## 5 Numerical Experiments
### Experiments on Small- and Medium-Scale Instances  
To validate the correctness of the proposed MILP model for the comprehensive CDP, as well as evaluate the performance of the LNS, ten small-scale instances and ten medium-scale instances are generated. All the instances are solved using both Gurobi and the LNS. The results of objective values and solving time are reported in Tables 4 and 5, respectively. Firstly, as presented in Tables 4 and 5, the results obtained from the small-scale and medium-scale instances provide strong evidence supporting the validity of the model. For all the small-scale instances, Gurobi is able to find the optimal solutions with an average solving time of 260 s and a maximum solving time of 729 s. As the instance scale increases, the solving time rises significantly. It is important to note that the complexity of the problem depends not only on the total number of requests but also on the number of composite requests. In short, it is determined by the total number of request vertices after decomposing the composite requests. Therefore, although instances 10 and 11 have the same number of requests, the split of composite requests substantially increases the computational burden,leading to Gurobi being unable to find the optimal solutions for instance 11 and other medium-scale instances within 1 h. However, the average gap between the objective value and the lower bound across the ten medium-scale instances is only 4.81%, demonstrating that Gurobi is capable of providing near-optimal solutions. Such a level of performance exhibited by the MILP model can be regarded as acceptable for practical scenarios. Secondly, Tables 4 and 5 also validate the outstanding performance of the proposed LNS. In particular, the LNS is able to optimally solve instances 1 to 5 in an average time of just 2.74 s, significantly faster than Gurobi, which takes an average of 49.60 s. For the remaining five small-scale instances and some medium-scale instances (11, 12, 13, 16, and 19), although the LNS yields slightly higher objective values than Gurobi, the average gap is only 2.88%. The reason why the solutions obtained by LNS are worse than those from Gurobi can be attributed to two factors. One factor is that LNS may become trapped in local optima. Another factor is that the \"sequential insertion\" operation proposed in this study is a heuristic method. It converts the solution sequence into a truck routing plan according to specific rules while ensuring feasibility. However, even if the solution sequence originates from an optimal truck routing plan, this approach does not guarantee the generation of an optimal solution. Although this limitation exists, the advantage of the LNS becomes more pronounced as the scale of the instances increases, both in terms of objective values and solving time. The LNS can solve instances 14, 15, 17, 18, and 20 within an average time of only 44.07 s, while it also provides better solutions than Gurobi within a 1 h time limit. For all the small-scale and medium-scale instances, the gap between the LNS and Gurobi is only 0.26%, while the LNS requires considerably less solving time, further validating its effectiveness and its potential for larger scale instances.