# The Vehicle Routing Problem: State-of-the-Art Classification and Review
## 5 Observations and Conclusions  
Based on the practical importance of VRPs in real life, such problems have attracted significant research attention in recent years. Most work has been devoted to classical cost objectives such as total cost, total travel distance, and CO\({}_{2}\) emission. Some studies have considered multiple objectives. In order to solve the problem of greenhouse gas emission, the discussion of trolley distribution has become a research trend. Time windows still account for a large proportion of modern papers and are mainstream in current research on the VRP and its variants. Time windows are closely related to the current mode of \"to C\" distribution, where customers focus on service satisfaction. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Solution**} \\ \cline{2-3}  & **Exact Methods** & **Heuristic Algorithms** & **Meta-Heuristic Algorithms** \\ \hline \multirow{4}{*}{8} & & tabu search metaheuristic & IBM ILOG CPLEX Optimization Studio 12.61 \\  & & tabu search metaheuristic & (Visual Studio 2015, C\#) on a DELL Precision 7600 workstation with two Xeon E5-2643 V3 CPUs (24 cores) @3.4 GHz with 128 GB RAM \\ \hline \multirow{4}{*}{9} & & Adaptive tabu search & C++ and all computational experiments were performed on a 3.30 GHz Intel Core i5 CPU on a single thread \\ \hline \multirow{4}{*}{10} & & ALNS algorithm & Intel Xeon E5 2.10 GHz CPU virtual machine with 16 GB RAM \\ \hline \multirow{4}{*}{11} & & ACO algorithm & JAVA computer language, executed using a computer with an Intel (R) Core (TM) i7 CPU @3.40 GHz and 4 GB RAM \\ \hline \multirow{4}{*}{12} & Knowledge-guided local & AMD Ryzen 3 1300X CPU @3.5 GHz on Windows 10 \\ \hline \multirow{4}{*}{13} & & C++ language and all 120 instances run \\  & Genetic local search algorithm & independently five times on a PC with two Intel i7-7820 CPUs @2.9 GHz and 32 GB RAM on the Windows 10 OS (64-bit) \\ \hline \multirow{4}{*}{14} & & ALNS algorithm & Java, run on a Huawei XH620 V3 computer with an Intel Xeon 2660v3 CPU @2.60 GHz \\ \hline \multirow{4}{*}{15} & & Intel® Xeon® Gold 6126 cluster, where each node \\  & Hybrid VNS/tabu search & operated at 2.6 GHz with 16 GB RAM \\  & algorithm & (hyper-threading disabled). Algorithms \\  & algorithm & implemented in Java SE and Gurobi Optimizer 8.1.0 used for solving MILPs (Gurobi \\  & & Optimization, 2018). \\ \hline \multirow{4}{*}{16} & & NSGA-II & MATLAB R2017a \\ \cline{2-3}  & & Gurobi 7.5.0, 12 GB RAM and on a cluster of \\ \cline{1-1} \cline{2-3}  & Exact heuristic algorithm & 27 computers, each with 12 cores and two Intel(R) \\ \cline{1-1} \cline{2-3}  & & Xeon X3675 @3.07 GHz CPUs \\ \hline \multirow{4}{*}{18} & Improved BAP & C\# and CPLEX with a 3.10 GHz Intel Core TM \\  & algorithm & i5-2400 CPU using the Microsoft Windows 7 OS \\ \cline{1-1} \cline{2-3}  & & with 8 GB RAM \\ \hline \multirow{4}{*}{19} & & Multi-adaptive PSO algorithm & Intel Core i5 2430M @2.40 GHz with 4 GB RAM on \\  & & algorithm & the Windows 7 Home Premium 64-bit OS \\ \hline \multirow{4}{*}{20} & & & Coded in Java and run on 12 computers with Intel \\  & & improved hybrid FA & i-5 @3.2 GHz CPUs and 4 GB RAM with 32-bit \\ \cline{1-1} \cline{2-3}  & & Windows 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: \(Cont.\)Regarding datasets, different studies make various adjustments to data and many use generated datasets in addition to real data, which makes it difficult to compare algorithms using a unified standard. There is still scope for significant further work in the field. Therefore, researchers should be motivated to develop publicly available datasets, and effective and efficient methods for dealing with VRPs. The gaps in the available literature mentioned above may motivate further work in these directions for researchers in this field. For solving algorithms, with the development of the processing speed and memory capacity of computers, using the exact way such as branch and X to solve VRPs is rapid growth.