# CiteSpace-Based Bibliometric Review of Pickup and Delivery Problem from 1995 to 2021
## 4 Visualization Results and Bibliometric Analysis Based on CiteSpace
### Reference Analysis
#### 4.5.2 Reference Co-Citation Clustering Analysis
However, with the deepening of the research contents, mathematical models with complex structure and multiple constraints impose higher requirements on the solution algorithm, the scenarios where the exact algorithms can be applied are somewhat limited. Therefore, more powerful and efficient heuristic algorithms and metaheuristics are often used by scholars in PDP research. It can be seen from Figure 12 that both Cluster #5 (metaheuristic approach) and Cluster #10 (heuristics) have roughly undergone a decade of development, and the references related to metaheuristics have been cited frequently in recent years. In fact, metaheuristic algorithm can be interpreted as a more general method compared to the heuristic algorithm, because it is an integrated algorithm of several modular algorithms and does not depend on problem-specific conditions. Specifically, LNS and variable neighborhood search (VNS) are the two most frequently adopted heuristic algorithms in the research on PDP; metaheuristic algorithms designed to address PDPs mainly include tabu search, genetic algorithm, simulated annealing, evolutionary algorithm, and particle swarm optimization. In recent years, some scholars focused on developing more computationally efficient hybrid algorithms, and its core concept is how to properly insert part or all of an algorithm into the execution of another method. Up to now, hybrid algorithms frequently used in PDP research include hybrid LNS [64; 65], hybrid evolutionary algorithm [66], hybrid genetic algorithm [44; 67], hybrid VNS or simulated annealing [68], and hybrid metaheuristic [46; 69; 70]. However, the hybrid algorithms are usually designed according to the characteristics of specific problem, the applicability and adaptability of them are relatively weak compared with underlying algorithms belonging to heuristics and metaheuristics. Cluster #1 (maritime transportation) occupies the second position with 145 members, which indicates one of the real-life scenarios where the PDPs are taken seriously by logistics practitioners. In the practical transportation and packaging activities, the impact of various constraints on the carrier vehicle's route choice behavior is not negligible. Among them, the first and most mentioned is Cluster #3 (PDPTW), many of the literatures mentioned above (e.g., [33; 38; 55; 56; 57]) can be regarded as enriching the study of PDPTW from different perspectives. Another critical constraint considered frequently is loading operations, which means the goods must be implemented with the specified operations for loading or unloading. [PERSON] and [PERSON] [71] conducted a comprehensive review of practically relevant loading constraints including weight limits, orientation, stacking, stability, LIFO, FIFO, and allocation, as well as summarized the contents of literatures related to how to introduce loading constraints into the model and solve them further. The current research directions related to loading constraints in PDP can be classified into two aspects: two-dimensional and three-dimensional, and the difference between them is whether packages are allowed to stack on top of each other. [PERSON] et al. [72] developed an ant colony algorithm to analyze the strategy space for two-dimensional loading VRP, and lower bounds, heuristics, and branch-and-bounds were used to examine the feasibility of loading on each route. In terms of three-dimensional loading, [PERSON] and [PERSON] [64] introduced five variants of the PDP with three-dimensional loading constraints (3L-PDP) from the perspective of routing and packing constraints discussed by [PERSON] et al. [73], including LIFO loading constraints, weight constraints, orientation constraints, and support constraints. A hybrid algorithm combining LNS with tree search was further proposed in [64] to determine the optimal solution of aforementioned five extension models. With the above discussions, it can be found that only a few exact algorithms and heuristic algorithms have been developed for loading constraints so far.