A Decision Model for the Robot Selection Problem Using Robust Regression*
针对制造企业选择工业机器人时面临众多型号的问题，提出一个基于稳健回归的决策模型，根据制造商规格识别给定成本下性能更优的机器人，并筛选出候选机器人进行测试验证。
Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased with many of these firms now offering a wide range of models. A potential user is thus faced with many options in both performance and cost. This paper proposes a decision model for the robot selection problem. The proposed model uses robust regression to identify, based on manufacturers' specifications, the robots that are the better performers for a given cost. Robust regression is used because it identifies and is resistant to the effects of outlying observations, key components in the proposed model. The robots selected by the model become candidates for testing to verify manufacturers' specifications. The model is tested on a real data set and an example is presented.