In a world where supply chains are increasingly complex and unpredictable,finding the optimal way to move goods through transshipment networks is more important and challenging than ever.In addition to addressing the ...In a world where supply chains are increasingly complex and unpredictable,finding the optimal way to move goods through transshipment networks is more important and challenging than ever.In addition to addressing the complexity of transportation costs and demand,this study presents a novel method that offers flexible routing alternatives to manage these complexities.When real-world variables such as fluctuating costs,variable capacity,and unpredictable demand are considered,traditional transshipment models often prove inadequate.To overcome these challenges,we propose an innovative fully fuzzy-based framework using LR flat fuzzy numbers.This framework allows for more adaptable and flexible decision-making in multi-objective transshipment situations by effectively capturing uncertain parameters.To overcome these challenges,we develop an innovative,fully fuzzy-based framework using LR flat fuzzy numbers to effectively capture uncertainty in key parameters,offering more flexible and adaptive decision-making in multi-objective transshipment problems.The proposed model also presents alternative route options,giving decisionmakers a range of choices to satisfy multiple requirements,including reducing costs,improving service quality,and expediting delivery.Through extensive numerical experiments,we demonstrate that the model can achieve greater adaptability,efficiency,and flexibility than standard approaches.This multi-path structure provides additional flexibility to adapt to dynamic network conditions.Using ranking strategies,we compared our multi-objective transshipment model with existing methods.The results indicate that,while traditional methods such as goal and fuzzy programming generate results close to the anti-ideal value,thus reducing their efficiency,our model produces solutions close to the ideal value,thereby facilitating better decision making.By combining dynamic routing alternatives with a fully fuzzybased approach,this study offers an effective tool to improve decision-making and optimize complex networks under real-world conditions in practical settings.In this paper,we utilize LINGO 18 software to solve the provided numerical example,demonstrating the effectiveness of the proposed method.展开更多
冬季渠道输水过程中浮冰容易在闸前形成堆积体,导致过水断面束窄,严重时影响渠道的输水效率和安全运行.为判断渠道闸前浮冰的输移状态,开展了明渠水槽平板闸孔自由出流条件下的室内物理模型试验,提出了一种基于主成分分析与支持向量机(p...冬季渠道输水过程中浮冰容易在闸前形成堆积体,导致过水断面束窄,严重时影响渠道的输水效率和安全运行.为判断渠道闸前浮冰的输移状态,开展了明渠水槽平板闸孔自由出流条件下的室内物理模型试验,提出了一种基于主成分分析与支持向量机(principal component analysis and support vector machine,PCA-SVM)的闸前冰堆积与输移判别模型.通过相关性分析法确定输入特征间存在信息重叠,进而采用主成分分析法对特征向量进行降维,提取贡献率为86%的第一主成分和贡献率为7%的第二主成分作为输入特征,利用网格搜索方法确定多项式、高斯径向基和Sigmoid核函数的最优参数,通过混淆矩阵确定最优核函数为高斯径向基,最优核函数参数C为137,γ为0.37,建立PCA-SVM模型对试验数据进行监督学习.结果显示,模型在验证集上预测精确率为0.94,准确率为0.97,F1-Score为0.97,上游水流弗汝徳数(Fr1)和闸前水流弗汝徳数(Fr2)是渠道闸前冰输移与堆积的主要影响因素,闸孔相对开度(H/e)和闸门相对淹没水深(H1/H)是次要影响因素.进一步将已建立的模型应用在倒虹吸口浮冰状态判别试验中,验证开发模型的分类性能.研究成果可为冬季输水渠道的调度管理和安全运行提供重要参考.展开更多
基金the financial support of the European Union under the REFRESH-Research Excellence for Region Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Programme Just Transition and has been done in connection with project Students Grant Competition SP2025/062"specific research on progressive and sustainable production technologies"and SP2025/063"specific research on innovative and progressive manufacturing technologies"financed by the Ministry of Education,Youth and Sports and Faculty of Mechanical Engineering VSB-TUOThe authors would like to extend their sincere appreciation to Researchers Supporting Project number(RSP2025R472)King Saud University,Riyadh,Saudi Arabia.
文摘In a world where supply chains are increasingly complex and unpredictable,finding the optimal way to move goods through transshipment networks is more important and challenging than ever.In addition to addressing the complexity of transportation costs and demand,this study presents a novel method that offers flexible routing alternatives to manage these complexities.When real-world variables such as fluctuating costs,variable capacity,and unpredictable demand are considered,traditional transshipment models often prove inadequate.To overcome these challenges,we propose an innovative fully fuzzy-based framework using LR flat fuzzy numbers.This framework allows for more adaptable and flexible decision-making in multi-objective transshipment situations by effectively capturing uncertain parameters.To overcome these challenges,we develop an innovative,fully fuzzy-based framework using LR flat fuzzy numbers to effectively capture uncertainty in key parameters,offering more flexible and adaptive decision-making in multi-objective transshipment problems.The proposed model also presents alternative route options,giving decisionmakers a range of choices to satisfy multiple requirements,including reducing costs,improving service quality,and expediting delivery.Through extensive numerical experiments,we demonstrate that the model can achieve greater adaptability,efficiency,and flexibility than standard approaches.This multi-path structure provides additional flexibility to adapt to dynamic network conditions.Using ranking strategies,we compared our multi-objective transshipment model with existing methods.The results indicate that,while traditional methods such as goal and fuzzy programming generate results close to the anti-ideal value,thus reducing their efficiency,our model produces solutions close to the ideal value,thereby facilitating better decision making.By combining dynamic routing alternatives with a fully fuzzybased approach,this study offers an effective tool to improve decision-making and optimize complex networks under real-world conditions in practical settings.In this paper,we utilize LINGO 18 software to solve the provided numerical example,demonstrating the effectiveness of the proposed method.
文摘冬季渠道输水过程中浮冰容易在闸前形成堆积体,导致过水断面束窄,严重时影响渠道的输水效率和安全运行.为判断渠道闸前浮冰的输移状态,开展了明渠水槽平板闸孔自由出流条件下的室内物理模型试验,提出了一种基于主成分分析与支持向量机(principal component analysis and support vector machine,PCA-SVM)的闸前冰堆积与输移判别模型.通过相关性分析法确定输入特征间存在信息重叠,进而采用主成分分析法对特征向量进行降维,提取贡献率为86%的第一主成分和贡献率为7%的第二主成分作为输入特征,利用网格搜索方法确定多项式、高斯径向基和Sigmoid核函数的最优参数,通过混淆矩阵确定最优核函数为高斯径向基,最优核函数参数C为137,γ为0.37,建立PCA-SVM模型对试验数据进行监督学习.结果显示,模型在验证集上预测精确率为0.94,准确率为0.97,F1-Score为0.97,上游水流弗汝徳数(Fr1)和闸前水流弗汝徳数(Fr2)是渠道闸前冰输移与堆积的主要影响因素,闸孔相对开度(H/e)和闸门相对淹没水深(H1/H)是次要影响因素.进一步将已建立的模型应用在倒虹吸口浮冰状态判别试验中,验证开发模型的分类性能.研究成果可为冬季输水渠道的调度管理和安全运行提供重要参考.
