I have a project proposal about Heuristic Methods and with your help, i proposed a project like this;

Can you help about it to accomplish these proposal tasks? It can be with or without related datum. I should justify the algorithm to solve the problem about Heuristic. Thanks.

Approach 1: In this project, I will be addressing the Capacitated Vehicle Routing problem, which involves finding the most efficient routes for a fleet of vehicles to deliver goods to various locations while considering factors such as the capacity of the vehicles and designated delivery times. To find a solution, I will use simulated annealing, a single-solution-based metaheuristic, and genetic algorithm, a population-based metaheuristic. Additionally, I will create my own heuristic called the "nearest neighbor" heuristic, which involves selecting the next location to visit based on its proximity to the current location. For references, I can use the following papers: - "A Survey of Metaheuristics for the Capacitated Vehicle Routing Problem" by Z. Z. Sari et al. - "A Hybrid Genetic Algorithm for the Capacitated Vehicle Routing Problem" by R. Tavakkoli-Moghaddam et al. - "Simulated Annealing for the Capacitated Vehicle Routing Problem" by M. F. F. El-Hadidy et al. For this project, I will be creating my own data set for the Capacitated Vehicle Routing problem, including details about the number of vehicles, their capacities, and the locations and delivery needs of the customers. I plan to implement the algorithms myself, possibly using some existing code as a starting point and Approach 2: For my course project, I will be working on the Capacitated Vehicle Routing problem, which involves identifying the most efficient routes for a fleet of vehicles to visit a group of customers while considering the vehicles' capacity limitations and minimizing the total travel distance. As a solution method, I will be using the Ant Colony Optimization (ACO) algorithm, a single-solution-based metaheuristic that employs the concept of artificial ants searching for the best path through a graph and using heuristics such as pheromone trails to guide their search. For my course project, I will also be using the Genetic Algorithm (GA) as a solution method. This is a population-based metaheuristic that uses principles from natural selection, such as crossover and mutation, to generate a population of potential solutions and gradually improve them until an optimal solution is achieved. As a third solution approach for my course project, I will be developing a heuristic called the Greedy Route Selection algorithm. This algorithm will begin at the depot and select the nearest customer to visit, adding them to the route until the vehicle's capacity is reached. It will then move on to the next nearest customer and repeat the process until all customers have been visited. I can use data from the TSPLIB library for my problem instance. The references I can use for the ACO and GA algorithms are: - Dorigo, M., \& Stutzle, T. (2004). Ant Colony Optimization. MIT Press. - Goldbarg, M. C., \& de Souza, C. E. (2008). A genetic algorithm for the capacitated vehicle routing problem with time windows. European Journal of Operational Research, 190(1), 259-270. I can also refer to relevant literature for the Greedy Route Selection algorithm, such as: - Shen, Z. (2005). A study of the greedy algorithm for the vehicle routing problem. European Journal of Operational Research, 166(1), 181-193. I am confident that the combination of these three solution methods will be able to effectively and efficiently solve the Capacitated Vehicle Routing