A Multi-objective Model for Location-Allocation Problem with Environmental Considerations

  • Malika Nisal Ratnayake
  • Voratas KachitvichyanukulEmail author
  • Huynh Trung Luong


The paper presents a multi-objective model for solving location-allocation problem (LAP) which considers the greenhouse gas. It involves the determination of the best strategy to distribute the product in a distribution network by selecting proper locations of plants and distribution centers as well as the allocation of products from plants to warehouses and from warehouses to customers. Two objective functions are considered simultaneously. The first objective is to minimize the total logistics costs and the second objective is to minimize the total amount of greenhouse gases generated by the activities. The model is validated using the test data that were derived from published benchmark test data set. The mathematical model was solved using CPLEX by converting one objective into a constraint with slack. The set of trade-off solutions is generated by solving the model repeatedly with varying slack values.


Location-allocation problem (LAP) Multiple objective optimization Greenhouse gas 


  1. 1.
    Abdallah, T., Diabat, A., & Simchi-Levi, D. (2010) A carbon sensitive supply chain network problem with green procurement. Awaji.Google Scholar
  2. 2.
    Ramudhin, A., Chaabane, A., & Paquet, M. (2010). Carbon market sensitive sustainable supply chain network design. International Journal of Management Science and Engineering Management, 5(1), 30–38.Google Scholar
  3. 3.
    Elhedhli, S., & Merrick, R. (2012). Green supply chain network design to reduce carbon emissions. Transportation Research Part D: Transport and Environment, 17(5), 370–379.CrossRefGoogle Scholar
  4. 4.
    Shaw, K., Shankar, R., Surendra, S. Y., & Lakshman, S. T. (2012). Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert Systems with Applications, 39(9), 8182–8192.CrossRefGoogle Scholar
  5. 5.
    Sabio, N., Kostin, A., Guillén-Gosálbez, G., & Jiménez, L. (2012). Holistic minimization of the life cycle environmental impact of hydrogen infrastructures using multi-objective optimization and principal component analysis. International Journal of Hydrogen Energy, 37(6), 5385–5405.CrossRefGoogle Scholar
  6. 6.
    Jamshidi, R., Ghomi, S. M. T. F., & Karimi, B. (2012) Multi-objective green supply chain optimization with a new hybrid memetic algorithm using the Taguchi method. Scientia Iranica, 19(6), 1876–1886.CrossRefGoogle Scholar
  7. 7.
    Wang, F., Lai, X., & Shi, N. (2011). A multi-objective optimization for green supply chain network design. Decision Support Systems, 51(2), 262–269.CrossRefGoogle Scholar
  8. 8.
    Giarola, S., Zamboni, A., & Bezzo, F. (2011). Spatially explicit multi-objective optimisation for design and planning of hybrid first and second generation biorefineries. Computers & Chemical Engineering, 35(9), 1782–1797.CrossRefGoogle Scholar
  9. 9.
    Bamrungbutr, C. (2011) Differential evolution algorithm for the multi-commodity distribution network design problem. Master thesis No. ISE 11-01, Asian Institute of Technology, Bangkok, Thailand.Google Scholar
  10. 10.
    Chiamchit, B. (2015). Multi-objective differential algorithm for the multi-commodity distribution network design problem. Bangkok, Thailand: Asian Institute of Technology.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Malika Nisal Ratnayake
    • 1
  • Voratas Kachitvichyanukul
    • 1
    Email author
  • Huynh Trung Luong
    • 1
  1. 1.Industrial and Manufacturing Engineering, School of Engineering and TechnologyAsian Institute of TechnologyPathumthaniThailand

Personalised recommendations