Hybrid evolutionary algorithm for maximizing medical equipment supply during pandemic✰

C. D James , Sandeep Mondal
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Abstract

Inadequate capacity and delayed delivery of electronic life support equipment was a major impediment in saving human lives during COVID-19. Capital intensive mass customised electronics and semiconductor manufacturing formed critical raw material for the same. Targeted efficiency achievement fails when variety and flexibility are prioritised in chip production. Digital manufacturing involves artificial intelligence for planning and autonomous execution with robotic hi-tech machines. However, large number of controlling factors fluctuate at extreme levels in the manufacturing environment leading to capacity shrinkage risk of these machines. In this paper, we make use of a simulation-based model to demonstrate solution to this problem because experimental setups involve high cost and delivery risks.
Firstly, we identified thirty-one factors that affect hi-tech machine efficiency. Of these, thirteen factors were shortlisted through confidential voting by the industry experts to mirror the actual challenges during pandemic. We developed a model, and simulated problem scenarios for shortlisted factors at three levels. Design of experiments was performed using Taguchi based orthogonal arrays. Signal to noise ratios were used to determine the main effects and robust combination of factor levels for high efficiency. Significant factors were identified from ANOVA for variance-reduction based robustness design.
A better solution was created using a learning-based fruit fly optimization algorithm and further using a hybrid fruit fly grasshopper leap optimization. This algorithm successfully supported the high customization scenario for manufacturing efficiency during pandemic for any pre-set parameters by accelerating learning cycles. In addition, a multifactor particle swarm optimization was also performed for managing dynamic changes in all 31 factors together and the results were compared with previous techniques. The managerial implications and conclusion are explained for the benefit of the electronics industry and academia.
流行病期间医疗设备供应最大化的混合进化算法
能力不足和电子生命维持设备的延迟交付是COVID-19期间挽救生命的主要障碍。资本密集型大规模定制电子产品和半导体制造业为其提供了关键的原材料。当芯片生产中优先考虑多样性和灵活性时,目标效率的实现就会失败。数字化制造涉及人工智能的规划和自主执行与机器人高科技机器。然而,在制造环境中,大量的控制因素在极端水平波动,导致这些机器的产能收缩风险。在本文中,我们利用基于仿真的模型来演示解决这个问题,因为实验设置涉及高成本和交付风险。首先,我们确定了影响高科技机器效率的31个因素。其中,有13个因素通过行业专家的秘密投票入围,以反映大流行期间的实际挑战。我们开发了一个模型,并在三个层次上模拟了入围因素的问题场景。实验设计采用基于田口的正交阵列。信噪比用于确定主效应和高效因素水平的稳健组合。通过方差减少稳健性设计的方差分析确定显著因素。利用基于学习的果蝇优化算法和混合果蝇蝗虫跳跃优化算法,得到了一个更好的解决方案。该算法通过加速学习周期,成功地支持大流行期间任何预设参数的高定制化生产效率场景。此外,还采用多因素粒子群优化方法对31个因素的动态变化进行了综合管理,并与已有的方法进行了比较。为了电子工业和学术界的利益,对管理意义和结论进行了解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.20
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