An improved optimization method for materials distribution based on spatiotemporal clustering in automobile assembly lines

The development of smart factories has put forward more flexible logistics needs for automobile assembly system, and efficient scheduling strategies to meet these requirements still demand prompt solution. Thus, this paper focuses on the problem of materials distribution with automated guided vehicles (AGVs) in automobile assembly lines. The mathematical model is established in the light of actual situation with mixed time windows and an improved genetic algorithm (GA) is developed. Considering the demand characteristics both in time and space, material demand points are clustered based on their spatiotemporal distance to generate the initial population. Then, selection, crossover and mutation operators of GA are also ameliorated as necessary to minimize the total travel cost. Finally, practical examples are carried out to demonstrate the effectiveness of this methodology.