# CiteSpace-Based Bibliometric Review of Pickup and Delivery Problem from 1995 to 2021
## 4 Visualization Results and Bibliometric Analysis Based on CiteSpace
### Country and Institution Analysis
#### 4.4.2 Keyword Citation Burst Detection  
In order to reflect the historical development of research hotspots, and highlight the current research focuses to inspire scholars to conduct follow-up studies, we list the top 27 keywords with strongest citation burst in Figure 10, where the bile line represents the entire time horizon (1995-2021), the red line represents the burst duration of a keyword. Generally, for a specific keyword, the longer the burst duration and the higher the burst intensity, and the more people pay attention to it within a certain period. As shown in Figure 10, \"to many operation\" ranks first with 5.2535 citation burst strength, which begins in 1995 and ends in 2007. Followed by keyword \"a ride problem\" with 7.2523 citation burst strength, which also begins in 1995 and ends in 2007. Therefore, we can infer that solving real-world operational problems with pickup and delivery demand is the main reason for the appearance of PDP research.  
Figure 10: The top 27 keywords with strongest citation burst between 1995 and 2021.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Freq.** & **Year** & **Keyword** \\ \hline
15 & 64 & 2002 & Genetic Algorithm \\
16 & 63 & 2007 & Branch \\
17 & 63 & 2003 & Delivery Problem \\
18 & 57 & 2004 & Logistics \\
19 & 56 & 2000 & Transportation \\
20 & 51 & 2000 & Routing \\ \hline \end{tabular}
\end{table}
Table 8: _Cont._In the middle of the development process, along with the popularity of the concept of PDP, scholars have switched their attention to \"backhaul\", \"DARP\", \"multiple stack\", \"cut\", \"bound algorithm\", \"heuristic algorithm\", \"metaheuristic algorithm\", and \"LIFO\". Among them, \"DARP\" as the special case of PDP, many of its results can be naturally applied to other works of PDP research. A typical example is that [PERSON] et al. [43] designed a genetic algorithm based on clustering and routing heuristic to address the DARP, whose methodology laid theoretical foundation for the core idea of [PERSON] et al. [44] and [PERSON] et al. [45]. Specifically, [PERSON] et al. [46] subtly combined local search metaheuristic with set partitioning to form hybrid algorithm, which can obtain high-quality solutions of multi-depot heterogeneous DARP within a short time. Another keyword worth noting is \"LIFO\", which is the abbreviation of \"last-in-first-out\" that guarantees only the last pickup customer request that has been loaded can be delivered [47], the opposite is \"first-in-first-out\" (FIFO). As shown in Figure 10, the citation burst strength of \"LIFO\" up to 3.6951 (ranks tenth) and this trend is likely to become hotspot again in the future. In 2011, [PERSON] et al. [48] proposed a new tree representation for the feasible solutions of PDP with LIFO loading, this method can automatically make sure the feasibility of solution and increase the opportunity of acquiring better strategy during the execution process. Recently, [PERSON] et al. [49] considered both splitting mechanism and Lin-Kernighan heuristic in their study, and then introduced a learning-based memetic algorithm for addressing the PDP considering multiple vehicle and LIFO loading.  
However, the emerging keywords including \"branch\", \"LNS\", \"hybrid algorithm\", \"simultaneous pickup\", and \"optimization\" can be seen as the hotspots in recent years. In terms of the distribution of emerging keywords, the current hotspots focus on addressing PDPs concerning more complicated service requirements. For example, [PERSON] et al. [50] designed a self-learning particle swarm optimization approach to deal with the PDPs with multiple cross-docks. [PERSON] et al. [51] focused on a class of time-dependent profitable PDPTWs, and designed an adaptive large neighborhood search (ALNS) to acquire its high-quality solutions. [PERSON] et al. [52] proposed a parallel simulated annealing algorithm to solve the mathematical optimization model of vehicle routing problem with simultaneous pickup-delivery and time windows (VRPSPDTW).