Modeling and analysis with real-world data for global production planning and location of automobile assembly plants

Abstract

The management of the automobile industry has changed recently because of the influence of the financial crisis and the industrial boom in developing countries such as Brazil, Russia, India, and China (BRICs). In this paper, we consider a global automobile-production optimization problem (GAPOP), which we model as a mixed integer programming (MIP) problem. The GAPOP determines global production bases and transportation plans to minimize the total cost of production, transportation, and facilities. It is a unified model that contains the facility location, production planning and transportation problems. We analyze the model for instances generated from real-world data with up to 20 production bases and 133 importing countries for the 18 years from 1997 to 2014. The computational results show that near-optimal solutions to our model are close to the present real-world situation. We also analyze our model with various parameter settings and observe from the results that, at each production base, changes in the number of production lines are affected mainly by labor and material costs. In addition, the proportion of each automobile type when allocating production is influenced by the material costs of all automobile types and the demands of nearby importing countries. Our model is expected to be of use to the automobile industry for making forecasts.

Keywords

• global automobile production optimization problem
• facility location problem
• real-world data simulation
• mixed integer programming
• automobile industry