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In this work, some counterexamples are given to refute some results in the paper by Kohli [RAIRO:OR 53 (2019) 1617–1632]. We correct the fault in some of his results.

Metamaterial has become a hotspot in many research fields, including electromagnetism, thermodynamics and mechanics, as it can offers additional design freedom for material to obtain novel properties. Especially for the electromagnetic devices, various interesting electromagnetic properties which cannot be found in nature materials can be realized, such as negative refraction, invisible cloak, etc. Herein, we provide an overview of paper-based metamaterial for microwave application. This work reviews the metamaterial realized on paper substrate, including the fabrication techniques, application fields, as well as the outlook on future directions of the paper-based metamaterial for the readership.

The paper describes the stages of paper production, considers the structure of a paper-making machine. Questions related to the proof and use of the Pontryagin’s maximum principle in the theory of optimal control are considered. Optimal paper web weight control system based on the Pontryagin’s maximum principle is presented. Adaptive learning methods for modeling nonlinear systems represent some of the latest advances in adaptive algorithms and machine learning techniques designed to model and identify nonlinear systems. Real-world problems always involve a certain degree of non-linearity, which makes linear models a suboptimal choice. This article may be of interest to research engineers and practitioners in the study and application of control systems using adaptive regulators. This book serves as an essential resource for researchers, graduate students and doctoral students working in the field of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, and computational intelligence. This book may also be of interest to the industry market and practitioners working with a wide range of nonlinear systems.

The design engineering is one of essential work in modern manufacturing environment. The optimization is principal technique to be used widely for searching the solution. However, primary process of optimization is to know the relation between design input parameters and target output. In this work, an artificial neural network (ANN) approach as an intelligent algorithm is proposed to construct the relation and also provides it in form of mathematic modeling. Even though the ANN modeling is so call a backblock due to difficulty to understand complicated equations, it is simply constructed by automate iteration process. A case of paper helicopter is used as an example of the application. The classical 2k Factorial design is used to provide an experiment plan to create training and testing data. 93 experiments are carried out. The architecture of ANN is set according to lowest Mean square error (MSE) of training and testing procedure. The result of 5-10-1 architecture has shown ability to accurately predict output, landing time, with MSE of 0.012. With such a highly quantitative accuracy of results, the developed model using the neural network approach can be used for finding the suitable input parameters to achieve a desired target output. In this case, the design of dimension (A) Depth of cut wing is 1.3 cm., (B) Length of wing is 12.9 cm., (C) Length of body is 9.0, (D) Width of body is 2.0 cm., and (E) Depth of cut body is 0 cm. yield the lowest area of a paper helicopter that can meet the target landing time, 2.85 + 5% second.

This article introduces the use of PCI software production day draw satellite is projective like production process, analyzes the key technologies in the production, summarizes the Suggestions to improve the quality of results, strengthen quality control method is proposed, for the day draw satellite orthogonal projection as mass production work has carried on the beneficial exploration.

The article deals with the issues and methods of teaching translation and interpretation of scientific and technical papers to engineering students from a linguistic point of view. The authors describe the concept and special features of technical translation as a professional activity compared to different kinds of translation. The paper analyzes stylistic, lexical and phraseological, and grammatical peculiarities of scientific and technical papers. Moreover, the authors suggest the main techniques of translating scientific and technical papers such as direct and indirect translation methods.

The contributions on technology are summarized.

International audience; No abstract available

A total of 14 contributions were presented in the Theory and Modelling sessions at EC-17. One Theory and Modelling paper was included in the ITER ECRH and ECE sessions each. Three papers were in the area of nonlinear physics discussing parametric processes accompanying ECRH. Eight papers were based on the quasi-linear theory of wave heating and current drive. Three of these addressed the application of ECCD for NTM stabilization. Two papers considered scattering of EC waves by edge density fluctuations and related phenomena. In this summary, we briefly describe the highlights of these contributions. Finally, the three papers concerning modelling of various aspects of ECE are reported in the ECE session.

An ever-increasing proportion of the algorithms employed in image processing are being translated into the language of image algebra. The aspects of this algebra of particular interest in electron optics are presented and the advantages of adopting it as a lingua franca are described.

The paper based on the analysis of the working conditions of railway transport shows that, ceteris paribus, an increase in actual durability and mean time before failure of rolling stock units is obtained through maintenance of wheelsets, especially during logistic processes on nonmechanized humps. One of the main causes for the service life decrease of wheelsets is brake damage. To eliminate such brake damage on highways, on-board braking dis-tance regulators mounted on the locomotive are used. In the hump yards, using such systems is not possible, since under marshalling, cars are detached from the locomotive. In this regard, an industrial challenge turns up to design a similar in purpose braking distance regulator for humps through skidding. To solve this problem, we developed a method of controlling the speed of cars to be sorted, which complements the widely used technology of the shoe braking and avoids the formation of one-way sliders. The paper presents a skid adjuster, which excludes brake damage to the rolling stock wheels by the slides on the humps using the shoe braking techniques. The motion equation of the rolling stock equipped with a skid adjuster on the hump tracks is developed. In the result of this study, an equation of car motion in the system of “rolling stock Hughes regulator way” allowing to enter the working elements of Hughes controller to an existing and debugged algorithm of any slides with minimal change it.

For a commodity, in addition to its quality, its external package is also very essential. This paper briefly introduced the intelligent support vector machine (SVM) algorithm for color design of paper packaging. The features were extracted from photos of packages using scale-invariant feature transform (SIFT), and the intelligent algorithm was trained and tested using photos of paper packaging for ceramic products collected at the ceramic crafts market as a sample set. Two paper package schemes designed in this study were used for further test. The SVM algorithm was compared with the back-propagation (BP) algorithm and the convolutional neural network (CNN) algorithm. The results showed that the three intelligent algorithms could evaluate the color design of paper packages, but the SVM algorithm was more accurate than the BP and CNN algorithms in evaluating the imagery of color design, both for the samples collected in the craft market and for the paper packaging scheme designed in this paper.

The parameters of electrical network modes often do not meet the requirements of Russian GOST 32144-2013 and the guidelines of Vietnam. In the actual operating conditions while there is the non-sinusoidal mode in electrical networks voltage and current harmonics are present. Harmonics result in overheating and damage of power transformers since they cause additional active power losses. Additional losses lead to the additional heat release, accelerating the process of insulating paper, transformer oil and magnetic structure deterioration consequently shortening the service life of a power transformer. In this regard there arises a need to develop certain scientific methods that would help demonstrate that low power quality, for instance could lead to a decrease in the electrical equipment service life. Currently we see a development of automated systems for continuous monitoring of power quality indices and mode parameters of electrical networks. These systems could be supplemented by characteristics calculating programs that give out a warning upon detection of the adverse influence of voltage and current harmonics on various electrical equipment of both electric power providers and electric power consumers. A software program presented in the article may be used to predict the influence of voltage and current harmonics on power transformers.

This paper provides a summary of the PIle Soil Analysis (PISA) project, completed in the UK during the period 2013 to 2018. The research led to the development of a new, computationally efficient, one dimensional design model for laterally loaded monopile foundations, particularly for offshore wind turbine support structures. The current form of the design model is applicable to monotonic loading only, but it could form a basis for extensions to cyclic loading. This short paper describes the background to the project, outlining the key research elements completed, as well as the main impacts that have been achieved. A number of publications describing the research in further detail are highlighted.

Sustainable construction contributed to the usage of recycled and waste materials to substitute conventional concrete. This research focuses on prediction of normalized bond strength of cement concrete substituted by large amounts of waste materials and products with strong mechanical properties and sustainability. It also emphases on using analytical model for the prediction of bond strength of the green concrete, so that there is a reduction in the cost of construction, con-serve energy, and it will lead to a reduction of CO2 production from cement industries within reliable limits. In this paper machine learning approach has been used to predict the normalized bond strength of green and sustainable concrete. Machine learning empowers machines to learn from their experiences and data provided. The system analyses the datasets and finds different patterns formed in the given data. Then, based on its learnings the machine can make certain predictions. In civil engineering application, a special computing technique called the Machine learning (ML) is in huge demand. ANN is a soft computing technique that learns from previous situations and adapts without constraints to a new environment. In this work, a ML network model for prediction of normalized bond strength of concrete has been illustrated. Different sets of data based upon several concrete design mixes were taken from technical literature and were fed to the model. The model is then trained for prediction, which are being influenced by several input attributes and were jotted down a linear regression analysis.

The amount of documents increases so fast. Those documents exist not only in a paper based but also in an electronic based. It can be seen from the data sample taken by the SpringerLink publisher in 2010, which showed an increase in the number of digital document collections from 2003 to mid of 2010. Then, how to manage them well becomes an important need. This paper describes a new method in managing documents called as inverted files system. Related with the electronic based document, the inverted files system will closely used in term of its usage to document so that it can be searched over the Internet using the Search Engine. It can improve document search mechanism and document save mechanism.

In the light of more and more urgent hidden fire abnormal detection problem in complex conditions of mine, a method which is used directional drilling technology is put forward. The method can avoid the obstacles in mine, and complete the fire abnormal detection. This paper based on analyzing the trajectory control of directional drilling, measurement while drilling and the characteristic of open branch process, the project of the directional drilling is formulated combination with a complex condition mine, and the detection of fire abnormal is implemented. This method can provide technical support for fire prevention, which also can provide a new way for fire anomaly detection in the similar mine.

This short paper describes the simulation results obtained with the elasto-hysteresis model implemented in the finite element software Herezh++ and compares them with the experimental data of the Roundrobin SMA Modelling benchmark.

This paper proposes a modelling approach for simulating mechanical ventilators for intensive care units (ICUs) The shortage of ventilators during the coronavirus disease 2019 (COVID-19) pandemic has focused attention on their design and performance The proposed modelling approach consists in using the Mathworks® Simulink software tool and the SimScape Fluids (gas) library, so as to use well-established subroutines to simulate all the pneumatic components of typical ventilators for ICUs, such as the pressure reducing valves, pressure relief valves, check valves, tanks, ON\OFF and proportional directional valves, etc The patient is simulated by setting the values of lung compliance and pressure losses occurring in the trachea The proposed modelling approach is used in this paper to simulate a pneumatic scheme employed in some commercial ventilators The model allows a very accurate prediction of fundamental parameters, such as the inspiratory flow rate, the inspiratory pressure, the end-expiratory pressure Since the software interface is user-friendly, it can easily be used by manufacturers to correctly choose the geometrical and operating parameters of the components during the design stage or to assess different scenarios © 2020 The Authors, published by EDP Sciences

This paper deals with the Extended Resource Constrained Project Scheduling Problem (ERCPSP) which is defined by events, nonrenewable resources and precedence constraints between pairs of events. The availability of a resource is depleted and replenished at the occurrence times of a set of events. The decision problem of ERCPSP consists of determining whether an instance has a feasible schedule or not. When there is only one nonrenewable resource, this problem is equivalent to find a feasible schedule that minimizes the number of resource units initially required. It generalizes the maximum cumulative cost problem and the two-machine maximum completion time flow-shop problem. In this paper, we consider this problem with some specific precedence constraints: parallel chains, series-parallel and interval order precedence constraints. For the first two cases, polynomial algorithms based on a linear decomposition of chains are proposed. For the third case, a polynomial algorithm is introduced to solve it. The priority between events is defined using the properties of interval orders.

This paper studies the equality generalized symmetric traveling salesman problem (EGSTSP). A salesman has to visit a predefined set of countries. S/he must determine exactly one city (of a subset of cities) to visit in each country and the sequence of the countries such that s/he minimizes the overall travel cost. From an academic perspective, EGSTSP is very important. It is NP-hard. Its relaxed version TSP is itself NP-hard, and no exact technique solves large difficult instances. From a logistic perspective, EGSTSP has a broad range of applications that vary from sea, air, and train shipping to emergency relief to elections and polling to airlines' scheduling to urban transportation. During the COVID-19 pandemic, the roll-out of vaccines further emphasizes the importance of this problem. Pharmaceutical firms are challenged not only by a viable production schedule but also by a flawless distribution plan especially that some of these vaccines must be stored at extremely low temperatures. This paper proposes an approximate tree-based search technique for EGSTSP. It uses a beam search with low and high level hybridization. The low-level hybridization applies a swap based local search to each partial solution of a node of a tree whereas the high-level hybridization applies 2-Opt, 3-Opt or Lin-Kernighan to the incumbent. Empirical results provide computational evidence that the proposed approach solves large instances with 89 countries and 442 cities in few seconds while matching the best known cost of 8 out of 36 instances and being less than 1.78% away from the best known solution for 27 instances. © The authors. Published by EDP Sciences, ROADEF, SMAI 2021.

Renewable energy represented by wind energy plays an increasingly important role in China’s national energy system. The accurate prediction of wind power generation is of great significance to China’s energy planning and power grid dispatch. However, due to the late development of the wind power industry in China and the lag of power enterprise information, there are little historical data available at present. Therefore, the traditional large sample prediction method is difficult to be applied to the forecasting of wind power generation in China. For this kind of small sample and poor information problem, the grey prediction method can give a good solution. Thus, given the seasonal and long memory characteristics of the seasonal wind power generation, this paper constructs a seasonal discrete grey prediction model based on collaborative optimization. On the one hand, the model is based on moving average filtering algorithm to realize the recognition of seasonal and trend features. On the other hand, based on the optimization of fractional order and initial value, the collaborative optimization of trend and season is realized. To verify the practicability and accuracy of the proposed model, this paper uses the model to predict the quarterly wind power generation of China from 2012Q1 to 2020Q1, and compares the prediction results with the prediction results of the traditional GM(1,1) model, SGM(1,1) model and Holt-Winters model. The results are shown that the proposed model has a strong ability to capture the trend and seasonal fluctuation characteristics of wind power generation. And the long-term forecasts are valid if the existing wind power expansion capacity policy is maintained in the next four years. Based on the forecast of China’s wind power generation from 2021Q2 to 2024Q2 in the future, it is predicted that China’s wind power generation will reach 239.09 TWh in the future, which will be beneficial to the realization of China’s energy-saving and emission reduction targets.

In this paper, we discuss a particular class of Nash games, where the participants of the game (the players) are divided into two groups (leaders and followers) according to their position or influence on the other players. Moreover, we consider the case, when the leaders’ and/or the followers’ game can be described as a potential game. This is a subclass of Nash games that has been introduced by Monderer and Shapley in 1996 and has beneficial properties to reformulate the bilevel Nash game. We develope necessary and sufficient conditions for Nash equilibria and present existence and uniqueness results. Furthermore, we discuss some Examples to illustrate our results. In this paper, we discussed analytical properties for multi-leader follower potential games, that form a subclass of hierarchical Nash games. The application of these theoretical results to various fields of applications are a future research topic. Moreover, they are meant to serve as a starting point for the developement of efficient numerical solution methods for multi-leader-follower games.

This paper addresses the Multi-Skill Resource-Constrained Project Scheduling Problem with Transfer Times (MSRCPSP-TT). A new model has been developed that incorporates the presence of transfer times within the multi-skill RCPSP. The proposed model aims to minimize project’s duration and cost, concurrently. The MSRCPSP-TT is an NP-hard problem; therefore, a Multi-Objective Multi-Agent Optimization Algorithm (MOMAOA) is proposed to acquire feasible schedules. In the proposed algorithm, each agent represents a feasible solution that works with other agents in a grouped environment. The agents evolve due to their social, autonomous, and self-learning behaviors. Moreover, the adjustment of environment helps the evolution of agents as well. Since the MSRCPSP-TT is a multi-objective optimization problem, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used in different procedures of the MOMAOA. Another novelty of this paper is the application of TOPSIS in different procedures of the MOMAOA. These procedures are utilized for: (1) detecting the leader agent in each group, (2) detecting the global best leader agent, and (3) the global social behavior of the MOMAOA. The performance of the MOMAOA has been analyzed by solving several benchmark problems. The results of the MOMAOA have been validated through comparisons with three other meta-heuristics. The parameters of algorithms are determined by the Response Surface Methodology (RSM). The Kruskal–Wallis test is implemented to statistically analyze the efficiency of methods. Computational results reveal that the MOMAOA can beat the other three methods according to several testing metrics. Furthermore, the impact of transfer times on project’s duration and cost has been assessed. The investigations indicate that resource transfer times have significant impact on both objectives of the proposed model.

This paper studies heuristics for the bandwidth reduction of large-scale matrices in serial computations. Bandwidth optimization is a demanding subject for a large number of scientific and engineering applications. A heuristic for bandwidth reduction labels the rows and columns of a given sparse matrix. The algorithm arranges entries with a nonzero coefficient as close to the main diagonal as possible. This paper modifies an ant colony hyper-heuristic approach to generate expert-level heuristics for bandwidth reduction combined with a Hill-Climbing strategy when applied to matrices arising from specific application areas. Specifically, this paper uses low-cost state-of-the-art heuristics for bandwidth reduction in tandem with a Hill-Climbing procedure. The results yielded on a wide-ranging set of standard benchmark matrices showed that the proposed strategy outperformed low-cost state-of-the-art heuristics for bandwidth reduction when applied to matrices with symmetric sparsity patterns.

This paper considers the minimization of makespan in the unrelated parallel batch processing machines scheduling problem with considering non-identical job size and dynamic job ready time. The considered unrelated machines have different capacity and different processing speed. Each machine processes a number of the jobs as a batch at the same time so that the machine’s capacity is not exceeded. The batch processing time and the batch ready time are equal to the largest processing time and the largest ready time of jobs in the same batch, respectively. In this paper, a Mixed Integer Linear Programming (MILP) model, two categories of the heuristic procedures (six heuristics) and a meta-heuristic algorithm are proposed to solve the problem. A lower bound is also presented by relaxing of the original problem to evaluate the quality of the proposed algorithms. The computational experiments show the performance of the proposed algorithms under the considered measures.

Data Envelopment Analysis (DEA) is a popular non-parametric technique for the assessment of efficiency of a set of homogeneous decision making units (DMUs) with the same set of inputs and outputs. In the conventional DEA models, it is assumed that all variables are fully controllable. However, in the real-world applications of DEA, some of the variables are completely uncontrollable or partially controllable. In this paper, we are concerned about partially controllable variables which are called semi-discretionary variables. In DEA models, in the presence of semi-discretionary variables, decision makers have partial control on these variables and the proportional changes are possible to some extent. Previous DEA models with semi-discretionary variables consider a certain level of control on the variables which is fixed and it is given by decision makers or a higher authority. Since this level is usually given by experts, it is possible that in some cases all experts may not come up with an agreement, so in this paper we consider variable instead of fixed level of control on each semi-discretionary variable. In the presence of semi-discretionary variables, the proportional changes in inputs and out-puts may not be feasible and as a result the obtained target value by conventional DEA models is not achievable for an inefficient DMU. Thus, we propose a bi-objective model to evaluate DMUs when modifying a variable to its target value should be managed by decision makers in a voting system. One of the advantages of the proposed model is including decision making conditions directly into a DEA model.

Aiming at the attitude solution accuracy and robustness for small UAVs in complex flight conditions, this paper proposes a dynamic adaptive attitude and heading systems(AHRS) estimator with singular value decomposition Cubature Kalman filter(SVDCKF). Considering the problem of random bias for the low-cost attitude sensor, this paper designs a method that the sensor random bias is used as the state vector to eliminate the effect of the sensor random bias. Due to the non-linearity of small UAVs AHRS model and the non-positive definite phenomenon of the covariance matrix, a nonlinear AHRS filter combined with the Cubature Kalman filter and singular value decomposition is designed to improve the attitude solution accuracy. In addition, when the UAV flies in the different flight conditions, the three-axis acceleration of the attitude sensor will affect the attitude solution. Thus, a dynamic adaptive factor based on adaptive filtering is used to adjust continuously the acceleration noise variance to improve the robustness of the AHRS. The experimental results show that the method and algorithm proposed not only improve the attitude solution accuracy, and satisfy the flight requirements of small UAVs, but also eliminate the influence of the attitude sensor random bias and three-axis acceleration for the attitude solution to improve the proposed algorithm robustness and anti-interference.

To plan a UAV's full-area reconnaissance path under uncertain information conditions, an unsupervised learning neural network based on the genetic algorithm is proposed. Firstly, the environment model, the UAV model and evaluation indexes are presented, and the neural network model for planning the UAV's full-area reconnaissance path is established. Because it is difficult to obtain the training samples for planning the UAV's full-area reconnaissance path, the genetic algorithm is used to optimize the unsupervised learning neural network parameters. Compared with the traditional methods, the evaluation indexes constructed in this paper do not need to specify UAV maneuver rules. The offline learning method proposed in the paper has excellent transfer performances. The simulation results show that the UAV based on the unsupervised learning neural network can plan effective full-area reconnaissance paths in the unknown environments and complete full-area reconnaissance missions.

To address the unmeasured velocity, external disturbance and internal model uncertainty for following the path of an under-actuated ship, the paper presents a sliding mode control method based on the radial basis function(RBF) neural network and the velocity observer. To enhance the RBF performance of approximating the unknown, an arc tangent function was exploited in the RBF neural network to update its weight values. Then, the nonlinear observer was built via the hyperbolic tangent function to deal with the unmeasured velocity of the ship. Furthermore, in order to avoid overshoots when the ship is moving to its way points, the virtual paths of a variable circle based on the turning angle were designed at the joints of the path of the ship to enhance its path following capability. Finally, the simulation results show that the sliding mode controller designed in the paper can force the ship to follow accurately the reference path in case of time-varying disturbances without measured velocity and enhance the path following performance of the ship and the accuracy of the RBF neural network, thus demonstrating its effectiveness.

This paper introduces a multi-factory scheduling problem with heterogeneous factories and parallel machines. This problem, as a major part of supply chain planning, includes the finding of a suitable factory for each job and the scheduling of the assigned jobs at each factory, simultaneously. For the first time, this paper studies multi-objective scheduling in the production network in which each factory has its customers and demands can be satisfied by itself or other factories. In other words, this paper assumes that jobs can transfer from the overloaded machine in the origin factory to the factory, which has fewer workloads by imposing some transportation times. For simultaneous minimization of the sum of the earliness and tardiness of jobs and total completion time, after modeling the scheduling problem as a mixed-integer linear program, the existing multi-objective techniques are analyzed and a new one is applied to our problem. Since this problem is NP-hard, a heuristic algorithm is also proposed to generate a set of Pareto optimal solutions. Also, the algorithms are proposed to improve and cover the Pareto front. Computational experiences of the heuristic algorithm and the output of the model implemented by CPLEX over a set of randomly generated test problems are reported.

Flow shop scheduling is a type of scheduling where sequence follows for each job on a set of machines for processing. In practice, jobs in flow shops can arrive at irregular times, and the no-wait constraint allows the changes in the job order to flexibly manage such irregularity. The flexible flow shop scheduling problems with no-wait have mainly addressed for flow optimization on the shop floor in manufacturing, processing, and allied industries. The scope of this paper is to identify the literature available on permutation and non-permutation flow shop scheduling with no-wait constraint. This paper organizes scheduling problems based on performance measures of variability and shop environments. The extended summary of two/three-machine and m-machine problems has been compiled, including their objectives, algorithms, parametric considerations, and their findings. A systematic appearance of both conceptual and analytical results summarizes various advances of the no-wait constraint. The paper includes independently investigated problems and suggestions for future research directions.

In this paper we deal with a variant of non-convex data envelopment analysis, called free replication hull model and try to obtain their anchor points. This paper uses a variant of super-efficiency model to characterize all extreme efficient decision making units and anchor points of the free replication hull models. A necessary and sufficient conditions for a decision making unit to be anchor point of the production possibility set of the free replication hull models are stated and proved. Since the set of anchor points is a subset of the set of extreme units, a definition of extreme units and a new method for obtaining these units in non-convex technologies are given. To illustrate the applicability of the proposed model, some numerical examples are finally provided.

This paper refers to a recent approach to two-stage DEA called the weak link approach. It underlines the lack of solution uniqueness in this approach to DEA and the fact that in order for the solution to the weak link approach to be unique, the decision maker needs to express a preference on which Pareto solution would be most satisfactory. In this paper, we propose to use a fuzzy set approach called fuzzy bicriterial programming to help the decision maker to express this preference. Fuzzy bicriterial programming is explained and then applied to the weak link approach to the DEA. It is shown that for each candidate (Pareto) solution to the original weak link approach, there exists an expert opinion that can lead to the unequivocal selection of this solution due to the use of the fuzzy approach. The proposal is illustrated with examples.

The paper stresses the importance of making an appropriate specification of inputs and outputs in technical efficiency measurement and provides empirical evidence that this initial step of an efficiency measurement project should not be underestimated. Oriented on a case study of Slovak commercial banks for the period from 2005 to 2016, the paper explores to what extent different input-output specifications affect the comparability or congruence of technical efficiency scores in a banking application produced by four different data envelopment models differing in the efficiency measure and orientation. Building on the long-standing controversy in the banking literature about the most appropriate description of banking production, the paper compares technical efficiency scores for 9 input-output specifications of the intermediation approach, 9 specifications of the production-like approaches and 3 network integrated specifications. All these specifications were empirically applied earlier in the literature. The efficiency scores produced by different input-output specifications and models are confronted by six measures of association or dependence, and their levels are explained in a regression framework. The results attest that the choice of the input-output set is a critical judgemental input to efficiency measurement since there is vast diversity in efficiency scores of input-output sets coming from different approaches but also for input-output sets associated with the same approach. In addition, intermediation input-output specifications tend to produce higher efficiency scores than production-like specifications.

This paper presents an efficient metaheuristic approach for optimizing the generalized ratio problems such as the sum and multiplicative of linear or nonlinear ratio objective function with affine constraints. This paper focuses on the significance of hybrid techniques, which are implemented by using GA and ER-WCA to increase efficiency and robustness for solving linear and nonlinear generalized ratio problems. Initially, GA starts with an initial random population and it is processed by genetic operators. ER-WCA will observe and preserve the GAs fittest chromosome in each cycle and every generation. This Genetic ER-WCA algorithm is provided with better optimal solutions while solving constrained ratio optimization problems. Also, the effectiveness of the proposed genetic ER-WCA algorithm is analyzed while solving the large scale ratio problems. The results and performance of the proposed algorithm ensures a strong optimization and improves the exploitative process when compared to the other existing metaheuristic techniques. Numerical problems and applications are used to test the performance of the convergence and the accuracy of the approached method. The behavior of this Genetic ER-WCA algorithm is compared with those of evolutionary algorithms namely Neural Network Algorithm, Grey Wolf Optimization, Evaporation Rate - Water Cycle Algorithm, Water Cycle Algorithm, Firefly algorithm, Cuckoo search algorithm. The evaluated results show that the proposed algorithm increases the convergence and accuracy more than other existing algorithms.

This work presents a comparative study between different resolutions approaches applied to the problem of power distribution. The main objective is to present a comparison between the various methods of resolution presented in the literature and the most used by the various authors. For this study, ninety papers that address the problem of reconfiguration of power distribution networks were analysed. The main objective is to reduce the real energy losses in the system, while several constraints regarding distribution are satisfied. The most recent papers were analysed, taking into account the approaches presented by the various authors, the formulation of the problem – namely its objective functions and constraints – the initialization methods and the stopping methods, as well as the results obtained. As such, an analysis and categorization of the various problems and approaches is presented, with the main focus being on the analysis and minimization of energy losses in 33-bus systems.

In this paper, we formulate and solve an economic order quantity model with default risk. Our main purpose is to investigate retailer’s optimal replenishment time and credit period for deteriorating items under selling price-dependent demand while maximizing profit per unit time. Here, shortages are allowed and items are partially backlogged for interested customers. We show that optimal replenishment time and credit period not only exist but they are also unique. We solve the proposed problem analytically. An algorithm is presented to derive the optimal solution of the model. The mathematical model is evaluated by numerical examples. We use Mathematica to obtain a global maximum solution to the optimal cycle time and the optimal credit period for the proposed model. A sensitivity analysis with respect to major parameters is performed in order to examine the stability of our model. At the end of the paper, conclusions are drawn and an outlook of possible future directions is depicted.

This paper presents modeling and optimization of batch production based on layout, cutting and project scheduling problems by considering scenario planning. In order to solve the model, a novel genetic algorithm with an improvement procedure based on variable neighborhood search (VNS) is presented. Initially, the model is solved in small sizes using Lingo software and the combined (proposed) genetic algorithm; then the results are compared. Afterwards, the model is solved in large sizes by utilizing the proposed algorithm and simple genetic algorithm. The main findings of this paper show: (1) The suggested algorithm is valid and able to achieve optimal and near-optimal solutions. This conclusion was made after proving the validity of the proposed method by solving a case study by employing the classical method (employing Lingo 11). And when the results were compared with the ones obtained by the proposed algorithm, they were found to be the same in both cases. (2) The combined genetic algorithm is more effective in obtaining optimal boundaries and the solutions close to them in all cases compared to the classical (simple) genetic algorithm. In other words, the main finding of this paper is a combined genetic algorithm to optimize batch production modeling problems, which is more efficient than the methods provided in the literature.

This paper deals with dynamic facility layout problem (DFLP) in a plant which is concerned with determining the best position of machines in the plant during a multi-period planning horizon. The material handling costs and machines rearrangement costs (MRC) are used to determine the best layout. In addition to the positions of machines, the details of transportation such as type of transporters and sequence of transportation operations have a direct effect on material handling costs (MHC). Therefore, it is more realistic to consider the transportation details during DFLP optimization. This paper proposes a new mathematical model to simultaneously determine the best position of machines in each period and to plan the transportation operations. Minimizing sum of MHC and MRC is considered as the objective function. A new hybrid meta-heuristic approach has been developed by combining modified genetic algorithm and cloud-based simulated annealing algorithm to solve the model. Finally, the proposed methodology is compared with two meta-heuristics on a set of test problems.

With the continuous development of UAV technology, the trend of using UAV in the military battlefield is increasingly obvious, but the autonomous air combat capability of UAV needs to be further improved. The air combat maneuvering decision is the key link to realize the UAV autonomous air combat, and the genetic algorithm has good robustness and global searching ability which is suitable for solving large-scale optimization problems. This paper uses an improved genetic algorithm to model UAV air combat maneuvering decisions. Based on engineering application requirements, a typical simulation test scenario is established. The simulation results show that the air combat maneuvering decision model based on reinforcement genetic algorithm in this paper can obtain the correct maneuvering decision sequence and gain a position advantage in combat.

The requirements of power system for the safety of overhead transmission lines are increasing. With the rapid development of mobile robot technology, the inspection of overhead transmission lines by inspection robots has become a research hotspot in recent years. Aiming at the task requirements of transmission line inspection robots and the environmental characteristics of transmission lines, researchers at home and abroad have developed a variety of inspection robots. However, most of these robots have problems such as inability to cross the strain tower, low obstacle crossing efficiency and poor safety. In order to solve the above problems, this paper proposes a new four-arm inspection robot mechanism. The robot can cross the strain tower with two different sets of arms working together. The rectangular frame structure on the walking arm improves the obstacle crossing efficiency of the robot, and the closed hanging mechanism ensures that the robot does not fall from the line. In this paper, the three-dimensional model of the robot is established, and the specific structure and motion parameters are given. Three typical obstacle-crossing modes are planned, and the motion analysis and force simulation analysis of the robot's obstacle-crossing process are carried out. The simulation result shows that the mechanism can efficiently cross the strain towers and common obstacles on the transmission line.

A scale-stretching combined filter is proposed for underwater acoustic mobile communication receivers. The combined filter consists of the sum of signals with different time stretching and shifting. Based on the matched filtering theory, Doppler stretching scales, time shift, and the order of each component of the combined filter are derived. The method proposed in this paper improves the matching degree of the filter with wideband Doppler echo signals with different moving speeds. Simulation results show that the combined filter proposed in this paper can improve the detection probability and synchronization time estimation accuracy of wideband Doppler echo. In addition, the combined filter proposed in this paper has the same calculation amount as the matched filter. This feature makes this filter suitable for the synchronous detection of underwater acoustic mobile communication with real-time operation and low power consumption requirements.