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Logistics horizontal collaboration:an agent-based simulation approach to model collaboration dynamics

机译:物流横向协作:一种基于代理的仿真方法来建模协作动力学

摘要

Underutilized capacity, long shipping lead time, high cost and lack of sufficient scale are examples of logistics inefficiencies that have troubled many supply chain operations. Logistics horizontal collaboration (LHC) is believed to be an innovative approach to tackle the increasing logistics challenges. This kind of collaborative logistics is quickly gaining momentum in practice but relevant contributions in literature are scarce. So far it remains unclear how LHC could be structured and operated given the limited understanding of the various characteristics and forms of LHC between companies. Furthermore, the explicit impact of LHC on the participating partners, as well as on the supply chain system is understudied. Very few studies have explored the process of collaboration and how it links to performance behaviours. Case studies and Agent-Based Simulation are employed in this thesis to study the research gaps identified above. Case studies are initially conducted to examine the key elements which can support the design of LHC, and to make a classification of models for collaboration. These are followed by Agent-Based Simulation to model a typical collaboration process and work out what benefits would emerge if participating in horizontal collaboration and how the collaboration can produce the impacts on the supply chain operations for individuals and the system as a whole. The case studies suggest that “collaboration structures”, “collaboration objectives”, “collaboration intensity”, and “collaboration modes” are the four key elements critical to the design of a LHC project. Each element represents an important aspect of the collaboration and exhibits different characteristics and forms. Based on these key elements, several typologies are derived which together provide a comprehensive view to explain the different types of LHC in practice. The simulation modelling demonstrates that LHC can significantly benefit the logistics efficiency in terms of capacity utilization and customer service in the sense of order fill-rate, and such beneficial effects are consistently observed in different supply chain environments. In particular, LHC can produce better logistics performance in a relationship-based supply chain network where downstream customers can support upstream shippers with more stable and predictable demand. On the other hand, information sharing in the collaboration, for the most part, does not facilitate the higher collaboration gains for partners. Specifically, sharing either the demand or supply information in the horizontal collaboration is not helpful in increasing collaboration gains. Hence there is a difference for the value of information sharing in the context of horizontal collaboration as opposed to vertical collaboration, the latter of which is often justified as providing more beneficial gains. The research findings provide insights for practitioners and scholars about how to develop a type of collaboration project or study, as well as enabling a better understanding of the dynamic collaboration effects.
机译:利用率不足,运输提前期长,成本高以及缺乏足够的规模是造成效率低下的问题,这些问题困扰了许多供应链运营。物流水平协作(LHC)被认为是解决日益严峻的物流挑战的一种创新方法。这种协作式物流在实践中迅速获得发展势头,但在文学上的相关贡献却很少。到目前为止,由于对公司之间的LHC的各种特征和形式的了解有限,因此尚不清楚LHC的结构和运作方式。此外,人们还没有研究大型强子对撞机对参与伙伴以及供应链系统的明显影响。很少有研究探讨协作的过程以及它如何与绩效行为联系起来。本文以案例研究和基于Agent的仿真为研究对象,对上述研究空白进行了研究。最初进行了案例研究,以检查可以支持LHC设计的关键要素,并对协作模型进行分类。接下来是基于代理的模拟,以模拟典型的协作过程,并确定参与横向协作会产生哪些好处,以及协作如何对个人和整个系统的供应链运作产生影响。案例研究表明,“协作结构”,“协作目标”,“协作强度”和“协作模式”是LHC项目设计的四个关键要素。每个元素代表合作的重要方面,并展现出不同的特征和形式。基于这些关键要素,得出了几种类型,它们在一起提供了一个全面的视图,以在实践中解释LHC的不同类型。仿真模型表明,就订单填充率而言,LHC可以在产能利用率和客户服务方面显着提高物流效率,并且可以在不同的供应链环境中持续观察到这种有益效果。特别是,LHC可以在基于关系的供应链网络中产生更好的物流绩效,下游客户可以以更稳定和可预测的需求为上游托运人提供支持。另一方面,协作中的信息共享在大多数情况下并不能促进合作伙伴获得更高的协作收益。具体而言,在横向协作中共享需求或供应信息对增加协作收益没有帮助。因此,与纵向协作相比,在横向协作的情况下信息共享的价值存在差异,后者通常被认为可以提供更多有益的收益。研究结果为从业者和学者提供了有关如何开发一种协作项目或研究的见识,以及使人们能够更好地了解动态协作效果。

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    Zhu Jie;

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