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Multi objective outbound logistics network design for a manufacturing supply chain

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Abstract

Outbound logistics network (OLN) in the downstream supply chain of a firm plays a dominant role in the success or failure of that firm. This paper proposes the design of a hybrid and flexible OLN in multi objective context. The proposed distribution network for a manufacturing supply chain consists of a set of customer zones (CZs) at known locations with known demands being served by a set of potential manufacturing plants, a set of potential central distribution centers (CDCs), and a set of potential regional distribution centers (RDCs). Three variants of a single product classified based on nature of demand are supplied to CZs through three different distribution channels. The decision variables include number of plants, CDCs, RDCs, and quantities of each variant of product delivered to CZs through a designated distribution channel. The goal is to design the network with multiple objectives so as to minimize the total cost, maximize the unit fill rates, and maximize the resource utilization of the facilities in the network. The problem is formulated as a mixed integer linear programming problem and a multiobjective genetic algorithm (MOGA) called non-dominated sorting genetic algorithm—II (NSGA-II) is employed to solve the resulting NP-hard combinatorial optimization problem. Computational experiments conducted on randomly generated data sets are presented and analyzed showing the effectiveness of the solution algorithm for the proposed network.

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References

  • Altiparamak F., Gen M., Lin L., Paksoy T. (2006) A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers and Industrial Engineering 51(1): 196–215

    Article  Google Scholar 

  • Altiparmak F., Gen M., Lin L., Karaoglan I. (2009) A steady state genetic algorithm for multi-product supply chain network design. Computers and Industrial Engineering 56(2): 521–537

    Article  Google Scholar 

  • Amiri A. (2006) Designing a distribution network in a supply chain system: Formulation and efficient solution procedure. European Journal of Operational Research 171(2): 567–576

    Article  Google Scholar 

  • Amodeo L., Chen H., El Hadji A. (2008) Supply chain inventory optimization with multiple objectives: An industrial case study. Studies in Computational Intelligence 144: 211–230

    Article  Google Scholar 

  • Bachlaus M., Pandey M. K., Mahajan C., Shankar R., Tiwari M.K. (2008) Designing an integrated multi-echelon agile supply chain network: A hybrid taguchi-particle swarm optimization approach. Journal of Intelligent Manufacturing 19(6): 747–761

    Article  Google Scholar 

  • Bhattacharya R., Bandyopadhyay S. (2010) Solving conflicting bi-objective facility location problem by NSGA II evolutionary algorithm. International Journal of Advanced Manufacturing Technology 51(1–4): 397–414

    Article  Google Scholar 

  • Bilgen B., Günther H. O. (2010) Integrated production and distribution planning in the fast moving consumer goods industry: A block planning application. OR Spectrum 32: 927–955

    Article  Google Scholar 

  • Chandra C., Grabis J. (2009) Configurable supply chain: Framework, methodology and application. International Journal of Manufacturing Technology and Management 17(1–2): 5–22

    Article  Google Scholar 

  • Chandra C., Kamrani A. K. (2003) Knowledge management for consumer-focused product design. Journal of Intelligent Manufacturing 14: 557–580

    Article  Google Scholar 

  • Cheng F., Ye F. (2011) A two objective optimisation model for order splitting among parallel suppliers. International Journal of Production Research 49(10): 2759–2769

    Article  Google Scholar 

  • Cheng F., Ye F., Yang J. (2009) Multi-objective optimization of collaborative manufacturing chain with time-sequence constraints. International Journal of Advanced Manufacturing Technology 40: 1024–1032

    Article  Google Scholar 

  • Chopra S., Meindl P., Kalra D.V. (2008) Supply chain management (2nd ed). Dorling Kindersley (India) Pvt. Ltd. (Licensee of Pearson Education), New Delhi

    Google Scholar 

  • Cintron A., Ravindran A. R., Ventura J. A. (2010) Multi-criteria mathematical model for designing the distribution network of a consumer goods company. Computers and Industrial Engineering 58(4): 584–593

    Article  Google Scholar 

  • Coello C. C. A. (1999) A comprehensive survey of evolutionary-based multi-objective optimization techniques. Knowledge and Information Systems: An International Journal 1(3): 269–308

    Article  Google Scholar 

  • Deb K. (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester

    Google Scholar 

  • Deb K., Pratap A., Agarwal S., Meyarivan T. (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2): 182–197

    Article  Google Scholar 

  • Ding H., Benyoucef L., Xie X. (2006) A simulation-based multi-objective genetic algorithm approach for networked enterprises optimization. Engineering Applications of Artificial Intelligence 19(6): 609–623

    Article  Google Scholar 

  • Dogan K., Goetschalckx M. (1999) A primal decomposition method for the integrated design of production-distribution systems. IIE Transactions (Institute of Industrial Engineers) 31(11): 1027–1036

    Google Scholar 

  • ElMaraghy H.A., Majety R. (2008) Integrated supply chain design using multi-criteria optimization. International Journal of Advanced Manufacturing Technology 37: 371–399

    Article  Google Scholar 

  • Farahani R. Z., Elahipanah M. (2008) A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain. International Journal of Production Economics 111(2): 229–243

    Article  Google Scholar 

  • Gebennini E., Gamberini R., Manzini R. (2009) An integrated production-distribution model for the dynamic location and allocation problem with safety stock optimization. International Journal of Production Economics 122(1): 286–304

    Article  Google Scholar 

  • Gen M., Altiparmak F., Lin L. (2006) A genetic algorithm for two-stage transportation problem using priority-based encoding. OR Spectrum 28(3): 337–354

    Article  Google Scholar 

  • Goetschalckx, M. (2008). Distribution system design. In G. Don Taylor (Ed.), Logistics engineering handbook. Florida: Taylor and Francis Group (CRC Press).

  • Goetschalckx M., Vidal C.J., Dogan K. (2002) Modeling and design of global logistics systems: A review of integrated strategic and tactical models and design algorithms. European Journal of Operational Research 143(1): 1–18

    Article  Google Scholar 

  • Goldberg D. E. (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, New York

    Google Scholar 

  • Ioannou G. (2005) Streamlining the supply chain of the Hellenic sugar industry. Journal of Food Engineering 70(3): 323–332

    Article  Google Scholar 

  • Jawahar N., Balaji A. N. (2009) A genetic algorithm for the two-stage supply chain distribution problem associated with a fixed charge. European Journal of Operational Research 194(2): 496–537

    Article  Google Scholar 

  • Jayaraman V., Ross A. (2003) A simulated annealing methodology to distribution network design and management. European Journal of Operational Research 144(3): 629–645

    Article  Google Scholar 

  • Jiao J., Simpson T. W., Siddique Z. (2007) Product family design and platform-based product development: A state-of-the-art review. Journal of Intelligent Manufacturing 18: 5–29

    Article  Google Scholar 

  • Kauder S., Meyr H. (2009) Strategic network planning for an international automotive manufacturer-balancing flexibility and economical efficiency. OR Spectrum 31: 507–532

    Article  Google Scholar 

  • Kazemi A., Fazel Zarandi M. H., Moattar Husseini S. M. (2009) A multi-agent system to solve the production-distribution planning problem for a supply chain: A genetic algorithm approach. International Journal of Advanced Manufacturing Technology 44(1–2): 180–193

    Article  Google Scholar 

  • Kim J., Moon I. (2008) Strategic design of hydrogen infrastructure considering cost and safety using multiobjective optimization. International Journal of Hydrogen Energy 33(21): 5887–5896

    Article  Google Scholar 

  • Konak A., Coit D. W., Smith A. E. (2006) Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering and System Safety 91: 992–1007

    Article  Google Scholar 

  • Koo L. Y., Adhitya A., Srinivasan R., Karimi I. A. (2008) Decision support for integrated refinery supply chains. Part 2: Design and operation. Computers and Chemical Engineering 32(11): 2787–2800

    Article  Google Scholar 

  • Liao S., Hsieh C., Lin Y. (2011) A multi-objective evolutionary optimization approach for an integrated location-inventory distribution network problem under vendor-managed inventory system. Annals of Operations Research 186(1): 213–229

    Article  Google Scholar 

  • Lim S. J., Jeong S. J., Kim K. S., Park M. W. (2006) Hybrid approach to distribution planning reflecting a stochastic supply chain. International Journal of Advanced Manufacturing Technology 28: 618–625

    Article  Google Scholar 

  • Lin L., Mitsuo G., Xiaoguang W. (2009) Integrated multistage logistics network design by using hybrid evolutionary algorithm. Computers and Industrial Engineering 56: 854–873

    Article  Google Scholar 

  • Luh G. C., Chueh C. H., Liu W. W. (2003) MOIA: Multi-objective immune algorithm. Engineering Optimization 35(2): 143–164

    Article  Google Scholar 

  • Manzini R., Bindi F. (2009) Strategic design and operational management optimization of a multi stage physical distribution system. Transportation Research Part E: Logistics and Transportation Review 45(6): 915–936

    Article  Google Scholar 

  • Manzini R., Gebennini E. (2008) Optimization models for the dynamic facility location and allocation problem. International Journal of Production Research 46(8): 2061–2086

    Article  Google Scholar 

  • Manzini R., Gamberi M., Gebennini E., Regattieri A. (2008) An integrated approach to the design and management of a supply chain system. International Journal of Advanced Manufacturing Technology 37: 625–640

    Article  Google Scholar 

  • Melo M. T., Nickel S., Saldanha-da-Gama F. (2009) Facility location and supply chain management—A review. European Journal of Operational Research 196(2): 401–412

    Article  Google Scholar 

  • Paksoy T., Cavlak E. B. (2011) Development and optimization of a new linear programming model for production/distribution network of an edible vegetable oil manufacturer. Journal of Logistics Systems and Management 9(1): 1–21

    Article  Google Scholar 

  • Pishvaee M. S., Farahani R. Z., Dullaert W. (2010) A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Computers and Operations Research 37(6): 1100–1112

    Article  Google Scholar 

  • Rabbani M., Tavakkoli-Moghaddam R., Parsa H. (2008) A new mathematical model for a distribution network problem in a multi-product supply chain system: A real case study. International Journal of Manufacturing Technology and Management 15(1): 1–11

    Article  Google Scholar 

  • Sarkar D., Modak J. M. (2005) Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm. Chemical Engineering Science 60: 481–492

    Article  Google Scholar 

  • Sarmiento A. M., Nagi R. (1999) A review of integrated analysis of production-distribution systems. IIE Transactions (Institute of Industrial Engineers) 31(11): 1061–1074

    Google Scholar 

  • Serrano V., Alvarado M., Coello C. A. (2007) Optimization to manage supply chain disruptions using the NSGA-II. Advances in Soft Computing 42: 476–485

    Article  Google Scholar 

  • Shen Z.-J. Max, Daskin M. S. (2005) Trade-offs between customer service and cost in integrated supply chain design. Manufacturing and Services Operations Management 7(3): 188–207

    Article  Google Scholar 

  • Srinivas N., Deb K. (1995) Multi-objective function optimisation using non-dominated sorting in genetic algorithms. Evolutionary Computations 2(3): 221–248

    Article  Google Scholar 

  • Syarif A., Gen M. (2003) Hybrid genetic algorithm for production/distribution system in supply chain. International Journal of Smart Engineering System Design 5(4): 289–298

    Article  Google Scholar 

  • Syarif A., Yun Y., Gen M. (2002) Study on multi-stage logistic chain network: A spanning tree-based genetic algorithm approach. Computers and Industrial Engineering 43(1–2): 299–314

    Article  Google Scholar 

  • Thanh P. N., Bostel N., Peton O. (2008) A dynamic model for facility location in the design of complex supply chains. International Journal of Production Economics 113(2): 678–693

    Article  Google Scholar 

  • Tiwari M. K., Raghavendra N., Agrawal S., Goyal S.K. (2010) A hybrid Taguchi-immune approach to optimize an integrated supply chain design problem with multiple shipping. European Journal of Operational Research 203(1): 95–106

    Article  Google Scholar 

  • Vidal C., Goetschalckx M. (1997) Strategic production–distribution models: A critical review with emphasis on global supply chain models. European Journal of Operational Research 98: 1–18

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India

    N. C. Hiremath, Sadananda Sahu & Manoj Kumar Tiwari

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  1. N. C. Hiremath
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  2. Sadananda Sahu
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  3. Manoj Kumar Tiwari
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Correspondence to Manoj Kumar Tiwari.

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Hiremath, N.C., Sahu, S. & Tiwari, M.K. Multi objective outbound logistics network design for a manufacturing supply chain. J Intell Manuf 24, 1071–1084 (2013). https://doi.org/10.1007/s10845-012-0635-8

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  • DOI: https://doi.org/10.1007/s10845-012-0635-8

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