Cross-Infection and Risk Prevention in Production Environment: A Multi-criteria Decision-making Approach

Abstract

Manufacturing relies heavily on the physical presence of human resources, and disease outbreaks that force separation, such as: COVID-19, harm output. As a solution to the problem, this paper proposes a new Hybrid Multi-criteria Decision-making (H-MCDM) model based on the Intuitionistic Fuzzy Weighted Geometric (IFWG) operator, the Intuitionistic Fuzzy Hamacher Interactive Weighting Averaging (IFHIWA) operator, and the Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (IF-TOPSIS) method for the evaluation, management, and prevention planning. In addition to the role that modeling played in solving the cross-infection problem, the results obtained from putting the model through its implementation show that the use of protective materials during manufacturing operations is the most suitable among the finite alternatives considered, when the safety of the workforce/workplace, cost of implementing risk-mitigating actions, virus transmission level, and ease of implementation are all taken into account. Thus, the proposed model has been validated through a feasibility test, so contributes to maintaining production and reducing cross-infections in workplaces while an epidemic is ongoing.