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The purpose of this paper is to focus on the development of a thorough method for the macromechanical analysis of fabrics.

The homogenization method was implemented for the generation of continuum equivalent model for the plain woven structure. Keystone of the method is the mesomechanical analysis of the textile unit cell for the evaluation of the apparent properties and the generation of an equivalent macromechanical model supporting the mechanical performance of the structure. The finite element method (FEM) using beam elements was applied for the mechanical analysis of the discrete model of the unit cell and the FEM using shell elements was applied for the analysis of the continuum macromechanical model.

The tensile, shear and bending test of the unit cell were simulated. The constitutive equations of the continuum model were formed considering equivalent performance with the discrete model.

The reliability of the equivalent model in tensile, shear (in‐plane) and bending (out‐of‐plane) deformation was achieved even for asymmetric woven structures. The low computational power demanded for the meso‐ and macro‐mechanical modelling and analysis is a beneficial feature of the proposed method.

The purpose of this paper is to present a simplified hybrid modeling (HYMOD) approach which overcomes limitations regarding computational cost and permits the simulation and prediction of the nonlinear inelastic behavior of full-scale RC structures.

The proposed HYMOD formulation was integrated in a research software ReConAn FEA and was numerically studied through the use of different numerical implementations. Then the method was used to model a full-scale two-storey RC building, in an attempt to demonstrate its numerical robustness and efficiency.

The numerical results performed demonstrate the advantages of the proposed hybrid numerical simulation for the prediction of the nonlinear ultimate limit state response of RC structures.

A new numerical modeling method based on finite element method is proposed for simulating accurately and with computational efficiency, the mechanical behavior of RC structures. Currently 3D detailed methods are used to model single structural members or small parts of RC structures. The proposed method overcomes the above constraints.

This paper aims to present a framework for deriving analytical and semi‐numerical models for coupled conductive‐convective heat transfer processes in a borehole heat exchanger subjected to general initial and boundary conditions.

The discrete Fourier transform and the spectral element method have been utilized for deriving two spectral models for a single U‐tube borehole heat exchanger in contact with a soil mass.

Verification and numerical examples have shown that the developed models are accurate and computationally very efficient. It is illustrated that one spectral element is capable of producing results which are more accurate than those produced by 200 finite elements.

The gained computational efficiency and accuracy will boost considerably the possibilities for more insight into geothermal analysis, which will improve the procedure for designing competitive energy extraction systems.

The models are capable of calculating exactly the temperature distribution in all involved borehole heat exchanger components and their thermal interactions.

With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.

To address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.

Following the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.

This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.

INSTRUCT is a multi‐user, text retrieval system which was developed as an interactive teaching package for demonstrating modern information retrieval techniques, these including natural language query processing, best match searching and automatic relevance feedback based on probabilistic term weighting. INSTRUCT has recently been extended and now additionally has facilities for query expansion using both relevance and term co‐occurrence data, for cluster‐based searching and for two browsing search strategies. These retrieval mechanisms are used to search a file of 26,280 titles and abstracts from the Library and Information Science Abstracts database; both menu‐based and command‐based searching are allowed.

The purpose of this study is the reduction of computational time demand of discrete element based modeling.

The methodology is the systematic changing of particle size and micromechanical parameters to reduce computational time requirements.

In some cases, the computational demand of discrete simulations can be reduced to about 95 per cent.

Based on the results and demonstrated methodology, the enormous computational time demand of discrete element-based modeling can be reduced significantly.

The purpose of this paper is twofold: first to carry out a comprehensive literature review for state of the art regarding airline schedule planning and second to identify some new research directions that might help academic researchers and practitioners.

The authors mainly focus on the research work appeared in the last three decades. The search process was conducted in database searches using four keywords: “Flight scheduling,” “Fleet assignment,” “Aircraft maintenance routing” (AMR), and “Crew scheduling”. Moreover, the combination of the keywords was used to find the integrated models. Any duplications due to database variety and the articles that were written in non-English language were discarded.

The authors studied 106 research papers and categorized them into five categories. In addition, according to the model features, subcategories were further identified. Moreover, after discussing up-to-date research work, the authors suggested some future directions in order to contribute to the existing literature.

The presented categories and subcategories were based on the model characteristics rather than the model formulation and solution methodology that are commonly used in the literature. One advantage of this classification is that it might help scholars to deeply understand the main variation between the models. On the other hand, identifying future research opportunities should help academic researchers and practitioners to develop new models and improve the performance of the existing models.

This study proposed some considerations in order to enhance the efficiency of the schedule planning process practically, for example, using the dynamic Stackelberg game strategy for market competition in flight scheduling, considering re-fleeting mechanism under heterogeneous fleet for fleet assignment, and considering the stochastic departure and arrival times for AMR.

In the literature, all the review papers focused only on one category of the five categories. Then, this category was classified according to the model formulation and solution methodology. However, in this work, the authors attempted to propose a comprehensive review for all categories for the first time and develop new classifications for each category. The proposed classifications are hence novel and significant.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

Motivated by a problem in the context of DiDi Travel, the biggest taxi hailing platform in China, the purpose of this paper is to propose a novel facility location problem, specifically, the single source capacitated facility location problem with regional demand and time constraints, to help improve overall transportation efficiency and cost.

This study develops a mathematical programming model, considering regional demand and time constraints. A novel two-stage neighborhood search heuristic algorithm is proposed and applied to solve instances based on data sets published by DiDi Travel.

The results of this study show that the model is adequate since new characteristics of demand can be deduced from large vehicle trajectory data sets. The proposed algorithm is effective and efficient on small and medium as well as large instances. The research also solves and presents a real instance in the urban area of Chengdu, China, with up to 30 facilities and demand deduced from 16m taxi trajectory data records covering around 16,000 drivers.

This study examines an offline and single-period case of the problem. It does not consider multi-period or online cases with uncertainties, where decision makers need to dynamically remove out-of-service stations and add other stations to the selected group.

Prior studies have been quite limited. They have not yet considered demand in the form of vehicle trajectory data in facility location problems. This study takes into account new characteristics of demand, regional and time constrained, and proposes a new variant and its solution approach.

The coping of demand oscillation is an important challenge in dynamic transport planning. A reliable request fulfillment must be provided even if the number of incoming requests temporarily climbs over the expected demand and resource scarceness appears. The aim of this paper is to propose an innovative planning approach that enables a transportation fleet to maintain a sufficiently high percentage of timely‐fulfilled customer requests even in demand peak situations.

The effectiveness of the new approach is verified in computational simulation experiments. Quantifications for the system's responsiveness are proposed. Then, the quantified knowledge about the intermediate responsiveness is exploited to adjust the decision model representing the next schedule update task in a rolling horizon re‐planning.

The observed simulation results suggest the suitability of the proposed approach. An adjustment of the plan update model supports the maintenance of a high percentage of timely completed requests during and after the demand peak.

The generic approach presented and evaluated here motivates an adaptation to other more practical problem settings, in order to show its general applicability.

The proposed methodology contributes to the current demand for computational support for increasing the responsiveness of logistic systems.

The original contribution of this paper is the autonomous feedback‐controlled adjustment of decision preferences which enables a rolling horizon re‐planning framework to maintain a stable output performance even if the input oscillates significantly over time.