An expert knowledge-based dynamic maintenance task assignment model using discrete stress–strength interference theory

Abstract

Expert knowledge has become an important factor in optimization decision-making for complex equipment maintenance. Motivated by the challenges of quantifying expert knowledge as a decision basis, we presented an expert knowledge-based dynamic maintenance task assignment model by using discrete stress–strength interference (DSSI) theory. We constructed the task assignment framework consisting of three parts: building expert database, selecting experts for tasks, and implementing the tasks, in which selecting experts for tasks based on expert knowledge is the key part of the model. To quantify tacit knowledge (experience) in optimization decision for expert recommendation, experience was defined as a probability, which is relevant to two random variables: quantity of task successfully implemented (strength) and quantity of task failed (stress), and experience is defined as the probability that the former (strength) is larger than the latter (stress). Further, universal generating function (UGF) method was used to calculate the experience, and decision rule was designed for the dynamic maintenance task assignment. The model can help collaborative maintenance platform periodically review experts’ performances and assign the corresponding task to the most suitable expert at different periods. A case study shows that the proposed model helps not only to achieve rational allocation of expert resources, but to promote positive competition among experts.