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Job Shop Scheduling
Job shop scheduling is one of the most frequently used types of scheduling in manufacturing facilities. In recent years, various research has been conducted to analyze the integration and impacts of the Industry 4.0 environment on job shop scheduling.
JSSP includes job operations with different machine sequences that have different processing times.  classified JSSP as an NP-hard optimization problem where different machines are assigned to various jobs while minimizing any of the applicable predefined criteria. Therefore, researchers and practitioners have gradually shifted their focus from traditional scheduling arrangement to smart-distributed scheduling (SDS) aided with technological pillars of the Industry 4.0 environment, such as Cyber-Physical Systems (CPS), Big data, Artificial Intelligence (AI), Internet of Things (IoTs), and Social, Mobile, Analytics, Cloud computing (SMAC). The transition from traditional scheduling to SDS faces two major research challenges: the integration of conventional JSSP scheduling techniques with SDS, and the development of new problem-solving techniques required for SDS.
Various models for JSSP have been proposed for improving the operational efficiency of a job shop production facility. A detailed review of the literature reveals that several studies have reviewed the integration of JSSP with Industry 4.0. Chaudhry & Khan (2016)  performed an extensive review of literature from 1990 to 2014 and highlighted various techniques and approaches used to solve the JSSP problem. A comprehensive review of job shop scheduling models, algorithms used for JSSPs, and the integration of techniques used in Industry 4.0 for solving JSSP were conducted by Zhang et al.
2. Latest Research Trends in SFFJSP
2.1. Use of IoT
2.2. Use of Genetic Algorithm
2.3. Decision Support System
2.4. Decentralization Outperformance
2.5. Use of Semi Hierarchal Configuration
2.6. Use of Heuristic Approaches
2.7. Maximizing Hamiltonian Function
2.8. Use of CBJSP
2.9. Use of RFID Based IoT
2.10. Industry 4.0 in SFFJSP
2.11. Use of Firefly Algorithm
2.12. Use of Lagrange Relaxation Method
2.13. Use of AGVs
2.14. Use of HSTL
2.15. Use of DSS with Big Data
2.16. Development of Standard Dataset
2.17. Use of Q-Learning Algorithm
2.18. Use of RRCF
The entry is from 10.3390/su13147684
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