Supply Chain Network Design Optimization Model for Multi-period Multi-product Under Uncertainty

M. S. Al-Ashhab

doi:doi:10.11648/j.ijmea.20170501.14

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Supply Chain Network Design Optimization Model for Multi-period Multi-product Under Uncertainty

M. S. Al-Ashhab

Published in International Journal of Mechanical Engineering and Applications (Volume 5, Issue 1)

Received: 4 September 2016 Accepted: 13 September 2016 Published: 17 February 2017

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Abstract

This research is a development of a stochastic mixed integer linear programming (SMILP) model considering stochastic customer demand, to tackle the multi-product SCND problems. It also considers multi-period, multi-echelons, products inventories, considering locations capacities and associated cost elements. The model represents both location and allocation decisions of the supply chain which maximize the total expected profit. The effect of demand mean on the total expected profit and the effect of the number of scenarios on the CPU time are studied. The results have shown the effect of customers’ demands for each product in each period on the quantities of material delivered from each supplier to each factory, the quantities of products delivered from each factory and factory store to each distributor, the inventory of each product in each factory and distributor, the quantities of each type of product delivered from each distributor to each customer in each period. The model has been verified through a detailed example.

Published in	International Journal of Mechanical Engineering and Applications (Volume 5, Issue 1)
DOI	10.11648/j.ijmea.20170501.14
Page(s)	28-40
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Supply Chain Network Design (SCND), Stochastic Mixed Integer Linear Programming (SMILP), Location, Allocation, Modeling, Multi-products, Multi-echelon and Multi-periods

References

[1]	Adabi, F., & Omrani, H. (2015). Designing a supply chain management based on distributors’ efficiency measurement. Uncertain Supply Chain Management, 3 (1), 87-96.‏
[2]	Badri, H., Bashiri, M., & Hejazi, T. H. (2013). Integrated strategic and tactical planning in a supply chain network design with a heuristic solution method. Computers & Operations Research, 40 (4), 1143-1154.‏
[3]	Ballou, R. H. (2007). Business Logistics/Supply Chain Management, 5/E (With Cd). Pearson Education India.‏
[4]	El-Sayed, M., Afia, N., & El-Kharbotly, A. (2010). A stochastic model for forward–reverse logistics network design under risk. Computers & Industrial Engineering, 58 (3), 423-431.‏
[5]	Haq, A. N., Vrat, P., & Kanda, A. (1991). An integrated production-inventory-distribution model for the manufacture of urea: a case. International Journal of production economics, 25 (1), 39-49.‏
[6]	Maqsood, I., Huang, G. H., & Yeomans, J. S. (2005). An interval-parameter fuzzy two-stage stochastic program for water resources management under uncertainty. European Journal of Operational Research, 167 (1), 208-225.‏
[7]	Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36 (8), 3433-3446.‏
[8]	Santoso, T., Ahmed, S., Goetschalckx, M., & Shapiro, A. (2005). A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research, 167 (1), 96-115.‏
[9]	Serdar E. T. & Al-Ashhab M. S. (2016). Supply Chain Network Design Optimization Model for Multi-period Multi-product. International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS, 16 (01), 122-140.
[10]	Wang, F., Lai, X., & Shi, N. (2011). A multi-objective optimization for green supply chain network design. Decision Support Systems, 51 (2), 262-269.‏
[11]	Wu, J., & Li, J. (2014). Dynamic Coal Logistics Facility Location under Demand Uncertainty. Open Journal of Social Sciences, 2 (09), 33.‏
[12]	www.FICO.com.
[13]	Xia R. & Matsukawa H., (2014). Optimizing the supply chain configuration with supply disruptions. Lecture Notes in Management Science, 6, 176–184.

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APA Style

M. S. Al-Ashhab. (2017). Supply Chain Network Design Optimization Model for Multi-period Multi-product Under Uncertainty. International Journal of Mechanical Engineering and Applications, 5(1), 28-40. https://doi.org/10.11648/j.ijmea.20170501.14

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ACS Style

M. S. Al-Ashhab. Supply Chain Network Design Optimization Model for Multi-period Multi-product Under Uncertainty. Int. J. Mech. Eng. Appl. 2017, 5(1), 28-40. doi: 10.11648/j.ijmea.20170501.14

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AMA Style

M. S. Al-Ashhab. Supply Chain Network Design Optimization Model for Multi-period Multi-product Under Uncertainty. Int J Mech Eng Appl. 2017;5(1):28-40. doi: 10.11648/j.ijmea.20170501.14

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@article{10.11648/j.ijmea.20170501.14,
  author = {M. S. Al-Ashhab},
  title = {Supply Chain Network Design Optimization Model for Multi-period Multi-product Under Uncertainty},
  journal = {International Journal of Mechanical Engineering and Applications},
  volume = {5},
  number = {1},
  pages = {28-40},
  doi = {10.11648/j.ijmea.20170501.14},
  url = {https://doi.org/10.11648/j.ijmea.20170501.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20170501.14},
  abstract = {This research is a development of a stochastic mixed integer linear programming (SMILP) model considering stochastic customer demand, to tackle the multi-product SCND problems. It also considers multi-period, multi-echelons, products inventories, considering locations capacities and associated cost elements. The model represents both location and allocation decisions of the supply chain which maximize the total expected profit. The effect of demand mean on the total expected profit and the effect of the number of scenarios on the CPU time are studied. The results have shown the effect of customers’ demands for each product in each period on the quantities of material delivered from each supplier to each factory, the quantities of products delivered from each factory and factory store to each distributor, the inventory of each product in each factory and distributor, the quantities of each type of product delivered from each distributor to each customer in each period. The model has been verified through a detailed example.},
 year = {2017}
}

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TY  - JOUR
T1  - Supply Chain Network Design Optimization Model for Multi-period Multi-product Under Uncertainty
AU  - M. S. Al-Ashhab
Y1  - 2017/02/17
PY  - 2017
N1  - https://doi.org/10.11648/j.ijmea.20170501.14
DO  - 10.11648/j.ijmea.20170501.14
T2  - International Journal of Mechanical Engineering and Applications
JF  - International Journal of Mechanical Engineering and Applications
JO  - International Journal of Mechanical Engineering and Applications
SP  - 28
EP  - 40
PB  - Science Publishing Group
SN  - 2330-0248
UR  - https://doi.org/10.11648/j.ijmea.20170501.14
AB  - This research is a development of a stochastic mixed integer linear programming (SMILP) model considering stochastic customer demand, to tackle the multi-product SCND problems. It also considers multi-period, multi-echelons, products inventories, considering locations capacities and associated cost elements. The model represents both location and allocation decisions of the supply chain which maximize the total expected profit. The effect of demand mean on the total expected profit and the effect of the number of scenarios on the CPU time are studied. The results have shown the effect of customers’ demands for each product in each period on the quantities of material delivered from each supplier to each factory, the quantities of products delivered from each factory and factory store to each distributor, the inventory of each product in each factory and distributor, the quantities of each type of product delivered from each distributor to each customer in each period. The model has been verified through a detailed example.
VL  - 5
IS  - 1
ER  -

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Author Information

M. S. Al-Ashhab

Design & Production Engineering Dept., Faculty of Engineering, Ain-Shams University, Cairo, Egypt

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APA Style

M. S. Al-Ashhab. (2017). Supply Chain Network Design Optimization Model for Multi-period Multi-product Under Uncertainty. International Journal of Mechanical Engineering and Applications, 5(1), 28-40. https://doi.org/10.11648/j.ijmea.20170501.14

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ACS Style

M. S. Al-Ashhab. Supply Chain Network Design Optimization Model for Multi-period Multi-product Under Uncertainty. Int. J. Mech. Eng. Appl. 2017, 5(1), 28-40. doi: 10.11648/j.ijmea.20170501.14

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AMA Style

M. S. Al-Ashhab. Supply Chain Network Design Optimization Model for Multi-period Multi-product Under Uncertainty. Int J Mech Eng Appl. 2017;5(1):28-40. doi: 10.11648/j.ijmea.20170501.14

Copy | Download

@article{10.11648/j.ijmea.20170501.14,
  author = {M. S. Al-Ashhab},
  title = {Supply Chain Network Design Optimization Model for Multi-period Multi-product Under Uncertainty},
  journal = {International Journal of Mechanical Engineering and Applications},
  volume = {5},
  number = {1},
  pages = {28-40},
  doi = {10.11648/j.ijmea.20170501.14},
  url = {https://doi.org/10.11648/j.ijmea.20170501.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20170501.14},
  abstract = {This research is a development of a stochastic mixed integer linear programming (SMILP) model considering stochastic customer demand, to tackle the multi-product SCND problems. It also considers multi-period, multi-echelons, products inventories, considering locations capacities and associated cost elements. The model represents both location and allocation decisions of the supply chain which maximize the total expected profit. The effect of demand mean on the total expected profit and the effect of the number of scenarios on the CPU time are studied. The results have shown the effect of customers’ demands for each product in each period on the quantities of material delivered from each supplier to each factory, the quantities of products delivered from each factory and factory store to each distributor, the inventory of each product in each factory and distributor, the quantities of each type of product delivered from each distributor to each customer in each period. The model has been verified through a detailed example.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Supply Chain Network Design Optimization Model for Multi-period Multi-product Under Uncertainty
AU  - M. S. Al-Ashhab
Y1  - 2017/02/17
PY  - 2017
N1  - https://doi.org/10.11648/j.ijmea.20170501.14
DO  - 10.11648/j.ijmea.20170501.14
T2  - International Journal of Mechanical Engineering and Applications
JF  - International Journal of Mechanical Engineering and Applications
JO  - International Journal of Mechanical Engineering and Applications
SP  - 28
EP  - 40
PB  - Science Publishing Group
SN  - 2330-0248
UR  - https://doi.org/10.11648/j.ijmea.20170501.14
AB  - This research is a development of a stochastic mixed integer linear programming (SMILP) model considering stochastic customer demand, to tackle the multi-product SCND problems. It also considers multi-period, multi-echelons, products inventories, considering locations capacities and associated cost elements. The model represents both location and allocation decisions of the supply chain which maximize the total expected profit. The effect of demand mean on the total expected profit and the effect of the number of scenarios on the CPU time are studied. The results have shown the effect of customers’ demands for each product in each period on the quantities of material delivered from each supplier to each factory, the quantities of products delivered from each factory and factory store to each distributor, the inventory of each product in each factory and distributor, the quantities of each type of product delivered from each distributor to each customer in each period. The model has been verified through a detailed example.
VL  - 5
IS  - 1
ER  -

Copy | Download