## 3 Solutions for Different Points of Optimization in Urban Cold Transportation
### Overview of Solutions Found in Current Logistics Literature  
Typical logistics and cold transportation research present a variety of technical as well as algorithmic solutions. The most researched types of algorithmic solutions relevant to the optimization of urban cold transportation are route optimization and traffic prediction. Routing optimization is carried out using different variations of the Vehicle Routing Problem. Most solutions to different VRPs relevant to urban logistics are different variations and combinations of the annealing algorithm, variable neighborhood search and genetic algorithms. Most of the algorithms include some variation of variable neighborhood search. Below is Table 1, in which solutions to relevant variations of the VRP found in the current literature are presented. We can see that variations of Green VRP are quite well covered in the research. The complement to route optimization is traffic prediction and control, which can be applied in the whole of urban transportation. In Table 2, different solutions to traffic prediction and traffic control are presented. The solutions remain in many cases quite theoretical, as the implementation of these methods is not discussed in most papers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Proposed Algorithm**} & \multicolumn{6}{c}{**Variable of VRP Present in the Problem**} \\ \cline{2-7}  & **Green** & \begin{tabular}{c} **Multi-** \\ **Depot** \\ \end{tabular} & \begin{tabular}{c} **Time-** \\ **Dependent** \\ \end{tabular} & \begin{tabular}{c} **Split** \\ **Delivery** \\ \end{tabular} & \
\begin{tabular}{c} **Multiple Time** \\ **Windows** \\ \end{tabular} & **Other** \\ \hline Hybrid simulated annealing & X & - & X & - & - & - \\ and tempering algorithm [11] & X & - & - & - & - & Joint Distribution \\ \hline Simulated annealing & X & - & - & - & - & Joint Distribution \\ distribution model [12] & & & & & & \\ \hline Genetic algorithm [13] & X & X & - & X & - & Multi-Tour \\ \hline General Variable & X & X & - & - & - & - \\ Neighborhood Search & X & & & & & \\ method/Tabu Search [14] & & & & & & \\ \hline Variable neighborhood search & & & & & & \\ combined with a & X & - & X & X & X & - \\ non-dominated sorting genetic & & & & & & \\ algorithm II [15] & & & & & & \\ \hline Hybrid Butterfly & X & - & - & - & - & - \\ Optimization Algorithm [16] & X & X & - & - & - & - \\ \hline Hybrid Evolutionary & X & X & - & - & - & - \\ Algorithm [17] & & & & & & \\ \hline Hybrid adaptive large & & & & & & \\ neighborhood search with & - & - & X & - & X &
\begin{tabular}{c} Duration- \\ minimizing \\ \end{tabular} \\ \hline Adaptive large neighborhood & & & & & & \\ search combined with variable & - & - & X & - & X & Multi-Trip \\ neighborhood descent [19] & & & & & & \\ \hline Ant Colony Optimizer [20] & - & - & - & - & - & Drones \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Different algorithms proposed as solutions to different variations of VRP and relevant to the maintenance of the cold chain in urban areas while reducing emissions. In the other optimization areas identified, algorithmic solutions specifically for the optimization of environmental performance are significantly fewer in number. Table 3 contains examples of such current algorithms, but many more gaps are identified. The economic effects of the degradation of goods during cold transport have been modeled by first using a set of first-order linear differential delay equations to model the development of the degradation process and then by adding a feedback control to represent the slowing of degradation by cooling, and finally by finding the optimal case for a set financial criterion, such as minimal costs [29]. To the authors' knowledge, this method has not been implemented in environmental optimization, although algorithms for preventing degradation do exist.