[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Nowadays, time and cost are familiar criteria for every logistic provider, and both have been long treated to be minimized simultaneously. However, the criteria are naturally conflicted even with flexibilities and/or constraints appeared in the logistic networks. This paper is concerned with three-level logistic networks with potential suppliers, distributed centers (DCs), and deterministic demands from available consumers. The networks also benefit from potential direct shipments from suppliers to consumers as long as suppliers and DCs facilities might face limited capacity in their own seasonal supplying and warehousing processes. The goal is (re)configure the networks in order to minimize response time to consumers, transportation cost and facility cost. Therefore, the networks are formulated as multiple criteria decision making problems, which have more than one configuration through the time and cost optimizing at the same time. Due to the flexibility and the constraints, the decision maker(s) needs a set of compromise solutions for the networks that represent optimal configurations based on the objectives without considering prior knowledge. To this end, the problems are formulated into four individual logistic network models varying with the flexibility option and/or the capacitated facilities. To find the compromise solutions, Pareto-based multi-objective evolutionary algorithm, NSGA-II is customized and then utilized to deal with an illustrative case study. The results are analyzed through the two performance measures, hypervolume and the number of optimal solutions obtained so far.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Aslam, T., Ng, A. H. C. (2010). Multi-objective optimization for supply chain management: a literature review and new development. In 8th International conference on supply chain management and information systems: logistics systems and engineering, Hong Kong.

  • Bader J. M. (2009) Hypervolume-based search for multiobjective optimization: Theory and methods. Swiss Federal Institute of Technology, Zurich

    Google Scholar 

  • Baker K. (1987) Adaptive selection methods for genetic algorithms. In: Grefenstette J. J. (eds) 2th International conference on genetic algorithms. Lawrence Erlbaum Associates, Hillsdale, NJ, pp 100–111

    Google Scholar 

  • Ballou R. H. (2004) Business logistics/supply chain management (5 ed.). Pearson Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

  • Baykasoglu A. (2006) Multi-rule multi-objective simulated annealing algorithm for straight and U type assembly line balancing problems. Journal of Intelligent Manufacturing 17(2): 217–232. doi:10.1007/s10845-005-6638-y

    Article  Google Scholar 

  • Baykasoglu A., ozbakir L., Sonmez A. I. (2004) Using multiple objective tabu search and grammars to model and solve multi-objective flexible job shop scheduling problems. Journal of Intelligent Manufacturing 15(6): 777–785. doi:10.1023/B:JIMS.0000042663.16199.84

    Article  Google Scholar 

  • Beamon B. M. (1998) Supply chain design and analysis: Models and methods. International Journal of Production Economics 55(3): 281–294

    Article  Google Scholar 

  • Chan F. T. S., Chung S. H. (2004) A multi-criterion genetic algorithm for order distribution in a demand driven supply chain. International Journal of Computer Integrated Manufacturing 17(4): 339–351

    Article  Google Scholar 

  • Chen C. L., Lee W. C. (2004) Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & Chemical Engineering 28(6–7): 1131–1144

    Article  Google Scholar 

  • Cheng R., Gen M. (1998) Compromise approch-based genetic algorithm for bicriterion shortest path problem. Ashikaga Institute of Technology, Ashikaga

    Google Scholar 

  • Closs D. J., Swink M., Nair A. (2005) The role of information connectivity in making flexible logistic programs successful. International Journal of Physical Distribution & Logistics Management 35(4): 258–277

    Article  Google Scholar 

  • Coello C. A. C., Lamont G. B., Veldhuizen D. A. V. (2007) Evolutionary algorithms for solving multi-objective problems (2nd ed.). Springer Science+Business Media, LLC, New York, NY

    Google Scholar 

  • Cooper L. (1963) Location-allocation problems. Operations Research 11: 331–343

    Article  Google Scholar 

  • Deb K. (2000) An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering 186(2–4): 311–338

    Article  Google Scholar 

  • Deb K., Pratap A., Agarwal S., Meyarivan T. (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2): 182–197. doi:10.1109/4235.996017

    Article  Google Scholar 

  • Dhahri I., Chabchoub H. (2007) Nonlinear goal programming models quantifying the bullwhip effect in supply chain based on ARIMA parameters. European Journal of Operational Research 177(3): 1800–1810. doi:10.1016/j.ejor.2005.10.065

    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 

  • Gen M., Cheng R., Lin L. (2008) Network models and optimization, multiobjective genetic algorithm approach. Springer, London

    Google Scholar 

  • Goldberg D. (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesely, Reading, MA

    Google Scholar 

  • Gong, Y.-c. Multi-facility Location Model with Timeliness constrains of Logistics Distribution Centers. In IEEE. (Ed.), 2008 IEEE international conference on automation and logistics, Hong Kong, China, 2008 (Vol. 1–6, pp. 2533–2536).

  • Harris, I., Mumford, C., & Naim, M. (2009). The multi-objective uncapacitated facility location problem for green logistics. In IEEE. (Ed.), IEEE congress on evolutionary computation, S Glam Wales, 2009 (Vol. 1–5, pp. 2732–2739).

  • Hitchcock F.L. (1941) The distribution of a product from several sources to numerous localities. Journal of Mathematical Physics 20: 220–230

    Google Scholar 

  • Kim, B.-I., & Son, S.-J. (2010). A probability matrix based particle swarm optimization for the capacitated vehicle routing problem. Journal of Intelligent Manufacturing, doi:10.1007/s10845-010-0455-7.

  • Knowles J. D. (2002) Local-search and hybrid evolutionary algorithms for pareto optimization. University of Reading, Reading

    Google Scholar 

  • Kursawe, F. (1990). A variant of evolution strategies for vector optimization. In The 1st workshop on parallel problem solving from nature, London, UK (pp. 193–197). Berlin: Springer.

  • Lau H. C. W., Chan T. M., Tsui W. T., Chan F. T. S., Ho G. T. S., Choy K. L. (2009) A fuzzy guided multi-objective evolutionary algorithm model for solving transportation problem. Expert Systems with Applications 36(4): 8255–8268

    Article  Google Scholar 

  • Laumanns, M., Zitzler, E., & Thiele, L. (2000). A unified model for multiobjective evolutionary algorithms with elitism. In Congress Evolutionary Computers (Vol. 1, pp. 46–53).

  • Lee S. S., Wang H.-P. B. (1992) Modified simulated annealing for multiple-objective engineering design optimization. Journal of Intelligent Manufacturing 3(2): 101–108. doi:10.1007/BF01474749

    Article  Google Scholar 

  • Lin C. Y., Hajela P. (1994) Design optimization with advanced genetic search strategies. Advances in Engineering Software 21(3): 179–189

    Article  Google Scholar 

  • Lin C. T., Chen C. B., Hsieh H. J. (2001) Effects of centralization on expected profits in a multi-location newsboy problem. Journal of the Operational Research Society 52(7): 839–841

    Article  Google Scholar 

  • Lin L., Gen M., Wang X. (2007) A hybrid genetic algorithm for logistics network design with flexible multistage model. International Journal of Information Systems for Logistics and Management 3(1): 1–12

    Google Scholar 

  • Liu, X.-j., Yi, H., & Ni, Z.-h. (2010). Application of ant colony optimization algorithm in process planning optimization. Journal of Intelligent Manufacturing, doi:10.1007/s10845-010-0407-2.

  • Masud A. S., Ravindran A. R. (2008) Multiple criteria decision making. In: Ravindaran A. R. (eds) Operations research and management science. Taylor & Francis Group, LLC, Raton

    Google Scholar 

  • Murata, T., & Ishibuchi, H. (1995). Multi-objective genetic algorithms. In the Second IEEE international conference on evolutionary computation (pp. 289–294).

  • Nanazawa Y., Suito H., Kawarada H. (2009) Mathematical study of trade-off relations in logistics systems. Journal of Computational and Applied Mathematics 232(1): 122–126

    Article  Google Scholar 

  • Paksoy T., Chang C. T. (2010) Revised multi-choice goal programming for multi-period, multi-stage inventory controlled supply chain model with popup stores in Guerrilla marketing. Applied Mathematical Modelling 34(11): 3586–3598. doi:10.1016/j.apm.2010.03.008

    Article  Google Scholar 

  • Park S., Lee T. E., Sung C. S. (2010) A three-level supply chain network design model with risk-pooling and lead times. Transportation Research Part E-Logistics and Transportation Review 46(5): 563–581. doi:10.1016/j.tre.2009.12.004

    Article  Google Scholar 

  • Pinto, E. G. (2004). Supply chain optimization using multi-objective evolutionary algorithms. http://www.coe.psu.edu/water/images/0/0d/Pinto.pdf. Accessed 25/5/2011 2011.

  • Purshouse R. (2003) On the evolutionary optimization of many objectives. University of Sheffield, Sheffield, UK

    Google Scholar 

  • Ruodolph, G. (2001). Evolutionary search under partially ordered fitness sets. Inthe International symposium on information science innovations in engineering of natural and artificial intelligent systems (ISI 2001) (pp. 818–822).

  • Schaffer J. D. (1984) Some experiments in machine learning using vector evaluted genetic algorithms. Vanderbilt University, Nashville, TN

    Google Scholar 

  • Selim H., Ozkarahan I. (2008) A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach. International Journal of Advanced Manufacturing Technology 36(3–4): 401–418. doi:10.1007/s00170-006-0842-6

    Article  Google Scholar 

  • Serrano V., Alvarado M., Coello C. A. C. (2007) Optimization to manage supply chain disruptions using the NSGA-II. Theoretical Advances and Applications of Fuzzy Logic and Soft Computing 42: 476–485. doi:10.1007/978-3-540-72434-6_47

    Article  Google Scholar 

  • Shimizu Y., Matsuda S., Wada T. (2006) A flexible design of logistic network against uncertain demands through hybrid meta-heuristic method. Computer Aided Chemical Engineering 21: 2051–2056. doi:10.1016/S1570-7946(06)80350-0

    Article  Google Scholar 

  • Stalk G., Hout T. M. (1990) Competing against time. Research-Technology Management 33(2): 19–24

    Google Scholar 

  • Thatte A. A. (2007) Competitive advantage of a firm through supply chain responsiveness and SCM practices. University of Toledo, Toledo

    Google Scholar 

  • Wang F., Lin L., Gen M. (2009) A flexible multistage logistics network modeling and optimization. In: Wang H. F., Neace M. B., Zhu Y. G., Duch W. (eds) 8th International conference on information and management sciences, Hong Kong, Vol. 8. California Polytechnic State Univ, Cal Poly, San Luis Obispo, pp 730–738

    Google Scholar 

  • While L., Hingston P., Barone L., Huband S. (2006) A faster algorithm for calculating hypervolume. IEEE Transactions on Evolutionary Computation 10(1): 29–38

    Article  Google Scholar 

  • Wong, J.-T. (2010). DSS for 3PL provider selection in global supply chain: Combining the multi-objective optimization model with experts’ opinion. Journal of Intelligent Manufacturing, doi:10.1007/s10845-010-0398-z.

  • Wu D. D., Zhang Y., Wu D., Olson D. L. (2010) Fuzzy multi-objective programming for supplier selection and risk modeling: A possibility approach. European Journal of Operational Research 200(3): 774–787

    Article  Google Scholar 

  • Yang W., Li L., Ma S. (2007) Coordinating supply chain response-time: A bi-level programming approach. International Journal of Advanced Manufacturing Technology 31(9–10): 1034–1043. doi:10.1007/s00170-005-0260-1

    Article  Google Scholar 

  • Zilinskas A. (2010) Multi-objective management in freight logistics: Increasing capacity, service level and safety with optimization algorithms. Interfaces 40(1): 87–88

    Google Scholar 

  • Zitzler E. (1999) Evolutionary algorithms for multiobjective optimization: Methods and applications. Swiss Federal Inst of Technology (ETH), Zurich

    Google Scholar 

  • Zitzler E., Thiele L. (1999) Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach. IEEE Transactions on Evolutionary Computation 3(4): 257–271

    Article  Google Scholar 

  • Zitzler E., Laumanns M., Thiele L. (2001) SPEA2: Improving the strength pareto evolutionary algorithm. Swiss Federal Institute of Technology (ETH) Zurich, Zurich

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, UTM, 81310, Skudai, Johor, Malaysia

    Hossein Rajabalipour Cheshmehgaz, Mohamad Ishak Desa & Antoni Wibowo

Authors
  1. Hossein Rajabalipour Cheshmehgaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamad Ishak Desa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antoni Wibowo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamad Ishak Desa.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rajabalipour Cheshmehgaz, H., Desa, M.I. & Wibowo, A. A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm. J Intell Manuf 24, 277–293 (2013). https://doi.org/10.1007/s10845-011-0584-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-011-0584-7

Keywords

Search

Navigation