Abstract
Facility location allocation (FLA) is considered as the problem of finding optimally a facility's location with the maximum customer satisfaction, the maximum profit of investors of the facility, and the minimum transportation cost of its oriented-customers. In practice, some factors of the FLA problem, i.e., customer demands, allocations, even locations of customers and facilities, are usually changing, and thus the problem features with uncertainty. To account for this uncertainty, some researchers have addressed the stochastic profit and cost issues of FLA. However, a decision-maker hopes to obtain the specific profit of investors of building facility and meanwhile to minimize the cost of target customers. To handle this issue via a more practical manner, it is essential to address the cost-profit tradeoff issue of FLA. Moreover, some region constraints can greatly influence FLA. By taking the vehicle inspection station as a typical automotive service enterprise example, this work presents new stochastic cost-profit tradeoff FLA models with region constraints. A hybrid algorithm integrating stochastic simulation and Genetic Algorithms (GA) is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and the effectiveness of the proposed algorithm.
Note to Practitioners-This work concerns the uncertainty involved in facility location allocation (FLA). To deal with such uncertainty, it proposes a novel stochastic tradeoff method to solve an FLA problem. The prior research handles such a problem through a methodology based on the stochastic profit or cost, which is ineffective without considering the balance between the obtained profit of investors and the transportation cost of target customers. The goal of this work is to establish the tradeoff FLA model, i.e., to obtain assuredly the specific profit of investors, while minimizing the transportation cost of the service costumers for FLA. Both theoretical and simulation results demonstrate that the proposed approach is effective and feasible. Such results can help decision makers perform better judgments when a practical FLA is executed.