Solving Large Scale Crew Pairing Problems

Crew pairing is one of the most critical processes in airline management operations. Taking a timetable as input, the objective of this process is to find an optimal way to partition flights of the timetable without breaking rules and regulations which are enforced by an airline. The problem has attracted many scientists in recent decades. The main challenge is that there is no general method to work well with all kinds of non-linear cost functions and rules. In order to overcome the non-linearity, the thesis follows a main idea to transfer this combinatorial optimization problem to a set partitioning problem which is one of the most popular \np-hard problems. Although this problem has been studied throughout decades, it becomes more complicated with the increasing size of the input. The complication is induced not only in the transformation process, but also in the methods to solve the resulting set partitioning problem. Finding quickly a good and robust solution for large scale problems is more and more critical to airlines. They are the main targets which are studied by the thesis. The thesis presents exact methods which are usually based on a branch-and-bound scheme. A branch-and-cut approach applies preprocessing techniques, cutting plane generation methods, and heuristics which are suitable for crew pairing problems. The implementation can solve small and medium sized problems. However, for large problems, a branch-and-price approach is necessary to cope with huge constraint matrices. The thesis improves the weakness of standard column generation methods by applying stabilized column generation variants with sophisticated parameter control schemes into this approach. The computation time is reduced significantly by a factor of three. Moreover, the work also focuses on the extensibility of the methods. This is quite important for large scale problems. Then, we easily obtain a heuristic solution method by controlling running parameters of the presented approaches or combining them together. Utilizing the available computing resources to deal with large scale crew pairing problems as effective as possible is also a target of the thesis. A new parallel branch-and-bound library is developed to support scientists to solve combinatorial optimization problems. With little effort, they can migrate their sequential codes to run on a parallel computer. The library contains several load balancing methods and control parameters in order to work well with specific problems. The sequential branch-and-cut code to solve set partitioning problems is parallelized by the library and introduces a good speedup for most crew pairing test problems. Parallel computing is also used to solve a so-called pricing subproblem, which is the most difficult problem in the branch-and-price approach, with a nearly linear speedup. The implementation solves large scale crew pairing problems to optimality within minutes, whereas previous methods ended up in the range of hours or more

HUPSMT: AN EFFICIENT ALGORITHM FOR MINING HIGH UTILITY-PROBABILITY SEQUENCES IN UNCERTAIN DATABASES WITH MULTIPLE MINIMUM UTILITY THRESHOLDS

The problem of high utility sequence mining (HUSM) in quantitative se-quence databases (QSDBs) is more general than that of frequent sequence mining in se-quence databases. An important limitation of HUSM is that a user-predened minimum tility threshold is used commonly to decide if a sequence is high utility. However, this is not convincing in many real-life applications as sequences may have diferent importance. Another limitation of HUSM is that data in QSDBs are assumed to be precise. But in the real world, collected data such as by sensor maybe uncertain. Thus, this paper proposes a framework for mining high utility-probability sequences (HUPSs) in uncertain QSDBs (UQS-DBs) with multiple minimum utility thresholds using a minimum utility. Two new width and depth pruning strategies are also introduced to early eliminate low utility or low probability sequences as well as their extensions, and to reduce sets of candidate items for extensions during the mining process. Based on these strategies, a novel ecient algorithm named HUPSMT is designed for discovering HUPSs. Finally, an experimental study conducted in both real-life and synthetic UQSDBs shows the performance of HUPSMT in terms of time and memory consumption

The impact of government support on firm performance in Vietnam. New evidence from a dynamic approach

Using a sample of private manufacturing small- and medium-sized enterprises (SMEs) in the period 2007\u20132015, this paper examines the effect of government support on firms\u2019 financial performance in Vietnam. Contrary to many previous studies, the study finds that government assistance affects firms\u2019 financial performance after controlling for heterogeneity, unobservable factors, and dynamic endogeneity. The finding supports the viewpoint of institutional theory. The study also reveals that assistance measures, such as tax exemptions, soft loans, and investment incentives to promote financial performance, are vital for the development of Vietnamese private SMEs

STUDY ON ANTIOXIDANT ACTIVITIES OF THE AERIAL PARTS AND SOME COMPOUNDS ISOLATED FROM Archidendron clypearia ((Jack) I. Niels Part 2. ISOLATING, DETERMINING STRUCTURE AND ANTIOXIDANT CAPABILITY OF SOME COMPOUNDS FROM ETHYL ACETATE EXTRACT

The antioxidant activity in vitro of methanol extract of Archidendron clypearia was evaluated by in vitro tests on isolated liver cells of mouses with ED50 value ​​ of 2.18 μg/mL compared to that of curcumin of 1.87 µg/mL. Using combined chromatographic methods, four compounds were isolated from chloroform extract of the Archidendron clypearia. Their structures were elucidated to be daucosterol, 1-octacosanol, docosenoic acid, and methyl gallate by 1D- and 2D-NMR spectroscopic methods and in comparison with those reported in the literature. This is the first report of these compounds from the plant. All four compounds showed quite high antioxidant activity, for which methyl gallate was the highest one

Taxonomic assignment for large-scale metagenomic data on high-perfomance systems

Metagenomics is a powerful approach to study environment samples which do not require the isolation and cultivation of individual organisms. One of the essential tasks in a metagenomic project is to identify the origin of reads, referred to as taxonomic assignment. Due to the fact that each metagenomic project has to analyze large-scale datasets, the metatenomic assignment is very much computation intensive. This study proposes a parallel algorithm for the taxonomic assignment problem, called SeMetaPL, which aims to deal with the computational challenge. The proposed algorithm is evaluated with both simulated and real datasets on a high performance computing system. Experimental results demonstrate that the algorithm is able to achieve good performance and utilize resources of the system efficiently. The software implementing the algorithm and all test datasets can be downloaded at http://it.hcmute.edu.vn/bioinfo/metapro/SeMetaPL.html

Least Expected Time Paths in Stochastic Schedule-Based Transit Networks

We consider the problem of determining a least expected time (LET) path that minimizes the number of transfers and the expected total travel time in a stochastic schedule-based transit network. A time-dependent model is proposed to represent the stochastic transit network where vehicle arrival times are fully stochastically correlated. An exact label-correcting algorithm is developed, based on a proposed dominance condition by which Bellman’s principle of optimality is valid. Experimental results, which are conducted on the Ho Chi Minh City bus network, show that the running time of the proposed algorithm is suitable for real-time operation, and the resulting LET paths are robust against uncertainty, such as unknown traffic scenarios

Collaborative Exploration of Plant Genetic Resources in the Central Highlands of Vietnam, 2018

This report describes the first collaborative expedition to collect Vietnamese plant genetic resources in the National Agriculture and Food Research Organization (NARO), Japan and the Plant Resources Center (PRC) of the Vietnamese Academy of Agricultural Science to establish an international research enterprise within the framework of the Plant Genetic Resources in Asia Phase 2 project. We conducted a field survey in the central highland of Vietnam (Gia Lai Province) between August 28 and September 4, 2018 and collected a total 113 accessions (16 of Amaranthus sp., 2 of Benincasa hispida, 33 of Cucumis melo, 10 of Cucumis sativus, 25 of Cucurbita moschata, 3 of Citrullus lanatus, 1 of Coccinia sp., 2 of Momordica charantia, 1 of Trichosanthes sp., 7 of Capsicum sp., 10 of Solanum sp., 1 of Solanum lycopersicum, 1 of Lablab purpureus, and 1 of Sorghum bicolor). All accessions were stored as seeds, and subsets were transferred to the Genetic Resources Center, NARO, Japan.本報告は国立研究開発法人農業・食品産業技術総合研究機構とベトナム植物遺伝資源センターと共同で実施された農林水産省委託プロジェクト研究「海外植物遺伝資源の民間等への提供促進」における，ベトナム中部高原における園芸作物遺伝資源の探索・収集に関わる調査結果である．中部高原のザライ省において 2018 年 8 月 28 日～ 9 月 4 日にかけて植物遺伝資源の探索・調査を行った．その結果，合計 113 点（アマランサス属 16 点，トウガン 2 点，メロン 33 点，キュウリ10 点，ニホンカボチャ 25 点，スイカ 3 点，コッキニア属 1 点，カラスウリ属 1 点，ニガウリ 2 点，トウガラシ属 7 点，ナス属 10 点，トマト 1 点，フジマメ 1 点およびソルガム 1 点）を収集した．収集された遺伝資源は，ベトナム植物資源センターで保存された後，農研機構遺伝資源センタージーンバンクに移送され

Current medical product development for diagnosis, surgical planning and treatment in the areas of Neurosurgery, Orthopeadic and Dental-Cranio-Maxillofacial surgery in Vietnam

With the population of 86 million and good GDP growth in recent decades, the medical market in Vietnam is growing fast. However, most of the medical technology products are imported, and the number of locally manufactured ones is limited and they do not have the high competition capability in term of quality, quantity and types. In this paper, the current product development in Vietnam for diagnosis, surgical planning and treatment in the areas of Rehabilitation, Neurosurgery, Orthopeadic and Dental-Cranio-Maxillofacial surgery is presented. A roadmap for medical technology development in Vietnam is propose

Mechanism of Inverse Magnetoresistance in High-TaT_{a} Annealed MnNi/Co/Ag(Cu)/Py Spin Valves

The magnetic transport properties -- magnetoresistive (MR) effects of MnNi/Co/Ag(Cu)/\break Py pinned spin valve structures (SVs) prepared by rf sputtering method and annealed at $T_{a} = 100$°C - 500°C for 30 minutes in high vacuum ($\sim 10^{ - 5}$ torr) are investigated. The received results show a change in the observed MR behaviors from a normal giant magnetoresistance effect to an inverse magnetoresistance effect after annealing at high temperatures, 300°C and 400°C, for these SVs. The origin and mechanism of the IMR behavior are analyzed and discussed. These results will suggest an ability to manufacture SV devices used the IMR effect for enhancing the application capacities for SV-sensor systems