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Closed Loop Supply Chain with Production Planning

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Języki publikacji
EN
Abstrakty
EN
We present a Closed Loop Supply Chain (CLSC) model that supports a production planning (PP) process. CLSC model is based on CLSC framework model which consists of four main centers: collection, recovery center, distribution and disposal centers. These logistics parts support main production lines. Some quantity of the products is recovered and the factories don’t need to spend money for production. This is a simple cost reduction process. In CLSC literature one can hardly meet the models of production planning processes supported by CLSC. Important problem with that models is the computational complexity when one wants to prepare production plans for more than one time period. This is connected with a number of the numerical variables of the CLSC and PP models which are usually Integer Programming models solved with Branch & Bound algorithms. We present some modifications of the widely known and used constraints in the CLSC models to optimize solving process. All the experiments were conducted with the CPLEX solver.
Słowa kluczowe
Rocznik
Strony
117--128
Opis fizyczny
Bibliogr. 11 poz., fig., tab.
Twórcy
autor
  • Faculty of Cybernetics Military University of Technology, ul. Kaliskiego 2, 00-908 Warsaw, Poland, wstecz@wat.edu.pl
Bibliografia
  • 1. Amin S.H. & Zhang G. (2013), A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return, Applied Mathematical Modelling, Vol. 37, pp. 4165-4176.
  • 2. Cooper M.C., Lambert D. & Pagh J.D. (1997), Supply chain management: more than a new name for logistics, International Journal of Logistics Management, Vol. 8, No. 1, pp. 1-9.
  • 3. Klibi W., Martel A. & Guitouni A. (2010), The design of robust value-creating supply chain networks: a critical review, Eur. J. Oper. Res., Vol. 203, pp. 283-293.
  • 4. Kramarz W., Kramarz M. (2015), Strategy of improving resistance of supply chain in conditions of disruptions, Research in Logistics & Production, Vol. 1, pp. 53-64.
  • 5. Meade L., Sarkis J. & Presley A. (2007), The theory and practice of Reverse Logistics, Int. J. Log. Syst. Manage., Vol. 3, pp. 56-84.
  • 6. Melo M.T., Nickel S. & Saldanha-da-Gama F. (2009), Facility location and supply chain management - a review, Eur. J. Oper. Res., Vol. 196, pp. 401-412.
  • 7. Nemhauser G.L. & Wolsey L.A. (1988), Integer and Combinatorial Optimization, John Wiley & Sons, New York.
  • 8. Nocedal J. & Wright S.J. (1999), Numerical optimization, Springer-Verlag, New York.
  • 9. Pishvaee M.S., Rabbani M. & Torabi S.A. (2011), A robust optimization approach to closed-loop supply chain network design under uncertainty, Applied Mathematical Modelling, Vol. 35, pp. 637-649.
  • 10. Uster H., Easwaran G., Akcali E. & Cetinkaya S. (2007), Benders decomposition with alternative multiple cuts for a multi-product closed-loop supply chain, network design model, Naval Research Logistics, Vol. 54, pp. 890-907.
  • 11. Wolsey L.A. & Neumhauser N.L. (1999), Integer and combinatorial optimization, Wiley-InterScience.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e0452c21-b2bb-4335-adbc-93d22e6c243d
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