Dynamic Coal Logistics Facility Location under Demand Uncertainty

Jiang Wu^*, Jingfeng Li

School of Economics and Management, Shanxi University, Taiyuan, China.

DOI: 10.4236/jss.2014.29006                

Abstract

In this paper, dynamic facility location and supply chain planning are studied through minimizing the costs of facility location, path selection and transportation of coal under demand uncertainty. The proposed model dynamically incorporates possible changes in transportation network, facility investment costs, operating cost and changes in facility location. In addition, the time variation and the demand uncertainty for coal in each period of the planning horizon is taken into account to determine the optimal facility location and the optimal production volumes. Computational process and results are presented for the model in a java language. Also, an empirical case study in Shanxi province is conducted in order to investigate the dynamic effects of traffic congestion and demand uncertainty on facility location design and total system costs.

Keywords

Supply Chain Design, Facility Location, Path Planning, Networks

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Amin, S.H. and Zhang, G. (2012) A Multi-Objective Facility Location Model for Closed-Loop Supply Chain Network under Uncertain Demand and Return. Applied Mathematical Modelling, 32, 133-234.
[2]	Aikens, C.H. (1985) Facility Location Models for Distribution Planning. European Journal of Operational Research, 22, 263-279. http://dx.doi.org/10.1016/0377-2217(85)90246-2
[3]	Altiparmak, F., Gen, M., Lin, L. and Paksoy, T. (2006) A Ge-netic Algorithm Approach for Multi-Objective Optimization of Supply Chain Networks. Computers & Industrial Engineering, 51, 196-215. http://dx.doi.org/10.1016/j.cie.2006.07.011
[4]	Fahimnia, B., Farahani, R.Z., Marian, R. and Luong, L. (2012) A Review and Critique on Integrated Production-Distribution Planning Models and Techniques. Journal of Manufacturing Systems, 85, 105-119.
[5]	Weber, G. (1957) Intramolecular Transfer of Electronic Energy in Dihydro Diphosphopy-ridine Nucleotide.
[6]	Daskin, M.S. (2011) Network and Discrete Location: Models, Algorithms, and Applications. John Wiley & Sons.
[7]	Drezner, Z. (1995) Facility Location: A Survey of Applications and Methods. Springer Verlag. http://dx.doi.org/10.1007/978-1-4612-5355-6
[8]	Altiparmak, F., Gen, M., Lin, L. and Paksoy, T. (2006) A Genetic Algorithm Approach for Multi-Objective Optimization of Supply Chain Networks. Computers & Industrial Engineering, 51, 196-215. http://dx.doi.org/10.1016/j.cie.2006.07.011
[9]	Snyder, L.V. (2006) Facility Location under Uncertainty: A Review. IIE Transactions, 38, 547-564. http://dx.doi.org/10.1080/07408170500216480
[10]	Liste?, O. and Dekker, R. (2005) A Stochastic Approach to a Case Study for Product Recovery Network Design. European Journal of Operational Research, 160, 268-287. http://dx.doi.org/10.1016/j.ejor.2001.12.001
[11]	Santoso, T., Ahmed, S., Goetschalckx, M. and Shapiro, A. (2005) A Stochastic Programming Approach for Supply Chain Network Design under Uncertainty. European Journal of Operational Research, 167, 96-115. http://dx.doi.org/10.1016/j.ejor.2004.01.046
[12]	Lin, L., Gen, M. and Wang, X. (2009) Integrated Multistage Logistics Network Design by Using Hybrid Evolutionary Algorithm. Computers & Industrial Engineering, 56, 854-873. http://dx.doi.org/10.1016/j.cie.2008.09.037