Research on Permutation Flow-shop Scheduling Problem based on Improved Genetic Immune Algorithm with vaccinated offspring

Tayeb, Fatima Benbouzid-Si; Bessedik, Malika; Benbouzid, Mohamed; Cheurfi, Hamza; Blizak, Ammar

doi:10.1016/j.procs.2017.08.055

Cited by 12 publications

(6 citation statements)

References 27 publications

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“… Class Optimization algorithm Year Abbreviated form Reference Evolution based Artificial Infections Disease 2016 AIDO Huang (2016) Earthworm Optimization 2018 EOA (G. . Wang et al, 2018 ) Improved Genetic Immune 2017 IGIA Benbouzid-Si Tayeb et al (2017) Virulence Optimization 2016 VOA Jaderyan & Khotanlou (2016) Plants based Artificial Flora Optimization 2018 AFO Cheng et al (2018) Natural Forest Regeneration 2016 NFR Moez et al (2016) Root Tree Optimization 2016 RTOA Labbi et al (2016) Tree Growth 2018 TGA Cheraghalipour et al (2018) Tree Physiology Optimization 2018 TPO Halim & Ismail (2018) Social Human Behavior based Adolescent Identity Search 2020 AISA Bogar & Beyhan (2020) Cognitive Behavior Optimization 2016 COA (M. Li et al, 2016 ) Swarm intelligence based Andean Condor 2019 ACA Almonacid & Soto (2019) Bald Eagle Search 2020 BES Alsattar et al (2020) Bison Behavior 2019 BBA Kazikova et al (2019) Biology Migration ...…”

Section: Classification Of Bio-inspired Optimizationmentioning

confidence: 99%

Application of bio-inspired optimization algorithms in food processing

Sarkar

Salauddin

Mukherjee³

et al. 2022

Current Research in Food Science

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Section: Classification Of Bio-inspired Optimizationmentioning

confidence: 99%

Application of bio-inspired optimization algorithms in food processing

Sarkar

Salauddin

Mukherjee³

et al. 2022

Current Research in Food Science

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“…Genes are combined to form a chromosome that can be interpreted as a "solution" in a scientific context in engineering manufacturing. The following step, called the fitness of the function, is observed to reveal how good it is through the fitness score of the values, and each score is based on probability [20][21][22][23][24]. Along with appointing individual members fit for the job to be performed, another perpetuation of the next generation is also selected.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

“…Complex problems can be solved with the use of genetic algorithms that are capable of finding an optimal solution [25,26]. GA is a natural process of evolution in biological sciences, and it has the power of solving heuristics-related problems [23,27]. The authors in [28] used a modified version of genetic algorithms to solve flexible job-shop scheduling problems (JSPs).…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

et al. 2020

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Numerous organizations are striving to maximize the profit of their businesses by the effective implementation of competitive advantages including cost reduction, quick delivery, and unique high-quality products. Effective production-scheduling techniques are methods that many firms use to attain these competitive advantages. Implementing scheduling techniques in high-mix low-volume (HMLV) manufacturing industries, especially in Industry 4.0 environments, remains a challenge, as the properties of both parts and processes are dynamically changing. As a reaction to these challenges in HMLV Industry 4.0 manufacturing, a newly advanced and effective real-time production-scheduling decision-support system model was developed. The developed model was implemented with the use of robotic process automation (RPA), and it comprises a hybrid of different advanced scheduling techniques obtained as the result of analytical-hierarchy-process (AHP) analysis. The aim of this research was to develop a method to minimize the total production process time (total make span) by considering the results of risk analysis of HMLV manufacturing in Industry 4.0 environments. The new method is the combination of multi-broker (MB) optimization and a genetics algorithm (GA) that uses general key process indicators (KPIs) that are easy to measure in any kind of production. The new MB–GA method is compatible with industry 4.0 environments, so it is easy to implement. Furthermore, MB–GA deals with potential risk during production, so it can provide more accurate results. On the basis of survey results, 16% of the asked companies could easily use the new scheduling method, and 43.2% of the companies could use it after a little modification of production.

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“…The following step is the fitness of the function. The fitness of the function observed how good it is and the observed through fitness score of values and all bases on probability [43,44,45,46,47]. The selection phase of selecting individual members who are fit selected for another perpetuation of the next generation.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Innovation, Artificial Intelligence in Contingent Work-Force Management

Matonya

2020

IJEMS

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In recent years, the global use of contingent workers is rapidly increasing despite the increasing quantity of artificial intelligence applications in business. The question is "how these companies leverage the use of artificial intelligence to enhance contingent workforce's management?". The ideal goal of this paper is to develop a purely conceptual application of innovation, artificial intelligence (AI) adjacent to contingent workforce management(CWM). The researcher used qualitative information gathered from various authors and observations to reinforce the usage of AI. One of the critical tools to integrate with contingent workforce management for reduction of time spent on human resource administrative tasks is AI. There must be a transformation of thinking, accepting positive organizational change, utilization of technology and openness to new technology to foster AI. Along with that, integrating contingent workforce management with AI reduces risks and costs, increases efficiency and quality of work. Innovation and Artificial intelligence have been used in five pillars performance of contingent workforce management to mitigate the challenges associated with it.

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Research on Permutation Flow-shop Scheduling Problem based on Improved Genetic Immune Algorithm with vaccinated offspring

Cited by 12 publications

References 27 publications

Application of bio-inspired optimization algorithms in food processing

Application of bio-inspired optimization algorithms in food processing

Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

Innovation, Artificial Intelligence in Contingent Work-Force Management

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