Solving fuzzy dynamic ship routing and scheduling problem through new genetic algorithm
This paper develops a model for shipping of container vessels to fulfill the demand and supply in various ports in a fixed time frame with dynamic demand and supply of each port under fuzzy environment. The time frame is divided into sub-frames which are operation time and travelling time. Speed optimization, simultaneous loading, unloading operation, and load factor are introduced to reduce fuel consumption and carbon emission. The risk factor is introduced to make the problem more realistic. In the real ship routing scenarios, different cost parameters are not always deterministic, and fluctuate imprecisely. The imprecise cost parameters are considered as Triangular Fuzzy Number (TFN). A modified genetic algorithm is used to solve the proposed model, and numerical examples are given to illustrate the efficiency of the proposed algorithm.
Aktar, M. S., De, M., Maity, S., Mazumder, S. K., & Maiti, M. (2020). Green 4D transportation problems with breakable incompatible items under type-2 fuzzy-random environment. Journal of Cleaner Production, 275, 1-26.
Alfandari, L., Davidović, T., Furini, F., Ljubić, I., Maraš, V., & Martin, S. (2019). Tighter MIP models for Barge Container Ship Routing. Omega, 82, 38-54.
Alhamad, K., Alrashidi, A., & Alkharashi, S. (2019). Metaheuristic algorithm for ship routing and scheduling problems with time window. Cogent Business & Management, 6(1), 1-17.
Bausch, D., Brown, G.G., & Ronen, D. (1998). Scheduling Short-Term Marine Transport of Bulk Products. Maritime Policy & Management, 25, 335-348.
De, A., Kumar, S.K., Gunasekaran, A., & Tiwari, M.K. (2017). Sustainable maritime inventory routing problem with time window constraints. Engineering Applications of Artificial Intelligence, 61, 77–95.
Dong, Z. & Bian, X. (2020). Ship Pipe Route Design Using Improved A* Algorithm and Genetic Algorithm. IEEE Access, 8, 153273-153296.
Fan, H., Yu, J., & Liu, X. (2019). Tramp Ship Routing and Scheduling with Speed Optimization Considering Carbon Emissions. Sustainability, 11(22), 1-19.
Hemmati, A., Hvattum, L.M., Fagerholt, K., & Norstad, I. (2014). Benchmark Suite for Industrial and Tramp Ship Routing and Scheduling Problems. Information Systems and Operational Research, 52(1), 28-38.
Homsi, G., Martinelli, R., Vidal, T., & Fagerholt, K. (2020). Industrial and tramp ship routing problems: Closing the gap for real-scale instances. European Journal of Operational Research, 283(3), 972-990.
Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.
Imran, M., Habib, M.S., Hussain, A., Ahmed, N., & Al-Ahmari, A.M. (2020). Inventory Routing Problem in Supply Chain of Perishable Products under Cost Uncertainty. Mathematics, 8, 1-29.
Lan, X., Zuo, X., & Tang, X. (2020). The Impact of Different Carbon Emission Policies on Linear Shipping. Journal of Marine Sciences, 2020, 1-12.
Laura, W., Anisa, R., Mareike, W., & Carlos, J. (2016). Modeling and Optimization Algorithms in Ship Weather Routing. International Journal of e-Navigation and Maritime Economy, 4, 31-45.
Liqan, Y., Gang, C., Jinlou, Z., & Niels, R. (2020). Ship Speed Optimization Considering Ocean Currents to Enhance Environmental Sustainability in Maritime Shipping. Sustainability, 12, 1-24.
Maity, S., Roy, A., & Maiti, M. (2018). Rough Genetic Algorithm for Constrained Solid TSP with Interval Valued Costs and Times. Fuzzy Information and Engineering, 10(2), 145-177.
Noshokaty, S. E. (2021). Ship routing and scheduling systems: forecasting, upscaling and viability. Maritime Business Review, 6 (1), 95-112.
Pratap, S., Zhang, M., Shen, C. L. D., & Huang, G. Q. (2019). A multi-objective approach to analyse the effect of fuel consumption on ship routing and scheduling problem. International Journal of Shipping and Transport Logistics, 11(2/3), 161-175.
Psaraftis, H.N. (2019). Ship routing and scheduling: the cart before the horse conjecture. Maritime Economics & Logistics, 21, 111–124.
Rabbani, M., Sadeghsa, S., Vaez-Alaei, M., & Farrokhi-Asl, H. (2019). Robust and sustainable full-shipload routing and scheduling problem considering variable speed: A real case study. Scientia Iranica, 26(3), 1881-1897.
Roy, A., Gao, R., Jia, L., Maity, S., & Kar, S. (2020). A Noble Genetic Algorithm to Solve a Solid Green Traveling Purchaser Problem with Uncertain Cost Parameters. American Journal of Mathematical and Management Sciences, 40(1), 17-31.
Sun, Y., Lu, Y., & Zhang, C. (2019). Fuzzy Linear Programming Models for a Green Logistics Center Location and Allocation Problem under Mixed Uncertainties Based on Different Carbon Dioxide Emission Reduction Methods. Sustainability, 11(22), 1-24.
United Nations Conference on Trade and Development (UNCTAD). Review of maritime transport 2018, https://unctad.org/en/Publications Library/rmt 2018-en.pdf. Accessed 26 September 2018.
Wang, H., Li, X., Li, P., Veremey, E., & Sotnikova, M. (2018). Application of Real-Coded Genetic Algorithm in Ship Weather Routing. Journal of Navigation, 71(4), 989-1010.
Yang, A., Cao, Y., Chen, K., Zeng, Q., & Chen, Z. (2021). An Optimization Model for Tramp Ship Scheduling considering Time Window and Seaport Operation Delay Factors. Journal of Advanced Transportation, 21, 1-19.
Zhang, G., Wang, H., & Zhao, W. (2021). Application of Improved Multi-Objective Ant Colony Optimization Algorithm in Ship Weather Routing. Journal of Ocean University of China, 20(1), 45–55.
Zhao, Y., Fan, Y., Zhou, J., & Kuang, H. (2019). Bi-Objective Optimization of Vessel Speed and Route for Sustainable Coastal Shipping under the Regulations of Emission Control Areas. Sustainability, 11(22), 1-24.