# CiteSpace-Based Bibliometric Review of Pickup and Delivery Problem from 1995 to 2021
## 7 Outlook and Future Trend  
There still exists an extensive research potential on the PDP, since we are witnessing great innovations in the evolution process of mathematical models and solution algorithms, coupled with the advancements in logistics engineering and computer technology. Here, we provide several interesting and promising research perspectives to logistics practitioners and enthusiastic researchers, which may trigger more results and contributions in the field of PDP. * Improved solution algorithm. Since the PDP is a kind of NP-hard problem, most of the aforementioned studies in this field concentrate on designing computationally efficient heuristic algorithms to deal with different intricate PDPs. Although a part of studies in the last decade has involved the concept of hybrid algorithms (e.g., hybrid LNS, hybrid genetic algorithm, and hybrid evolutionary algorithm), most algorithms presented so far have been proposed for very specific scenarios and mild size (only a few hundred pickup and delivery points). Judging from the bright prospect of e-commerce, a fleet of vehicles serving thousands of pickup and delivery points will also be seen in the following years, then the latest solution methods may not necessarily apply in future studies. With the development of parallel computing, we believe there are opportunities to develop more novel, advanced, or alternative algorithms for addressing general and large-scale PDPs. * Uncertain demand and dynamic travel time. PDPs in reality are always full of dynamics and uncertainty, particularly because delays and errors occurred frequently in the process of information transmission. In most real-world situations, customers' demand at each pickup /delivery location cannot be known correctly in advance, and the travel time on road usually varies with the real time road condition. All of these can have serious impact on the choice of optimal solution of PDPs. Due to the extreme difficulty of synchronously addressing uncertainty and dynamics, only a few latest literatures made preliminary investigation on it. But most of them chose to adopt the methodology of stochastic dynamic programming for obtaining the optimal route of vehicles (i.e., most of them are model-oriented), none of them have considered the adoption of machine learning, a widely used artificial intelligence technology to predict uncertain input parameters by training machine learning model using a sample of historical instances. * Multi-objective optimization. The objective of PDP-related optimization models in most studies is the minimization of cost, distance, or time. Although this hypothesis is of theoretical interest, logistics management is essentially a multi-objective programming, because managers are often faced to consider multiple but conflicting criteria at a time. In recent years, scholars have attempted to use standard multi-objective algorithms (e.g., non-dominated sorting genetic algorithm and pareto archived evolution strategy) to resolve PDPs with multiple objectives, but these standard algorithms do not fit each specific PDP model perfectly. To overcome it, developing multi-objective metaheuristic algorithms with better computational performance, such as multi-objective memetic algorithm and multi-objective evolutionary algorithm, deserves more attention in the future. * Sustainability. With the increasingly worldwide concern on reducing pollution and natural resources consumption, logistics industry should adopt more environmentally friendly actions to respond to this trend. In contrast to traditional economic optimization in the past, several metrics that are highly relevant to society and environment should now be incorporated in the general framework of VRP progressively, e.g., carbon emission, air pollution, and the rate of resource degradation.