# Two-Stage Genetic Algorithm for Optimization Logistics Network for Groupage Delivery
## 5 Discussion
### Future Work  
One avenue for further exploration is the possibility of self-adapting parameters within the GA framework [53; 54] to enhance its performance in solving optimization problems. Self-adaptation involves allowing the algorithm to dynamically adjust its own parameters during the optimization process based on the feedback received from previous iterations. This adaptive mechanism can lead to an improved convergence speed, robustness, and overall effectiveness of the GA. For instance, techniques such as self-adaptive mutation [55] operators or evolutionary strategies can be employed to automatically tune the mutation rate during the optimization process. Similarly, the crossover rate determines the extent to which genetic information is exchanged between individuals. Self-adaptation of the crossover [56] rate can be achieved by monitoring the effectiveness of the crossover operations in generating offspring solutions and adjusting the rate accordingly. So, by incorporating self-adaptive mechanisms [57; 58; 59] into the GA framework, it becomes possible to create more adaptive and robust optimization algorithms that can effectively tackle a wide range of transport optimization problems without the need for manual parameter tuning.  
Another promising avenue is using multi-objective optimization with GA [60; 61] for solving transportation problems, which opens up the possibility of effectively considering multiple conflicting objectives [62; 63] and finding compromise solutions. The GA can be configured to simultaneously optimize multiple criteria, such as minimizing the travel time or total number of vehicles. Additionally, the GA can be tailored to accommodate different types of cargo while considering various cargo needs and limitations. Includingenvironmental criteria [64], such as emission levels [65] or fuel consumption [66], allows the GA to find environmentally sustainable solutions that reduce negative impacts on the environment while performing transportation tasks.  
To utilize multi-objective optimization with GAs for solving transportation problems, it is necessary to define appropriate objective functions and evaluation criteria that efficiently explore the solution space [67]. Additionally, the specific characteristics of the transportation problem should be considered, such as network sizes, cargo types, and constraints, to adapt optimization methods to specific conditions and requirements.  
Future work on applying GAs to the VRP could focus on several key areas. First, developing problem-specific genetic operators, such as custom crossover and mutation, would help ensure valid solutions that respect the VRP constraints, like city repetition and route feasibility. Additionally, addressing multi-truck scenarios is crucial, as current GAs typically represent VRP solutions as single permutations, which is not ideal for problems involving multiple vehicles.  
Hybrid approaches combining GAs with local search methods could also improve the solution quality by refining routes and escaping local optima. Furthermore, enhancing GAs to account for real-world logistics constraints, such as time windows and traffic conditions, would make the solutions more applicable to practical scenarios. To improve the scalability, parallel and distributed computing techniques could speed up the optimization process for large-scale problems. Finally, exploring multi-objective optimization would allow GAs to handle conflicting objectives, like minimizing both the travel distance and fuel consumption, providing more realistic solutions.