A Genetic Algorithm for Multiple Inspections with Multiple Objectives

DOI: 10.4236/ajor.2013.36045   PDF   HTML     4,763 Downloads   6,746 Views  


 This research presents a genetic algorithm to address the problem where multiple inspections are done to test conformity of multiple product characteristics. The genetic algorithm is employed to find an inspection plan where the multiple inspections are carried out, motivated to optimize two objectives: minimization of the total cost associated with the inspection; and maximization of probability of accepting conforming units. The genetic algorithm includes a constraint to induce variety into the characteristics being tested, so that the inspections are not dominated by “specialized” product characteristics. The resulting solutions are compared to optimal solutions, and it is determined that formidable solutions are found via the Genetic Algorithm approach.

Share and Cite:

P. McMullen, "A Genetic Algorithm for Multiple Inspections with Multiple Objectives," American Journal of Operations Research, Vol. 3 No. 6, 2013, pp. 463-473. doi: 10.4236/ajor.2013.36045.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] A. Tzimerman and Y. T. Herer, “Off-Line Inspections under Inspection Errors,” IIE Transactions, Vol. 41, No. 7, 2009, pp. 626-641.
[2] G. D. Eppenand G. Hurst, “Optimal Location of Inspection Stations in a Multistage Production Process,” Management Science, Vol. 20, No. 8, 1974, pp. 1194-1200.
[3] T. Raz and M. Kaspi, “Location and Sequencing of Imperfect Inspection Operations Serial Multi-Stage Production Systems,” International Journal of Production Research, Vol. 8, No. 3, 1991, pp. 1645-1659.
[4] D. P. Ballou and H. L. Pazer, “The Impact of Inspector Fallibility on the Inspection Policy in Serial Production Systems,” Management Science, Vol. 28, No. 4, 1982, pp. 387-399.
[5] T. Raz and M. U. Thomas, “A Method for Sequencing Inspection Activities Subject to Errors,” IIE Transactions, Vol. 15, No. 1, 1983, pp. 12-18.
[6] T. Raz and D. Bricker, “Sequencing of Imperfect Inspection Operations Subject to Constraints on the Quality of Accepted and Rejected Units,” International Journal of Production Research, Vol. 25, No. 6, 1987, pp. 809-821.
[7] T. Raz and D. Bricker, “Optimal and Heuristic Solutions to the Variable Inspection Policy Problem,” Computers & Operations Research, Vol. 18, No. 1, 1991, pp. 115-123.
[8] J. W. Schmidt and G. K. Bennett, “Economic multiattribute Acceptance Sampling,” AIIE Transactions, Vol. 4, No. 3, 1972, pp. 194-199.
[9] K. Tang, R. Plante and H. Moskowitz, “Multiattribute Bayesian Acceptance Sampling Plans under Non-Destructive Inspection,” Management Science, Vol. 32, No. 6, 1986, pp. 739-750. http://dx.doi.org/10.1287/mnsc.32.6.739
[10] K. Tang and J. Tang, “Design of Product Specifications for Multi-Characteristic Inspection,” Management Science, Vol. 35, No. 6, 1989, pp. 743-756.
[11] L. L. Hau, “On the Optimality of a Simplified Multicharacteristic Component Inspection Model,” IIE Transactions, Vol. 20, No. 4, 1988, pp. 393-398.
[12] C. Shaoxiang and M. Lambrecht, “The Optimal Frequency and Sequencing of Tests in the Inspection of Multicharacteristic Components,” IIE Transactions, Vol. 29, No. 12, 1997, pp. 1039-1049.
[13] T. Avinadav and D. Sarne, “Sequencing Counts: A Combined Approach for Sequencing and Selecting Costly Unreliable Off-Line Inspections,” Computers & Operations Research, Vol. 39, No. 11, 2012, pp. 2488-2499.
[14] T. Raz and D. Bricker, “Sequencing of Inspection Operations Subject to Errors,” European Journal of Operational Research, Vol. 68, No. 2, 1993, pp. 251-264.
[15] J. S. Park, M. H. Peters and K. Tang, “Optimal Inspection Policy in Sequential Screening,” Management Science, Vol. 37, No. 8, 1991, pp. 1058-1061.
[16] D. E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning,” Addison-Wesley, Reading, 1989.
[17] Z. Michelawicz and D. Fogel, “How to Solve It: Modern Heuristics,” Springer, New York, 2010.
[18] P. R. McMullen, “JIT Mixed-Model Sequencing with Batching and Setup Considerations via Search Heuristics,” International Journal of Production Research, Vol. 48, No. 22, 2010, pp. 6559-6582.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.