Abstract
Supply chain decision makers are constantly trying to improve the customer demand fulfillment process and reduce the associated costs via decision making models and techniques. As two of the most important parameters in a supply chain, supply and demand quantities are subject to uncertainty in many real-world situations. In addition, in recent decades, there is a trend to think of the impacts of supply chain design and strategies on society and environment. Especially, transportation of goods not only imposes costs to businesses but also has socioeconomic influences. In this paper, a fuzzy nonlinear programming model for supply chain design and planning under supply/demand uncertainty and traffic congestion is proposed and a hybrid meta-heuristic algorithm, based on electromagnetism-like algorithm and simulated annealing concepts, is designed to solve the model. The merit of this paper is presenting a realistic model of current issues in supply chain design and an efficient solution method to the problem. These are significant findings of this research which can be interesting to both researchers and practitioners. Several numerical examples are provided to justify the model and the proposed solution approach.
Similar content being viewed by others
References
Agustina, D., Lee, C., & Piplani, R. (2014). Vehicle scheduling and routing at a cross docking center for food supply chains. International Journal of Production Economics, 152, 29–41.
Almansoori, A., & Shah, N. (2012). Design and operation of a stochastic hydrogen supply chain network under demand uncertainty. International Journal of Hydrogen Energy, 37(5), 3965–3977.
Baghalian, A., Rezapour, S., & Farahani, R. Z. (2013). Innovative applications of OR: Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research, 227(1), 199–215.
Bai, Y., Hwang, T., Kang, S., & Ouyang, Y. (2011). Biofuel refinery location and supply chain planning under traffic congestion. Transportation Research Part B, 45(1), 162–175.
Bashiri, M., Badri, H., & Talebi, J. (2012). A new approach to tactical and strategic planning in production-distribution networks. Applied Mathematical Modelling, 36(4), 1703–1717.
Bhatnagar, R., & Sohal, A. S. (2005). Supply chain competitiveness: Measuring the impact of location factors, uncertainty and manufacturing practices. Technovation, 25(5), 443–456.
Bidhandi, H. M., & Yusuff, R. M. (2011). Integrated supply chain planning under uncertainty using an improved stochastic approach. Applied Mathematical Modelling, 35, 2618–2630.
Birbil, S. I., & Fang, S. C. (2003). An electromagnetism-like mechanism for global optimization. Journal of Global Optimization, 25(3), 263–282.
Boukherroub, T., Ruiz, A., Guinet, A., & Fondrevelle, J. (2015). An integrated approach for sustainable supply chain planning. Computers & Operations Research, 54, 180–194.
Brandenburg, M. (2015). Low carbon supply chain configuration for a new product—a goal programming approach. International Journal of Production Research, 53, 1–23.
Cardoso, S. R., Barbosa-Póvoa, A. P. F. D., & Relvas, S. (2013). Design and planning of supply chains with integration of reverse logistics activities under demand uncertainty. European Journal of Operational Research, 226(3), 436–451.
Chang, P., Chen, S., & Chin-Yuan, F. (2009). A hybrid electromagnetism-like algorithm for single machine scheduling problem. Expert Systems with Applications, 36, 1259–1267.
Chen, C., & Fan, Y. (2011). Bioethanol supply chain system planning under supply and demand uncertainties. Transportation Research Part E. doi:10.1016/j.tre.2011.1008.1004.
Chen, C., & Fan, Y. (2012). Bioethanol supply chain system planning under supply and demand uncertainties. Transportation Research Part E, 48(1), 150–164.
Chopra, S., & Meindel, P. (2007). Supply chain management: Strategy, planning, and operation (3rd ed.). New Jersey: Upper Saddle River.
Dal-Mas, M., Giarola, S., Zamboni, A., & Bezzo, F. (2011). Strategic design and investment capacity planning of the ethanol supply chain under price uncertainty. Biomass and Bioenergy, 35(5), 2059–2071.
Davarzani, H., & Rezapour, S. (2009). Supply Network Design. In R. Z. Farahani, et al. (Eds.), Supply Chain and Logistics in National, International and Governmental Environment (pp. 105–128). Berlin: Springer.
Davoudpour, H., & Hadji Molana, M. (2008). Solving flow shop sequencing problem for deteriorating jobs by using electro magnetic algorithm. Journal of Applied Sciences, 8(22), 4121–4128.
Debels, D., De Reyck, B., & Leus, R. (2006). A hybrid scatter search/electromagnetism meta-heuristic for project scheduling. European Journal of Operational Research, 169, 638–653.
Demirhan, M., Özdamar, L., Helvacıoğlu, L., & Birbil, Ş. I. (1999). FRACTOP: A geometric partitioning metaheuristic for global optimization. Journal of Global Optimization, 14(4), 415–436.
Döyen, A., Aras, N., & Barbarosoglu, G. (2011). A two-echelon stochastic facility location model for humanitarian relief logistics. Optim Lett. doi:10.1007/s11590-11011-10421-11590.
Döyen, A., Aras, N., & Barbarosoglu, G. (2012). A two-echelon stochastic facility location model for humanitarian relief logistics. Optim Lett, 6(6), 1–23.
Dullaert, W., Braysy, O., Goetschalckx, M., & Raa, B. (2007). Supply chain (re)design: Support for managerial and policy decisions. European Journal of Transportation Infrastruct Research, 7(2), 73–92.
Erenguc, S. S., Simpson, N. C., & Vakharia, A. J. (2006). Integrated production/distribution planning in supply chains: An invited review. European Journal of Operational Research, 115(2), 219–236.
Fazel Zarandi, M. H., & Avazbeigi, M. (2012). A multi-agent solution for reduction of bullwhip effect in fuzzy supply chains. Journal of Intelligent and Fuzzy Systems, 23(5), 259–268.
Figliozzi, M. A. (2011). The impacts of congestion on time-definitive urban freight distribution networks CO2 emission levels: Results from a case study in Portland Oregon. Transportation Research Part C, 19(5), 766–778.
Georgiadis, M. C., Tsiakis, P., Longinidis, P., & Sofioglou, M. K. (2011). Optimal design of supply chain networks under uncertain transient demand variations. Omega, 39(3), 254–272.
Harris, I., Naim, M., Palmer, A., Potter, A., & Mumford, C. (2011). Assessing the impact of cost optimization based on infrastructure modelling on CO\(_2\) emissions. International Journal of Production Economics, 131(1), 313–321.
Jabbarzadeh, A., Jalali Naini, S. G., Davoudpour, H., & Azad, N. (2012). Designing a Supply Chain Network under the Risk of Disruptions. Mathematical Problems in Engineering, 2012.
Jamili, A., Shafia, M. A., & Tavakkoli-Moghaddam, R. (2011). A hybridization of simulated annealing and electromagnetism-like mechanism for a periodic job shop scheduling problem. Expert Systems with Applications, 38(5), 5895–5901.
Javadian, N., Gol Alikhani, M., Tavakkoli-Moghaddam, R., Huang, D.-S., Wunsch, D., Levine, D., et al. (2008). A discrete binary version of the electromagnetism-like heuristic for solving traveling salesman problem. Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence (pp. 123–130). Springer, Berlin / Heidelberg.
Jolai, F., Tavakkoli-Moghaddam, R., Golmohammadi, A., & Javadi, B. (2012). An electromagnetism-like algorithm for cell formation and layout problem. Expert Systems with Applications, 39(2), 2172–2182.
Jouzdani, J., Barzinpour, F., & Fathian, M. An improved electromagnetism-like algorithm for global optimization. Proceedings of 42nd conference on computers and industrial engineering.
Jouzdani, J., Barzinpour, F., Shafia, M. A., & Fathian, M. (2013a). Applying simulated annealing to a generalized cell formation problem considering alternative routings and machine reliability. Asia-Pacific Journal of Operational Research
Jouzdani, J., Sadjadi, S. J., & Fathian, M. (2013b). Dynamic dairy facility location and supply chain planning under traffic congestion and demand uncertainty: A case study of Tehran. Applied Mathematical Modelling (in press).
Jung, H., & Jeong, S.-J. (2012). Managing demand uncertainty through fuzzy inference in supply chain planning. International Journal of Production Research, 50(19), 5415–5429.
Jung, J. Y., Blau, G., Pekny, J. F., Reklaitis, G. V., & Eversdyk, D. (2004). A simulation based optimization approach to supply chain management under demand uncertainty. Computers & Chemical Engineering, 28(10), 2087–2106.
Kadadevaramath, R. S., Chen, J. C., Latha Shankar, B., & Rameshkumar, K. (2012). Application of particle swarm intelligence algorithms in supply chain network architecture optimization. Expert Systems with Applications, 39(11), 10160–10176.
Kirkpatrick, S., Gelatt, C. D, Jr, & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.
Konur, D., & Geunes, J. (2011). Competitive multi-facility location games with non-identical firms and convex traffic congestion costs. Transportation Research Part E. doi:10.1016/j.tre.2011.1006.1005.
Kostin, A. M., Guillén-Gosálbez, G., Mele, F. D., Bagajewicz, M. J., & Jiménez, L. (2012). Design and planning of infrastructures for bioethanol and sugar production under demand uncertainty. Chemical Engineering Research and Design, 90, 359–376.
Kusiak, A., Zeng, Y., & Xu, G. (2013). Minimizing energy consumption of an air handling unit with a computational intelligence approach. Energy and Buildings, 60, 355–363.
Le, T. P. N., & Lee, T.-R. (2013). Model selection with considering the CO2 emission alone the global supply chain. Journal of Intelligent Manufacturing, 24(4), 653–672.
Le, T. P. N., & Lee, T. (2011). Model selection with considering the CO2 emission alone the global supply chain. Journal of Intelligent Manufacturing. doi:10.1007/s10845-10011-10613-10846.
Lee, C.-T., Chiu, H.-N., Yeh, R. H., & Huang, D.-K. (2012). Application of a fuzzy multilevel multiobjective production planning model in a network product manufacturing supply chain. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 226(12), 2064–2074.
Liao, S., Hsieh, C., & Lai, P. (2011a). An evolutionary approach for multi-objective optimization of the integrated location-inventory distribution network problem in vendor-managed inventory. Expert Systems with Applications, 38(6), 6768–6776.
Liao, S., Hsieh, C., & Lin, Y. (2011b). A multi-objective evolutionary optimization approach for an integrated location-inventory distribution network problem under vendor-managed inventory systems. Annals of Operations Research, 186, 213–229.
Liu, K., Zhou, Y., & Zhang, Z. (2010). Capacitated location model with online demand pooling in a multi-channel supply chain. European Journal of Operational Research, 207, 218–231.
Mirzapour Al-e-hashem, S. M. J., Malekly, H., & Aryanezhad, M. B. (2011). A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. Int. J. Production Economics, 134(1), 28–42.
Mirzapour Al-e-hashema, S. M. J., Malekly, H., & Aryanezhad, M. B. (2011). A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. International Journal of Production Economics, 134, 28–42.
Mizgier, K. J., Wagner, S. M., & Holyst, J. A. (2012). Modeling defaults of companies in multi-stage supply chain networks. International Journal of Production Economics, 135(1), 14–23.
Montgomery, D. C., & Runger, G. C. (2010). Applied statistics and probability for engineers. New York: Wiley.
Mousavi, S., Bahreininejad, A., Musa, S. N., & Yusof, F. (2014). A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network. Journal of Intelligent Manufacturing, 1–16.
Naderi, B., Tavakkoli-Moghaddam, R., & Khalili, M. (2010). Electromagnetism-like mechanism and simulated annealing algorithms for flowshop scheduling problems minimizing the total weighted tardiness and makespan. Knowledge-Based Systems, 23(2), 77–85.
Naji-Azimi, Z., Toth, P., & Galli, L. (2010). An electromagnetism metaheuristic for the unicost set covering problem. European Journal of Operational Research, 205(2), 290–300.
Nemhauser, G. L., & Wolsey, L. A. (1988). Integer and combinatorial optimization. New York: Wiley-Interscience.
Nieuwenhuis, P., Beresford, A., & Choi, A. K.-Y. (2012). Shipping or local production? CO\(_2\) impact of a strategic decision: An automotive industry case study. International Journal of Production Economics, 140(1), 138–148.
Oliver, R. K., & Webber, M. D. (1982). Supply-chain management: Logistics catches up with strategy. Outlook, 5(1), 42–47.
Osman, I. B. Metaheuristics: Models, Design and Analysis. Proc., Fifth Asia Pacific Industrial Engineering and Management Systems Conference, Asia Pacific Industrial Engineering and Management Society, 1.2.1-1.2.16.
Paksoy, T., Pehlivan, N. Y., & Özceylan, E. (2012). Application of fuzzy optimization to a supply chain network design: A case study of an edible vegetable oils manufacturer. Applied Mathematical Modelling, 36(3), 2762–2776.
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637–649.
Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network under uncertainty. Fuzzy Sets and Systems, 161, 2668–2683.
Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A robust design for a closed-loop supply chain network under an uncertain environment. The International Journal of Advanced Manufacturing Technology, 66(5–8), 825–843.
Rentizelas, A. A., & Tatsiopoulos, I. P. (2010). Locating a bioenergy facility using a hybrid optimization method. International Journal Production Economics, 123, 196–209.
Sadjady, H., & Davoudpour, H. (2012). Two-echelon, multi-commodity supply chain network design with mode selection, lead-times and inventory costs. Computers and Operations Research, 39(7), 1345–1354.
Salema, M. I. G., Barbosa-Povoa, A. P., & Novais, A. Q. (2010). Simultaneous design and planning of supply chains with reverse flows: A generic modelling framework. European Journal of Operational Research, 203, 336–349.
Simangunsong, E., Hendry, L. C., & Stevenson, M. (2012). Supply-chain uncertainty: A review and theoretical foundation for future research. International Journal of Production Research, 50(16), 4493–4523.
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2003). Designing and managing the supply Chain: Concepts, strategies, and case sudies. Boston: Mcgraw-Hill/Irwin.
Soleimani, H., & Kannan, G. (2014). A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Applied Mathematical Modelling(0).
Soleimani, H., Seyyed-Esfahani, M., & Shirazi, M. (2013). Designing and planning a multi-echelon multi-period multi-product closed-loop supply chain utilizing genetic algorithm. The International Journal of Advanced Manufacturing Technology, 68(1–4), 917–931.
Subramanian, P., Ramkumar, N., Narendran, T., & Ganesh, K. (2013). PRISM: PRIority based SiMulated annealing for a closed loop supply chain network design problem. Applied Soft Computing, 13(2), 1121–1135.
van Hop, N. (2007). Fuzzy stochastic goal programming problems. European Journal of Operational Research, 176(1), 77–86.
Venkatesan, S. P., & Kumanan, S. (2012). A multi-objective discrete particle swarm optimisation algorithm for supply chain network design. International Journal of Logistics Systems and Management, 11(3), 375–406.
Wang, F., Lai, X., & Shi, N. (2011). A multi-objective optimization for green supply chain network design. Decision Support Systems, 51(2), 262–269.
Wang, S., & Watada, J. (2012). A hybrid modified PSO approach to VaR-based facility location problems with variable capacity in fuzzy random uncertainty. Information Sciences, 192, 3–18.
Wei, J., Zhao, J., & Li, Y. (2012). Pricing decisions for a closed-loop supply chain in a fuzzy environment. Asia-Pacific Journal of Operational Research, 29(01), 1240003.
White, J. A., Agee, M. H., & Case, K. E. (1983). Principles of engineering economics analysis. New York: Wiley.
Wu, P., Yang K. J., & C., F. H. (2006). A revised EM-like algorithm + K-OPT method for solving traveling salesman problem. First international conference on innovative computing, information and control, IEEE, Beijing, China, pp 546–549.
Xu, J., He, Y., & Gen, M. (2009). A class of random fuzzy programming and its application to supply chain design. Computers and Industrial Engineering, 56, 937–950.
Yang, G., & Liu, Y. (2013). Designing fuzzy supply chain network problem by mean-risk optimization method. Journal of Intelligent Manufacturing, 26, 1–12.
Yang, G., Liu, Y., & Yang, K. (2011). Modeling supply chain network design problem with joint service level constraint. Knowledge engineering and management (pp. 311–318) Berlin: Springer.
Yiqing, L., Xigang, Y., & Yongjian, L. (2007). An improved PSO algorithm for solving non-convex NLP/MINLP problems with equality constraints. Computers and Chemical Engineering, 31, 153–162.
Zeng, Y., Zhang, Z., Kusiak, A., Tang, F., & Wei, X. (2015). Optimizing wastewater pumping system with data-driven models and a greedy electromagnetism-like algorithm. Stochastic Environmental Research and Risk Assessment, 1–13.
Zhang, S., Lee, C., Chan, H., Choy, K., & Wu, Z. (2015). Swarm intelligence applied in green logistics: A literature review. Engineering Applications of Artificial Intelligence, 37, 154–169.
Zhang, S., Lee, C., Choy, K., Ho, W., & Ip, W. (2014). Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem. Transportation Research Part D: Transport and Environment, 31, 85–99.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Fathian, M., Jouzdani, J., Heydari, M. et al. Location and transportation planning in supply chains under uncertainty and congestion by using an improved electromagnetism-like algorithm. J Intell Manuf 29, 1447–1464 (2018). https://doi.org/10.1007/s10845-015-1191-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-015-1191-9