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The application of reduced order models (ROMs) in the aerodynamic/aeroelastic analysis of long-span bridges, unlike the aeronautical structures, has not been extensively studied. ROMs are computationally efficient techniques, which have been widely used for predicting unsteady aerodynamic response of airfoils and wings. This paper aims to discuss the application of a reduced order computational fluid dynamics (CFD) model based on the eigensystem realization algorithm (ERA) in the aeroelastic analysis of the Great Belt Bridge (GBB).

The aerodynamic impulse response of the GBB section is used to construct the aerodynamic ROM, and then the aerodynamic ROM is coupled with the reduced DOF model of the system to construct the aeroelastic ROM. Aerodynamic coefficients and flutter derivatives are evaluated and compared to those of the advanced discrete vortex method-based CFD code.

Results demonstrate reasonable prediction power and high computational efficiency of the technique that can serve for preliminary aeroelastic analysis and design of long-span bridges, optimization and control purposes.

The application of a system identification tool like ERA into the aeroelastic analysis of long-span bridges is performed for the first time in this work. Authors have developed their earlier work on the aerodynamic analysis of long-span bridges, published in the Journal of Bridge Engineering, by coupling the aerodynamic forces with reduced DOF of structural system. The high computational efficiency of the technique enables bridge designers to perform preliminary aeroelastic analysis of long-span bridges in less than a minute.

The purpose of this paper is to introduce a novel approach to solving linear systems arising from applying a Boundary Element Method (BEM) to elasticity problems.

The key idea is based on using wavelet transforms as a tool to change dense and fully populated matrices of BEM systems into sparse matrices. Wavelets are then used again to produce an algorithm to solve the resultant sparse linear systems. The wavelet transformation part of the method can be added as a black box to existing BEM codes.

Numerical results focusing on the sensitivity of the solution for various physical variables to the thresholding parameters, and savings in computer time and memory are presented. The results show that the proposed method is efficient for large problems.

Application of the proposed method is restricted to problems with number of DOF equal to an integer power of 2.

The novel algorithm to solve transformed algebraic linear equations uses NS‐form of the modified matrix, taking the advantage of the hierarchical nature of Multi‐Resolution Analysis (MRA) to decompose a parent system into descendant systems with reduced size. These smaller systems are then solved iteratively using generalized minimal residual method.

The purpose of this paper is to improve the recent models for the evaluation of the efficiency of decision making units (DMUs) comprising a network structure with undesirable intermediate measures and fuzzy data.

In this paper a three-stage network structure model with desirable and undesirable data is presented and is solved as linear triangular fuzzy planning problems.

A new three stage network data envelopment analysis (DEA) model is established to evaluate the efficiency of industries with undesirable and desirable indicators in fuzzy environment.

The implication of this study is to evaluate the furniture services and the chipboard industries of wood lumber as a three-stage process.

In some cases, DMUs include two or multi-stage process (series or parallel) operating with a structure called a network DEA. Also, in the real world problems, the data are often presented imprecisely. Additionally, the intermediate measures under the real-world conditions include desirable and undesirable data. These mentioned indexes show the value of the proposed model.

The aim of this study is to (1) construct a standard framework for assessing the capability of bioenergy enterprises' digital green innovation partners; (2) quantify the choice of partners for digital green innovation by bioenergy enterprises; (3) propose based on a dual combination empowerment niche digital green innovation field model.

Fuzzy set theory is combined into field theory to investigate resource complementarity. The successful application of the model to a real case illustrates how the model can be used to address the problem of digital green innovation partner selection. Finally, the standard framework and digital green innovation field model can be applied to the practical partner selection of bioenergy enterprises.

Digital green innovation technology of superposition of complementarity, mutual trust and resources makes the digital green innovation knowledge from partners to biofuels in the enterprise. The index rating system included eight target layers: digital technology innovation level, bioenergy technology innovation level, bioenergy green level, aggregated digital green innovation resource level, bioenergy technology market development ability, co-operation mutual trust and cooperation aggregation degree.

This study helps to (1) construct the evaluation standard framework of digital green innovation capability based on the dual combination empowerment theory; (2) develop a new digital green innovation domain model for bioenergy enterprises to select digital green innovation partners; (3) assist bioenergy enterprises in implementing digital green innovation practices.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

The purpose of this paper is to determine the criteria weights of outsourcing and their key role in ranking outsourcing suppliers.

A new hybrid multi-criteria decision-making approach merges three tools, namely, decision making trial and evaluation (DEMATLE), fuzzy analytic network process (FANP) and ordinal/cardinal data envelopment analysis (DEA) model. Afterwards, experts’ opinions were gathered from a Pipe and Fittings company. Finally, their opinions were incorporated in three-stage approach for outsourcing suppliers’ selection.

The findings of this study show that among the selective criteria for outsourcing, business development, focus on basic activities and order delays are the three most important criteria. Also, the proposed approach ranks suppliers to facilitate decision making for selection.

The number of suppliers, selection criteria and the number of members of the respondents’ team have been identified as some of the limitations of the present study.

The study has significant and practical implications for the managers and for the organizations which have to choose top suppliers, particularly in the case of dealing with numerous and qualitative/quantitative criteria.

This paper proposed a new three-stage approach that incorporates outputs of previous as inputs of next stage to increasing results accuracy. Also, it showed that by incorporating results of FANP method into DEA model, key role of experts’ opinions as a qualitative and quantitative criteria can be caused by increasing flexibility of decision process.

Drugs are strategic products with essential functions in human health. An optimum design of the pharmaceutical supply chain is critical to avoid economic damage and adverse effects on human health. The vehicle-routing problem, focused on finding the lowest-cost routes with available vehicles and constraints, such as time constraints and road length, is an important aspect of this. In this paper, the vehicle routing problem (VRP) for a pharmaceutical company in Turkey is discussed.

A mixed-integer programming (MIP) model based on the vehicle routing problem with time windows (VRPTW) is presented, aiming to minimize the total route cost with certain constraints. As the model provides an optimum solution for small problem sizes with the GUROBI® solver, for large problem sizes, metaheuristic methods that simulate annealing and adaptive large neighborhood search algorithms are proposed. A real dataset was used to analyze the effectiveness of the metaheuristic algorithms. The proposed simulated annealing (SA) and adaptive large neighborhood search (ALNS) were evaluated and compared against GUROBI® and each other through a set of real problem instances.

The model is solved optimally for a small-sized dataset with exact algorithms; for solving a larger dataset, however, metaheuristic algorithms require significantly lesser time. For the problem addressed in this study, while the metaheuristic algorithms obtained the optimum solution in less than one minute, the solution in the GUROBI® solver was limited to one hour and three hours, and no solution could be obtained in this time interval.

The VRPTW problem presented in this paper is a real-life problem. The vehicle fleet owned by the factory cannot be transported between certain suppliers, which complicates the solution of the problem.

The activities of the oil industry from discovery to distribution of oil products have adverse effects on human and environment. Thus, the companies that are active in this industry should identify and manage their risks. The purpose of this paper is to prioritize the identified risks based on different measures such as cost, occurrence, etc. Then, selecting the most important corrective actions using goal-programming approach is another objective of this study.

To identify the health, safety and environment (HSE) risks, the Fuzzy Delphi method was used. The failure mode and effects analysis (FMEA) and fuzzy Vlse Kriterijumsk Optimizacija Kompromisno Resenje (VIKOR) methods covering the deficits of FMEA were used to rank the HSE risks. Unlike similar researches, in the proposed FMEA–VIKOR method, the risk priority number was not calculated. In addition to severity, occurrence and detection, the parameters such as time, cost and quality, being considered for ranking the risks, were weighted by the Eigenvector method. Then, a fuzzy goal-programming model was developed for determining the best solutions of risk response.

The research findings indicated that the most important risks include fire and blast because of tank and pipeline, leakage of connections and pipelines and industrial waste. Also, the most important risk responses include using and strengthening the alarm and fire extinguishing systems, using fiberglass tanks to prevent pipeline corrosion, using modern technology to have more efficient oil refining.

The main contribution of this paper is using hybrid approach of FMEA–VIKOR for risk ranking by considering different measures such as time, cost and quality besides severity, occurrence and detection. Providing a fuzzy goal-programming framework for determining the main risk responses is another value for this research.

This paper develops a decision-analysis model to prioritize and select the site to establish a new hospital over different indicators such as cost, market conditions, environmental factors, government factors, locations and demographics. In this way, an integrated model is proposed under the intuitionistic fuzzy information (IFI), the standard deviation (SD), the rank-sum (RS) and the measurement of alternatives and ranking using the compromise solution (MARCOS) approach for ranking hospital sites (HSs).

The IF-SD-RS model is presented to obtain the combined weight with the objective and subjective weights of diverse sub-criteria and indicators for ranking sites to establish the hospital. The IF-MARCOS model is discussed to prioritize the various sites to establish the hospital over several crucial indicators and sub-criteria.

The authors implement the developed model on a case study of HSs assessment for the construction of new hospital. In this regard, inclusive set of 6 key indicators and 18 sub-criteria are considered for the evaluation of HSs. This study distinguished that HS (h₂) with combined utility function 0.737 achieves highest rank compared to the other three sites for the given information. Sensitivity analysis is discussed with different parameter values of sub-criteria to examine how changes in weight parameter ratings of the sub-criteria affect the prioritization of the options. Finally, comparative discussion is made with the diverse extant models to show the reasonability of the developed method.

This study aims to develop an original hybrid weighting tool called the IF-SD-RS model with the integration of IF-SD and IF-RS approaches to find the indicators' weights for prioritizing HSs. The developed integrated weighting model provides objective weight by IF-SD and subjective weight with the IF-RS model. The model presented in the paper deals with a consistent multi-attribute decision analysis (MADA) concerning the relations between indicators and sub-criteria for choosing the appropriate options using the developed IF-SD-RS-MARCOS model.