Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era

Leusin, Matheus Eduardo; Frazzon, Enzo Morosini; Maldonado, Maurício Uriona; Kück, Mirko; Freitag, Michael

doi:10.3390/technologies6040107

Cited by 80 publications

(40 citation statements)

References 76 publications

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“…Leusin et al [30] developed a multi agent system in a cyber-physical system to solve the dynamic job-shop scheduling problem. The proposed solution had self-configuring features in the production line.…”

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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“…Intelligent manufacturing proposed by Industry 4.0 is an information manufacturing process facing the whole life cycle of products and realizing ubiquitous perception [44,45]. As one of the pillar technologies of Industry 4.0 technology, IoT technology can promote better operation of intelligent manufacturing by improving productivity, flexibility, and quality [46]. IoT technology is used to connect all links involved in the production-delivery process, including suppliers, production, products, storage, transportation, and etc.…”

Section: Iot-based Sustainable Architecturementioning

confidence: 99%

A Novel Collaborative Optimization Model for Job Shop Production–Delivery Considering Time Window and Carbon Emission

Liao

Wang

2019

Sustainability

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The manufacturing industry is undergoing transformation and upgrading from traditional manufacturing to intelligent manufacturing, in which Internet of Things (IoT) technology plays a central role in promoting the development of intelligent manufacturing. In order to solve the problem that low production efficiency and machine utilization lead to serious pollution emissions in the workshop caused by untimely transmission of information in all links of the production and manufacturing process to whole supply chains, this study establishes an intelligent production scheduling and logistics delivery model with IoT technology to promote green and sustainable development of intelligent manufacturing. Firstly, an application framework of IoT technology in production–delivery supply chain systems was established to improve efficiency and achieve the integration of production and delivery. Secondly, an integrated production–delivery model was constructed, which takes into account time and low carbon constraints. Finally, a two-layer optimization algorithm was proposed to solve this integration problem. Through a case study, the results show this integration production–delivery model can reduce the cost of supply chains and improve customer satisfaction. Moreover, it proves that carbon emission cost is a major factor affecting total cost, and it could help enterprises to realize the profit and sustainable development of the environment. The production–delivery model could also support the last kilometer distribution problem and extension under E-commerce applications.

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“…Therefore, finding an optimal solution in acceptable amount of time is challenging due to the highly vulnerable environment of smart IoT-enabled devices. 7,10,21 There are three approaches used for handling the JSP: Heuristic, classical optimization, and Artificial Intelligence (AI) approaches. 21 Typically, resource allocation in JSP is considered as an optimization problem, and several heuristic algorithms have been proposed based on different assumptions and constraints.…”

Section: Related Workmentioning

confidence: 99%

“…18,[22][23][24][25][26][27][28] These algorithms directly influence the volume, frequency, and intensity of data exchange in job-shop as well as the scheduling quality. 21 Most of heuristic algorithms can be classified into three types with respect to the real-time processing. The first type requires only information (ie, ordered list of tasks) and is used in task-level scheduling heuristics, 8 while the second type considers the processing time as constant.…”

Section: Related Workmentioning

confidence: 99%

Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Hasan

Al‐Rizzo

2019

Concurrency and Computation

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Summary Internet of Things (IoT) is steadily growing in support of current and projected real‐time distributed Internet applications in civilian and military applications, while Cloud Computing has the ability to meet the performance expectations of these applications. In this paper, we present the implementation of logistics management applications relying on cooperative resources with optimized performances. To dynamically incorporate smart manufacturing objects into logistics management IoT applications within a ubiquitous environment, task scheduling must be provided for resource allocation in an optimized way. Within such environment, we propose a task scheduling algorithm based on a robust Canonical Particle Swarm Optimization (CPSO) algorithm to solve the problem of resource allocation and management in both homogeneous and heterogeneous IoT Cloud Computing. Our objective is to satisfy the Makespan by performing optimal task scheduling while considering different policies of incoming tasks. Performance evaluation from simulation experiments reveals that optimizing the Makespan can be significantly improved by Longest Processing Time (LPT), Shortest Processing Time (SPT), Earliest Computation Time (ECT), Earliest Starting Time (EST), Earliest Deadline First (EDF), and Earliest Duedate (EDD) using our CPSO algorithm as compared with traditional list task scheduling algorithms.

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Solving the Job-Shop Scheduling Problem in the Industry 4.0 Era

Cited by 80 publications

References 76 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

A Novel Collaborative Optimization Model for Job Shop Production–Delivery Considering Time Window and Carbon Emission

Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

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