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This study shows a review and brief analysis of the most concepts and models in the supply chain of solar energy. The presented work of this study possesses two parts. In the first section, a brief introduction on supply chain of solar energy is addressed and then, in the second part, a detailed bibliometric analysis is performed on supply chain of solar energy. The bibliometric analysis has been performed as an influential tool for using in scientometrics and reviews, which for this aim, keywords and subject areas are discussed, and a review of problems and solution methods are provided as well. The results show that in terms of the subject area, energy fuels, green sustainable science technology, environmental sciences, environmental engineering, and chemical engineering are the most discussed areas amongst scholars. Also, based on findings, the majority of studies are deterministic approaches, while there is an urgent need to provide robust approaches for tackling uncertain situations. In the end, the conclusion and discussion are provided as the final section of this study.

The next release problem (NRP) refers to implementing the next release of software in the software industry regarding the expected revenues; specifically, constraints like limited budgets indicate that the total cost corresponding to the next software release should be minimized. This paper uses and investigates the comparative performance of nineteen state-of-the-art evolutionary multi-objective algorithms, including NSGA-II, rNSGA-II, NSGA-III, MOEAD, EFRRR, tDEA, KnEA, MOMBIII, SPEA2, RVEA, NNIA, HypE, ANSGA-III, BiGE, GrEA, IDBEA, SPEAR, SPEA2SDE, and MOPSO, that can tackle this problem. The problem was designed to maximize the customer satisfaction and minimize the total required cost. Three indicators, namely hyper-volume (HV), spread, and runtime, were examined to compare the algorithms. Two types of datasets, i.e., classic and realistic data, from small to large scale were also examined to verify the applicability of the results. Overall, NSGA-II exhibited the best CPU run time in all test scales, and, also, the results shows that the HV and spread values of 1st and 2nd best algorithms (NNIA and SPEAR), for which most HV values for NNIA are bigger 0.708 and smaller than 1, while the HV values for SPEAR vary between 0.706 and 0.708. Finally, the conclusion and direction for future works are discussed.

This presented study provides a novel analysis of scholarly literature on constraint handling techniques for single-objective and multi-objective population-based algorithms according to the most relevant journals, keywords, authors, and articles. The paper reviews the main ideas of the most state-of-the-art constraint handling techniques in multi-objective population-based optimization, and then the study addresses the bibliometric analysis in the field. The extracted papers include research articles, reviews, book/book chapters, and conference papers published between 2000 and 2020 for the analysis. The results indicate that the constraint handling techniques for multi-objective optimization have received much less attention compared with single-objective optimization. The most promising algorithms for such optimization were determined to be genetic algorithms, differential evolutionary algorithms, and particle swarm intelligence., 38 pages, 16643 words

Scheduling and planning problems are generally complex, large‐scale, challenging topics, and involve numerous constraints. To discover a real solution for these problems, most real‐world problems must be formulated as discrete or mixed variable optimization problems. In this study, scientometric analysis is used to analyze scientific literature in this field. Here, scientometric mapping technique illustrates who works on what area, and it is attained by cognitive and density maps and network visualization. The chapter presents those research areas which are most inspired by evolutionary computation (EC) in scheduling. Also, the chapter presents a scientometric mapping analysis to identify who works on what in the field of EC in scheduling. This goal is attained by the visualization of different maps and networks. Finally, summary, conclusions, and future research in the field are presented

Multiobjective combinatorial optimization problems appear in a wide range of applications including operations research/management, engineering, biological sciences, and computer science. This work presents a brief analysis of most concepts and studies of solution approaches applied to multiobjective combinatorial optimization problems. A detailed scientometric analysis presents an influential tool for bibliometric analyses that were performed on multiobjective combinatorial optimization problems and the solution approaches data from the Scopus databases. To this end, we address social, keywords, and subject areas by employing two well-known tools: VOSviewer and Mendeley. Finally, the conclusion and discussion are provided with a couple of directions for future researches.

NSGA-II is an evolutionary multi-objective optimization algorithm that has been applied to a wide variety of search and optimization problems since its publication in 2000. This study presents a review and bibliometric analysis of numerous NSGA-II adaptations in addressing scheduling problems. This paper is divided into two parts. The first part discusses the main ideas of scheduling and different evolutionary computation methods for scheduling and provides a review of different scheduling problems, such as production and personnel scheduling. Moreover, a brief comparison of different evolutionary multi-objective optimization algorithms is provided, followed by a summary of state-of-the-art works on the application of NSGA-II in scheduling. The next part presents a detailed bibliometric analysis focusing on NSGA-II for scheduling applications obtained from the Scopus and Web of Science (WoS) databases based on keyword and network analyses that were conducted to identify the most interesting subject fields. Additionally, several criteria are recognized which may advise scholars to find key gaps in the field and develop new approaches in future works. The final sections present a summary and aims for future studies, along with conclusions and a discussion.

This chapter focuses on determining the location of bank branches under competitive conditions considering different levels of customer attraction. Finding an optimum location for bank branches depends on many criteria and such problems are known as NP-hard problems. From marketing point of view, the optimum location of a new branch should be far enough from other branches. Also, the sum of distances between all customers and the new branch should be minimized. In this chapter, Multiobjective Charged System Search is applied for finding the optimum location of bank branches with conflicting purposes. The algorithm has capability of finding the optimum location for new branches under competitive conditions with conflicting purposes. To fulfill this purpose, a part of Tabriz city is selected for implementation. The results of the proposed optimization algorithm are compared with other well-known algorithms. The evaluation reveals that the presented algorithm is able to achieve many of the solutions on the Pareto Front; furthermore, the distribution of the solutions on the Pareto Front and their convergence has been better than Multiobjective Particle Swarm Optimization and Nondominated Sorting Genetic Algorithm II.

Almost all real-world and engineering problems involve multi-objective optimization of some sort that is often constrained. To solve these constrained multi-objective optimization problems, constrained multi-objective optimization evolutionary algorithms (CMOEAs) are enlisted. These CMOEAs require specific constraint handling techniques. This study aims to address and test the most successful constraint handling techniques, seven different penalty constraint techniques, as applied to the Non-dominated Sorting Genetic Algorithm II (NSGA-II). In this paper, NSGA-II is chosen because of its high popularity amongst evolutionary algorithms. Inverted Generational Distance and Hypervolume are the main metrics that are discussed to compare the constraint handling techniques. NSGA-II is applied on 13 constrained multi-objective problems known as CF1-CF10, C1-DTLZ1, C2-DTLZ2, and C3-DTLZ4. The result of IGD and HV values are compared and the feasibility proportions of each combination on each problem are shown. The results of simulation present interesting findings that have been presented at the end of paper as discussion and conclusion.

The Novel coronavirus (COVID-19) has distributed to more than 200 territory worldwide leading to about 24 million confirmed cases as of August 25, 2020. Several models have been released that forecast the outbreak globally. This work presents a review of the most important forecasting models against COVID-19 and shows a short analysis of each one. The work presented in this study possesses two parts. A detailed scientometric analysis was done in the first section that provides an influential tool for describing bibliometric analyses. The analysis was performed on data corresponding to COVID-19 using the Scopus and Web of Science databases. For analysis, keywords and subject areas were addressed while classification of forecasting models, criteria evaluation and comparison of solution approaches were done in the second section of the work. Conclusion and discussion are provided as the final sections of this study.

In this chapter, we describe the ant colony optimization (ACO) algorithm and other advanced algorithms that are derived from the basic approach. ACO is applied to solve NP‐hard problems with combinatorial nature, and this algorithm is classified in soft computing branch. In the first section, we articulate the history of ACO and characteristics of ACO are described, and then the inspiration of the ant's nature along with double bridge experiment is explained. In the second section, we introduce ACO as a meta‐heuristic, and the mechanism and steps of the algorithm are then described. In the third section, the newest versions of ACO are introduced and described. We categorize all versions into two classes comprising approaches with different pheromone trail updating strategy, and hybrid approaches. In the fourth section, we introduce some multi‐objective ACO in literature, and lastly, the new application of bi‐objective ACO for surgical case scheduling (SCS) problem is described. In the last section, we suggest the meta‐heuristic ant system (AS) to tackle the combinatorial nature of bi‐objective SCS problem. Therefore, we give a terse description of basic ACO and equations of some advanced ACO in literature.

The novel Coronavirus disease, known as COVID-19, is an outbreak that started in Wuhan, one of the Central Chinese cities. In this report, a short analysis focusing on Australia, Italy, and the United Kingdom has been conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia as compared with Italy and the United Kingdom, and the outbreak in different Australian cities. Mathematical approaches based on the susceptible, infected, and recovered case (SIR) and susceptible, exposed, infected, and recovered (SEIR) models were proposed to predict the epidemiology in the countries. Since the performance of the classic form of SIR and SEIR depends on parameter settings, some optimization algorithms, namely, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), L-BFGS-B, and Nelder-Mead are proposed to optimize the parameters of SIR and SEIR models and improve its predictive capabilities. The results of optimized SIR and SEIR models are compared with the Prophet algorithm and logistic function as two known ML algorithms. The results show that different algorithms display different behaviours in different countries. However, the improved version of the SIR and SEIR models have a better performance compared with other mentioned algorithms described in this study. Moreover, the Prophet algorithm works better for Italy and the United Kingdom cases than for Australian cases and Logistic function compared with Prophet algorithm has a better performance in these cases. It seems that Prophet algorithm is suitable for data with increasing trend in pandemic situations. Optimization of the SIR and SEIR models parameters has yielded a significant improvement in the prediction accuracy of the models. Although there are several algorithms for prediction of this Pandemic, there is no certain algorithm that would be the best one for all cases.

© 2020, CIMNE, Barcelona, Spain. Many optimization problems encountered in the real-world have more than two objectives. To address such optimization problems, a number of evolutionary many-objective optimization algorithms were developed recently. In this paper, we tested 18 evolutionary many-objective algorithms against well-known combinatorial optimization problems, including knapsack problem (MOKP), traveling salesman problem (MOTSP), and quadratic assignment problem (mQAP), all up to 10 objectives. Results show that some of the dominance and reference-based algorithms such as non-dominated sort genetic algorithm (NSGA-III), strength Pareto-based evolutionary algorithm based on reference direction (SPEA/R), and Grid-based evolutionary algorithm (GrEA) are promising algorithms to tackle MOKP and MOTSP with 5 and 10 while increasing the number of objectives. Also, the dominance-based algorithms such as MaOEA-DDFC as well as the indicator-based algorithms such as HypE are promising to solve mQAP with 5 and 10 objectives. In contrast, decomposition based algorithms present the best on almost problems at saving time. For example, t-DEA displayed superior performance on MOTSP for up to 10 objectives.

© 2020, CIMNE, Barcelona, Spain. A comprehensive review and analysis of operating room (OR) theatre scheduling problems as well as a comparison of solution approaches along with suggestions for future studies are presented in this work. A detailed scientometric analysis was performed, which is a powerful tool for conducting bibliometric analyses and comprehensive reviews. OR scheduling problems were categorized into three decision levels, including strategical, tactical, and operational levels. Since optimization of OR problems is an NP-hard optimization problem, we evaluated research studies that employed different mathematical and metaheuristic methods to address OR optimization problems. The comprehensive review presented in this work is divided into two sections. The first section is focused on mathematical modeling, including deterministic and uncertainty modeling, and the second section is focused on solution approaches. The latter section reviews single and multi-objective solution methods. An additional section of this paper is focused on application software that are developed to address the previously mentioned problems. The final section of the paper presents conclusions of this work.

The purpose of this paper is to obtain an optimal DEA efficiency score simultaneously with facility location pattern for two-stage supply chain since there is a lack in the literature of multi-objective facility location and facility efficiency in supply chain network design. First network DEA has been introduced as a possible model to solve this problem. Secondly, we have presented a solution approach based on Benders decomposition algorithm (BDA) to deal with the large-scale size and last compare the result of the solution found via BDA with the result found from original problem via CPLEX. From the solution approach perspective, results of Benders decomposition algorithm depict that this algorithm even solving this problem for large-scale possesses better performance than CPLEX when the original problem is solved. Applicability and originality of the model have been explored with a real case study which validates the solution approach as well.

There are different definitions of big data, and among them, the most common definition refers to three or five characteristics, called volume, velocity, variety, value, and veracity from (Laney (2001)). Volume could include Tera Byte, Peta Byte, Exa Byte, and Zetta Byte. Velocity describes how fast the data are retrieved and processed ‘‘Batch or streaming”. Variety describes structured, semi-structured, and unstructured data (Laney, 2001, Zikopoulos and Eaton, 2011). Veracity explains the integrity and disorderliness of data, while value refers to how good is the “value” we derive from analyzing data? (Zicari et al., 2016). Electrical power systems are networks of components arrayed to supply, transfer, and use electric power. In power system since models are used to predict and characterize operations. However, there is a necessity for powerful optimization algorithms for information processing to learn models as the size increase of data is becoming a global problem to solve large-scale optimization problems. Any optimization problem includes a real function to be maximized or minimized by systematically determination of input values from an allowed set of values. Richness and quantity of large data sets provide the potential to enhance statistical learning performance but require smart models that use the latent low-dimensional structure for effective 2 data separation. This chapter reviews the most recent scientific articles related to large and big data optimization in power systems. Optimization issues such as logistics in power systems and techniques including nonsmooth, nonconvex, and unconstrained large-scale optimization are presented. After a brief review of big data, scientometric analysis has been applied using keywords of “big data” and “power system.” Besides, keywords analysis, network visualization, journal map, and bibliographic coupling analysis have been done to draw a path on big data works in power system problems. Also, the most common useful techniques in large-scale optimization in power system have been reviewed. At the end of this chapter, metaheuristic techniques in big data optimization are reviewed to show that many efforts have been involved in big data optimization in power system and systematically highlight some perspectives on big data optimization.

Electronic apical foramen locators are now widely used to determine working length. This study was designed to determine whether tooth length influenced the accuracy of the Root ZX device.Forty extracted maxillary canine teeth with a length range of 27-29 mm were selected. Access cavities were prepared and coronal flaring of canals performed. The teeth were mounted in self-polymerizing acrylic resin to facilitate horizontal sectioning except for the apical 3-4-mm portion of the root and embedded in alginate as the electronic medium. Electronic measurements were taken at the major foramen, 'zero' reading using the Root ZX and compared with the actual root canal length. The teeth were sectioned 3 mm from the coronal reference point to create a second group with shorter length; these reductions in the length continued six times in all to create seven groups of 40 specimens each. The actual and electronic lengths of specimens in each group were measured. Data were analyzed by Pearson's correlation coefficient.Identical measurements between the actual and electronic root canal length from the longest to the shortest groups were 12.5%, 10.0%, 20.0%, 27.5%, 37.5%, 35.0% and 45.0%, respectively. There was a mild negative correlation between the precise measurements of the Root ZX and root canal lengths in the seven groups (r = -0.964, p0.001).Under the conditions of the study, the Root ZX device was more accurate in shorter teeth compared to longer ones.

The aim of this paper is the scientometric analysis of articles on “scheduling in renewable energy” that have been published between 2000 and 2017. For this purpose, the terms “scheduling in renewable energy” and “unit commitment in renewable energy” have been searched. We have found 1009 articles in the Scopus database which include the terms mentioned above. VOSviewer and CiteSpace, as two well-known the scientometric analysis tools, have been used. Keywords analysis and citation analysis are two analysis methods that have been used in this paper. China and the US are two countries with a high contribution to this topic among other countries. Two methods of counting, including full counting and fractional counting, have been applied. Citation analysis based on countries, document, source, author, and organization have been addressed, and results have been presented. Co-authorship and co-citation analysis also have been shown. IEEE Transactions journals have the most activity in published articles on the topic, while Energy and Engineering are two subjects with the most documents. The remaining part of the paper has focused on citation analysis and co-citation analysis based on authors, documents, sources, countries, organizations, and references.

The objective of this article is an evaluation and assessment efficiency of the poultry meat farm as a case study with the new method. As it is clear poultry farm industry is one of the most important sub- sectors in comparison to other ones. The purpose of this study is the prediction and assessment efficiency of poultry farms as decision making units (DMUs). Although, several methods have been proposed for solving this problem, the authors strongly need a methodology to discriminate performance powerfully. Their methodology is comprised of data envelopment analysis and some data mining techniques same as artificial neural network (ANN), decision tree (DT), and cluster analysis (CA). As a case study, data for the analysis were collected from 22 poultry companies in Iran. Moreover, due to a small data set and because of the fact that the authors must use large data set for applying data mining techniques, they employed k-fold cross validation method to validate the authors’ model. After assessing efficiency for each DMU and clustering them, followed by applied model and after presenting decision rules, results in precise and accurate optimizing technique.

In the present study, the problem of non-linear model arising in heat transfer through the porous fin in a natural convection environment is presented and the homotopy perturbation method is employed to obtain an approximate solution, which admits a remarkable accuracy.

A parental history of cardiovascular disease has a strong relationship with risk factor clusters in the offspring. This study was performed to identify major cardiovascular risk factors in middle school-aged children and their parents in both high and low-risk families.A school-based, cross-sectional study.The middle schools of the 6th district of Tehran were divided randomly into two groups. A total of 169 high-risk children with their families were recruited from the first group and 105 low-risk children with their families were recruited from the second group of schools. Anthropometric and metabolic measurements were performed.The means of the waist circumference and waist-to-hip ratio were significantly higher in high-risk fathers. The means of total and low-density lipoprotein (LDL) cholesterol were significantly higher in both parents and children of the high-risk group. The means of the fasting plasma glucose were significantly higher in fathers and offspring of high-risk families. More fathers in high-risk families were smokers. The prevalence of increased total cholesterol, LDL-cholesterol and hyperglycemia (or = 100 mg/dl) were higher in high-risk parents and children. The prevalence of increased body mass index (or = 25 kg/m for parents and 85th percentile for children) was higher in fathers and children of high-risk families.Cardiovascular risk factors are more prevalent and clustered in high-risk families. The screening of high-risk families is essential to prevent the progression of atherosclerosis from childhood and reduce the burden of cardiovascular disease in adulthood.

There is a large armamentarium of pain-reducing interventions and analgesic choices available to anesthesiologists, but oligoanalgesia continues to be a large problem. We studied the attitudes of residents and faculty members of anesthesiology towards different domains of pain medicine.anonymous questionnaires were mailed to 68 professionals containing demographic and personal data plus 40 items in 10 domains: control, emotion, disability, solicitude, cure, opioids, harm, practice settings, training, and barriers. Internal consistency was 0.70 and the test-retest reliability was 0.80.With 81% response rate, we observed desirable beliefs towards all domains except moderately undesirable beliefs towards the domain solicitude. Scores of residents and faculties were not significantly different.Continuing education programs on both the international guidelines, routine professional education, are needed to improve attitudes towards pain control.

Rapid unmanaged growth of population and properties in metropolitan areas has increased the societal vulnerability to disasters. While the first step in disaster economic risk management is the quantification of economic losses, modeling unexpected events by equilibrium-oriented economic models which assume gradual and incremental changes over time and uniform spatial impacts poses analytical challenges. In this paper an integrated, operational methodology for evaluating the effects of earthquake on the economy based on the various types of losses and their relations is developed. A case study of Tehran shows future losses caused by studied earthquake scenarios will be more than 70% of gross regional product (GRP). Finally, a number of solutions for reducing negative impacts are introduced.

Data envelopment analysis (DEA) is a mathematical programming based method to measure empirically the efficiency and productivity of operating units using multiple inputs to secure multiple outputs. Typically the inputs and the output are incommensurate. In large data set, discussion regarding the forecast and output calculating of decision making units to measure their efficiency is important task specially. In this paper, one new hybrid method of two old forecasting models (fuzzy time series and recurrent neural network), that about data envelopment analysis has been considered, is used in order to get more accurate results than using each of methods individually. In the end of paper, each of methods (fuzzy time series, recurrent neural network, and hybrid method) on large data set of decision making units has been used and the results have been compared to each other.

Recurrent neural network (RNN) is an efficient tool not only for modeling production control process but also for modeling services. In this paper the combination model of RNN, regression and stepwise regression analysis (SRA) were employed in order to predict the variables of process control chart. Therefore, one maintenance process in workshop of Esfahan Oil Refining Co. (EORC) was taken for illustration of hybrid model. First, the most important factors on forecasting response time as inputs were selected according to SRA. Then, the regression was made for predicting the response time of process based upon obtained inputs, and then the error between actual and predicted response time as output along with input were used in RNN. Finally, according to predicted data from combined model, it is scrutinized for test values in statistical process control whether forecasting efficiency is acceptable. Meanwhile, design of experiments (DOE) was set so as to optimize the RNN in training process of it.

Objective Laryngoscopy and tracheal intubation may cause significant cerebral and systemic hemodynamic responses. Many drugs have been shown to be effective in modifying these hemodynamic responses, including fentanyl, sufentanil, alfentanil and remifentanil. The purpose of the current study was to compare the efficacy of fentanyl, sufentanil, alfentanil and remifentanil on blunting cardiovascular changes during laryngoscopy and intubation in children. Methods Eighty children, 1–6 years old, classified as American Society of Anesthesiologists physical status I and II who were scheduled for elective surgery with general anesthesia and orotracheal intubation, were enrolled in this randomized and double-blinded study. Patients were randomly assigned into four groups of 20 patients. Group F received fentanyl 1µg/kg−1, group S received sufentanil 0.1 µg/kg−1, group A received alfentanil 10 µg/kg−1 and group R received remifentanil 1 µg/kg−1 intravenously. After establishment of neuromuscular blockade confirmed with a nerve stimulator, laryngoscopy and orotracheal intubation were performed 3 min after induction. Hemodynamic variables including systolic and diastolic blood pressure (SAP, DAP) and heart rate (HR) were recorded at base line (before opioid administration), before laryngoscopy and one minute after orotracheal intubation. Findings The patients' characteristics and laryngoscopy grade were similar in all groups. There was no significant difference in the mean values of SAP, DAP and HR at each measured time between the four groups. There was significant difference in the mean values of SAP, DAP and HR measured over time in each group. Conclusion The intravenous fentanyl attenuated laryngoscopy-induced SAP, DAP and HR increases better than sufentanil, alfentanil or remifentanil and hemodynamic stability is better preserved with fentanyl.

Priming is a well-known method to accelerate the onset of action of nondepolarizing neuromuscular-blocking agents. It consists of administration of a small dose of neuromuscular blocking agent several minutes before the principal dose is given. Ephedrine has been shown to improve the intubating conditions of rocuronium following its priming with a small dose. However, the potential effects of ephedrine on intubating conditions using atracurium after its priming with a small dose have not yet been studied. Since rocuronium is not available in Iran, atracurium is widely used as an alternative.We studied two groups of patients, each group consisting of 32 patients. One group received ephedrine after priming (PE) and the other received placebo following priming (P). There were no significant differences between the two groups in age, sex, physical status (assessed using the American Society of Anesthesiologists classification), baseline mean arterial pressure and baseline heart rate. Patients with anticipated difficult airway, hypertension, obesity, any evidence of neuromuscular, cardiovascular, respiratory, hepatic, or renal disease, as well as those taking medications known to interact with atracurium or ephedrine were excluded. The anesthesiologist, the physician responsible for recording the results, and the statistician interpreting them were blinded to group allocations. Intubating conditions were graded according to Cooper's criteria. A clinically acceptable outcome was defined as good or excellent intubating conditions, represented by overall scores of 6-7 and 8-9, respectively.Intubating conditions were clinically acceptable in 22 patients in the PE group and 15 patients in the P group (p = 0.13). Vocal cord position and jaw relaxation scores during intubation and response to intubation did not differ significantly between the two groups. The mean arterial pressure showed significant differences over the course of time between the two groups (p0.01). Heart rate exceeded 120 bpm more frequently in the PE than in the P group (p0.01).The effects of ephedrine on improving intubating conditions following atracurium priming were not statistically significant. Given the risk of exacerbation of coronary ischemia by tachycardia, and the negative results on intubating conditions in our study, we cannot recommend the use of ephedrine for improving intubating conditions following priming with atracurium.