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Improved particle swarm optimization based on blockchain mechanism for flexible job shop problem

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Abstract

The emergence and massive growth of cloud computing increased the demand for task scheduling strategies to utilize the full potential of virtualization technology. Efficient task scheduling necessitates efficiency, reduced makespan and execution time, and improvement ratio. Additionally, secure scheduling is a pivotal element in highly distributed environments. Task scheduling is an NP-complete problem where the time required to locate the resource depends on the problem size. Despite the several proposed algorithms, optimal task scheduling lacks an ideal solution and requires further efforts from academia and industry. Recently, blockchain has evolved as a promising technology for combining cloud clusters, secure cloud transactions, data access, and application codes. This study leverages the advantages of blockchain to propose a novel encoding technique to improve the makespan value and scheduling time. The proposed algorithm is an optimal solution for effective and efficient job shop scheduling where an Improved Particle Swarm Optimization (IPSO) and blockchain technology is used to provide efficiency and security. IPSO algorithm is hybridized by acquiring the best data from methods, and selective particles are kept for further iteration generation. The IPSO algorithm effectively traverses to the solution space and obtains optimal solutions by altering the dominant operations. The performance of IPSO is evaluated concerning the makespan, improvement ratio, execution time, and efficiency. Experiment results indicate that the proposed algorithm is practical and secure in handling flexible job scheduling, and outperforms the state-of-the-art task scheduling algorithms. Results suggest that IPSO minimizes the execution time by 8% and increases the efficiency by 35% than the existing scheduling approaches.

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All the data generated and material analyzed in the research work were included in this manuscript.

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All the code generated/analyzed in the research work were included in this manuscript.

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Funding

This work is supported by the Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2019JM-348).

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Authors and Affiliations

  1. College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an, 710054, Shaanxi, China

    Muhammad Usman Sana, Zhanli Li & Muhammad Wahab Hanif

  2. Department of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an, 710054, Shaanxi, China

    Fawad Javaid

  3. Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, 38541, Republic of Korea

    Imran Ashraf

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  1. Muhammad Usman Sana
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  2. Zhanli Li
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  4. Muhammad Wahab Hanif
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Contributions

MdUS, ZL, FJ, MdWH, and IA designed the model, conceptualized the framework, collected the data material, generated codes, analyzed the model and visualized the results.

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Correspondence to Muhammad Usman Sana.

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Sana, M.U., Li, Z., Javaid, F. et al. Improved particle swarm optimization based on blockchain mechanism for flexible job shop problem. Cluster Comput 26, 2519–2537 (2023). https://doi.org/10.1007/s10586-021-03349-6

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  • DOI: https://doi.org/10.1007/s10586-021-03349-6

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