Novel Heuristic Algorithm & its Application for Reliability Optimization

Tripti Dahiya, Nakul Vashishth, D. Garg, A. Shrivastava, P. Kapur
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Abstract

Heuristic algorithms are practical, easy to implement, and work fast to provide short-term, feasible solutions for any kind of problem within economical budgets as compared to other meta-heuristic algorithms. This paper presents a novel heuristic algorithm named the Dahiya-Garg Heuristic Algorithm (DG-Alg) to find the optimal solution for constrained reliability redundancy allocation optimization problems. The cornerstone of the novel DG-Alg is its novel selection factor, which is a mathematical formula that helps the heuristic algorithm search for optimal subsystems for reliability optimization. A novel formulated selection factor in DG-Alg has increased its effectiveness and efficiency. To analyze the performance of the proposed heuristic algorithm and the other three existing heuristic algorithms, they are applied to a problem taken from a pharmaceutical manufacturing plant named Yaris Pharmaceuticals. During the application of the heuristic algorithms, it was ensured that redundancy allocation was done within stipulated cost constraints. Further, a comparative analysis of the obtained results has been done to judge the performance of the proposed heuristic algorithm. It is deduced that the proposed heuristic algorithm gives optimized and computationally efficient results in comparison to the other existing heuristic algorithms.
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新型启发式算法及其在可靠性优化中的应用
与其他元启发式算法相比,启发式算法实用、易于实现,并且工作速度快,可以在经济预算内为任何类型的问题提供短期可行的解决方案。本文提出了一种新的启发式算法——Dahiya-Garg启发式算法(DG-Alg)来寻找约束可靠性冗余分配优化问题的最优解。新的DG算法的基石是其新的选择因子,这是一个数学公式,有助于启发式算法搜索最优子系统进行可靠性优化。DG算法中一种新的公式化选择因子提高了其有效性和效率。为了分析所提出的启发式算法和其他三种现有启发式算法的性能,将它们应用于一家名为Yaris Pharmaceuticals的制药厂的问题。在启发式算法的应用过程中,确保了冗余分配在规定的成本约束内完成。此外,还对所获得的结果进行了比较分析,以判断所提出的启发式算法的性能。结果表明,与现有的启发式算法相比,所提出的启发式算法给出了优化和计算高效的结果。
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来源期刊
CiteScore
3.80
自引率
6.20%
发文量
57
审稿时长
20 weeks
期刊介绍: IJMEMS is a peer reviewed international journal aiming on both the theoretical and practical aspects of mathematical, engineering and management sciences. The original, not-previously published, research manuscripts on topics such as the following (but not limited to) will be considered for publication: *Mathematical Sciences- applied mathematics and allied fields, operations research, mathematical statistics. *Engineering Sciences- computer science engineering, mechanical engineering, information technology engineering, civil engineering, aeronautical engineering, industrial engineering, systems engineering, reliability engineering, production engineering. *Management Sciences- engineering management, risk management, business models, supply chain management.
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