# CiteSpace-Based Bibliometric Review of Pickup and Delivery Problem from 1995 to 2021
## 5 Discussion of Evolution Trend  
Keyword burst analysis in Section 4.4.2 identifies the chronological research hotspots, the timeline view of reference co-citation clusters in Section 4.5.2 presents the dynamic accumulation of knowledge base, both of them moderately reflect the research features and scopes in a time-series. Thus, we put the historical development of research hotspots and knowledge clusters on the same timeline, and consider their common and unique characteristics simultaneously. Then the evolution trend of PDP research can be roughly summarized into three stages, namely, start-up, initial development, and rapid development stage. (1) Start-up stage (1995-1999). In this stage, route choice made by practitioner in logistics system considering customers' pickup and delivery demand was often inefficient. In order to improve the operation revenue and avoid unnecessary expenses, a fraction of scholars and academic teams was entrusted to resolve this dilemma, who utilized the knowledge base sourced from the discipline of \"Transportation Engineering\" and \"Operations Research\" to develop a number of basic mathematical models and algorithms. In this stage attention is paid to the topics of \"VRP\", \"algorithm\", \"time window\", \"model\", \"to many operation\", and \"a ride problem\". Indeed, it can be seen that the main contents during this stage focused on developing simple but appropriate mathematical models based on the theories and methodologies of VRP research, and these newly developed models can capture the picture of reality better. A most typical example is that [PERSON] and sol [98] proposed a general PDP model from the perspective of practical transportation scenarios, and elaborated the differences between PDPs and traditional VRPs. In terms of the modeling of different types of PDPs, location problem [99; 100], DARP [101; 102; 103], and PDPTW [61; 62; 63; 104] were especially of interest to scholars. Although the research literatures in this stage were relatively scattered, the characteristic of \"pickup and delivery\" began to attract attention from academia and industry. Moreover, reference co-citation analysis confirmed that these few but high-value literatures did make a great contribution to the development and innovation of PDP research, most of the subsequent works have also referred to them. (2) Initial development stage (2000-2010). From 2000 to 2010, there was a gradual increase in the quantity of publications, and a series of mathematical models and methodologies began to be proposed, which constituted a formal knowledge system of PDP research. During this stage, \"TSP\", \"backhaul\", \"DARP\", \"LIFO\", \"tabu search\", \"heuristics\", and \"search\" have become the hot topics. Moreover, it can be seen in the timeline view of knowledge clusters that various relevant knowledge domains in this stage have regularly organized into specific knowledge clusters, and these clusters were closely correlated. Cluster #0, #1, #2, #3, and #10 have been all gradually developed since 2000. From the perspective of the distribution of hotspots and knowledge clusters, more specific, powerful, and computationally efficient algorithms were required to solve the variant and extension of PDP-related models with multiple intractable constraints. For example, [PERSON] et al. [105] considered the situation that the pickup demands can be partially satisfied, and only profitable pickup locations will be visited due to capacity constraints. In terms of algorithm optimization, they proposed tabu search heuristic algorithm to obtain the near-optimal route choice after reviewing the construction process of classical heuristics. [PERSON] et al. [106] presented splitting loads in pickup and delivery activities can reduce the total transportation cost and the number of vehicles dispatched. The results of computational tests demonstrated that the maximum benefits of splitting loads occur with loadsthat are just over half the capacity of the vehicle.