## 2 Literature Review  
Over the past twenty years, extensive research has been conducted in the field of CDP, focusing on various types of requests as well as practical constraints and scenarios, including resource constraints, trucking operation modes, empty container repositioning strategies, multiple depots and multiple terminals, etc. [12]. This body of work has led to numerous significant insights through the exploration of the CDP and its various adaptations. In this section, we review the literature from the perspective of the types of transport requests addressed. The CDP is essentially the pickup and delivery problem of containers. [PERSON] and [PERSON] [4] were the first to introduce the CDP supporting rail or maritime intermodal operations as the local full truckload pickup and delivery problem with time windows (FTPDPTW). In their study, there was only one type of container transport request, which was defined as picking up a container from a dedicated origin and delivering it to a specified destination. The FTPDPTW was modeled as a \"multi-Traveling Salesman Problem with Time Windows\" (m-TSPTW) using integer programming and solved by designing a heuristic algorithm that discretizes time window constraints. Following this initial breakthrough, the problem began to attract increasing attention from scholars and practitioners alike. [PERSON] et al. [13] extended [PERSON] and [PERSON] [4] by proposing an exact algorithm based on dynamic programming for small size instances and a hybrid method combining genetic algorithms and dynamic programming for large size instances. To more accurately capture the practical scenarios or constraints encountered by the Korean trucking industry, [PERSON] et al. [14] developed several variants of the basic model presented in [PERSON] and [PERSON] [4] by considering multi-size containers, heterogeneous fleets, and vehicles with different ownership, and solved them with an insertion heuristic. Furthermore, [PERSON] et al. [15] formulated a generalized multiple matching problem model applicable to any type of vehicle capable of simultaneously transporting multiple 20 ft and 40 ft containers, while [PERSON] and [PERSON] [16] modeled the pickup and delivery process of 20 ft and 40 ft containers with heterogeneous fleets as a MILP. To solve larger instances, the former designed a variable neighborhood search (VNS) heuristic, and the latter proposed a decomposition and aggregation heuristic. Recently, [PERSON] et al. [17] established a more practical variant of the CDP with release and due dates by considering the multi-trip multi-period features and designed combinatorial Benders' Cuts methods. Since the haulage of more than one multi-size trailer by a truck is legal, [PERSON] et al. [18] focused on solving a dynamic multi-size multi-trailer CDP with a rolling-horizon approach. Aiming at handling the requests with uncertain arrival times, [PERSON] et al. [19] designed a Markov decision process model for the CDP and combined reinforcement learning with integer programming to solve it. With the emergence of e-commerce drayage platforms, traditional drayage operations are facing new challenges. Thus, [PERSON] et al. [20] investigated a dynamic drayage container booking and routing problem with regular and flexible requests. Against the backdrop of the \"dual carbon\" goal [21], electric trucks hold the promise of tackling climate-related issues. Hence, [PERSON] et al. [22] introduced electric trucks into the CDP, considering charging resource constraints. They developed an improved adaptive LNS algorithm by combining it with an exact method. However, the above articles did not clearly distinguish between inbound and outbound containers, nor did they differentiate between empty and full containers. In practice, after an inbound full container is unpacked at its consignee location, an empty container will be left behind which can be moved back to a terminal, a depot or a shipper demanding an empty container.