technologies, barriers and goals. It appeals to managers who can use its contents along with the web application to derive recommendations and suggestions tailored to their Industry's 4.0 journeys.

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“…This model involves the setup time of machines in the dynamic re-scheduling. Ortíz et al [68] proposed a dispatching rule to solve practical FJSP. As one of the heuristic algorithm, the effectiveness of their proposed dispatching rule is higher than the related swarm intelligence algorithms in practical textile manufacturing.…”

Section: Heuristicsmentioning

confidence: 99%

“…3, pp. [67][68][69][70][71][72][73][74][75][76][77] This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/) transportation time. An effective TLBO was proposed by Xu et al [146] to solve the FJSP with fuzzy processing time.…”

Section: Population-based Meta-heuristicsmentioning

confidence: 99%

Review on flexible job shop scheduling

Xie

Gao

Peng

et al. 2019

IET Collaborative Intelligent Manufacturing

158

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Flexible job shop scheduling problem (FJSP) is an NP‐hard combinatorial optimisation problem, which has significant applications in the real world. Due to its complexity and significance, lots of attentions have been paid to tackle this problem. In this study, the existing solution methods for the FJSP in recent literature are classified into exact algorithms, heuristics and meta‐heuristics, which are reviewed comprehensively. Moreover, the real‐world applications of the FJSP are also introduced. Finally, the development trends of the manufacturing industry are analysed, and the future research opportunities of the FJSP are summarised in detail.

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Dispatching algorithm for production programming of flexible job-shop systems in the smart factory industry

Cited by 23 publications

References 32 publications

A Decision Support System for Dynamic Job-Shop Scheduling Using Real-Time Data with Simulation

A Decision Support System for Dynamic Job-Shop Scheduling Using Real-Time Data with Simulation

Industry’s 4.0 transformation process: how to start, where to aim, what to be aware of

Review on flexible job shop scheduling

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