Open Access

ARTICLE

Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing

Caiyun Liu, Peng Liu^*

School of Management, Shenyang University of Technology, Shenyang, 110870, China

* Corresponding Author: Peng Liu. Email:

Intelligent Automation & Soft Computing 2023, 36(3), 3221-3242. https://doi.org/10.32604/iasc.2023.035114

Received 08 August 2022; Accepted 26 October 2022; Issue published 15 March 2023

Abstract

Shared manufacturing is recognized as a new point-to-point manufacturing mode in the digital era. Shared manufacturing is referred to as a new manufacturing mode to realize the dynamic allocation of manufacturing tasks and resources. Compared with the traditional mode, shared manufacturing offers more abundant manufacturing resources and flexible configuration options. This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment, and the characteristics of shared manufacturing resource allocation. The execution of manufacturing tasks, in which candidate manufacturing resources enter or exit at various time nodes, enables the dynamic allocation of manufacturing tasks and resources. Then non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithms are designed to solve the model. The optimal parameter settings for the NSGA-II and MOPSO algorithms have been obtained according to the experiments with various population sizes and iteration numbers. In addition, the proposed model’s efficiency, which considers the entries and exits of manufacturing resources in the shared manufacturing environment, is further demonstrated by the overlap between the outputs of the NSGA-II and MOPSO algorithms for optimal resource allocation.

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Keywords

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