The airline crew pairing (ACP) problem is one of the most challenging problems in airline operations. It aims to decide the optimal connections among pairs of flights assigned to flight crews. However, the number of connections grows exponentially with the increasing number of flights. Conventional approaches usually follow a two-stage method, i.e., divide-and-conquer. The flight sequences (pairings) spanning multiple days are firstly generated for each crew. Then, the best pairing set is chosen based on minimum operational costs. When the number of flights is large, it becomes too difficult to generate all feasible pairings and find the optimum. In order to solve large-scale ACP problems efficiently, we propose a novel iterative optimization framework based on monte carlo tree search (MCTS). Thus, the speed of pairing generation and solution accuracy can be improved. To evaluate the performance of the proposed method, we conduct experiments on different scales of real-world instances provided by airline companies. The empirical results show that the proposed approach is capable of producing high-quality solutions, especially in large-scale instances.

The global warming has made arctic marine traffic possible, but the harsh navigation environment threatens its safety and restrains its development. It is necessary to evaluate the safety of arctic...

In this paper, an eco-efficiency analysis is conducted using the epsilon-based measure data envelopment analysis (EBM-DEA) model for Russian cities along the Northern Sea Route (NSR). The EBM-DEA model includes five input variables: population, capital, public investment, water supply, and energy supply and four output variables: gross regional product (GRP), greenhouse gas (GHG) emissions, solid waste, and water pollution. The pattern of eco-efficiency of 28 Russian cities along the NSR is empirically analyzed based on the associated real data across the years from 2010 to 2019. The empirical results obtained from the analysis show that St. Petersburg, Provideniya, Nadym, N. Urengoy, and Noyabrsk are eco-efficient throughout the 10 years. The results also indicate that the cities along the central section of the NSR are generally more eco-efficient than those along other sections, and the cities with higher level of GRPs per capita have relatively higher eco-efficiency with a few exceptions. The study provides deeper insights into the causes of disparity in eco-efficiency, and gives further implications on eco-efficiency improvement strategies. The contributions of this study lie in the fact that new variables are taken into account and new modeling techniques are employed for the assessment of the eco-efficiency of the Russian cities.

As a result of the opening Arctic waters, Arctic Northeast Passage (NEP) can be used as a shortcut between Asian and European ports, which may bring considerable economic benefits. Due to the harsh environment and substantial costs, it is difficult to construct many fulcrum ports along the Russian Arctic coast, which provide support services for ships navigating the NEP. A multi-criteria decision model is hence constructed to select fulcrum ports among candidates to support Arctic shipping through the NEP. Based on the model, the evidential reasoning (ER) approach is used to aggregate quantitative data and qualitative expert judgments to generate distributed assessments for candidate ports, as well as modeling uncertainties such as incompleteness and ignorance in the assessment of candidate ports. Their suitability as fulcrum ports are prioritized according to the utilities. The results show that Pevek, Sabetta, and Murmansk are the ideal fulcrum ports for the eastern, central, and western section, respectively, of the Russian Arctic coast. The associated policy recommendations include prioritizing the improvement of selected fulcrum ports, attaching importance to promising candidate ports, updating outdated infrastructure, and implementing more related projects. A sensitivity analysis conducted in this study validates the feasibility and reasonability of the proposed model.

The Arctic Northeast Passage is of growing economic and strategic importance, while its harsh and highly variable navigation environment poses substantial challenges for vessel operation. Existing assessment approaches often rely on predefined structural assumptions or sacrifice interpretability for numerical performance. This study proposes a data-driven evolutionary belief rule base model that integrates an adaptive reference value field mechanism with differential evolution-based structural optimization for rule pruning and growth. The inputs are observed vessel-operational and environmental variables from the Oden and Tian En datasets, and the output targets are three-grade expert-consensus navigation safety risk distributions aggregated from 100 independent votes per sample. Under the repeated random-split evaluations, ARVF-DEBRB achieved the lowest mean MAE on Oden and errors comparable to the leading learned baselines; on Tian En, its mean errors were comparable to those of the strongest learned baselines while lower than those of several conventional and deep baselines. Complementary chronological rolling-window evaluations over three disjoint future test windows provided future-only evidence as follows: ARVF-DEBRB ranked second in RMSE and third in MAE on Oden, and first in RMSE and second in MAE on Tian En. The rolling factorial ablation obtained the lowest mean errors for the complete configuration on both datasets. These results indicate competitive predictive performance under the reported evaluation protocols, while preserving transparent IF-THEN reasoning semantics and a compact rule structure. The data-adaptive ARVF construction and DE-based rule-structure refinement provide a methodological basis for recalibrating the framework to new operational settings.