# The Environmental Impact of Inland Empty Container Movements Within Two-Depot Systems
## 2 Literature Review  
The inland movement of empty containers plays a considerable role in increasing carbon emissions, releasing millions of metric tons of CO2 into the atmosphere. Consequently, numerous authors have addressed this issue by utilizing operational research techniquesto improve the analysis of distribution planning and attain the optimal balance between operational costs and superior service quality. However, many existing studies do not comprehensively capture the wide array of specific operational decisions related to inland empty container repositioning, such as coordinated depot utilization, and adaptive truck routing strategies. The details of these operational challenges are discussed in the following sections. The limited exploration of these strategies in research leaves critical gaps regarding the environmental and operational implications of different logistical decisions. Addressing these clearly defined gaps forms the basis and justification for employing our ABM approach. While ABM has been widely applied in various areas of logistics and supply chain management, including warehouse operations [21], last-mile delivery [22], and supply chain resilience [23], these examples reflect only part of its broader use across this field. However, a review of the existing literature reveals that ABM has not yet been applied to the problem of inland empty container movements and their associated emissions. One of the earliest attempts to define and address the inland empty container repositioning problem can be found in foundational studies that helped shape this research area. [PERSON] and [PERSON] [24] conducted a review on the management of empty flows. They stated that, despite significant attention being paid to this problem, there was a lack of original models specifically addressing the allocation of empty containers within land-based distribution systems. Subsequently, [PERSON] et al. [25] discussed the strategic issue of assigning customers to depots in an inland transportation network managed by a maritime shipping company. They introduced an optimization model aimed at minimizing both the costs associated with depot establishment and the transportation of empty containers. Numerous mathematical models have been developed to improve empty container allocation in inland logistics, but many fail to address real-world factors like container leasing options and substitution between container types. [PERSON] et al. [26] presented a general framework to address the specific characteristics of the empty container allocation problem in the land distribution system of a shipping company. They developed two deterministic dynamic models, one for a single-commodity scenario and another for a multi-commodity variant. However, their formulations did not incorporate the management opportunities presented by flexible leased containers, and no computational results were provided. Similarly, [PERSON] et al. [27] investigated the distribution planning of empty cars in a railway transportation system. To mitigate the inefficiencies of the existing planning process, they proposed a time-extended network optimization model that accounted for multiple car types, all assumed to be of uniform length. However, their model did not consider substitution opportunities. Additionally, [PERSON] et al. [28] examined the impact of the end-of-horizon on the management of empty containers within a land-based distribution system. Their study considered a single container type, thereby excluding substitution possibilities. Additionally, company-owned containers were modeled as leased containers, as empty containers could be leased at any given time period. Some studies focused on strategic decisions in inland container logistics, like where to place depots and how to manage container flows, but they often used simplified assumptions and did not consider practical factors like using different container types or leasing options. [PERSON] et al.