近年来，电子商务的发展为货物运输行业带来巨大的增长。同时，长途卡车司机作为城市间物流货运的主体，其交通安全问题也日益凸显。睡眠相关疲劳被认为是导致长途货运交通事故的重要原因之一。面对传统的事后型疲劳管理方法，本文提出了前瞻型疲劳管理方法，以“构建生物数学疲劳模型-验证模型-依据模型进行工作设计”为主线，探索“基于生物数学疲劳模型的卡车司机工作设计”问题。 本文构建了个性化生物数学疲劳模型，该模型以经典SAFTE模型为基础，通过调整SAFTE模型参数刻画个体疲劳特征。本文采用基于自适应矩估计的梯度下降算法估计个性化模型参数，并由此构建个性化生物数学疲劳模型。本文基于文献中已有的实验室数据展开数值实验，实验结果初步展示了个性化模型的预测优势。本文随后搭建了基于可穿戴设备和智能手机的数据采集系统，分别采集文职人员和长途卡车司机在真实工作场景中的睡眠活动数据和疲劳水平数据，并验证了个性化疲劳模型的预测准确性。实验结果表明，和过往模型相比，个性化生物数学疲劳模型能够更准确地预测真实工作场景中个体的疲劳水平。 在构建并验证疲劳模型之后，本文利用生物数学疲劳模型指导长途卡车司机排班规划，保证司机既能完成运输任务又能降低疲劳风险。本文将考虑生物数学疲劳模型的排班规划问题建模为混合整数规划问题，设计了启发式排班算法求解该问题。数值实验结果显示，在排班规划时引入生物数学疲劳模型能够提升司机警戒性水平。随后，本文将生物数学疲劳模型引入司机路径规划问题，从更顶层的工作设计视角平衡运输效率与安全。本文将考虑生物数学疲劳模型的卡车司机路径规划问题建模为两阶段混合整数规划问题，并设计了自适应大规模邻域搜索元启发式算法求解该问题。数值实验结果显示，考虑生物数学疲劳模型的路径规划能够提升司机警戒性水平，且能够帮助司机更好地应对潜在的疲劳风险因素。 综上所述，本文对生物数学疲劳建模领域进行深化和拓展，构建了适用于个体司机的生物数学疲劳模型，为个体层面的前瞻型疲劳管理提供理论基础；此外，本文还研究了考虑生物数学疲劳模型的卡车司机排班和路径规划问题，从建模和求解方法的层面上，对已有的文献进行了补充和拓展，同时，本文也为长途卡车司机的前瞻型疲劳管理提供了管理洞见和实践指引。

With the tremendous growth of urbanization and e-commerce, the transportation industry has increased recently. Meanwhile, the number of traffic accidents related to long-haul truck drivers, who act as the main body of inter-city logistics freight, is increasing. Sleep-related fatigue is one of the major reasons for long-haul traffic accidents. The traditional fatigue management methods detect and intervene against fatigue after it occurs. Considering the limitations of traditional methods, this study proposes a novel fatigue management approach, following the stages of building bio-mathematical models of fatigue (BMMF), validating BMMFs, and designing drivers' transportation tasks based on BMMFs. The main objective of this dissertation is to perform truck driver task design based on the bio-mathematical models of fatigue.Firstly, this study established the individual BMMF by adjusting the model parameters in the classical SAFTE model to describe individual fatigue attributes. This study develops an adaptive moment estimation gradient descent to estimate the model parameters of the individual BMMF. Experimental results based on existing laboratory data in the literature show that the proposed individual BMMF can improve prediction accuracy. Subsequently, this study develops a data collection system based on wearable devices and smartphones to collect the sleep activities data and fatigue data of civil servants and long-haul truck drivers in real working scenarios to validate the individual BMMF. The experimental results show that the individual BMMF can predict the fatigue level of individual truck drivers and civil servants more accurately than the previous BMMFs.After establishing BMMFs, this study designs schedules for long-haul truck drivers according to BMMFs. The truck driver scheduling problem focuses on how to arrange drivers' driving and rest activities to fulfill the transportation task and to avoid fatigue risk. We formulate the truck driver scheduling problem considering BMMFs as a mixed-integer programming model and develop a heuristic scheduling algorithm to solve it. The numerical experiments results demonstrate the value of BMMFs in scheduling planning. After performing scheduling planning considering BMMFs, this study turns to the upstream routing planning decisions to balance efficiency and safety. We model the truck driver routing problem considering the bio-mathematical model as a two-stage mixed integer programming problem and design an adaptive large neighbor search heuristic algorithm to solve it. The numerical experimental results show that BMMFs can improve drivers' alertness and deal with various fatigue risk factors in drivers' working scenarios.The present study is a further investigation of the BMMF research field. The proposed individual BMMF could explain the substantial differences in sleep-related fatigue among individuals, and provide a theoretical basis for prospective individual fatigue management. In addition, this study also pays attention to truck driver scheduling and routing problems considering BMMFs. The models and algorithms employed in this paper extend the existing literature. This study also provides management insights and practical guidance for the prospective fatigue management of long-haul truck drivers.