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This paper develops a platform that can be used to determine how to effectively and efficiently deal with a large number of temporary facilities under a constrained site condition(s). The ultimate goal is to reduce the material handling costs between transformation phases of construction works occurring during the project's development period.

Empirical and deductive research is first adopted to mathematical model dynamic site layout planning using the branch and bond algorithm (B&B). Second, a real-life construction project is examined to illustrate how dynamic site layout planning (using the aforementioned B&B algorithm and a computer software program called LINGO) can reduce the material handling costs. The application of the proposed methodology is then showcased against a case study that utilizes a comparative analysis between the “dynamic” and “statistic” site planning approaches.

By dividing the construction period into different phases, the developed model is shown to be capable of optimizing the material handling costs between the phases of transformation during construction works. Optimal costs are also considered using the site boundary and unit cost for moving construction materials between two facilities. The comparative analysis results illustrate that the B&B algorithm reduces material handling costs by 33%.

The proposed model offers an effective planning algorithm for the site layout and location of temporary facilities. More specifically, it can make a substantial improvement in reducing the travel time and material handling cost between the temporary facilities in the construction sites.

The primary knowledge contribution of this study to the site layout is successfully deal with the unequal area problem of temporary site facilities and incorporates the concept of dynamics site planning into the algorithm.

The purpose of this paper is to present an algorithm for an additive/subtractive rapid pattern manufacturing (RPM) process where thick slabs of material are sequentially stacked and then cut to 3D shapes. Unlike traditional rapid prototyping processes where layer thickness is typically uniform, this process is able to vary the layer thickness in order to most effectively generate feature shapes.

This paper discusses the factors affecting layer thickness decisions and then presents an algorithm to determine layer thicknesses for a given part model. The system is designed to import a computer‐aided design file and use the algorithm to automatically generate the set of layers based on the slab height, material and bonding properties and the process parameters used in the system.

The layer thickness algorithm is implemented and tested using an additive/subtractive manufacturing system developed in the laboratory. The algorithm has proved effective in determining appropriate layer heights for thick slab machining, taking into account a variety of geometries. Several sand casting patterns have been successfully created using the proposed system, which could significantly improve traditional sand casting pattern manufacturing.

The proposed RPM process is a new process presented by the authors, developed for rapid sand castings. The layer thickness algorithm is an original contribution that enables automatic process planning for this new process.

This research study aims to develop regular cylindrical pore network models (RCPNMs) to calculate topology and geometry properties of explosively created fractures along with their resulting hydraulic permeability. The focus of the investigation is to define a method that generates a valid geometric and topologic representation from a computational modelling point of view for explosion-generated fractures in rocks. In particular, extraction of geometries from experimentally validated Eulerian smoothed particle hydrodynamics (ESPH) approach, to avoid restrictions for image-based computational methods.

Three-dimensional stabilized ESPH solution is required to model explosively created fracture networks, and the accuracy of developed ESPH is qualitatively and quantitatively examined against experimental observations for both peak detonation pressures and crack density estimations. SPH simulation domain is segmented to void and solid spaces using a graphical user interface, and the void space of blasted rocks is represented by a regular lattice of spherical pores connected by cylindrical throats. Results produced by the RCPNMs are compared to three pore network extraction algorithms. Thereby, once the accuracy of RCPNMs is confirmed, the absolute permeability of fracture networks is calculated.

The results obtained with RCPNMs method were compared with three pore network extraction algorithms and computational fluid dynamics method, achieving a more computational efficiency regarding to CPU cost and a better geometry and topology relationship identification, in all the cases studied. Furthermore, a reliable topology data that does not have image-based pore network limitations, and the effect of topological disorder on the computed absolute permeability is minor. However, further research is necessary to improve the interpretation of real pore systems for explosively created fracture networks.

Although only laboratory cylindrical rock specimens were tested in the computational examples, the developed approaches are applicable for field scale and complex pore network grids with arbitrary shapes.

It is often desirable to develop an integrated computational method for hydraulic conductivity of explosively created fracture networks which segmentation of fracture networks is not restricted to X-ray images, particularly when topologic and geometric modellings are the crucial parts. This research study provides insight to the reliable computational methods and pore network extraction algorithm selection processes, as well as defining a practical framework for generating reliable topological and geometrical data in a Eulerian SPH setting.

This paper aims to use a derivative of genetic programming to predict the bond strength of glass fiber-reinforced polymer (GFRP) bars in concrete under the effects of design guidelines. In developing bond strength prediction models, this paper prioritized simplicity and meaningfulness over extreme accuracy.

Assessing the bond strength of GFRP bars in concrete is a critical issue in designing and building reinforced concrete structures.

Ultimately, the equation of a linear form of a particular design guideline was suggested as the optimal prediction model. Improvements to the current design guidelines suggested by this model include setting a 1.31 magnification and considering the effects of the three significant parameters of bar diameter (db), minimum cover-to-bar diameter (C/db) and development length to bar diameter (l/db) under an acceptable root mean square error accuracy of around 2 MPa. Furthermore, the model suggests that the original influence parameter of concrete compressive strength (f_c) may be removed from bond strength calculations.

The model suggests that the original influence parameter of concrete compressive strength (f_c) may be removed from bond strength calculations.

This study aims to develop a framework for the optimal selection of construction project portfolios for a construction holding company. The objective is to minimize risks, align the portfolio with the organization’s strategic objectives and maximize portfolio returns and net present value (NPV).

The study develops a multi-objective genetic algorithm approach to optimize the portfolio selection process. The construction company’s portfolio is categorized into four main classes: water projects, building projects, road projects and healthcare projects. A mathematical model is developed, and a genetic algorithm is implemented using MATLAB software. Data from a construction holding company in Iraq, including budget and candidate projects, are used as a case study.

The case study results show that out of the 34 candidate projects, 13 have been recommended for execution. These selected projects span different portfolio classes, such as water, building, road and healthcare projects. The total budget required for executing the selected projects is $64.55m, within the organization’s budget limit. The convergence diagram of the genetic algorithm indicates that the best solutions were achieved around generation 20 and further improved from generation 60 onwards.

The study introduces a specialized framework for project portfolio management in the construction industry, focusing on risk management and strategic alignment. It uses a multi-objective genetic algorithm and risk analysis to minimize risks, increase returns and improve portfolio performance. The case study validates its practical applicability.

This study contributes to project portfolio management by developing a framework specifically tailored for construction holding companies. Integrating a multi-objective genetic algorithm allows for a comprehensive optimization process, taking into account various objectives, including portfolio returns, NPV, risk reduction and strategic alignment. The case study application provides practical insights and validates the effectiveness of the proposed framework in a real-world setting.

Japanese regional banks have actively expanded their overseas business in emerging markets, and this topic is quite important for regional banks that have confronted severe business environments over the decades. An aging population suppresses long-term increases in loan demands, and stagnant economic conditions lead to lowered interest rates in the medium-term. Overseas business is a promising business field for regional banks, but recent developments have not been investigated in detail.

This chapter examines overseas investments using data from regional banks’ financial reports. Our sample comprises 44 regional banks without overseas branches, and a research period from FY2011 to FY2015. We demonstrate different overseas business patterns among regional banks. This investigation uses X-means clustering, which is nonhierarchical, as this method automatically presents an optimal number of clusters, and sorts regional banks into their appropriate clusters.

The X-means clustering method indicates five business patterns among regional banks. This also characterizes respective clusters and demonstrates that medium-sized banks actively develop security investments, which increases overseas business’s contributions to profits. Meanwhile, small banks cannot expand overseas investments, which differ from other banks. These banks must seek other business models to compensate for this decline in their earning power.

Having defined liquidity, the aim is to assess the predictive capacity of its representative variables, so that economic fluctuations may be better understood.

Conceptual variables that are representative of liquidity will be used to formulate the predictions. The results of various machine learning models will be compared, leading to some reflections on the predictive value of the liquidity variables, with a view to defining their selection.

The predictive capacity of the model was also found to vary depending on the source of the liquidity, in so far as the data on liquidity within the private sector contributed more than the data on public sector liquidity to the prediction of economic fluctuations. International liquidity was seen as a more diffuse concept, and the standardization of its definition could be the focus of future studies. A benchmarking process was also performed when applying the state-of-the-art machine learning models.

Better understanding of these variables might help us toward a deeper understanding of the operation of financial markets. Liquidity, one of the key financial market variables, is neither well-defined nor standardized in the existing literature, which calls for further study. Hence, the novelty of an applied study employing modern data science techniques can provide a fresh perspective on financial markets.

因此,就了解經濟週期和經濟發展而言,流動資金是一個極其重要的概念。本研究擬在安全資產的價格預測方面取得進步。安全資產代表了經濟的實際情況,特別是美國的十年期國債。

流動資金的定義上面已說明了; 為進一步了解經濟波動,本研究擬對流動資金代表性變量的預測能力進行評估。

研究使用作為流動資金代表的概念變項去規劃預測。各機器學習模型的結果會作比較,這會帶來對流動資金變量的預測值的深思,而深思的目的是確定其選擇。

只要在私營部門內流動資金的數據比公營部門的流動資金數據、在預測經濟波動方面貢獻更大時,我們發現、模型的預測能力也會依賴流動資金的來源而存在差異。國際流動資金被視為一個晦澀的概念,而它的定義的標準化,或許應是未來學術研究的焦點。當應用最先進的機器學習模型時,標桿分析法的步驟也施行了。

若我們對有關的變量加深認識,我們就可更深入地理解金融市場的運作。流動資金,雖是金融市場中一個極其重要的變量,但在現存的學術文獻裏,不但沒有明確的定義,而且也沒有被標準化; 就此而言,未來的研究或許可在這方面作進一步的探討。因此,本研究為富有新穎思維的應用研究,研究使用了現代數據科學技術,這可為探討金融市場提供一個全新的視角。

This paper presents a combined method of ensemble learning and genetics to rebalance the corporate portfolio. The primary purpose of this paper is to determine the amount of investment in each of the shares of the listed company and the time of purchase, holding or sale of shares to maximize total return and reduce investment risk.

To achieve the goals of the problem, a two-level combined intelligent method, such as a support vector machine, decision tree, network Bayesian, k-nearest neighbors and multilayer perceptron neural network as heterogeneous basic models of ensemble learning in the first level, was applied. Then, the majority vote method (weighted average) in the second stage as the final model of learning was collectively used. Therefore, the data collected from 208 listed companies active in the Tehran stock exchange (http://tsetmc.com) from 2011 to 2015 have been used to teach the data. For testing and analysis, the data of the same companies between 2016 and 2020 have been used.

The results showed that the method of combined ensemble learning and genetics has the highest total stock portfolio yield of 114.12%, with a risk of 0.905%. Also, by examining the rate of return on capital, it was observed that the proposed method has the highest average rate of return on investment of 110.64%. As a result, the proposed method leads to higher returns with lower risk than the purchase and maintenance method for fund managers and companies and predicts market trends.

In the forthcoming research, there were no limitations to obtain research data were easily extracted from the site of Tehran Stock Exchange Technology Management Company and Rahvard Novin software, and simulation was performed in MATLAB software.

In this paper, using combined machine learning methods, companies’ stock prices are predicted and stock portfolio optimization is optimized. As companies and private organizations are trying to increase their rate of return, so they need a way to predict stock prices based on specific indicators. It turned out that this algorithm has the highest stock portfolio return with reasonable investment risk, and therefore, investors, portfolio managers and market timers can be used this method to optimize the stock portfolio.

The homogeneous and heterogeneous two-level hybrid model presented in the research can be used to predict market trends by market timers and fund managers. Also, adjusting the portfolio with this method has a much higher return than the return on buying and holding, and with controlled risk, it increases the security of investors’ capital, and investors invest their capital in the funds more safely. And will achieve their expected returns. As a result, the psychological security gained from using this method for portfolio arrangement will eventually lead to the growth of the capital market.

This paper tries to present the best combination of stock portfolios of active companies of the Tehran Stock Exchange by using the two-level combined intelligent method and genetic algorithm.

The purpose of this paper is to offer empirical evidence on the role of self-esteem and social bonding in explaining citizenship behaviour of students at international university branch campuses (IBCs).

A sample of 400 students from four IBCs in Malaysia was administered in a questionnaire. Data were analysed using SPSS and partial least squares 3.0.

This research demonstrates that students’ self-esteem and social bonds have positive direct effects on customer citizenship behaviour (CCB). Moreover, self-esteem has an indirect effect on CCB via intervening of attachment, commitment and involvement of social bonds.

CCB of IBC students can be explained by self-consistency theory via mediation of social bonds from social bonding theory.

To encourage CCB in IBCs, university management should target students who have high self-esteem, closely tied to parents and lecturers, committed to university, highly involved in co-curricular activities and comply with university regulations.

Greater understanding of students’ citizenship behaviour may help transnational universities to improve relationship marketing strategy and enhance students’ campus experience.