Mathematical modelling of a robust inspection process plan: Taguchi and Monte Carlo methods

Abstract

This study develops a new optimisation framework for process inspection planning of a manufacturing system with multiple quality characteristics, in which the proposed framework is based on a mixed-integer mathematical programming (MILP) model. Due to the stochastic nature of production processes and since their production processes are sensitive to manufacturing variations; a proportion of products do not conform the design specifications. A common source of these variations is misadjustment of each operation that leads to a higher number of scraps. Therefore, uncertainty in misadjustment is taken into account in this study. A twofold decision is made on the subject that which quality characteristic needs what kind of inspection, and the time this inspection should be performed. To cope with the introduced uncertainty, two robust optimisation methods are developed based on Taguchi and Monte Carlo methods. Furthermore, a genetic algorithm is applied to the problem to obtain near-optimal solutions. To validate the proposed model and solution approach, several numerical experiments are done on a real industrial case. Finally, the conclusion is provided.

Uncertainty quantification in dynamic system risk assessment: a new approach with randomness and fuzzy theory

“…Techniques such as the Monte Carlo method are frequently used to quantify uncertainty based on random variables. The Monte Carlo method (see Mooney 1997;Rubinstein and Kroese 2011;Moghaddam 2015;Mohammadi et al 2015), is a computational technique based on repeated random sampling to obtain numerical results.…”

Design of a reliable logistics network with hub disruption under uncertainty

“…Similar to most other evolutionary algorithms such as genetic algorithms [38,39] and particle swarm optimization, the ICA is a population-based algorithm [40,41] and it was introduced by Atashpaz-Gargari and Lucas [42] . Thus, the ICA begins with an initial population of random solutions, where each member of the population is called a country (see Mohammadi et al [26] for further details).…”

Cost engineering for variation management during the product and process development

“…In almost all manufacturing organisations, inspections are used during the processes to reach quality specifications instead of having an acceptance or a rejection inspection at the end. To achieve this goal, effective inspection planning should efficiently be integrated with the production logistics [25]. The application 2 tries to integrate production logistics and quality control and design an effective IPP to makes decision regarding to which quality characteristics of the product need what kind of inspections (i.e., which-what decision); and when these inspections should be performed through the production process (i.e., when decision) in order to minimize manufacturing and inspection cost and maximize customer satisfaction.…”

“…The application 2 tries to integrate production logistics and quality control and design an effective IPP to makes decision regarding to which quality characteristics of the product need what kind of inspections (i.e., which-what decision); and when these inspections should be performed through the production process (i.e., when decision) in order to minimize manufacturing and inspection cost and maximize customer satisfaction. Through this problem, two kinds of product conformity (CI) and process monitoring (MI) inspections are considered [25].…”