Solving Vehicle Routing Problem with Time Window Based on Matlab Genetic Algorithm

Solving Vehicle Routing Problem with Time Window Based on Matlab Genetic Algorithm

Optimizing vehicle routing problems is an important and common challenge in the field of logistics and transportation. Especially when considering that each client has different time window requirements, the problem becomes more complicated. In order to effectively solve this problem, we can use genetic algorithm (Genetic Algorithm, GA) to find the best vehicle path.

Genetic algorithm is an optimization method based on the principle of biological evolution. It gradually optimizes the solution by simulating the selection, crossover and mutation operations in the "evolution" process. The following will introduce how to use Matlab to realize the vehicle path planning problem based on genetic algorithm.

First, we need to define the input to the problem. Assume that there are N customers who need to deliver goods, and each customer has a time window, which means that the delivery can only be carried out within this time window. We also need to determine the distance between each customer and the maximum driving distance of the vehicle. At the same time, it is necessary to define the encoding method of the chromosome, that is, the data structure representing the vehicle path.

1. Initializing the population
   We represent vehicle paths using binary encoding. For example, assuming there are 3 customers, then a path can be expressed as [0 1 2], where 0 represents the starting point, and 1 and 2 represent two customer points. Each individual in the population represents a vehicle path.

2. Fitness function
   The fitness function is used to evaluate the degree of fitness of each individual, that is, the quality of the path. We can define a fitness function in terms of total distance traveled, violation of time window constraints, etc. The smaller the fitness value, the better the path.

3. Selection operation
   The selection operation is a fitness-based strategy for selecting the next generation of individuals. Common selection strategies include roulette selection and ranking selection. In roulette selection, the probability of an individual being selected is proportional to its fitness.

4. Crossover operations
   Crossover operations generate new individuals by exchanging chromosome segments between two individuals. Methods such as single-point crossing, multi-point crossing, or uniform crossing can be used.

5. Mutation operation
   Mutation operation is to increase the diversity of the search space, and introduce new solutions by randomly changing the chromosomes of individuals. Commonly used mutation strategies include positional mutation and insertional mutation. </