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Reverse Logistics Network Problem using simulated annealing with and without priority-algorithm

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In recent years, Reverse Logistics (RL) has become a field of importance for all organizations due to growing environmental concerns, legislation, corporate social responsibility and sustainable competitiveness. In Reverse logistics, the used or returned products are collected after their acquisition and inspected for sorting into the different categories. The next step is to disposition them for repair, remanufacturing, recycling, reuse or final disposal. Manufacturers may adopt reverse logistics by choice or by force, but they have to decide whether performing the activities themselves or outsourcing to a third party (Martin et al., 2010). Lourenço et al., (2003) described three main areas of improvement within the RL process. Firstly, companies can reduce the level of returns through the analysis of their causes. Secondly, they can work on the improvement of the return’s process and, thirdly, they can create value from the returns. This paper considers the multistage reverse Logistics Network Problem (mrLNP) proposed by Lee et al., (2008). With minimizing the total of costs to reverse logistics shipping cost. We will demonstrate the mrLNP model will be formulated as a three-stage logistics network model. Since such network design problems belong to the class of NP-hard problems we propose a Simulated Annealing (SA) and simulated annealing with priority (priSA) with special neighborhood search mechanisms to find the near optimal solution consisting of two stages. Computer simulations show the several numerical examples by using, SA, priSA and priGA(Genetic algorithm with priority-based encoding method) and effectiveness of the proposed method.
Rocznik
Strony
7--17
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
autor
  • University of Sfax, OASIS Laboratory, ISGI, Sfax, Tunisia
autor
  • University of Sfax, MODILS Research Unit, FSEG Sfax, Tunisia
autor
  • Emirates College of Technology, Abu Dhabi, UAE
Bibliografia
  • [1] BARROS, A.I., DEKKER, R., SCHOLTEN, V., 1998. A two-level network for recycling sand: a case study. European journal of operational research, 110, 199–214.
  • [2] GEN, M., ALTIPARMAK, F., LIN, L. 2006 A genetic algorithm for two stage transportation problem using priority-based encoding. OR Spectrum 28, 337–354
  • [3] JAYARAMAN, V., GUIDE, V.D.R.J, SRI-VASTAVA, R., 1999. A closed loop logistics model for remanufacturing. Journal of the Operational Research Society, 50, 497–508.
  • [4] JAYARAMAN, V., PATTERSON, R.A., ROLLAND, E., 2003. The design of reverse distribution networks: models and solution procedures. European journal of operational research, 150, 128–149.
  • [5] KIM, K., SONG, I., KIM, J., JEONG, B., 2006. Supply planning model for remanufacturing system in reverse logistics environment. Computers & Industrial Engineering, 51, 279–287.
  • [6] KIRKPATRICK, S., GELATT, C. D., VECCHI, M. P., 1983. Optimization by simulated annealing. science, 220(4598), 671-680.
  • [7] KIRKKE, H.R., HARTEN, A.V., SCHUUR, P.C., 1999. Business case Oce: reverse logistic network redesign for copiers. OR Spectrum, 21, 381–409.
  • [8] KO, H.J., EVANS, G.W., 2007. A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Computer Operational Research, 34, 346–66.
  • [9] LEWCZUK, K., 2015. The concept of genetic programming in organizing internal transport processes. Archives of Transport, 34(2), 61-74.
  • [10] LEE, J.E, GEN. M., RHEE, K.G., 2008. Network model and optimization of reverse logistics by hybrid genetic algorithm. Computers and Industrial Engineering, 56, 951–64.
  • [11] SOTO, J.P., RAMALHINHO-LOURENÇO, H., 2002 A Recoverable Production Planning Model (July 2002). UPF Economics and Business Working Paper No. 636. Available at SSRN: https://ssrn.com/ab-stract=394302 or htt://dx.doi.org/10.2139/ssrn.394302
  • [12] KARKULA, M., 2014. Selected aspects of simulation modelling of internal transport processes performed at logistics facilities. Archives of Transport, 30(2), 43-56
  • [13] MARTIN, P., GUIDE JR., V.D.R., CRAIGHEAD, C.W., 2010. Supply chain sourcing in remanufacturing operations: an empirical investigation to remake versus buy. Decision Sciences, 41(2), 301–321.
  • [14] ILGIN, M. A., GUPTA, S. M. 2010. Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art. Journal of environmental management, 91(3), 563-591.
  • [15] METROPOLIS, N., ROSENBLUTH, A. W., ROSENBLUTH, M. N., TELLER, A. H., TELLER, E. 1953. Equation of state calculations by fast computing machines. The journal of chemical physics, 21(6), 1087-1092.
  • [16] MIN, H., KO, H.J., PARK, B.I., 2005. A Lagrangian relaxation heuristic for solving the multi-echelon, multi-commodity, closed-loop supply chain network design problem. International Journal Logistics System Management, 1, 382–404.
  • [17] MIN, H., KO, H.J., KO, C.S., 2006. A genetic algorithm approach to developing the multiechelon reverse logistics network for product returns. Omega, 34, 56–69.
  • [18] ŻOCHOWSKA, R., SOCZÓWKA, P., 2018. Analysis of selected transportation network structures based on graph measures. Scientific Journal of Silesian University of Technology. Series Transport, 98, 223-233.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-838ad5c0-4fe3-49c0-ad91-b679b60f1eda
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