A multi-objective optimization framework for an inspection planning problem under uncertainty and breakdown

Abstract : Quality inspection in multistage production systems (MPSs) has become an issue and this is because the MPS presents various possibilities for inspection. The problem of finding the best inspection plan is an “inspection planning problem”. The main simultaneous decisions in an inspection planning problem in a MPS are: 1) which quality characteristics need to be inspected, 2) what type of inspection should be performed for the selected quality characteristics, 3) where these inspections should be performed, and 4) how the inspections should be performed. In addition, lack of information about production processes and several environmental factors has become an important issue that imposes a degree of uncertainty to the inspection planning problem. This research provides an optimization framework to plan an inspection process in a MPS, wherein, input parameters are uncertain and inspection tools and production machines are subject to breakdown. This problem is formulated through several mixed-integer mathematical programming models with the objectives of minimizing total manufacturing cost, maximizing customer satisfaction, and minimizing total production time. Furthermore, Taguchi and Monte Carlo methods are applied to cope with the uncertainties. Due to the complexity of the proposed models, meta-heuristic algorithms are employed to find optimal or near-optimal solutions. Finally, this research implements the findings and methods of the inspection planning problem in another application as hub location problem. General and detail concluding remarks are provided for both inspection and hub location problems.
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Submitted on : Wednesday, July 13, 2016 - 11:58:09 AM
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Mehrdad Mohammadi. A multi-objective optimization framework for an inspection planning problem under uncertainty and breakdown. Mechanical engineering [physics.class-ph]. Ecole nationale supérieure d'arts et métiers - ENSAM, 2015. English. ⟨NNT : 2015ENAM0055⟩. ⟨tel-01345201⟩

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