Optimization of LNG Logistics System to Meet Gas Supply at Gresik LNG Receiving Terminal

Abstract

The construction of Gresik LNG receiving terminal is aimed to fulfil the 109 MMSCFD (0.87 MTPA) gas requirement for combined-cycle power plant (PLTGU) Jawa-3 which is integrated with LNG Gresik Terminal. There are several potential LNG plants in Indonesia and abroad that can be the source of gas for Gresik LNG Terminal. Each of these LNG plants has varying gas price and distance to Gresik. The modelling of the LNG logistics system for Gresik LNG Terminal was built to illustrate the LNG supply chain from the LNG plants to Gresik LNG Terminal and several supply scenarios will be proposed. The built model is linear and optimized by using linear programming. Linear programming involves the determination of objective functions, decision variables and constraints. The optimization of the logistics system aims to obtain the cheapest gas supply cost for Gresik LNG receiving terminal. The result shows that the cheapest gas supply cost is obtained through direct supply scenario with gas source from domestic and abroad with combination of delivery from Bontang equal to 0.34 MTPA (40 %) with 12 shipments, Tangguh equal to 0.26 MTPA (30 %) with 9 shipments, and Bintulu Malaysia equal to 0.26 MTPA (30 %) with 9 shipments.

A mathematical model on liquefied natural gas supply chain with uncertain demand

“…When studies are examined in terms of modes, the studies [1,2,5,[7][8][9][10][11][12][13][14][15][16]21] include only one mode. Studies [3,4,6,[17][18][19][20] include two modes of the transportation types. The studies examined were also evaluated in terms of storage areas.…”

“…Misra et al [17] took into consideration a supply chain optimization problem for packaged liquefied gaseous products, which considered a warehouse stock management. Raharjo and Sudibandriyo [18] proposed a model to minimize the total cost of the LNG supply chain, which took into consideration plant capacity with a case study.…”

“…Fodstad et al [3] and Rakke et al [4] consider inventory, routing, and scheduling. Studies [1,12,16,18,21] propose models on LNG supply chain. Most of the studies assume that demand, inventory, service time, and other factors are fixed.…”

Liquefied Natural Gas Logistics Management Through Optimal Road-Rail Intermodal Logistics Planning Considering Community Safety

“…The aim of optimization studies is to propose an optimal solution of the problem. Indeed, optimization studies allow to formulate the problem using various mathematical models, depending on the problem characteristics; for example, linear programming (Raharjo & Sudibandriyo, 2019;Utku & Soyöz, 2020), integer linear programming (Budiyanto et al, 2020), mixed integer linear programming (Andersson et al, 2015;Bittante et al, 2018), integer nonlinear programming (Paulauskas et al, 2022;Zhao et al, 2022), and mixed integer nonlinear programming (Atmayudha et al, 2021;Sangaiah et al, 2020). The optimal solution or local optimal solution is determined by optimization techniques such as branch-and-bound and metaheuristic, depending on the size and complexity of the problem.…”

“…The single objective involves only one objective function, whereas the multiobjective involves more than one objective function. LNG transportation studies dealing with the single objective include the following authors' works: Andersson et al (2015), Baalisampang et al (2019), Bittante et al (2018), Buyanova and Mudrova (2019), Jianhua and Zhenghua (2012), Kanda (2006), Hariraj and Ramanath (2015), Hart and Morrison (2015), Palestini & Sassu (2021), Raharjo and Sudibandriyo (2019), Sangaiah et al (2020), Songbandid (2016), Utku and Soyöz (2020), Vasilevich et al (2016), andVithal et al (2021). The multiobjective problem is more complex than the single-objective problem as the weights of the objectives must be optimally determined.…”