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Least-looping stepping-stone-based ASM approach for transportation and triangular intuitionistic fuzzy transportation problems

机译:基于循环的踩踏石头的ASM方法,用于运输和三角直觉模糊运输问题

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Transportation problem (TP) is a popular branch of Linear Programming Problem in the field of Transportation engineering. Over the years, attempts have been made in finding improved approaches to solve the TPs. Recently, in Quddoos et al. (Int J Comput Sci Eng (IJCSE) 4(7): 1271–1274, 2012), an efficient approach, namely ASM, is proposed for solving crisp TPs. However, it is found that ASM fails to provide better optimal solution in some cases. Therefore, a new and efficient ASM appoach is proposed in this paper to enhance the inherent mechanism of the existing ASM method to solve both crisp TPs and Triangular Intuitionistic Fuzzy Transportation Problems (TIFTPs). A least-looping stepping-stone method has been employed as one of the key factors to improve the solution quality, which is an improved version of the existing stepping-stone method (Roy and Hossain in, Operation research Titus Publication, 2015). Unlike stepping stone method, least-looping stepping-stone method only deals with few selected non-basic cells under some prescribed conditions and hence minimizes the computational burden. Therefore, the framework of the proposed method (namely LS-ASM) is a combination of ASM (Quddoos et al. 2012) and least-looping stepping-stone approach. To validate the performance of LS-ASM, a set of six case studies and a real-world problem (those include both crisp TPs and TIFTPs) have been solved. The statistical results obtained by LS-ASM have been well compared with the existing popular modified distribution (MODI) method and the original ASM method, as well. The statistical results confirm the superiority of the LS-ASM over other compared algorithms with a less computationl effort.
机译:运输问题(TP)是交通工程领域线性规划问题的流行分支。多年来,在寻找解决TPS的改进方法方面取得了尝试。最近,在Quddoos等。 (INT J COPPL SCI ENG(IJCSE)4(7):1271-1274,2012),提出了一种有效的方法,即ASM,用于解决酥脆TPS。但是,发现ASM在某些情况下无法提供更好的最佳解决方案。因此,本文提出了一种新的高效的ASM Appoach,提高了现有ASM方法的固有机制,以解决清晰的TPS和三角直觉模糊运输问题(TIFTPS)。一种最小循环的踩踏石方法是改善解决方案质量的关键因素之一,这是现有踩踏石方法的改进版本(Roy和Hossain,操作研究Titus Publication,2015)。与步进石方法不同,最小循环踩踏石方法仅在一些规定的条件下涉及少量选择的非基本单元,因此最大限度地减少计算负担。因此,所提出的方法(即LS-ASM)的框架是ASM(QUDDOOS等,2012)和最小循环踩踏石方法的组合。为了验证LS-ASM的表现,已经解决了一组六个案例研究和真实世界的问题(包括清晰TPS和TIFT)。与现有的流行改性分布(MODI)方法和原始ASM方法相比,LS-ASM获得的统计结果也很好。统计结果证实了LS-ASM的优势与其他比较算法的优势,具有较少的计算。

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