Pair Of Problems Research Articles

Solving an integrated mathematical model for crew pairing and rostering problems by an ant colony optimisation algorithm

Solving an integrated mathematical model for crew pairing and rostering problems by an ant colony optimisation algorithm

Joint Computation and Communication Resource Allocation With NOMA and OMA Offloading for Multi-Server Systems in F-RAN

Since mobile devices typically have limited computation resources, offloading computation tasks to fog access points (F-APs) is a promising approach to support delay-sensitive and computation-intensive applications. This paper considers joint computation and communication resource allocation for multiuser multi-server systems, which aims to maximize the number of users being served and minimize the total energy consumption subject to delay tolerance constraints. The joint computation and communication resource allocation problem is solved optimally for both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes. The joint user pairing and fog access point assignment problem for NOMA is proved to be NP-hard. For both NOMA and OMA, heuristic and optimal algorithms based on graph matching are designed. The optimal algorithms, though of high complexity, allow NOMA and OMA to be compared at their full potential and serve as benchmarks for evaluating the heuristic algorithms. Simulation results show that NOMA significantly outperforms OMA in terms of outage probability and energy consumption, especially for tight delay tolerance constraints and large computational tasks. Simulation results also demonstrate that our proposed NOMA and OMA schemes significantly outperform the swap-enabled matching algorithm widely used in the literature.

Efficient Pairing in Unknown Environments: Minimal Observations and TSP-Based Optimization

Generating paired sequences with maximal compatibility from a given set is one of the most important challenges in various applications, including information and communication technologies. However, the number of possible pairings explodes in a double factorial order as a function of the number of entities, manifesting the difficulties of finding the optimal pairing that maximizes the overall reward. In the meantime, in real-world systems, such as user pairing in non-orthogonal multiple access (NOMA), pairing often needs to be conducted at high speed in dynamically changing environments; hence, efficient recognition of the environment and finding high reward pairings are highly demanded. In this paper, we demonstrate an efficient pairing algorithm to recognize compatibilities among elements as well as to find a pairing that yields a high total compatibility. The proposed pairing strategy consists of two phases. The first is the observation phase, where compatibility information among elements is obtained by only observing the sum of rewards. We show an efficient strategy that allows obtaining all compatibility information with minimal observations. The minimum number of observations under these conditions is also discussed, along with its mathematical proof. The second is the combination phase, by which a pairing with a large total reward is determined heuristically. We transform the pairing problem into a traveling salesman problem (TSP) in a three-layer graph structure, which we call Pairing-TSP. We demonstrate heuristic algorithms in solving the Pairing-TSP efficiently. This research is expected to be utilized in real-world applications such as NOMA, social networks, among others.

Cockpit crew pairing Pareto optimisation in a budget airline

Crew pairing is the primary cost checkpoint in airline crew scheduling. Because the crew cost comes second after the fuel cost, a substantial cost saving can be gained from effective crew pairing. In this paper, the cockpit crew pairing problem (CCPP) of a budget airline was studied. Unlike the conventional CCPP that focuses solely on the cost component, many more objectives deemed to be no less important than cost minimisation were also taken into consideration. The adaptive non-dominated sorting differential algorithm III (ANSDE III) was proposed to optimise the CCPP against many objectives simultaneously. The performance of ANSDE III was compared against the NSGA III, MOEA/D, and MODE algorithms under several Pareto optimal measurements, where ANSDE III outperformed the others in every metric.

A Transgenic Heavy Chain IgG Mouse Platform as a Source of High Affinity Fully Human Single-Domain Antibodies for Therapeutic Applications.

The antibody repertoires of transgenic mice expressing human heavy chain only antibodies (HCAbs) can be retrieved from immune cells after antigen challenge. Compared with genetically modified rodents expressing conventional human antibodies (tetramers consisting of two heavy chains paired with two light chains), there is no chain pairing problem, since each antibody consists of a heavy chain dimer which is solely responsible for antigen binding. HCAbs can be obtained by classical hybridoma fusion, or the generation of phage libraries or eukaryotic cell libraries displaying or secreting HCAbs. Combined transcriptomic/serum proteomic approaches can also be used to determine the repertoire of antibodies, as well as single cell technologies such as the Beacon system that enable capture of immune cells of interest, analysis, and sequencing of antibodies in a short period of time. Here, we describe a protocol for obtaining monoclonal HCAbs from immunized Harbour transgenic mice through the generation and screening of HEK cell libraries of secreted antibodies. The method can be used routinely and is fast and affordable for everyone. Selected VH regions (single domains) are sequenced and individual HCAbs can be produced and purified from the same expression vector that is used for library generation (hIgG1 Fc). They can also be cloned into other expression plasmids and reformatted to equip them with a particular effector function, modify lifespan in serum, or optimize valency and avidity depending on the specific aim.

Extremal problems for multigraphs

An (n,s,q)-graph is an n-vertex multigraph in which every s-set of vertices spans at most q edges. Turán-type questions on the maximum of the sum of the edge multiplicities in such multigraphs have been studied since the 1990s. More recently, Mubayi and Terry (2019) [13] posed the problem of determining the maximum of the product of the edge multiplicities in (n,s,q)-graphs. We give a general lower bound construction for this problem for many pairs (s,q), which we conjecture is asymptotically best possible. We prove various general cases of our conjecture, and in particular we settle a conjecture of Mubayi and Terry on the (s,q)=(4,6a+3) case of the problem (for a≥2); this in turn answers a question of Alon. We also determine the asymptotic behaviour of the problem for ‘sparse’ multigraphs (i.e. when q≤2(s2)). Finally we introduce some tools that are likely to be useful for attacking the problem in general.

Solving Multi-Objective Paired Single Row Facility Layout Problem Using Hybrid Variable Neighborhood Search

The footwear industry is distinguished by its manual assembly line and a high proportion of shared workstation configuration. This study focuses on a subset of the single row facility layout problem known as the paired single row facility layout problem. As one of type of single-row facility layout, the paired single row facility layout problem cannot be solved quickly. Further, different objectives also need to be considered in the decision-making process. Therefore, multi-objective approaches are proposed to minimize the penalty of material handler usage while maximizing the adjacency function based on each workstation's closeness rating. A Single Row Facility Layout is an NP-hard problem; this problem also belongs to the NP-hard problem class. As a result, we propose a hybrid method combining variable neighborhood search (VNS) and genetic algorithm (GA) to solve the problem of obtaining the optimal configuration of a multi-objective paired single-row assembly line. A heuristic approach was used to create the schematic representation solution. To obtain the neighborhood solutions, a hybrid VNSGA was used. The schematic representation solution employs crossover and variable neighborhood descent. Using the concept of VNS, the neighborhood was changed in each generation.

Query Pixel Guided Stroke Extraction with Model-Based Matching for Offline Handwritten Chinese Characters

Stroke extraction and matching are critical for structural interpretation based applications of handwritten Chinese characters, such as Chinese character education and calligraphy analysis. Stroke extraction from offline handwritten Chinese characters is difficult because of the missing of temporal information, the multi-stroke structures and the distortion of handwritten shapes. In this paper, we propose a comprehensive scheme for solving the stroke extraction problem for handwritten Chinese characters. The method consists of three main steps: (1) fully convolutional network (FCN) based skeletonization; (2) query pixel guided stroke extraction; (3) model-based stroke matching. Specifically, based on a recently proposed architecture of FCN, both the stroke skeletons and cross regions are firstly extracted from the character image by the proposed SkeNet and CrossNet, respectively. Stroke extraction is solved by simulating the human perception that once given a certain pixel from non-cross region of a stroke, the whole stroke containing the pixel can be traced. To realize this idea, we formulate stroke extraction as a problem of pairing and connecting skeleton-wise stroke segments which are adjacent to the same cross region, where the pairing consistency between stroke segments is measured using a PathNet [1]. To reduce the ambiguity of stroke extraction, the extracted candidate strokes are matched with a character model consisting of standard strokes by tree search to identify the correct strokes. For verifying the effectiveness of the proposed method, we train and test our models on character images with stroke segmentation annotations generated from the online handwriting datasets CASIA-OLHWDB and ICDAR13-Online, as well as a dataset of Regularly-Written online handwritten characters (RW-OLHWDB). The experimental results demonstrate the effectiveness of the proposed method and provide several benchmarks. Particularly, the precisions of stroke extraction for ICDAR13-Online and RW-OLHWDB are 89.0% and 94.9%, respectively.

Multi-scale affinities with missing data: Estimation and applications.

Many machine learning algorithms depend on weights that quantify row and column similarities of a data matrix. The choice of weights can dramatically impact the effectiveness of the algorithm. Nonetheless, the problem of choosing weights has arguably not been given enough study. When a data matrix is completely observed, Gaussian kernel affinities can be used to quantify the local similarity between pairs of rows and pairs of columns. Computing weights in the presence of missing data, however, becomes challenging. In this paper, we propose a new method to construct row and column affinities even when data are missing by building off a co-clustering technique. This method takes advantage of solving the optimization problem for multiple pairs of cost parameters and filling in the missing values with increasingly smooth estimates. It exploits the coupled similarity structure among both the rows and columns of a data matrix. We show these affinities can be used to perform tasks such as data imputation, clustering, and matrix completion on graphs.

Towards Semantically-Rich Spatial Network Representation Learning via Automated Feature Topic Pairing.

Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of spatial data for characterization. However, SRL extracts features by internal layers of DNNs, and thus suffers from lacking semantic labels. Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models. How can we teach a SRL model to discover appropriate topic labels in texts and pair learned features with the labels? This paper formulates a new problem: feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO) based deep learning framework. Specifically, we formulate the feature-topic pairing problem into an automated alignment task between 1) a latent embedding feature space and 2) a textual semantic topic space. We decompose the alignment of the two spaces into: 1) point-wise alignment, denoting the correlation between a topic distribution and an embedding vector; 2) pair-wise alignment, denoting the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. We design a PSO based solver to simultaneously select an optimal set of topics and learn corresponding features based on the selected topics. We develop a closed loop algorithm to iterate between 1) minimizing losses of representation reconstruction and feature-topic alignment and 2) searching the best topics. Finally, we present extensive experiments to demonstrate the enhanced performance of our method.

A matheuristic for the robust integrated airline fleet assignment, aircraft routing, and crew pairing problem

We address an integrated airline scheduling problem that combines three airline planning processes: fleet assignment, aircraft routing, and crew pairing. For a given daily flight schedule, the problem requires simultaneously assigning aircraft and crews to each scheduled flight, taking into account aircraft maintenance restrictions and crew work rules. We propose to solve this complex problem of integrated flight planning while taking into account robustness considerations. In this regard, robustness is achieved by restricting tight connections in the schedule and increasing the number of connections where crews follow the aircraft. We formulate the problem using a very large-scale, yet compact, mixed-integer programming model, and we propose a matheuristic consisting of a decomposition approach and a proximity search algorithm. Computational experiments carried out on real instances from a major airline and having up to 14,014 itineraries, 646 flights, and 202 aircraft provide evidence of the proposed approach’s efficacy. In particular, we find that the average deviation from a conservative bound is at most equal to 0.6%.

An exact lexicographic approach for the maximally risk-disjoint/minimal cost path pair problem in telecommunication networks

The paper addresses the lexicographically maximal risk-disjoint/minimal cost path pair problem that aims at finding a pair of paths between two given nodes, which is the shortest (in terms of cost) among those that have the fewest risks in common. This problem is of particular importance in telecommunication network design, namely concerning resilient routing models where both a primary and a backup path have to be calculated to minimize the risk of failure of a connection between origin and terminal nodes, in case of failure along the primary path and where bandwidth routing costs should also be minimized. An exact combinatorial algorithm is proposed for solving this problem which combines a path ranking method and a path labelling algorithm. Also an integer linear programming (ILP) formulation is shown for comparison purposes. After a theoretical justification of the algorithm foundations, this is described and tested, together with the ILP procedure, for a set of reference networks in telecommunications, considering randomly generated risks, associated with Shared Risk Link Groups (SRLGs) and arc costs. Both methods were capable of solving the problem instances in relatively short times and, in general, the proposed algorithm was clearly faster than the ILP formulation excepting for the networks with the greatest dimension and connectivity.

A flexible mathematical model for crew pairing optimization to generate n-day pairings considering the risk of COVID-19: a real case study

PurposeOne of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.Design/methodology/approachOne of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.FindingsThe proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.Originality/valueThe authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

Coalitional game‐based user pairing and power allocation in downlink non‐orthogonal multiple access networks

AbstractThis article studies the user pairing and power allocation in a downlink non‐orthogonal multiple access (NOMA) network in which users form different pairs accessing orthogonal channels and NOMA is utilized in each channel. Existing studies mainly focused on throughput optimization, while throughput cannot completely reflect users' quality of experience. Therefore, we aim to maximize the network's sum of mean opinion scores rather than throughput to meet different traffic demands of users. The mixed integer nonlinear programming problem is decomposed into two subproblems. The first user pairing problem is modeled as a coalition formation game, and a Nash‐stable suboptimal pairing scheme is obtained via proposed coalition formation algorithm. The second power allocation problem is modeled as a knapsack problem, and we obtain the optimal power allocation scheme through dynamic programming with bisection method. These two subproblems are solved alternately until converging to the final resource allocation strategy. Simulation results show that the proposed strategy outperforms existing NOMA resource allocation techniques and achieves near‐optimal performance.

The Device-Object Pairing Problem: Matching IoT Devices with Video Objects in a Multi-Camera Environment.

IoT technologies enable millions of devices to transmit their sensor data to the external world. The device–object pairing problem arises when a group of Internet of Things is concurrently tracked by cameras and sensors. While cameras view these things as visual “objects”, these things which are equipped with “sensing devices” also continuously report their status. The challenge is that when visualizing these things on videos, their status needs to be placed properly on the screen. This requires correctly pairing visual objects with their sensing devices. There are many real-life examples. Recognizing a vehicle in videos does not imply that we can read its pedometer and fuel meter inside. Recognizing a pet on screen does not mean that we can correctly read its necklace data. In more critical ICU environments, visualizing all patients and showing their physiological signals on screen would greatly relieve nurses’ burdens. The barrier behind this is that the camera may see an object but not be able to see its carried device, not to mention its sensor readings. This paper addresses the device–object pairing problem and presents a multi-camera, multi-IoT device system that enables visualizing a group of people together with their wearable devices’ data and demonstrating the ability to recover the missing bounding box.

Eksperimentasi TAPPS dan GI terhadap Pengetahuan dan Sikap Sosial Ditinjau dari Gaya Belajar

<em>Education is faced with multi-dimensional problems, including the low competence of mathematical knowledge and the crisis of social aspects. This study aims to determine the difference in the effect of Thinking Aloud Pair Problem Solving (TAPPS), Group Investigation (GI), and Conventional models on knowledge competence and social attitudes in terms of learning styles. This is a quasi-experimental research, with a population of all eighth-grade students at SMP Negeri 24 Jakarta. Hypothesis testing used two-way multivariate analysis with unequal cells. The results of the research included: (1) TAPPS produced knowledge competence better than GI and conventional, and GI was better than conventional, (2) TAPPS and GI produced the same social attitudes, and both were better than conventional, (3) visual and auditorial resulted in the same good knowledge competence, and both were better than kinesthetic, (4) visual and auditory produced the same good social attitudes, and both were better than kinesthetic, (5) TAPPS and GI, knowledge competence of the three learning styles were equally good, whereas conventionally there were differences, (6) TAPPS and GI, social attitudes of the three types of learning styles were equally good, while conventionally there were differences, (7) in visual and auditory, knowledge competence of the three models were the same, while there were differences in kinesthetic, and (8) in visual and auditory, the social attitudes of the three models were the same, while there were differences in kinesthetic.</em>

Perbedaan Kemampuan Pemecahan Masalah Matematis dan Self-Confidence Siswa antara Model TPS dan PBL

This research is motivated by the low ability to solve mathematical problems. There is also another important aspect, namely self-confidence, which is one of the attitudes that get attention. The learning model that is supposed to be used to improve students' mathematical problem-solving skills and self-confidence is the Think Pair Share and Problem Based Learning cooperative learning model. This study aims to determine the differences in mathematical problem-solving abilities and self-confidence between students who received the TPS and PBL models. The research method is quasi-experimental with the entire population of class X MAN 2 Garut. The research sample consisted of two classes, namely class X MIA-1 as many as 29 students and class X MIA-3 as many as 31 students. The research instrument is a description of the problem-solving ability test and a self-confidence questionnaire. The results of the analysis showed that there was no difference in the achievement and improvement of mathematical problem-solving abilities between students who received the TPS and PBL models. The students' self-confidence interpretation of the TPS and PBL models is in the sufficient category.

Peningkatan Kemampuan Pemahaman Konsep Matematis Siswa dengan Model Think Pair Share dan Problem Based Learning

The ability to understand mathematical concepts is still low. Efforts are needed to overcome these problems, including using the Think Pair Share and Problem Based Learning learning models. The research was conducted to determine the improvement and quality of the improvement in students' mathematical understanding abilities with the two learning models. The research method is quasi-experimental with a population of all seventh-grade students of one of the public junior high schools in Garut. Samples were chosen randomly, namely class VII-G as experimental class I and class VII-H as experimental class II. The instrument is a description test. The results showed that the improvement and quality of the improvement in the ability to understand mathematical concepts of students who received the Think Pair Share learning model was better than Problem Based Learning. Think Pair Share learning model is more recommended to be applied in improving the ability to understand mathematical concepts.

Similar or Different Neuropsychological Profiles? Only Set Shifting Differentiates Women With Bipolar vs. Borderline Personality Disorder.

There is ongoing debate about the similarities and differences between bipolar disorder (BD) and borderline personality disorder (BPD). Very few studies have concurrently assessed their neuropsychological profile and only on a narrow array of neuropsychological tests. We aimed to investigate the differences of these two patient groups on visual memory, executive function, and response inhibition. Twenty-nine BD patients, 27 BPD patients and 22 controls (all female) were directly compared on paired associates learning (PAL), set shifting (ID/ED), problem solving (SOC), and response inhibition (SSRT) using Cambridge Neuropsychological Test Automated Battery (CANTAB). Rank-normalized outcomes were contrasted in one-way ANOVA tests. Discriminant analysis was finally performed to predict BD or BPD patient status. BD patients performed significantly worse than controls on all tasks. BPD patients performed significantly worse than HC on all tests except SST. Significant differences between the two patient groups were recorded only on ID/ED, where BPD patients performed worse (p = 0.044). A forward stepwise discriminant analysis model based on ID/ED and SOC predicted correctly patients' group at 67.9% of cases. In conclusion, BD and BPD female patients appear to be more similar than different as regards their neuropsychological functions. This study is the first to show that BPD patients display more deficits than BD patients when directly compared on the set shifting executive function test, a marker of cognitive flexibility. Discerning BD from BPD patients through neuropsychological performance is promising but would improve by using additional subtler tests and psychometric evaluation.

IMPLEMENTATION OF THINKING ALOUD PAIR PROBLEM SOLVING MODEL WITH MIND MAP TO INCREASE STUDENT’S ACTIVITIES AND THEIR LEARNING OUTCOME

This research was based on the problem that occur in SMAN 1 Lembah Melintang where the method of learning that applied with the usual discussion method lack influence on learning competencies of students in learning biology. Therefore, researches apply giving homework by making mind map task before cooperative learning with Thinking Aloud Pair Problem Solving type that aims to improve student learning competency through activities and learning outcomes at XII grade of SMAN 1 Lembah Melintang. Sample of this study was taken from XII IPA3 class because compared to other class of XII IPA, the activity and learning achievement were lowest. This research was conducted in two cycles in which each cycle consisted of planning, acting, observing and reflecting. Based on the results of the researcher’s observation on student’s learning activities of eight indicators from the first cycle to the second cycle, there was an improvement on the number of student’s activities in the first cycle were in good category and the second cycle, were in very good category. Student’s learning achievement improved from 57,94% in the first cycle into 85,29% in the second cycle. Based on the result of the research, the researcher concluded that the use cooperative learning Thinking Aloud Pair Problem Solving assisted with mind map task, can improve student’s activities and ther learning achievement as well at XII IPA3 class in SMAN 1 Lembah Melintang.