Optimization of China Crude Oil Transportation Network with Genetic Ant Colony Algorithm

Abstract

Taking into consideration both shipping and pipeline transport, this paper first analysed the risk factors for different modes of crude oil import transportation. Then, based on the minimum of both transportation cost and overall risk, a multi-objective programming model was established to optimize the transportation network of crude oil import, and the genetic algorithm and ant colony algorithm were employed to solve the problem. The optimized result shows that VLCC (Very Large Crude Carrier) is superior in long distance sea transportation, whereas pipeline transport is more secure than sea transport. Finally, this paper provides related safeguard suggestions on crude oil import transportation.

Developing the method of rational trucking routing based on the modified ant algorithm

“…Application of the ant algorithm for planning multiprofile operations in production systems is given in [19,20]. A multi-purpose programming model for optimizing an import crude oil transportation network based on the use of genetic and ant algorithms is given in [21].…”

Altitude-dependent gaseous emissions from freight trucks along the China-Pakistan Economic Corridor in Pakistan

“…The China-Pakistan Economic Corridor (CPEC) project, part of the Chinese Belt and Road Initiative, has strengthened road-based trade and economic cooperation between China and Pakistan and is intended as a model of regional cooperation [ 1 ]. Recently, with the promotion of environmental sustainability, efforts have focused on transforming the CPEC transport structure [ [2] , [3] , [4] ]. In Pakistan, however, emission-intensive [ 5 ] road-based transport remains the dominant mode for freight [ 6 ], accounting for 96% of domestic freight in 2018 [ 7 ].…”

Optimization of Oil-Flow Scheduling in Branched Pipeline Systems

“…There are many articles featuring different approaches to oil transportation scheduling in pipeline systems. In some studies (Arya and Honwad 2015;De la Cruz et al 2003;Narvaez and Galeano 2004a, b), a genetic algorithm was proposed to solve the problem of oil-flow scheduling optimization, whereas in others (Vlot 2017;Oosterhuis 2015;Wang and Lu 2015;Jamshidifar et al 2009;Grelli 1985;Osiadacz 1994) dynamic programming was used to optimize pipeline networks. Nowadays, gradient search techniques (Mercado et al 2002;Rozer 2003;Tabkhi 2007) and heuristic methods (Ferber et al 1999;Conrado and Rozer 2005) have become quite widespread in optimization of transport of oil and gas pipeline systems.…”