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The air transportation industry is one big network that is expected to grow continuously, and Asia-Pacific is the fastest growing region as of 2018 data. We apply weighted network analysis to explore the key network characteristics of the Asian international passenger aviation market in 2014 and 2018. For the analysis, networks are constructed based on international passengers' origin-destination pair data from 28 Asian airlines in 2014 and 2018. Previously published social network analysis (SNA) on the air transportation network largely used unweighted centrality metrics, whereas the actual situation is a weighted network. We implement Mean Associations (MA), Triangle Betweenness Centrality (TBC), and Weighted PageRank (WPR), as new weighted centrality metrics, to effectively reflect actual passenger traffic among airports beyond simple connections. Our weighted network analysis indicates that SIN, ICN, HKG, NRT, MNL, BKK, TPE, HND, PEK, and KUL are the top-ranked hub airports, followed by SGN, PVG, CAN, DEL, SYD, KIX and CGK, and that the low-cost carriers have almost fully developed a ‘hub-and-spoke’ structure over the 4-year study period and are increasing their impact on the centrality of airports in the region.

Owing to growing economic growth in tandem with regional economic developments on the route connecting Asia and sub-Saharan Africa (SSA), several countries in the SSA region such as South Africa have been developing container ports. This paper analyzes potential trunk line routes and transshipment flows that could be captured by potential hub ports in the SSA region. To this end, integer programing models are developed to represent liner shipping networks in the SSA region and a wide variety of numerical experiments based on realistic data are conducted for the SSA-Europe and SSA-Asia trade routes. This paper draws various meaningful policy and managerial implications through scenario analyses such as the effects of network evolution, different vessel sizes and speeds, carbon taxes and insights in association with the 21st Century Maritime Silk Road initiated by China.

This paper provides an algorithm to minimize the fixed ordering, purchase, and inventory-carrying costs associated with bunker fuel together with ship time costs; and environmental costs associated with greenhouse gas emissions. It determines the optimum ship speed, bunkering ports, and amounts of bunker fuel for a given ship’s route. To solve the problem, we use an epsilon-optimal algorithm by deriving a property. The algorithm is illustrated by applying it to typical sample data obtained and the effects of bunker prices, carbon taxes, and ship time costs on the ship speed are analyzed. The results indicate that the ship speed and CO2 emissions are highly sensitive to the factors considered.

The lanthanide complexes have been applied as emitting materials and have been anticipated to exhibit high efficiency along with a narrow emission spectrum since both singlet and triplet excitons of ligands are involved in the luminescence process. With increasing current, however, the quantum efficiencies have decreased significantly due to the triplet–triplet annihilation. The effect of the energy levels of singlet and triplet states of ligands have been studied and the efficient energy transfer mechanism to overcome triplet–triplet annihilation is proposed. The calculation shows that the triplet state as well as the singlet state of the ligand affects the quantum efficiency.

We consider the problem of determining the disassembly schedules of used products while satisfying the demand of their parts/components over a planning horizon. The case of multiple product types with parts commonality is considered for the objective of minimizing the sum of setup, disassembly operation, and inventory holding costs. A heuristic algorithm is proposed using the linear programming relaxation approach. Test results of the case study on the data obtained from a disassembly shop show that the heuristic algorithm suggested in this paper works well for practical problems.