A Multi-Objective Optimization Model for Locating a Vehicle Inspection Station with Fuzzy Demands

Automobile service enterprise location determination is an interesting and important issue in the logistic research field. To deal with this uncertainty, some researchers addressed the fuzzy time and cost issues for locating an automobile service enterprise. However, a decision maker hopes to minimize the travel time of customers meanwhile minimizing their costs when locating a facility. They prefer arriving at the destination within the specific time period and cost. To handle this issue in a more practical manner, by taking the vehicle inspection station as a typical automobile service enterprise example, we proposed a fuzzy change-constrained multi-objective optimization approach to handle it. Moreover, some region-wide constraints can greatly influence FLA and travel time is also has indeterminate due to the influence of some unpredictable factors. To handle them, we developed a fuzzy chance-constrained multi-objective programming model to determine its location with regional constraints, fuzzy inspection demands and varying velocities. A hybrid algorithm integrating fuzzy simulation, neural networks (NN), and genetic algorithms (GA), namely a random weight based multi-objective NN-GA, is proposed to solve the proposed models. A numerical example is provided to illustrate the effectiveness of the proposed model and the proposed algorithm.