A simulation-optimization model for liquefied natural gas transportation considering product variety

References

• Al-Sobhi, S. A., & Elkamel, A. (2015). Simulation and optimization of natural gas processing and production network consisting of LNG, GTL, and methanol facilities. Journal of Natural Gas Science and Engineering, 23, 500–508.
   Web of Science ®Google Scholar
• Babaee Tirkolaee, E., Alinaghian, M., Bakhshi Sasi, M., & Seyyed Esfahani, M. M. (2016). Solving a robust capacitated arc routing problem using a hybrid simulated annealing algorithm: A waste collection application. Journal of Industrial Engineering and Management Studies, 3(1), 61–76.
   Google Scholar
• Budiyanto, M. A., Riadi, A., Buana, I. S., & Kurnia, G. (2020). Study on the LNG distribution to mobile power plants utilizing small-scale LNG carriers. Heliyon, 6(7), e04538.
   PubMed Web of Science ®Google Scholar
• Cao, W., Beggs, C., & Mujtaba, I. M. (2015). Theoretical approach of freeze seawater desalination on flake ice maker utilizing LNG cold energy. Desalination, 355, 22–32.
   Web of Science ®Google Scholar
• Castilla-Rodríguez, I., Expósito-Izquierdo, C., Melián-Batista, B., Aguilar, R. M., & Moreno-Vega, J. M. (2020). Simulation-optimization for the management of the transshipment operations at maritime container terminals. Expert Systems with Applications, 139, 112852.
   Web of Science ®Google Scholar
• Collier, P. I. (1980). Simulation as an aid to the study of a port as a system. Ship Operation Automation, 3, 51–56.
   Google Scholar
• Dai, L., Jing, D., Hu, H., & Wang, Z. (2021). An environmental and techno-economic analysis of transporting LNG via Arctic route. Transportation Research Part A: Policy and Practice, 146, 56–71.
   Web of Science ®Google Scholar
• Daskin, M. S. (2011). Network and discrete location: Models, algorithms, and applications. Illinois, John Wiley & Sons.
   Google Scholar
• Fathollahi-Fard, A. M., Govindan, K., Hajiaghaei-Keshteli, M., & Ahmadi, A. (2019). A green home health care supply chain: New modified simulated annealing algorithms. Journal of Cleaner Production, 240, 118200.
   Web of Science ®Google Scholar
• Ferrín, J. L., & Pérez-Pérez, L. J. (2020). Numerical simulation of natural convection and boil-off in a small size pressurized LNG storage tank. Computers & Chemical Engineering, 138, 106840.
   Web of Science ®Google Scholar
• Fu, Y., Li, W., Chen, S., Wang, Z., & Liu, Y. (2020). Experimental and simulation study on the capacitive liquid level gauge of LNG vehicular cylinder. Measurement, 164, 107930.
   Web of Science ®Google Scholar
• Ghasemi, P., & Babaeinesami, A. (2020). Simulation of fire stations resources considering the downtime of machines: A case study. Journal of Industrial Engineering and Management Studies, 7(1), 161–176.
   Google Scholar
• Ghasemi, P., & Khalili-Damghani, K. (2021). A robust simulation-optimization approach for pre-disaster multi-period location–allocation–inventory planning. Mathematics and Computers in Simulation, 179, 69–95.
   Web of Science ®Google Scholar
• Ghasemi, P., Khalili-Damghani, K., Hafezolkotob, A., & Raissi, S. (2017). A decentralized supply chain planning model: A case study of hardboard industry. The International Journal of Advanced Manufacturing Technology, 93(9), 3813–3836.
   Web of Science ®Google Scholar
• Ghorbani, N., Aghahosseini, A., & Breyer, C. (2020). Assessment of a cost-optimal power system fully based on renewable energy for Iran by 2050–Achieving zero greenhouse gas emissions and overcoming the water crisis. Renewable Energy, 146, 125–148.
   Web of Science ®Google Scholar
• Goli, A., Tirkolaee, E. B., Malmir, B., Bian, G. B., & Sangaiah, A. K. (2019). A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand. Computing, 101(6), 499–529.
   Web of Science ®Google Scholar
• Goodarzian, F., Taleizadeh, A. A., Ghasemi, P., & Abraham, A. (2021). An integrated sustainable medical supply chain network during COVID-19. Engineering Applications of Artificial Intelligence, 100, 104188.
   PubMed Web of Science ®Google Scholar
• Grassmann, W. K. (2008). Warm-up periods in simulation can be detrimental. Probability in the Engineering and Informational Sciences, 22(3), 415.
   Web of Science ®Google Scholar
• Hafezi, R., Wood, D. A., Akhavan, A. N., & Pakseresht, S. (2020). Iran in the emerging global natural gas market: A scenario-based competitive analysis and policy assessment. Resources Policy, 68, 101790.
   Web of Science ®Google Scholar
• Kakemem, U., Jafarian, A., Husinec, A., Adabi, M. H., & Mahmoudi, A. (2021). Facies, sequence framework, and reservoir quality along a Triassic carbonate ramp: Kangan Formation, South Pars Field, Persian Gulf Superbasin. Journal of Petroleum Science and Engineering, 198, 108166.
   Web of Science ®Google Scholar
• Kana, A. A., & Harrison, B. M. (2017). A Monte Carlo approach to the ship-centric Markov decision process for analyzing decisions over converting a containership to LNG power. Ocean Engineering, 130, 40–48.
   Web of Science ®Google Scholar
• Karbassi Yazdi, A., Kaviani, M. A., Emrouznejad, A., & Sahebi, H. (2020). A binary particle swarm optimization algorithm for ship routing and scheduling of liquefied natural gas transportation. Transportation Letters, 12(4), 223–232.
   Web of Science ®Google Scholar
• Karbassi Yazdi, A., Rashidi Komijan, A., Raissi, S., & Modiri, M. (2019). A robust model for supplying LNG from different contracts considering overall and incremental discount options. Energy Sources, Part A: Recovery Utilization, and Environmental Effects, 12, 1–20.
   Web of Science ®Google Scholar
• Katebah, M. A., Hussein, M. M., Shazed, A., Bouabidi, Z., & Al-musleh, E. I. (2020). Rigorous simulation, energy and environmental analysis of an actual baseload LNG supply chain. Computers & Chemical Engineering, 141, 106993.
   Web of Science ®Google Scholar
• Kim, H., Watanabe, D., Toriumi, S., & Hirata, E. (2021). Spatial analysis of an emission inventory from liquefied natural gas fleet based on automatic identification system database. Sustainability, 13(3), 1250.
   Web of Science ®Google Scholar
• Kolahi, S. S. (2011). Simulation model, warm-up period, and simulation length of cellular systems. In 2011 Second International Conference on Intelligent Systems, Modelling and Simulation, Phnom Penh, Cambodia 375–379. IEEE.
   Google Scholar
• Lim, K. G., & Lim, M. (2020). Financial performance of shipping firms that increase LNG carriers and the support of eco-innovation. Journal of Shipping and Trade, 5(1), 1–25.
   Google Scholar
• Merrick, J. R., van Dorp, J. R., & Dinesh, V. (2005). Assessing uncertainty in simulation‐based maritime risk assessment. Risk Analysis: An International Journal, 25(3), 731–743.
   PubMed Web of Science ®Google Scholar
• Miana, M., Del Hoyo, R., Rodrigálvarez, V., Valdés, J. R., & Llorens, R. (2010). Calculation models for prediction of Liquefied Natural Gas (LNG) ageing during ship transportation. Applied Energy, 87(5), 1687–1700.
   Web of Science ®Google Scholar
• Nevins, M. R., Macal, C. M., Love, R. J., & Bragen, M. J. (1998). Simulation, animation and visualization of seaport operations. Simulation, 71(2), 96–106.
   Google Scholar
• Nikhalat-Jahromi, H., Bell, M. G., Fontes, D. B., Cochrane, R. A., & Angeloudis, P. (2016). Spot sale of uncommitted LNG from Middle East: Japan or the UK? Energy Policy, 96, 717–725.
   Web of Science ®Google Scholar
• Oglend, A., Osmundsen, P., & Kleppe, T. S. (2020). Time commitments in LNG shipping and natural gas price convergence. The Energy Journal, 41(2). doi:https://doi.org/10.5547/01956574.41.2.aogl
   Google Scholar
• Park, T., So, S., Jeong, B., Zhou, P., & Lee, J. U. (2021). Life cycle assessment for enhanced Re-liquefaction systems applied to LNG carriers; effectiveness of partial re-liquefaction system. Journal of Cleaner Production, 285, 124832.
   Web of Science ®Google Scholar
• Ramani, K. V. (1996). An interactive simulation model for the logistics planning of container operations in seaports. Simulation, 66(5), 291–300.
   Google Scholar
• Rida, M. (2014). Modeling and optimization of decision-making process during loading and unloading operations at container port. Arabian Journal for Science and Engineering, 39(11), 8395–8408.
   Web of Science ®Google Scholar
• Said, G. A. E. N. A., Mahmoud, A. M., & El-Horbaty, E. S. M. (2014). Simulation and optimization of container terminal operations: A case study. arXiv Preprint, arXiv:1407.6257.
   Google Scholar
• Sarkheil, H. (2021). Risk and incident analysis on key safety performance indicators and anomalies feedback in south pars gas complex. Results in Engineering, 9, 100210.
   Google Scholar
• Shafipour-omran, B., Khalili-Damghani, K., & Ghasemi, P. (2020). Solving a supply chain problem using two approaches of fuzzy goal programming based on TOPSIS and fuzzy preference relations. Journal of Industrial and Systems Engineering, 13(2), 27–48.
   Google Scholar
• Shibasaki, R., Kanamoto, K., & Suzuki, T. (2020). Estimating global pattern of LNG supply chain: A port-based approach by vessel movement database. Maritime Policy & Management, 47(2), 143–171.
   Web of Science ®Google Scholar
• Shirani, M., Akbari, A., Hassani, M., Goli, A., Habibollahi, S., & Akbarian, P. (2018). Homogeneous liquid-liquid microextraction via flotation assistance coupled with gas chromatography-mass spectrometry for determination of myclobutanil in cucumber, tomato, grape, and strawberry using genetic algorithm. International Journal of Environmental Analytical Chemistry, 98(3), 271–285.
   Web of Science ®Google Scholar
• Sun, S., & Ertz, M. (2020). Life cycle assessment and Monte Carlo simulation to evaluate the environmental impact of promoting LNG vehicles. MethodsX, 7, 101046.
   PubMed Web of Science ®Google Scholar
• Tahar, R. M., & Hussain, K. (2000). Simulation and analysis for the Kelang container terminal operations. Logistics Information Management. 13(1), 14–20
   Google Scholar
• Tuǧcu, S. (1983). A simulation study on the determination of the best investment plan for Istanbul seaport. Journal of the Operational Research Society, 34(6), 479–487.
   Web of Science ®Google Scholar