Joint optimisation of all inspection parameters for multistage processes: evolutionary algorithm and simulation. (14th June 2010)

• Record Type: Journal Article
• Authors: Volsem, Sofie Van
• Is Part Of: International journal of innovative computing and applications. Volume 2:Number 3(2010)
• Page Start: 170
• Page End: 177
• Publication Date: 2010-06-14
• Subjects: metaheuristics -- evolutionary algorithms -- simulation -- inspection allocation -- joint optimisation -- test sets -- quality economics -- multistage manufacturing -- inspection parameters -- inspection location -- inspection type -- inspection limits -- sampling characteristics; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

The allocation of inspection effort in multistage manufacturing systems has been well studied over the last decades. However, joint optimisation of all inspection parameters (inspection location, inspection type, inspection limits and sampling characteristics) in order to obtain an efficient inspection strategy that results in the lowest total inspection cost, has to our knowledge not been addressed before. To solve this problem, we propose a metaheuristic solution approach, namely an evolutionary algorithm (EA). Simulation is used to calculate the inspection costs for every candidate solution. A test set of problem instances is suggested to explore the characteristics and possibilities of the proposed EA metaheuristic.