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Description自雇司机是公路货运司机中比例人数最多、最基层的一员,他们在公路物流行业中扮演着极为重要的角色,他们承担着各种来源的压力。本文以疫情前后按揭购买卡车的自雇司机为研究样本,基于本研究收集到的独特数据,研究发现自雇卡车司机在面临按揭压力时,倾向采取更为激进的经营及驾驶行为,表现为更少的休息天数、更长的工作时长以及更危险的高速驾驶行为,并在一系列稳健性检验中基本结论仍然存在;基于新冠疫情事件研究发现,新冠疫情带来的非预期性经济停摆和收入中断,导致疫情前的发生的按揭贷款的卡车司机面临更强的还款压力,在经济恢复后面对按揭压力更有可能采用激进的经营和驾驶行为;进一步,通过机制检验研究本文发现这种按揭压力主要表现为担心当前或者未来发生不能及时偿还按揭款。再者,基于人格性征和家庭支持的调节效应检验,本文发现神经质人格特征、谨慎尽责性人格特征以及工作压力感没有在按揭压力与自雇卡车司机激进的经营和驾驶选择上起到调节作用,这可能是自雇卡车司机面临的按揭压力都很大,个体性格特征很大程度无法缓和其压力感,而家庭的支持和家庭-工作平衡可以有效缓解自雇卡车司机面临按揭压力时提高工作时长和危险驾驶行为的倾向。 最后,本文设计一项随机对照干预实验,向自雇卡车司机发送短息或者微信,提醒他们避免疲劳驾驶和危险超速驾驶,然后观察发送短信微信前后自雇卡车司机经营及驾驶行为的变化,识别考察外界积极主动的关心和提醒能否起到相应的后果。本文发现对自雇卡车司机获得外部主动积极地的关心和提醒,在面临按揭压力时意识到简单地减少休息增加运营时长以及采用危险驾驶行为抢时间的策略可能给其带来很大的风险,从而相应地缓解对按揭压力的过度反应;进一步调节作用检验表明,短信干预实验在神经质和谨慎尽责性人格司机中起到更大的减缓作用,同时家庭支持较少时短信干预实现效应也更为明显。
ContributorsMa, Liqun (Author) / Shen, Wei (Thesis advisor) / Wu, Fei (Thesis advisor) / Zhang, Zhen (Committee member) / Arizona State University (Publisher)
Created2021
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Description
This dissertation studies load balancing algorithms for many-server systems (with N servers) and focuses on the steady-state performance of load balancing algorithms in the heavy traffic regime. The framework of Stein’s method and (iterative) state-space collapse (SSC) are used to analyze three load balancing systems: 1) load balancing in the

This dissertation studies load balancing algorithms for many-server systems (with N servers) and focuses on the steady-state performance of load balancing algorithms in the heavy traffic regime. The framework of Stein’s method and (iterative) state-space collapse (SSC) are used to analyze three load balancing systems: 1) load balancing in the Sub-Halfin-Whitt regime with exponential service time; 2) load balancing in the Beyond-Halfin-Whitt regime with exponential service time; 3) load balancing in the Sub-Halfin-Whitt regime with Coxian-2 service time.

When in the Sub-Halfin-Whitt regime, the sufficient conditions are established such that any load balancing algorithm that satisfies the conditions have both asymptotic zero waiting time and zero waiting probability. Furthermore, the number of servers with more than one jobs is o(1), in other words, the system collapses to a one-dimensional space. The result is proven using Stein’s method and state space collapse (SSC), which are powerful mathematical tools for steady-state analysis of load balancing algorithms. The second system is in even “heavier” traffic regime, and an iterative refined procedure is proposed to obtain the steady-state metrics. Again, asymptotic zero delay and waiting are established for a set of load balancing algorithms. Different from the first system, the system collapses to a two-dimensional state-space instead of one-dimensional state-space. The third system is more challenging because of “non-monotonicity” with Coxian-2 service time, and an iterative state space collapse is proposed to tackle the “non-monotonicity” challenge. For these three systems, a set of load balancing algorithms is established, respectively, under which the probability that an incoming job is routed to an idle server is one asymptotically at steady-state. The set of load balancing algorithms includes join-the-shortest-queue (JSQ), idle-one-first(I1F), join-the-idle-queue (JIQ), and power-of-d-choices (Pod) with a carefully-chosen d.
ContributorsLiu, Xin (Author) / Ying, Lei (Thesis advisor) / Maguluri, Siva Theja (Committee member) / Wang, Weina (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2019