Design of a robust production facility layout with minimum handling cost （MHC） presents an appropriate approach to tackle facility layout problems in a dynamic volatile environment, in which product demands randomly...Design of a robust production facility layout with minimum handling cost （MHC） presents an appropriate approach to tackle facility layout problems in a dynamic volatile environment, in which product demands randomly change in each planning period. The objective of the design is to find the robust facility layout with minimum total material handling cost over the entire multiperiod planning horizon. This paper proposes a new mathematical model for designing robust machine layout in the stochastic dynamic environment of manufacturing systems using quadratic assignment problem （QAP） formulation. In this investigation, product demands are assumed to be normally distributed random variables with known expected value, variance, and covariance that randomly change from period to period. The proposed model was verified and validated using randomly generated numerical data and benchmark examples. The effect of dependent product demands and varying interest rate on the total cost function of the proposed model has also been investigated. Sensitivity analysis on the proposed model has been performed. Dynamic programming and simulated annealing optimization algorithms were used in solving the modeled example problems.展开更多
Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing. Despite these adv...Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing. Despite these advancements, efficiently programming GPUs remains a daunting challenge, often relying on trial-and-error optimization methods. This paper introduces an optimization technique for CUDA programs through a novel Data Layout strategy, aimed at restructuring memory data arrangement to significantly enhance data access locality. Focusing on the dynamic programming algorithm for chained matrix multiplication—a critical operation across various domains including artificial intelligence (AI), high-performance computing (HPC), and the Internet of Things (IoT)—this technique facilitates more localized access. We specifically illustrate the importance of efficient matrix multiplication in these areas, underscoring the technique’s broader applicability and its potential to address some of the most pressing computational challenges in GPU-accelerated applications. Our findings reveal a remarkable reduction in memory consumption and a substantial 50% decrease in execution time for CUDA programs utilizing this technique, thereby setting a new benchmark for optimization in GPU computing.展开更多
针对城市居民区回收箱布局规划和路径优化问题,首先构建居民区回收箱数量与人口、回收频率、回收阈值的线性函数,并构建双层优化模型,回收总利润最大化作为上层目标,运输成本最小化作为下层目标。其次,为求解具有NP-hard特征的新模型,...针对城市居民区回收箱布局规划和路径优化问题,首先构建居民区回收箱数量与人口、回收频率、回收阈值的线性函数,并构建双层优化模型,回收总利润最大化作为上层目标,运输成本最小化作为下层目标。其次,为求解具有NP-hard特征的新模型,设计加入团体学习算子和自适应选择策略的人类学习优化算法,并与禁忌搜索算法嵌套构建混合人类学习算法(hybrid human learning optimization algorithm,HHLO)。再次,采用不同规模算例,并将新算法与基本人类学习算法、遗传算法、自适应粒子群算法、红嘴蓝鹊算法进行对比分析,验证了模型的可行性和算法的有效性。最后,通过上海杨浦区某实例进行灵敏度分析,探讨回收箱容量、分时定价策略和分区定价策略对回收中心总利润与居民满意度的影响。展开更多
基金Supported by the Ministry of Higher Education of Malaysia through the Foundation Research(Grant Scheme no.FRGS/1/2012/TK01/MMU/02/2)
文摘Design of a robust production facility layout with minimum handling cost （MHC） presents an appropriate approach to tackle facility layout problems in a dynamic volatile environment, in which product demands randomly change in each planning period. The objective of the design is to find the robust facility layout with minimum total material handling cost over the entire multiperiod planning horizon. This paper proposes a new mathematical model for designing robust machine layout in the stochastic dynamic environment of manufacturing systems using quadratic assignment problem （QAP） formulation. In this investigation, product demands are assumed to be normally distributed random variables with known expected value, variance, and covariance that randomly change from period to period. The proposed model was verified and validated using randomly generated numerical data and benchmark examples. The effect of dependent product demands and varying interest rate on the total cost function of the proposed model has also been investigated. Sensitivity analysis on the proposed model has been performed. Dynamic programming and simulated annealing optimization algorithms were used in solving the modeled example problems.
文摘Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing. Despite these advancements, efficiently programming GPUs remains a daunting challenge, often relying on trial-and-error optimization methods. This paper introduces an optimization technique for CUDA programs through a novel Data Layout strategy, aimed at restructuring memory data arrangement to significantly enhance data access locality. Focusing on the dynamic programming algorithm for chained matrix multiplication—a critical operation across various domains including artificial intelligence (AI), high-performance computing (HPC), and the Internet of Things (IoT)—this technique facilitates more localized access. We specifically illustrate the importance of efficient matrix multiplication in these areas, underscoring the technique’s broader applicability and its potential to address some of the most pressing computational challenges in GPU-accelerated applications. Our findings reveal a remarkable reduction in memory consumption and a substantial 50% decrease in execution time for CUDA programs utilizing this technique, thereby setting a new benchmark for optimization in GPU computing.
文摘针对城市居民区回收箱布局规划和路径优化问题,首先构建居民区回收箱数量与人口、回收频率、回收阈值的线性函数,并构建双层优化模型,回收总利润最大化作为上层目标,运输成本最小化作为下层目标。其次,为求解具有NP-hard特征的新模型,设计加入团体学习算子和自适应选择策略的人类学习优化算法,并与禁忌搜索算法嵌套构建混合人类学习算法(hybrid human learning optimization algorithm,HHLO)。再次,采用不同规模算例,并将新算法与基本人类学习算法、遗传算法、自适应粒子群算法、红嘴蓝鹊算法进行对比分析,验证了模型的可行性和算法的有效性。最后,通过上海杨浦区某实例进行灵敏度分析,探讨回收箱容量、分时定价策略和分区定价策略对回收中心总利润与居民满意度的影响。
