Abstract
The spatial layout of the port industrial zone problem is a core issue in port industrial zone planning, and it directly affects the actual effects of the port industrial zone. Firstly, considering that existing port industrial zone planning lacks in methods of quantitative analysis, this paper constructs a Mathematical model based on multi-objective programming, and the optimal scale of various industries of port industrial zone is obtained. Secondly, the paper takes the maximum dependence degree of port as objective function by using systematic layout planning tools, and solves the spatial layout of the port industrial zone. Finally, by taking Binhai Port industrial zone of China as an example, a port industrial zone spatial layout model is constructed and solved through simulated annealing algorithm. The optimal spatial layout program for Binhai Port industrial zone of China was obtained, which verifies the feasibility and accuracy of the model.
Similar content being viewed by others
References
Verhetsel, A., Kessels, R., Goos, P., Zijlstra, T., Blomme, N., Cant, J.: Location of logistics companies: a stated preference study to disentangle the impact of accessibility. J. Transp. Geogr. 42(42), 110–121 (2015)
Fujita, M.: Thunen and the new economic geography. Reg. Sci. Urban Econ. 42(6), 907–912 (2000)
Wang, J.J., Slack, B.: The evolution of a regional container port system: the Pearl River delta. J. Transp. Geogr. 8(4), 263–275 (2000)
Imai, A., Sun, X., Nishimura, E., Papadimitriou, S.: Berth allocation in a container port: using a continuous location space approach. Transp. Res. Part B 39(3), 199–221 (2005)
Zhang, H., Zhao, X.: Quantitative analysis of organizational behavior of container shipping in the upper and middle reaches of the Yangtze River based on hub-and-spoke network. J. Coast. Res. 73, 119–125 (2015)
Baird, A.J.: Port privatisation: objectives, process and financing. Compos. B Eng. 45(1), 995–1000 (2000)
Rimmer, P.J.: A conceptual framework for examining urban and regional transport needs in Southeast Asia. Pac. View 18, 133–147 (1977)
Slack, Brain: Intermodal transportation in North America and the development of inland load centers. Prof. Geogr. 42(1), 72–83 (2010)
Feng, X., Jiang, L., Zhang, Y., Wang, W.: Optimization of capacity of ports within a regional port system. Transp. Res. Record J. Transp. Res. Board 2222, 10–16 (2011)
Martin, Jeffrey, Thomas, Brian J.: The container terminal community. Marit. Policy Manag. 28(3), 279–292 (2001)
Notteboom, T.E.: Container shipping and ports: an overview. Rev. Netw. Econ. 3(2), 86–106 (2004)
Ma, Y., Luan, W., Zhang, R., Feng, P.: Research on the size and layout of iron ore wharf in Bohai Rim region based on Freight Demand. Int. Conf. Logist. Eng. Manag. Comput. Sci. 2015(117), 846–850 (2015)
Cao, C., Li, C., Yang, Q., Liu, Y., Qu, T.: A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters. J. Clean. Prod. 174, 1422–1435 (2018)
Tsao, Y.C., Thanh, V.V., Lu, J.C., Yu, V.: Designing sustainable supply chain networks under uncertain environments: fuzzy multi-objective programming. J. Clean. Prod. 174, 1550–1565 (2017)
Capitanescu, F., Marvuglia, A., Benetto, E., Ahmadi, A., Tiruta-Barna, L.: Linear programming-based directed local search for expensive multi-objective optimization problems: application to drinking water production plants. Eur. J. Oper. Res. 262(1), 322–334 (2017)
Xue, L., Villalobos, J.R.: A multi-objective optimization primary planning model for a poe (port-of-entry) inspection. J. Transp. Secur. 5(3), 217–237 (2012)
Wang, B., Yang, T.: Multi-objective optimization model of export and transit containers storage in a transshipment port yard. Appl. Mech. Mater. 220–223, 272–278 (2012)
Cano, A., Ventura, S., Cios, K.J.: Multi-objective genetic programming for feature extraction and data visualization. Soft. Comput. 21(8), 2069–2089 (2017)
Lachhwani, K.: Modified FGP approach for multi-level multi objective linear fractional programming problems. Appl. Math. Comput. 266, 1038–1049 (2015)
Rao, R.V., Patel, V.: Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Appl. Math. Model. 37(3), 1147–1162 (2013)
Hui, S.: Multi-objective optimization for hydraulic hybrid vehicle based on adaptive simulated annealing genetic algorithm. Eng. Appl. Artif. Intell. 23(1), 27–33 (2010)
Cakir, B., Altiparmak, F., Dengiz, B.: Multi-objective optimization of a stochastic assembly line balancing: a hybrid simulated annealing algorithm. Comput. Ind. Eng. 60(3), 376–384 (2011)
Montreuil, B.: A modelling framework for integrating layout design and flow network design. Material Handling 90, pp. 95–115. Springer, Berlin (1991)
Meller, R.D., Chen, W., Sherali, H.D.: Applying the sequence-pair representation to optimal facility layout designs. Operat. Res. Lett. 35(5), 651–659 (2007)
Amar, S. H., & Abouabdellah, A. (2017). Facility layout planning problem: Effectiveness and reliability evaluation system layout designs. In: International Conference on System Reliability and Science (pp. 110-114).
Ning, X., Lam, K.C., Lam, C.K.: Dynamic construction site layout planning using max-min ant system. Autom. Constr. 19(1), 55–65 (2010)
Kim, B.I., Jeong, S., Shin, J., Koo, J., Chae, J., Lee, S.: A layout- and data-driven generic simulation model for semiconductor fabs. IEEE Trans. Semicond. Manuf. 22(2), 225–231 (2009)
Kulturel-Konak, S., Konak, A.: A simulated annealing algorithm with a dynamic temperature schedule for the cyclic facility layout problem. In: Informs Computing Society Conference (2015)
Gkegkas, T., Farmaki, P., Stylianidis, E.: Spatial planning system’s structure focus on urban development: urban layout planning, plot arrangement-compensation acts, urban planning implementation acts. Spatial, Design, Landscape & Socio-economic Dimensions, Changing Cities (2017)
Mavridou, T.D., Pardalos, P.M.: Simulated annealing and genetic algorithms for the facility layout problem: a survey. Comput. Optim. Appl. 7(1), 111–126 (1997)
Allahyari, M.Z., Azab, A., Allahyari, M.Z., Azab, A.: Mathematical modeling and multi-start search simulated annealing for unequal-area facility layout problem. Expert Syst. Appl. 91, 46–62 (2017)
Hu, C., Ren, G., Liu, C., Li, M., Jie, W.: A spark-based genetic algorithm for sensor placement in large scale drinking water distribution systems. Clust. Comput. 3, 1–11 (2017)
Acknowledgement
Research for this paper was funded by the National Natural Science Foundation of China (No. 41401120), Fundamental Research Funds for the Central Universities (Project No. 2014B00214), and College Students’ innovation and entrepreneurship training program project (Project No. 2017102941063). The authors thank every teacher of research institute, for their comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jiang, L., Ji, J., Lu, Y. et al. Mathematical modeling and simulated annealing algorithm for spatial layout problem. Cluster Comput 22 (Suppl 3), 6383–6391 (2019). https://doi.org/10.1007/s10586-018-2137-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-018-2137-8