Abstract
Nowadays robots have been commonly adopted in various manufacturing industries to improve product quality and productivity. The selection of the best robot to suit a specific production setting is a difficult decision making task for manufacturers because of the increase in complexity and number of robot systems. In this paper, we explore two key issues of robot evaluation and selection: the representation of decision makers’ diversified assessments and the determination of the ranking of available robots. Specifically, a decision support model which utilizes cloud model and TODIM (an acronym in Portuguese of interactive and multiple criteria decision making) method is developed for the purpose of handling robot selection problems with hesitant linguistic information. Besides, we use an entropy-based combination weighting technique to estimate the weights of evaluation criteria. Finally, we illustrate the proposed cloud TODIM approach with a robot selection example for an automobile manufacturer, and further validate its effectiveness and benefits via a comparative analysis. The results show that the proposed robot selection model has some unique advantages, which is more realistic and flexible for robot selection under a complex and uncertain environment.
Highlights
Due to the explosion of information technology and engineering sciences, robots have been widely utilized in many manufacturing practices
To bridge theses gaps, this paper attempts to developed a novel behavioral decision making model to address the problem of robot evaluation and selection with hesitant linguistic information
This section develops a novel decision supporting method by combining cloud model with a modified TODIM to cope with the robot evaluation and selection problems considering subjective and objective criteria weights
Summary
Due to the explosion of information technology and engineering sciences, robots have been widely utilized in many manufacturing practices. Researchers have applied cloud model theory to address linguistic decision making problems in many areas. Sang and Liu [23] proposed an interval type-2 fuzzy set-based TODIM approach to address multiple criteria green supplier selection problems. Hu et al [27] addressed the online diagnosis and medical treatment selection problems by using a TODIM-based three-way decision model, and Wang et al [28] managed the non-homogeneous information and experts’ psychological behavior in group emergency decision making by using a fuzzy TODIM method. This article aims to develop a cloud model based-TODIM (cloud TODIM) approach to handle robot selecting problems within an uncertain linguistic context.
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