A production optimization model of supply-driven chain with quality uncertainty

Renbin Xiao , Zhengying Cai , Xinhui Zhang

Journal of Systems Science and Systems Engineering ›› 2012, Vol. 21 ›› Issue (2) : 144 -160.

PDF
Journal of Systems Science and Systems Engineering ›› 2012, Vol. 21 ›› Issue (2) : 144 -160. DOI: 10.1007/s11518-011-5184-8
Article

A production optimization model of supply-driven chain with quality uncertainty

Author information +
History +
PDF

Abstract

Supply-driven chain’s production is different from traditional demand-driven production because its supplies must guide the full production flow toward the markets and respond actively to customer demand. According to the control theory, a novel multi-variable operation model of supply-driven chain is discussed here, integrating suppliers, manufacturers, distributors and market demands. Especially the coordination problem between suppliers and manufacturers is discussed where suppliers play more important role than manufacturers. Because defect is common in real production system, the production operation of supply-driven chain with imperfect quality is described on the basis of fuzzy set to model the ambiguity of quality and to provide appropriate supply coordination mechanism. In a designed numerical example, it is apparent that both response and robustness performances of supply-driven production system on demand with imperfect quality are improved by a fuzzy proportional-integral-differential regulator. The proposed model may apply to similar productions with imperfect quality.

Keywords

Supply-driven chain / quality / production planning / fuzzy regulator

Cite this article

Download citation ▾
Renbin Xiao, Zhengying Cai, Xinhui Zhang. A production optimization model of supply-driven chain with quality uncertainty. Journal of Systems Science and Systems Engineering, 2012, 21(2): 144-160 DOI:10.1007/s11518-011-5184-8

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Ayers J.. Demand-driven supply chain implementation. Chemical Engineering Progress, 2006, 102(12): 21-23.

[2]

Balakrishnan A., Geunes J.. Production planning with flexible product specifications: an application to specialty steel manufacturing. Operations Research, 2003, 51(1): 94-112.

[3]

Batson R.G., Mcgough K.D.. A new direction in quality engineering: supply chain quality modeling. International Journal of Production Research, 2007, 45(23): 5455-5464.

[4]

Bullow J., Shapiro C.. Kwoka J.E., White L.J.. The British petroleum/ARCO merger: Alaskan crude oil. The antitrust revolution: economics, competition and policy, 2003, Oxford: Oxford University Press 22

[5]

Cen Y.T.. Fuzzy quality and analysis on fuzzy probability. Fuzzy Sets and Systems, 1996, 83(2): 283-290.

[6]

Chan E.Y.Y., Griffiths S.M., Chan C.W.. Public-health risks of melamine in milk products. The Lancet, 2008, 372(9648): 1444-1445.

[7]

Chang H.C.. An application of fuzzy sets theory to the EOQ model with imperfect quality items. Computers & Operations Research, 2004, 31(12): 2079-2092.

[8]

Fisher M.L.. What is the right supply chain for your product?. Harvard Business Review, 1997, 75(2): 105-116.

[9]

Hameri A.P., Palsson J.. Supply chain management in the fishing industry: the case of Iceland. International Journal of Logistics: Research and Applications, 2003, 6(3): 137-150.

[10]

Handfield R., Warsing D., Wu X.. (Q, r) inventory policies in a fuzzy uncertain supply chain environment. Journal of Operational Research, 2009, 197: 609-619.

[11]

Hougaard J.L.. A simple approximation of productivity scores of fuzzy production plans. Fuzzy Sets and Systems, 2005, 152(3): 455-465.

[12]

Hull B.Z.. Are supply (driven) chains forgotten?. The International Journal of Logistics Management, 2005, 16(2): 218-236.

[13]

Jaber M.Y., Goyal S.K., Imran M.. Economic production quantity model for items with imperfect quality subject to learning effects. International Journal of Production Economics, 2008, 115(1): 143-150.

[14]

Jaksic M., Rusjan B.. The effect of replenishment policies on the bullwhip effect: a transfer function approach. European Journal of Operational Research, 2008, 184(3): 946-961.

[15]

Kulkarni S.S.. Loss-based quality costs and inventory planning: general models and insights. European Journal of Operational Research, 2008, 188(2): 428-449.

[16]

Lejeune M.A., Ruszczynski A.. An efficient trajectory method for probabilistic production-inventory-distribution problems. Operations Research, 2007, 55(2): 378-394.

[17]

Lin P.H., Wong D.S.H., Jang S.S., Shieh S.S., Chu J.Z.. Controller design and reduction of bullwhip for a model supply chain system using z-transform analysis. Journal of Process Control, 2004, 14(5): 487-499.

[18]

Mula J., Poler R., Garcia-Sebater J.P., Lario F.C.. Models for production planning under uncertainty: a review. International Journal of Production Economics, 2006, 103(1): 271-285.

[19]

Nagatani T., Helbing D.. Stability analysis and stabilization strategies for linear supply chains. Physica A: Statistical and Theoretical Physics, 2004, 335(3–4): 644-660.

[20]

Ojha D., Sarker B.R., Biswas P.. An optimal batch size for an imperfect production system with quality assurance and rework. International Journal of Production Research, 2007, 45(14): 3191-3214.

[21]

Paulsen K., Hensel F.. Design of an autarkic water and energy supply driven by renewable energy using commercially available components. Desalination, 2007, 203(1–3): 455-462.

[22]

Ponzi A., Yasutomi A., Kaneko K.. A non-linear model of economic production processes. Physical A: Statistical Mechanics and Its Applications, 2003, 324(1–2): 372-379.

[23]

Radhoui M., Rezg N., Chelbi A.. Integrated model of preventive maintenance, quality control and buffer sizing for unreliable and imperfect production systems. International Journal of Production Research, 2009, 47(2): 389-402.

[24]

Rong A., Lahdelma R.. Fuzzy chance constrained linear programming model for optimizing the scrap charge in steel production. European Journal of Operational Research, 2008, 186(3): 953-964.

[25]

Samanta B., Al-Araimi S.A.. An inventory control model using fuzzy logic. International Journal of Production Economics, 2001, 73(3): 217-226.

[26]

Schultmann F., Zumkeller M., Rentz O.. Modeling reverse logistic tasks within closed-loop supply chains: an example from the automotive industry. European Journal of Operational Research, 2006, 171(3): 1033-1050.

[27]

Sethi R., Sethi A.. Marketing strategy driven e-supply chains: a conceptual framework. Proceedings of American Marketing Association Conference, 2001, 12: 118-119.

[28]

Singh N.A., Muraleedharan K.A., Gomathy K.. Damping of low frequency oscillation in power system network using swarm intelligence tuned fuzzy controller. International Journal of Bio-inspired computation, 2010, 2(1): 1-8.

[29]

Spitter J.M., Hurkens C.A.J., de Kok A.G., Lenstra J.K., Negenman E.G.. Linear programming models with planned lead times for supply chain operations planning. European Journal of Operational Research, 2005, 163(3): 706-720.

[30]

Wang G., Huang S.H., Dismukes J.P.. Product-driven supply chain selection using integrated multi-criteria decision-making methodology. International Journal of Production Economics, 2004, 91(1): 1-15.

[31]

Wang J., Shu Y.F.. Fuzzy decision modeling for supply chain management. Fuzzy Sets and Systems, 2005, 150: 107-127.

[32]

Wang R.C., Liang T.F.. Application of fuzzy multi-objective linear programming to aggregate production planning. Computers & Industrial Engineering, 2004, 46(1): 17-41.

[33]

Zhu K., Zhang R.Q., Tsung F.. Pushing quality improvement along supply chains. Management Science, 2007, 53(3): 421-436.

AI Summary AI Mindmap
PDF

148

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/