# 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
Meanwhile, they designed a hybrid LNS that combines tabu search with five insertion/removal operators to address revised model, and verified the proposed algorithm can obtain better results in three test sets compared with CPLEX and three-step heuristic [82]. In dynamic case, [PERSON] et al. [83] proposed a dynamic BRP model considering the weighted sum objectives of unfulfilled user demands and target fill level, capacity constraints of carrier vehicle, and travel time. [PERSON] and [PERSON] [84] developed a modular algorithm based on fuzzy decision support system to determine the optimal route choice decision, time intervals, and the quantity of bikes to be rebalanced in a dynamic system concerning rebalancing cost and user's satisfaction level. Cluster #2 (collaborative vehicle routing) is another manifestation of resource sharing, which refers to the providers of logistics service want to reduce transportation costs and improve logistics operation efficiency by sharing their limited logistics resources (e.g., vehicles and facilities) [85]. Due to the increasing variety and volume of freights to be transported, classical independent distribution scenario, i.e., a single logistics provider utilizes his resources to serve all customers in the distribution area, will lead to low service quality, roundabout delivery, and high distribution price. Therefore, collaborative distribution scenario that several logistics service providers form partnerships to fulfill the orders is becoming more and more prevalent in the activities of logistics management. However, the optimal coalition structure and feasible cost allocation are two main tasks of multiple participators in collaborative vehicle routing. [PERSON] et al. [85] formulated the collaborative multicenter pickup and delivery problem with time windows assignment (CMPDPTW) into a bi-objective programming model, and a hybrid algorithm including k-means clustering, clarke-wright saving, and non-dominated sorting genetic algorithm-II was developed for solving it. By applying snowball theory to evaluate the stability of alliance caused by different profit allocation schemes, game quadratic programming mechanism has the highest probability of potential collaborators agreeing to join the alliance. [PERSON] and [PERSON] [86] proposed a hierarchical heuristic algorithm to determine the optimal coalition structure in collaborative PDPTW considering maximum cardinality constraint, and the cost allocation mechanism realized by calculating the weighted sum of contributions of collaborators, i.e., Shapely value method, was further designed for guaranteeing the fairness in allocations. In recent years, as customer enthusiasm for electric vehicle (EV) has grown rapidly in emerging markets, logistics service providers have shown a keen interest in taking EVs into their fleets. In contrast to traditional fuel vehicle (FV), electric vehicle (EV) as a type of new energy vehicle can significantly reduce greenhouse gas emissions and operation costs in transport sector, and targeted government policies and particular public demands greatly drive the sustainable development of EV industry in global countries. However, Cluster #8 (electric vehicle routing) as an extension of the previous VRP studies involves additional non-negligible considerations while finding the optimal visit paths for electric vehicles, such as charging technology [87], battery swapping [88; 89], energy consumption [90; 91], recharging strategies [92; 93], and the capacity of charging stations [94]. If customers in electric VRP have three types of demands, i.e., pickup, delivery, or both pickup and delivery, and EVs are dispatched to satisfy the pickup and delivery requests during a single visit, electric VRP will be reformulated into electric PDP. [PERSON] et al. [95] developed three variants of evolutionary VNS algorithm to improve the quality of solutions of electric DARP considering battery swapping of EVs.