Unreliable manufacturing supply chain optimisation based on an infinitesimal perturbation analysis

ABSTRACTThis paper studies the optimisation of a manufacturing/remanufacturing system, which produces one type of product and is composed of a serviceable inventory, two parallel machines, a recovery inventory, two quality inspection centres and customers who demand a constant quantity of product. The proposed system takes into account the withdrawn products and the return of the used products from the market. The manufacturing and remanufacturing products are stored in the serviceable inventory. The used products are inspected then disposed of or stored in the recovery inventory for remanufacturing. The withdrawn products are inspected and stored in the serviceable inventory for selling or stored in the recovery inventory for remanufacturing. The objective of this work is to find the optimal serviceable inventory level that minimises the cost function, then to study the influence of withdrawn products and used products on the value of the optimal serviceable inventory level. Infinitesimal perturbation an...

[1]  Debasis Mitra,et al.  Performance and fluid simulations of a novel shared buffer management system , 1998, Proceedings. IEEE INFOCOM '98, the Conference on Computer Communications. Seventeenth Annual Joint Conference of the IEEE Computer and Communications Societies. Gateway to the 21st Century (Cat. No.98.

[2]  Turki Sadok,et al.  Performance evaluation of a failure-prone manufacturing system with time to delivery and stochastic demand , 2009 .

[3]  Nathalie Sauer,et al.  Perturbation analysis based-optimization for discrete flow model: a failure-prone manufacturing system with constant delivery time and stochastic demand , 2010 .

[4]  Nathalie Sauer,et al.  Perturbation analysis based-optimization for a failure-prone manufacturing system with constant delivery time and stochastic demand , 2009, 2009 International Conference on Computers & Industrial Engineering.

[5]  Christos G. Cassandras,et al.  Perturbation Analysis and Optimization of Multiclass Multiobjective Stochastic Flow Models , 2011, Discret. Event Dyn. Syst..

[6]  Bernd Irlenbusch,et al.  Fairness Crowded Out by Law. An Experimental Study on Withdrawal Rights , 2007 .

[7]  Yao Zhao,et al.  IPA derivatives for a discrete model of make-to-stock production-inventory systems with backorders , 2010, Ann. Oper. Res..

[8]  Michael Manitz,et al.  Queueing-model based analysis of assembly lines with finite buffers and general service times , 2008, Comput. Oper. Res..

[9]  Zied Hajej,et al.  Modelling and analysis for sequentially optimising production, maintenance and delivery activities taking into account product returns , 2015 .

[10]  Ivo J. B. F. Adan,et al.  Output analysis of multiclass fluid models with static priorities , 2008, Perform. Evaluation.

[11]  Paul Glasserman,et al.  Gradient Estimation Via Perturbation Analysis , 1990 .

[12]  Nathalie Sauer,et al.  Perturbation analysis for continuous and discrete flow models: a study of the delivery time impact on the optimal buffer level , 2013 .

[13]  Christos G. Cassandras,et al.  Perturbation analysis for production control and optimization of manufacturing systems , 2004, Autom..

[14]  Xi-Ren Cao,et al.  Perturbation analysis of discrete event dynamic systems , 1991 .

[15]  H. Zied,et al.  Joint optimisation of maintenance and production policies with subcontracting and product returns , 2014 .

[16]  Christos G. Cassandras,et al.  Using infinitesimal perturbation analysis of stochastic flow models to recover performance sensitivity estimates of discrete event systems , 2012, Discret. Event Dyn. Syst..

[17]  Christos G. Cassandras,et al.  Perturbation analysis and control of two-class stochastic fluid models for communication networks , 2003, IEEE Trans. Autom. Control..

[18]  R. V. Adams Infinitesimal perturbation analysis of a multi-stage tandem of fluid queue with additive loss feedback , 2014, Syst. Control. Lett..

[19]  Pierre Dejax,et al.  Production planning of a hybrid manufacturing–remanufacturing system under uncertainty within a closed-loop supply chain , 2012 .

[20]  Y. C. Ho,et al.  A survey of the perturbation analysis of discrete event dynamic systems , 1985 .

[21]  C.G. Panayiotou,et al.  IPA for delay threshold violation using stochastic fluid models , 2008, 2008 9th International Workshop on Discrete Event Systems.

[22]  Turki Sadok,et al.  Infinitesimal perturbation analysis based optimization for a manufacturing-remanufacturing system , 2013, 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA).

[23]  Chun Su,et al.  Buffer allocation for hybrid manufacturing/remanufacturing system considering quality grading , 2014 .

[24]  Yugang Yu,et al.  Robust production control policy for a single machine and single part-type manufacturing system with inaccurate observation of production surplus , 2012 .

[25]  Christos G. Cassandras,et al.  Perturbation analysis for online control and optimization of stochastic fluid models , 2002, IEEE Trans. Autom. Control..

[26]  Li Xia,et al.  Performance optimization of queueing systems with perturbation realization , 2012, Eur. J. Oper. Res..

[27]  Turki Sadok,et al.  Optimization of stochastic fluid model using perturbation analysis: A manufacturingremanufacturing system with stochastic demand and stochastic returned products , 2014, Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control.

[28]  Albert Corominas,et al.  Optimal manufacturing-remanufacturing policies in a lean production environment , 2008, Comput. Ind. Eng..

[29]  N. Rezg,et al.  Optimization of Manufacturing Supply Chain with Stochastic Demand and Planned Delivery Time , 2017 .

[30]  Yu-Chi Ho,et al.  A gradient technique for general buffer storage design in a production line , 1979 .

[31]  Marisa de Brito,et al.  Reverse Logistics: A Review of Case Studies , 2004 .

[32]  Turki Sadok,et al.  Perturbation analysis for optimal production planning of a manufacturing system with influence machine degradation , 2014 .

[33]  Christos G. Cassandras,et al.  A Solution to the Optimal Lot-Sizing Problem as a Stochastic Resource Contention Game , 2012, IEEE Transactions on Automation Science and Engineering.

[34]  Xiaolan Xie,et al.  Simulation-based optimization of a single-stage failure-prone manufacturing system with transportation delay , 2008 .

[35]  Christos G. Cassandras,et al.  Infinitesimal Perturbation Analysis and Optimization for Make-to-Stock Manufacturing Systems Based on Stochastic Fluid Models , 2006, Discret. Event Dyn. Syst..

[36]  Marc Salomon,et al.  Strategic Issues in Product Recovery Management , 1995 .

[37]  Christos G. Cassandras,et al.  Infinitesimal and finite perturbation analysis for queueing networks , 1982, 1982 21st IEEE Conference on Decision and Control.

[38]  Ronald S. Tibben-Lembke,et al.  Going Backwards: Reverse Logistics Trends and Practices , 1999 .

[39]  Christoforos Panayiotou,et al.  Optimization of discrete event system parameters using SFM-based infinitesimal perturbation analysis estimates , 2007, 2007 46th IEEE Conference on Decision and Control.