Flexible design-planning of supply chain networks
Nowadays market competition is essentially associated to supply chain (SC) improvement. Therefore, the locus of value creation has shifted to the chain network. The strategic decision of determining the optimal SC network structure plays a vital role in the later optimization of SC operations. This...
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| Vydané v: | AIChE journal Ročník 55; číslo 7; s. 1736 - 1753 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Wiley Subscription Services, Inc., A Wiley Company
01.07.2009
Wiley American Institute of Chemical Engineers |
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| ISSN: | 0001-1541, 1547-5905 |
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| Abstract | Nowadays market competition is essentially associated to supply chain (SC) improvement. Therefore, the locus of value creation has shifted to the chain network. The strategic decision of determining the optimal SC network structure plays a vital role in the later optimization of SC operations. This work focuses on the design and retrofit of SCs. Traditional approaches available in literature addressing this problem usually utilize as departing point a rigid predefined network structure which may restrict the opportunities of adding business value. Instead, a novel flexible formulation approach which translates a recipe representation to the SC environment is proposed to solve the challenging design-planning problem of SC networks. The resulting mixed integer linear programming model is aimed to achieve the best NPV as key performance metric. The potential of the presented approach is highlighted through illustrative examples of increasing complexity, where results of traditional rigid approaches and those offered by the flexible framework are compared. The implications of exploiting this potential flexibility to improve the SC performance are highlighted and are the subject of our further research work. © 2009 American Institute of Chemical Engineers AIChE J, 2009 |
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| AbstractList | Nowadays market competition is essentially associated to supply chain (SC) improvement. Therefore, the locus of value creation has shifted to the chain network. The strategic decision of determining the optimal SC network structure plays a vital role in the later optimization of SC operations. This work focuses on the design and retrofit of SCs. Traditional approaches available in literature addressing this problem usually utilize as departing point a rigid predefined network structure which may restrict the opportunities of adding business value. Instead, a novel flexible formulation approach which translates a recipe representation to the SC environment is proposed to solve the challenging design-planning problem of SC networks. The resulting mixed integer linear programming model is aimed to achieve the best NPV as key performance metric. The potential of the presented approach is highlighted through illustrative examples of increasing complexity, where results of traditional rigid approaches and those offered by the flexible framework are compared. The implications of exploiting this potential flexibility to improve the SC performance are highlighted and are the subject of our further research work. © 2009 American Institute of Chemical Engineers AIChE J, 2009 Nowadays market competition is essentially associated to supply chain (SC) improvement. Therefore, the locus of value creation has shifted to the chain network. The strategic decision of determining the optimal SC network structure plays a vital role in the later optimization of SC operations. This work focuses on the design and retrofit of SCs. Traditional approaches available in literature addressing this problem usually utilize as departing point a rigid predefined network structure which may restrict the opportunities of adding business value. Instead, a novel flexible formulation approach which translates a recipe representation to the SC environment is proposed to solve the challenging design-planning problem of SC networks. The resulting mixed integer linear programming model is aimed to achieve the best NPV as key performance metric. The potential of the presented approach is highlighted through illustrative examples of increasing complexity, where results of traditional rigid approaches and those offered by the flexible framework are compared. The implications of exploiting this potential flexibility to improve the SC performance are highlighted and are the subject of our further research work. [PUBLICATION ABSTRACT] Nowadays market competition is essentially associated to supply chain (SC) improvement. Therefore, the locus of value creation has shifted to the chain network. The strategic decision of determining the optimal SC network structure plays a vital role in the later optimization of SC operations. This work focuses on the design and retrofit of SCs. Traditional approaches available in literature addressing this problem usually utilize as departing point a rigid predefined network structure which may restrict the opportunities of adding business value. Instead, a novel flexible formulation approach which translates a recipe representation to the SC environment is proposed to solve the challenging design-planning problem of SC networks. The resulting mixed integer linear programming model is aimed to achieve the best NPV as key performance metric. The potential of the presented approach is highlighted through illustrative examples of increasing complexity, where results of traditional rigid approaches and those offered by the flexible framework are compared. The implications of exploiting this potential flexibility to improve the SC performance are highlighted and are the subject of our further research work. |
| Author | Kopanos, Georgios Puigjaner, Luis Laínez, José Miguel Espuña, Antonio |
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| Keywords | Design MILP supply chain management Markets Linear programming Mixed integer programming Planning network design Flexibility Modeling Optimization Mathematical programming |
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| References | You F,Grossmann IE. Design of responsive supply chains under demand uncertainty. Comput Chem Eng. 2008; 32: 3090-3111. Guillén G,Mele F,Bagajewicz M,Espuña A,Puigjaner L. Multiobjective supply chain design under uncertainty. Chem Eng Sci. 2005; 60: 1535-1553. Jordan WC,Graves SC. Principles on the benefits of manufacturing process flexibility. Manage Sci. 1995; 41: 577-594. Kondili E,Pantelides CC,Sargent RW. A general algorithm for short term scheduling of batch operations. Comput Chem Eng. 1993; 17: 211-227. Ferrio J,Wassick J. Chemical supply chain network optimization. Comput Chem Eng. 2008; 32: 2481-2504. Handfield RF,Nichols EL. Introduction to Supply Chain Management. New Jersey: Prentice Hall, 1999. Graves SC,Tomlin BT. Process flexibility in supply chains. Manage Sci. 2003; 49: 907-919. Lamming R. Japanese supply chain relationships in recession. Long Range Plann. 2000; 33: 757-778. Jackson JR,Grossmann IE. Temporal decomposition scheme for nonlinear multisite production planning and distribution models. Ind Eng Chem Res. 2003; 42: 3045-3055. Shah N. Process industry supply chains: advances and challenges. Comput Chem Eng. 2005; 29: 1225-1235. Beamon BM. Supply chain design and analysis: models and methods. Int J Prod Econ. 1998; 55: 281-294. Oh H,Karimi IA. Regulatory factors and capacity-expansion planning in global chemical supply chains. Ind Eng Chem Res. 2004; 43: 3364-3380. Tsiakis P,Shah N,Pantelides CC. Design of multi-echelon supply chain networks under demand uncertainty. Ind Eng Chem Res. 2001; 40: 3585-3604. Meixell MJ,Gargeya VB. Global supply chain design: a literature review and critique. Transportation Res Part E. 2005; 41: 531-550. Vidal CJ,Goetschalckx M. Strategic production-distribution models: a critical review with emphasis on global supply chain models. Eur J Oper Res. 1997; 98: 1-18. Bok JW,Grossmann IE,Park S. Supply chain optimization in continuous flexible process networks. Ind Eng Chem Res. 2000; 39: 1279-1290. Laínez JM,Guillén-Gozálbez G,Badell M,Espuña A,Puigjaner L. Enhancing corporate value in the optimal design of chemical supply chains. Ind Eng Chem Res. 2007; 46: 7739-7757. Brown GG,Graves GW,Honczarenko M. Design and operation of a multicommodity production/distribution system using primal goal decomposition. Manage Sci. 1987; 33: 1469-1480. Mele FD,Guillén G,Espuña A,Puigjaner L. An agent-based approach for supply chain retrofitting under uncertainty. Comput Chem Eng. 2007; 31: 722-735. Schmidt G,Wilhelm WE. Strategic, tactical and operational decisions in multinational logistics networks: a review and discussion of modelling issues. Int J Prod Res. 2000; 38: 1501-1523. Cakravastia A,Toha IS,Nakamura N. A two-stage model for the design of supply chain networks. Int J Prod Econ. 2002; 80: 231-248. Tang CS. Perspectives in supply chain risk management. Int J Prod Econ. 2006; 103: 451-488. Kallrath J. Combined strategic and operational planning: an MILP success story in chemical industry. OR Spectrum. 2002; 24: 315-341. Hugo A,Pistikopoulos EN. Environmentally conscious long-range planning and design of supply chain networks. J Cleaner Prod. 2005; 13: 1471-1491. 2004; 43 1995; 41 1987; 33 2000; 38 1993; 17 2000; 39 1997; 98 2002; 24 2000; 33 2005; 41 2003; 49 2008; 32 2005; 60 2002; 80 2007; 31 2005; 29 2001; 40 2007; 46 2003; 42 1998; 55 2006; 103 2005; 13 2003; 11 1999 e_1_2_8_23_2 e_1_2_8_25_2 e_1_2_8_26_2 e_1_2_8_9_2 Handfield RF (e_1_2_8_4_2) 1999 e_1_2_8_2_2 e_1_2_8_3_2 e_1_2_8_6_2 e_1_2_8_5_2 e_1_2_8_8_2 Graves SC (e_1_2_8_24_2) 2003 e_1_2_8_7_2 e_1_2_8_20_2 e_1_2_8_21_2 e_1_2_8_22_2 e_1_2_8_16_2 e_1_2_8_17_2 e_1_2_8_18_2 e_1_2_8_19_2 e_1_2_8_12_2 e_1_2_8_13_2 e_1_2_8_14_2 e_1_2_8_15_2 e_1_2_8_10_2 e_1_2_8_11_2 |
| References_xml | – reference: Beamon BM. Supply chain design and analysis: models and methods. Int J Prod Econ. 1998; 55: 281-294. – reference: Meixell MJ,Gargeya VB. Global supply chain design: a literature review and critique. Transportation Res Part E. 2005; 41: 531-550. – reference: Jordan WC,Graves SC. Principles on the benefits of manufacturing process flexibility. Manage Sci. 1995; 41: 577-594. – reference: Jackson JR,Grossmann IE. Temporal decomposition scheme for nonlinear multisite production planning and distribution models. Ind Eng Chem Res. 2003; 42: 3045-3055. – reference: Lamming R. Japanese supply chain relationships in recession. Long Range Plann. 2000; 33: 757-778. – reference: Schmidt G,Wilhelm WE. Strategic, tactical and operational decisions in multinational logistics networks: a review and discussion of modelling issues. Int J Prod Res. 2000; 38: 1501-1523. – reference: Laínez JM,Guillén-Gozálbez G,Badell M,Espuña A,Puigjaner L. Enhancing corporate value in the optimal design of chemical supply chains. Ind Eng Chem Res. 2007; 46: 7739-7757. – reference: Brown GG,Graves GW,Honczarenko M. Design and operation of a multicommodity production/distribution system using primal goal decomposition. Manage Sci. 1987; 33: 1469-1480. – reference: Ferrio J,Wassick J. Chemical supply chain network optimization. Comput Chem Eng. 2008; 32: 2481-2504. – reference: Hugo A,Pistikopoulos EN. Environmentally conscious long-range planning and design of supply chain networks. J Cleaner Prod. 2005; 13: 1471-1491. – reference: Bok JW,Grossmann IE,Park S. Supply chain optimization in continuous flexible process networks. Ind Eng Chem Res. 2000; 39: 1279-1290. – reference: Graves SC,Tomlin BT. Process flexibility in supply chains. Manage Sci. 2003; 49: 907-919. – reference: Kondili E,Pantelides CC,Sargent RW. A general algorithm for short term scheduling of batch operations. Comput Chem Eng. 1993; 17: 211-227. – reference: Tang CS. Perspectives in supply chain risk management. Int J Prod Econ. 2006; 103: 451-488. – reference: Guillén G,Mele F,Bagajewicz M,Espuña A,Puigjaner L. Multiobjective supply chain design under uncertainty. Chem Eng Sci. 2005; 60: 1535-1553. – reference: Vidal CJ,Goetschalckx M. Strategic production-distribution models: a critical review with emphasis on global supply chain models. Eur J Oper Res. 1997; 98: 1-18. – reference: Mele FD,Guillén G,Espuña A,Puigjaner L. An agent-based approach for supply chain retrofitting under uncertainty. Comput Chem Eng. 2007; 31: 722-735. – reference: Handfield RF,Nichols EL. Introduction to Supply Chain Management. New Jersey: Prentice Hall, 1999. – reference: Tsiakis P,Shah N,Pantelides CC. Design of multi-echelon supply chain networks under demand uncertainty. Ind Eng Chem Res. 2001; 40: 3585-3604. – reference: Kallrath J. Combined strategic and operational planning: an MILP success story in chemical industry. OR Spectrum. 2002; 24: 315-341. – reference: Shah N. Process industry supply chains: advances and challenges. Comput Chem Eng. 2005; 29: 1225-1235. – reference: You F,Grossmann IE. Design of responsive supply chains under demand uncertainty. Comput Chem Eng. 2008; 32: 3090-3111. – reference: Cakravastia A,Toha IS,Nakamura N. A two-stage model for the design of supply chain networks. Int J Prod Econ. 2002; 80: 231-248. – reference: Oh H,Karimi IA. Regulatory factors and capacity-expansion planning in global chemical supply chains. Ind Eng Chem Res. 2004; 43: 3364-3380. – volume: 98 start-page: 1 year: 1997 end-page: 18 article-title: Strategic production‐distribution models: a critical review with emphasis on global supply chain models publication-title: Eur J Oper Res. – volume: 38 start-page: 1501 year: 2000 end-page: 1523 article-title: Strategic, tactical and operational decisions in multinational logistics networks: a review and discussion of modelling issues publication-title: Int J Prod Res. – volume: 80 start-page: 231 year: 2002 end-page: 248 article-title: A two‐stage model for the design of supply chain networks publication-title: Int J Prod Econ. – volume: 41 start-page: 577 year: 1995 end-page: 594 article-title: Principles on the benefits of manufacturing process flexibility publication-title: Manage Sci. – volume: 103 start-page: 451 year: 2006 end-page: 488 article-title: Perspectives in supply chain risk management publication-title: Int J Prod Econ. – volume: 29 start-page: 1225 year: 2005 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| SubjectTerms | Applications of mathematics to chemical engineering. Modeling. Simulation. Optimization Applied sciences Chemical engineering Competition Design Exact sciences and technology flexibility Market analysis MILP network design Retrofitting Supply chain management Supply chains |
| Title | Flexible design-planning of supply chain networks |
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