Increase in the consumption of natural resources in recent decades, followed by increased waste volume, has caused irreparable damage to the environment and society. The problems brought about by the waste in today's societies, if not attended to, may become more devastating in the future for generations after us. A myriad of research has been published in the area of industrial waste management; however, no study until now has pointed to the management of wastes produced in the automotive industry. Therefore, this study sought to describe an industrial waste transportation system in the automotive industry by proposing a capacitated location routing model with a heterogeneous fleet of vehicles. This research attempted to demonstrate the model efficiency considering a case from the SAIPA car Company in Iran. First, the waste collection network was considered based on a mixed integer bi-objective optimization model. Decisions about the route of collection vehicles, the location of collection centers, the inventory level in these centers, the reduction of the costs, the risk posed to the population, the categorization of transportation of different waste types and the estimation of the number of vehicles in the transportation phase were also made in this study. Second, to test the model as well as performing several numerical examples, the improved version of the epsilon-constraint was applied via the CPLEX solver embedded in the GAMS software. Decisions were also made regarding the optimal technology for the disposal of non-recyclable wastes and the selection of optimal sites for both disposal and recycle facilities, considering the irreconcilable economic, social, environmental, and technical criteria. Moreover, a hybrid multi-criteria decision-making approach, combining best worst method with PROMETHEE-II was proposed. Generally, numerical examples proved that the proposed MIP model with the consideration heterogeneous transport fleet and waste collection in separated manner was a safe solution in order to reduce logistics costs in the presence of transportation risk criteria.