A Robust Optimization Approach To Semi-obnoxious Facility Location Problem And Returned Logistics Optimization

Semi-obnoxious facility provides a benefit or service to society, while adversely affecting the quality of life or social values in a number of possible ways. As a key link in the returned logistics, semi-obnoxious facility location problem has come to be a hot research area. From the comprehensive survey of research on the semi-obnoxious facility location problem, most of the research results are based on the deterministic background, the influence of uncertainty on the facility location is neglected. The study on returned logistics is focused on the network design, which is under the deterministic or stochastic environment. However, the probability distribution of the uncertain data can’t be easily got in actual production condition, or system can’t hold the influence from the occurrence of small probability event. Thus the rubost approach to semi-facility location problem and returned logistics optimization has a great of academic and realistic meaning.This paper apply the robust optimization method to semi-obnoxious facility location problem and returned logistics optimization, detailed research contents, research methods and corresponding conclusions including:(1) With capacity constraint and without capacity constraint, this paper takes an overall consideration on a minisum function to represent the location costs and another minisum function to represent the obnoxious effects of the facility, and two basic models for semi-obnoxious facility location problem based on bi-objective particle swarm are established. Discrete binary particle swarm optimization algorithm and VC++6.0software are applied to solve the model under capacity constraint.(2) Two forms for recovery amount uncertainty are considered in this paper: disposal rate uncertainty and average recovery amount uncertainty. Under the above uncertain conditions, interval analysis and scenario analysis are applied to describe recovery amount uncertainty, semi-obnoxious facility location robust optimization models are established based on Bertsimas robust optimization method. Finally, a numerical study is designed to analyze the model.(3) Considering demand uncertainty、transportation cost uncertainty and recovery amount uncertainty, a robust model for returned logistics optimization is established based on Ben-Tal robust method. Finally, a numerical study is designed to analyze the model. Focuses on the analysis of the performance for the robust model and deterministic model in the returned logistics optimization under different problem size、 different uncertainty level. Results of a computational example verify the robust model is more superior and stable than the deterministic model.The research work has shown the following:Results of a numerical example verify the model robustness and solution robustness for the semi-obnoxious facility location robust optimization model, and in returned logistics optimization, the robust model is more superior and stable than the deterministic model.