## 2 Literature Review
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Paper**} & \multicolumn{6}{c}{**Constraints**} & \multicolumn{6}{c}{**Solution Method**} \\ \cline{2-9}  & \begin{tabular}{c} **Multi-** \\ **Dept** \\ \end{tabular} & \begin{tabular}{c} **Time-** \\ **Window** \\ \end{tabular} & \begin{tabular}{c} **Heterogeneous** \\ **Fleet** \\ \end{tabular} & \begin{tabular}{c} **Multi-** \\ **Product** \\ \end{tabular} & \begin{tabular}{c} **Periodic** \\ **Delivery** \\ \end{tabular} & \begin{tabular}{c} **Pick Up \&** \\ **Inventory** \\ \end{tabular} & \
\begin{tabular}{c} **Inventory** \\ **Exact** \\ \end{tabular} &
\begin{tabular}{c} **Metaheuristic** \\ \end{tabular} \\ \[PERSON] et al. (2015) & & \(\surd\) & & & & & \(\surd\) & & \(\surd\) & \(\surd\) \\ Ai and Kochitvichyanukul (2016) & \(\surd\) & & & & & \(\surd\) & & \(\surd\) & \(\surd\) \\ [PERSON] et al. (2011) & \(\surd\) & & & & & \(\surd\) & & \(\surd\) & \(\surd\) \\ Hasani-Coodarzi and & & & & & & \(\surd\) & & \(\surd\) & & \(\surd\) \\ [PERSON] (2016) & \(\surd\) & & & & & \(\surd\) & & \(\surd\) & & \(\surd\) \\ [PERSON] et al. (2014) & \(\surd\) & & \(\surd\) & & & \(\surd\) & & \(\surd\) & & \(\surd\) \\ [PERSON] et al. (2013) & \(\surd\) & \(\surd\) & \(\surd\) & & & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. (2014) & \(\surd\) & \(\surd\) & \(\surd\) & & & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. (2018) & \(\surd\) & & \(\surd\) & & & & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. (2017) & \(\surd\) & & \(\surd\) & & & & & \(\surd\) & \(\surd\) \\ [PERSON] (2019) & \(\surd\) & & \(\surd\) & & & & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. (2012) & \(\surd\) & & & \(\surd\) & & & \(\surd\) & \(\surd\) & \(\surd\) \\ [PERSON] et al. (2015) & \(\surd\) & & & \(\surd\) & & \(\surd\) & \(\surd\) & & \(\surd\) \\ [PERSON] et al. (2016) & \(\surd\) & & \(\surd\) & \(\surd\) & & & \(\surd\) & \(\surd\) & & \(\surd\) \\ This research & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & & & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of literature for the VRP. objective function is fuel consumption. [PERSON] et al. [26] presented a DE with knowledge-based control parameters and strategy adaptation, combining three well-known test suites. Later, [PERSON] et al. [27] proposed the parallel DE to minimize transport costs and penalties due to logistics service providers' delays. [PERSON] et al. [28] developed a distance-related DE to maximize the total price collected. Recently, [PERSON] and [PERSON] [29] proposed a hybrid DE to solve the multi-trip vehicle routing problem with backhauls and heterogeneous fleets in the beverage logistics industry. They demonstrated the benefit of using self-adaptive strategies because the user does not have to change the control parameters. The total computational effort was able to converge faster and significantly reduced into efficient solutions. As a result, this paper proposes a new Hybrid and Self-Adaptive Differential Evolution algorithm (HSADE) that combines their respective benefits, increasing the robustness to solve the problem. We suggest the HSADE that includes Hybrid and Self-Adaptive modification of the DE algorithm, which can also include self-adaptive mutation strategies, control parameters for crossover rate probability and hybrid neighborhood search structures using the probability exchange equation. We focus on addressing egg distribution planning transportation by improving the efficient solution in terms of the total cost, including the transportation cost, labor cost and holding cost. There is no work in the literature to the best of our knowledge interested in the interaction between these constraints: multi-depot, heterogeneous fleet, time window, multi-product and inventory restriction.