A Simulation-Optimization Approach to Control Production, Subcontracting and Maintenance Decisions for a Deteriorating Production System
This research studies the joint production,
maintenance and subcontracting control policy for an unreliable
deteriorating manufacturing system. Production activities are
controlled by a derivation of the Hedging Point Policy, and given that
the system is subject to deterioration, it reduces progressively its
capacity to satisfy product demand. Multiple deterioration effects are
considered, reflected mainly in the quality of the parts produced and
the reliability of the machine. Subcontracting is available as support
to satisfy product demand; also, overhaul maintenance can be
conducted to reduce the effects of deterioration. The main objective
of the research is to determine simultaneously the production,
maintenance and subcontracting rate, which minimize the total,
incurred cost. A stochastic dynamic programming model is
developed and solved through a simulation-based approach
composed of statistical analysis and optimization with the response
surface methodology. The obtained results highlight the strong
interactions between production, deterioration and quality, which
justify the development of an integrated model. A numerical example
and a sensitivity analysis are presented to validate our results.
[1] Olsder, G. J. and Suri, R., “Time-Optimal Control of Parts-Routing in a
Manufacturing System with Failure-Prone Machines”, IEEE, pp. 722-
727, 1980.
[2] Gerhswin, S. B., “Design and Operation of Manufacturing Systems: The
Control–Point Policy”, IIE Transaction 32, pp. 891-906, 2000.
[3] Kim, J. and Gershwin, S., “Integrated Quality and Quantity Modeling of
a Production Line”, OR Spectrum 27, pp. 287-314.2005.
[4] Kim J. and Gershwin S., “Analysis of Long Flow Lines with Quality and
Operational Failures”, IIE Transactions, 40, pp. 284-296, 2008.
[5] Radhoui, M., Rezg, N. and Chelbi, A., “Joint Quality Control and
Preventive Maintenance Strategy for Imperfect Production Processes”,
Journal of Intelligent Manufacturing, 21, pp. 205-212, 2010.
[6] Bouslah, B., Gharbi, A., and Pellerin, R., “Joint Production and Quality
Control of Unreliable Batch Manufacturing Systems with Rectifying
Inspection”, International Journal of Production Research, vol. 1, no.
15, pp. 1-15, 2013.
[7] Love, C. E., Zhang, Z. G., Zitron, M.A., Guo, R., “A Discrete Semi-
Markov Decision Model to Determine the Optimal Repair/Replacement
Policy under General Repairs”, European Journal of Operational
Research, 125 pp. 398-409, 2000.
[8] Soro, I.W., Nourelfath, M. and Ait-Kadi, D., “Performance Evaluation
of Multi-State Degraded Systems with Minimal Repairs and Imperfect
Preventive Maintenance”, Reliability Engineering and System Safety, 65,
pp. 65-69, 2010.
[9] Khatab, A., Ait-Kadi, D., Rezg, N., “Availability Optimization for
Stochastic Degrading Systems under Imperfect Preventive
Maintenance”, International Journal of Production Research, 52:14, pp.
4132-4141, 2014.
[10] Colledani, M., and Tolio, T., “Integrated Quality, Production Logistics
and Maintenance Analysis of Multi-Stage Asynchronous Manufacturing
Systems with Degrading Machines”, CIRP Annals–Manufacturing
Technology, Vol. 61, No. 1, pp. 455-458, 2012.
[11] Tan, B. and Gershwin, S.B., “Production and Subcontracting Strategies
for Manufacturers with Limited Capacity and Volatile Demand”, Annals
of Operations Research, 125, 205-232, 2004.
[12] Dahane, M., Clementz, C., and Rezg, N., “Effects of Extension of
Subcontracting on a Production System in a Joint Maintenance and
Production Context”, Computers & Industrial Engineering, 58, pp. 88-
96, 2010.
[13] Gharbi A., Hajji, A., and Dhouib, K., “Production Rate Control of an
Unreliable Manufacturing Cell with Adjustable Capacity”, International
Journal of Production Research, 49:21, 6539-6557, 2011.
[14] Assid, M., Gharbi, A., Dhouib. K., “Joint Production and Subcontracting
Planning of Unreliable Multi-Facility Multi-Product Production
Systems”, Omega: The International Journal of Management Science, to
be published, 2014.
[15] Dror, M., Smith, K. R., and Yano, C. A., “Deux Chemicals Inc. Goes
Just–in-Time, Interfaces”, Vol. 39, No. 6, pp. 503-515, 2009. [16] Rivera-Gómez, H., Gharbi, A. and Kenne, J.P., “Joint Production and
Major Maintenance Planning Policy of a Manufacturing System with
Deteriorating Quality”, International Journal of Production Economics,
146, pp. 575-587, 2013.
[17] Dehayem-Nodem, F.I., Kenne, J.P., and Gharbi, A., “Simultaneous
Control of Production, Repair/Replacement and Preventive Maintenance
of Deteriorating Manufacturing Systems”, International Journal of
Production Economics, 134, pp. 271-282, 2011.
[18] Kushner, H.J. and Dupuis, P.G., Numerical Methods for Stochastic
Control Problems in Continuous Time, Springer, New York, NY, 1992.
[19] Boukas, E.K. and Hauire, A., “Manufacturing Flow Control and
Preventive Maintenance: A Stochastic Control Approach”, IEEE
Transactions on Automatic Control, 33, pp. 1024-1031, 1990.
[20] Gharbi, A., and Kenne, J. P., “Production and Preventive Maintenance
Rates Control for a Manufacturing System: An Experimental Design
Approach”, International Journal of Production Economics, 65, pp.
275-287, 2000.
[1] Olsder, G. J. and Suri, R., “Time-Optimal Control of Parts-Routing in a
Manufacturing System with Failure-Prone Machines”, IEEE, pp. 722-
727, 1980.
[2] Gerhswin, S. B., “Design and Operation of Manufacturing Systems: The
Control–Point Policy”, IIE Transaction 32, pp. 891-906, 2000.
[3] Kim, J. and Gershwin, S., “Integrated Quality and Quantity Modeling of
a Production Line”, OR Spectrum 27, pp. 287-314.2005.
[4] Kim J. and Gershwin S., “Analysis of Long Flow Lines with Quality and
Operational Failures”, IIE Transactions, 40, pp. 284-296, 2008.
[5] Radhoui, M., Rezg, N. and Chelbi, A., “Joint Quality Control and
Preventive Maintenance Strategy for Imperfect Production Processes”,
Journal of Intelligent Manufacturing, 21, pp. 205-212, 2010.
[6] Bouslah, B., Gharbi, A., and Pellerin, R., “Joint Production and Quality
Control of Unreliable Batch Manufacturing Systems with Rectifying
Inspection”, International Journal of Production Research, vol. 1, no.
15, pp. 1-15, 2013.
[7] Love, C. E., Zhang, Z. G., Zitron, M.A., Guo, R., “A Discrete Semi-
Markov Decision Model to Determine the Optimal Repair/Replacement
Policy under General Repairs”, European Journal of Operational
Research, 125 pp. 398-409, 2000.
[8] Soro, I.W., Nourelfath, M. and Ait-Kadi, D., “Performance Evaluation
of Multi-State Degraded Systems with Minimal Repairs and Imperfect
Preventive Maintenance”, Reliability Engineering and System Safety, 65,
pp. 65-69, 2010.
[9] Khatab, A., Ait-Kadi, D., Rezg, N., “Availability Optimization for
Stochastic Degrading Systems under Imperfect Preventive
Maintenance”, International Journal of Production Research, 52:14, pp.
4132-4141, 2014.
[10] Colledani, M., and Tolio, T., “Integrated Quality, Production Logistics
and Maintenance Analysis of Multi-Stage Asynchronous Manufacturing
Systems with Degrading Machines”, CIRP Annals–Manufacturing
Technology, Vol. 61, No. 1, pp. 455-458, 2012.
[11] Tan, B. and Gershwin, S.B., “Production and Subcontracting Strategies
for Manufacturers with Limited Capacity and Volatile Demand”, Annals
of Operations Research, 125, 205-232, 2004.
[12] Dahane, M., Clementz, C., and Rezg, N., “Effects of Extension of
Subcontracting on a Production System in a Joint Maintenance and
Production Context”, Computers & Industrial Engineering, 58, pp. 88-
96, 2010.
[13] Gharbi A., Hajji, A., and Dhouib, K., “Production Rate Control of an
Unreliable Manufacturing Cell with Adjustable Capacity”, International
Journal of Production Research, 49:21, 6539-6557, 2011.
[14] Assid, M., Gharbi, A., Dhouib. K., “Joint Production and Subcontracting
Planning of Unreliable Multi-Facility Multi-Product Production
Systems”, Omega: The International Journal of Management Science, to
be published, 2014.
[15] Dror, M., Smith, K. R., and Yano, C. A., “Deux Chemicals Inc. Goes
Just–in-Time, Interfaces”, Vol. 39, No. 6, pp. 503-515, 2009. [16] Rivera-Gómez, H., Gharbi, A. and Kenne, J.P., “Joint Production and
Major Maintenance Planning Policy of a Manufacturing System with
Deteriorating Quality”, International Journal of Production Economics,
146, pp. 575-587, 2013.
[17] Dehayem-Nodem, F.I., Kenne, J.P., and Gharbi, A., “Simultaneous
Control of Production, Repair/Replacement and Preventive Maintenance
of Deteriorating Manufacturing Systems”, International Journal of
Production Economics, 134, pp. 271-282, 2011.
[18] Kushner, H.J. and Dupuis, P.G., Numerical Methods for Stochastic
Control Problems in Continuous Time, Springer, New York, NY, 1992.
[19] Boukas, E.K. and Hauire, A., “Manufacturing Flow Control and
Preventive Maintenance: A Stochastic Control Approach”, IEEE
Transactions on Automatic Control, 33, pp. 1024-1031, 1990.
[20] Gharbi, A., and Kenne, J. P., “Production and Preventive Maintenance
Rates Control for a Manufacturing System: An Experimental Design
Approach”, International Journal of Production Economics, 65, pp.
275-287, 2000.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:71032", author = "Héctor Rivera-Gómez and Eva Selene Hernández-Gress and Oscar Montaño-Arango and Jose Ramon Corona-Armenta", title = "A Simulation-Optimization Approach to Control Production, Subcontracting and Maintenance Decisions for a Deteriorating Production System", abstract = "This research studies the joint production,
maintenance and subcontracting control policy for an unreliable
deteriorating manufacturing system. Production activities are
controlled by a derivation of the Hedging Point Policy, and given that
the system is subject to deterioration, it reduces progressively its
capacity to satisfy product demand. Multiple deterioration effects are
considered, reflected mainly in the quality of the parts produced and
the reliability of the machine. Subcontracting is available as support
to satisfy product demand; also, overhaul maintenance can be
conducted to reduce the effects of deterioration. The main objective
of the research is to determine simultaneously the production,
maintenance and subcontracting rate, which minimize the total,
incurred cost. A stochastic dynamic programming model is
developed and solved through a simulation-based approach
composed of statistical analysis and optimization with the response
surface methodology. The obtained results highlight the strong
interactions between production, deterioration and quality, which
justify the development of an integrated model. A numerical example
and a sensitivity analysis are presented to validate our results.", keywords = "Deterioration, simulation, subcontracting, production
planning.", volume = "9", number = "10", pages = "1765-10", }
