Outsourcing optimization in two-echelon supply chain network under integrated production-maintenance constraints
- 296 Downloads
In this paper, we study a two-echelon supply chain network consisting of multi-outsourcers and multi-subcontractors. Each one is composed of a failure-prone production unit that produces a single product to fulfil market demands with variable production rates. Sometimes the manufacturing systems are not able to satisfy demand; in this case, outsourcing option is adopted to improve the limited in-house production capacity. The outsourcing is not justified by the production lack of manufacturing systems, but is also considered for the costs minimization issues. In the considered problem, we assume that the failure rate is dependent on the time and production rate. Preventive maintenance activities can be conducted to mitigate the deterioration effects, and minimal repairs are performed when unplanned failures occurs. We consider that the production cost depends on the rate of the machine utilization. The aim of this research is to propose a joint policy based on a mixed integer programming formulation to balance the trade-off between two-echelon of supply chain. We seek to assist outsourcers to determine the integrated in-house/ outsourcing, and maintenance plans, and the subcontractors to determine the integrated production-maintenance plans so that the benefit of the supply chain is maximized over a finite planning horizon. We develop an improved optimization procedure based on the genetic algorithms, and we discuss and conduct computational experiments to study the managerial insights for the developed framework.
KeywordsProduction-maintenance planning In-house production Outsourcing Multiple costing schedule Genetic algorithm Outsourcing providers’ selection Failure-prone single machine
This work is supported by: the Direction for Cooperation and Inter-university Exchanges of MESRS research ministry and LAP research laboratory – Algeria; LGIPM research laboratory and Université de Lorraine – France. We thank the reviewers for their thorough review and highly appreciate comments and suggestions, which significantly contributed to improving the quality of the paper.
- Budai, G., Dekker, R., & Nicolai, R. P. (2008). A review of planning models for maintenance and production. Springer Series in Reliability Engineering, Complex System Maintenance Handbook.Google Scholar
- Dahane, M., & Rezg, N. (2011). Economic model of outsourcing for a subcontractor manufacturing system in a single subcontractor - multi-outsourcers relationship. IEEE conference on automation science and engineering (CASE), Trieste, Italy, 24–27 Aug. 2011, pp. 450–455.Google Scholar
- Haoues, M., Dahane, M., Mouss, N. K., & Rezg, N. (2011). Optimization of outsourcing activity under a win-win single outsourcer – single subcontractor relationship, Proceedings of the 41st international conference on computers & industrial engineering (pp. 343–348).Google Scholar
- Haoues, M., Dahane, M., Mouss, N. K., & Rezg, N. (2013). Integrated optimization of in-house production and outsourcing strategy: Genetic algorithm based approach. The 11th IFAC workshop on intelligent manufacturing systems, IFAC proceedings (Vol. 46(7), pp. 420–425).Google Scholar
- Lee, S., & Lan, S. (2013). Production lot sizing with a secondary outsourcing facility. International Journal of Production Economics., 141(1), 414–424.Google Scholar
- Najid, N., & Alaoui-selsouli, M. (2011). An integrated production and maintenance planning model with time windows and shortage cost. International Journal of Production Research, 49(8), 37–41.Google Scholar
- Rivera-Gómez, H., Gharbi, A., Kenné, J.-P., Montaño-Arango, O., & Hernandez-Gress, E. S. (2016). Production control problem integrating overhaul and subcontracting strategies for a quality deteriorating manufacturing system. International Journal of Production Economics, 171(1), 134–150.CrossRefGoogle Scholar
- Wen, D., Ershun, P., Ying, W., & Wenzhu, L. (2014). An economic production quantity model for a deteriorating system integrated with predictive maintenance strategy. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-014-0954-z.