Consensus-based generalized TODIM approach for occupational health and safety risk analysis with opinion interactions

Occupational health and safety (OHS) risk analysis serves as a foundation for identifying, preventing, and controlling OHS hazards to reduce occupational accidents. As a representative risk analysis approach, FineKinney has been commonly applied to control hazards. However, current Fine-Kinney studies ranked hazards without considering the consensus reaching process (CRP) with incomplete information, insufficient to tackle decision makers' (DMs') dissatisfaction. Besides, risk analysis mainly relies on DMs' subjective assessments, where opinion interactions inevitably exist because of DMs' communication during the assessment process. This paper aims to develop a hybrid generalized TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) approach in the Fine-Kinney framework, integrating CRP with dynamic social influence network (SIN), and probabilistic linguistic terms (PLTSs). The PLTSs are used to cope with the complex and incomplete DMs' opinions. The dynamic SIN is proposed to calculate the weights of DMs and describe the opinion interactions considering the psychological behaviors of DMs. Then, a new CRP is developed including a two-fold personalized feedback mechanism based on the dynamic SIN. The generalized TODIM method is introduced to rank all identified potential occupational hazards based on the collective opinions after CRP. Finally, a numerical example is conducted to verify the efficiency of the proposed approach. Comparison and sensitivity studies are also carried out to test the rationality and efficiency of the proposed approach.

Automated summary

This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.