## 5 Numerical Experiments
### Experiments Setting and Instances Generation  
Since the proposed comprehensive CDP encompasses eight types of requests, no publicly available benchmark instances can be found in the literature. Therefore, the experimental instances are generated randomly, using a method similar to those applied in prior studies, such as [PERSON] et al. (2010) and [PERSON] et al. (2011). For each instance, the locations of a container terminal, six inland depots, and a certain number of customers (shippers and consignees) are stochastically distributed on a Euclidean plane, with both the length and width corresponding to a 3 h distance by truck. It is important to highlight that the  
Figure 4: Move operations.  
terminal fulfills two distinct functions in this context. For the IF and OF requests, the terminal is considered a bypass node, and its function is solely to calculate the transfer time on the arc passing through the terminal. In contrast, for the IE and OE requests by shipping companies, their locations are actually the terminal. However, in the vertex configuration, these locations are assigned distinct numerical identifiers, despite having identical coordinates. The time needed for loading (or unloading) a container onto (or from) a truck is 0.1 h, while the time required for packing or unpacking a container is 0.4 h. Additionally, the left boundary of the request time window is randomly assigned within a range of [0, 4] h, with the width of the time window varying between 4 and 8 h.  
The traveling speed of a truck is assumed to be constant, irrespective of whether it is loaded or empty. The cost parameters \(C_{1}\), \(C_{2}\), and \(C_{3}\) are set to 6.516, 1744, and 56.295, respectively, based on the values reported by [PERSON] et al. [50] and [PERSON] et al. [42]. It should be noted that \(C_{1}\) is only the total fuel cost and carbon emissions cost per unit of traveling time, which does not include the fixed one-time startup cost of dispatching a truck for a request. The parameters of the LNS algorithm, including the number of initial solutions, maximum number of iterations, and removal coefficient, are tailored to the specific characteristics of each instance under consideration. The scope of the removal coefficient ranges from 0.2 to 0.8. To ensure the results' reliability, each experimental instance was solved multiple times with the LNS, which may vary depending on the problem size, thus reducing the impact of randomness.  
In practical scenarios, a random combination of diverse request types may arise. To assess the performance of the proposed LNS, fifty experimental instances of varying sizes, spanning small-, medium-, large-, and super-large-scale instances, are generated. These instances are characterized by the quantities of each request type and the number of empty containers at each depot. Taking instance 6 as an example, the column \"No. of Requests\" indicates that the total number of requests is 12, with the distribution of eight request types (IF, ER, ED, OF, IE, OE, IFER, and OFED) as follows: 1, 1, 2, 0, 1, 2, 2, and 3, respectively. Additionally, the column \"No. of Empty Containers\" shows that the number of empty containers at depots 1 through 6 is 1, 0, 0, 2, 1, and 0, respectively. However, it is common for some customers to require trucks to remain at their locations awaiting packing or unpacking when handling composite requests. This necessitates the use of the Stay-with mode and adds complexity to the scheduling of trucks. Such customers are included in some of the super-large-scale instances. For example, in instance 42, \"40 (10)\" indicates that there are 40 customers with IFER requests, of which 10 need trucks to wait for packing or unpacking.