Mohammad Mahdi Ershadi and Mohamad Sajad Ershadi
Appropriate logistic planning for the pharmaceutical supply chain can significantly improve many financial and performance aspects. To this aim, a multi-objective optimization…
Abstract
Purpose
Appropriate logistic planning for the pharmaceutical supply chain can significantly improve many financial and performance aspects. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of pharmaceuticals, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified requests.
Design/methodology/approach
The main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized requests for pharmaceuticals in the network. Besides, the total transportation activities of different types of vehicles and related costs are considered as other objectives. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize the second objective function while maintaining the optimality of the first objective function. The third objective function is optimized based on the optimality of other objective functions, as well. A non-dominated sorting genetic algorithm II-multi-objective particle swarm optimization heuristic method is designed for this aim.
Findings
The performances of the proposed model were analyzed in different cases and its results for different problems were shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.
Practical implications
The proposed methodology can be applied to find the best logistic plan in real situations.
Originality/value
In this paper, the authors have tried to use a multi-objective optimization model to guide and correct the pharmaceutical supply chain to deal with the related requests. This is important because it can help managers to improve their plans.
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M.M. Ershadi, M.J. Ershadi and S.T.A. Niaki
Healthcare failure mode and effect analysis (HFMEA) identifies potential risks and defines preventive actions to reduce the effects of risks. In addition, a discrete event…
Abstract
Purpose
Healthcare failure mode and effect analysis (HFMEA) identifies potential risks and defines preventive actions to reduce the effects of risks. In addition, a discrete event simulation (DES) could evaluate the effects of every improvement scenario. Consequently, a proposed integrated HFMEA-DES model is presented for quality improvement in a general hospital.
Design/methodology/approach
In the proposed model, HFMEA is implemented first. As any risk in the hospital is important and that there are many departments and different related risks, all defined risk factors are evaluated using the risk priority number (RPN) for which related corrective actions are defined based on experts' knowledge. Then, a DES model is designed to determine the effects of selected actions before implementation.
Findings
Results show that the proposed model not only supports different steps of HFMEA but also is highly in accordance with the determination of real priorities of the risk factors. It predicts the effects of corrective actions before implementation and helps hospital managers to improve performances.
Practical implications
This research is based on a case study in a well-known general hospital in Iran.
Originality/value
This study takes the advantages of an integrated HFMEA-DES model in supporting the limitation of HFMEA in a general hospital with a large number of beds and patients. The case study proves the effectiveness of the proposed approach for improving the performances of the hospital resources.
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Mohammad Mahdi Ershadi and Hossein Shams Shemirani
Proper planning for the response phase of humanitarian relief can significantly prevent many financial and human losses. To this aim, a multi-objective optimization model is…
Abstract
Purpose
Proper planning for the response phase of humanitarian relief can significantly prevent many financial and human losses. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of injured people, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified injured people.
Design/methodology/approach
The main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized injured people in the network. Besides, the total transportation activities of different types of vehicles are considered as another objective function. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize transportation activities as the second objective function while maintaining the optimality of the first objective function.
Findings
The performances of the proposed model were analyzed in different cases and its robust approach for different problems was shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.
Practical implications
The proposed methodology can be applied to find the best response plan for all crises.
Originality/value
In this paper, we have tried to use a multi-objective optimization model to guide and correct response programs to deal with the occurred crisis. This is important because it can help emergency managers to improve their plans.
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Hamid Hassani, Azadeh Mohebi, M.J. Ershadi and Ammar Jalalimanesh
The purpose of this research is to provide a framework in which new data quality dimensions are defined. The new dimensions provide new metrics for the assessment of lecture video…
Abstract
Purpose
The purpose of this research is to provide a framework in which new data quality dimensions are defined. The new dimensions provide new metrics for the assessment of lecture video indexing. As lecture video indexing involves various steps, the proposed framework containing new dimensions, introduces new integrated approach for evaluating an indexing method or algorithm from the beginning to the end.
Design/methodology/approach
The emphasis in this study is on the fifth step of design science research methodology (DSRM), known as evaluation. That is, the methods that are developed in the field of lecture video indexing as an artifact, should be evaluated from different aspects. In this research, nine dimensions of data quality including accuracy, value-added, relevancy, completeness, appropriate amount of data, concise, consistency, interpretability and accessibility have been redefined based on previous studies and nominal group technique (NGT).
Findings
The proposed dimensions are implemented as new metrics to evaluate a newly developed lecture video indexing algorithm, LVTIA and numerical values have been obtained based on the proposed definitions for each dimension. In addition, the new dimensions are compared with each other in terms of various aspects. The comparison shows that each dimension that is used for assessing lecture video indexing, is able to reflect a different weakness or strength of an indexing method or algorithm.
Originality/value
Despite development of different methods for indexing lecture videos, the issue of data quality and its various dimensions have not been studied. Since data with low quality can affect the process of scientific lecture video indexing, the issue of data quality in this process requires special attention.
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Masoumeh Nabizadeh, Mohammad Khalilzadeh, Sadoullah Ebrahimnejad and Mohammad Javad Ershadi
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Zeinab Rahimi Rise and Mohammad Mahdi Ershadi
This paper aims to analyze the socioeconomic impacts of infectious diseases based on uncertain behaviors of social and effective subsystems in the countries. The economic impacts…
Abstract
Purpose
This paper aims to analyze the socioeconomic impacts of infectious diseases based on uncertain behaviors of social and effective subsystems in the countries. The economic impacts of infectious diseases in comparison with predicted gross domestic product (GDP) in future years could be beneficial for this aim along with predicted social impacts of infectious diseases in countries.
Design/methodology/approach
The proposed uncertain SEIAR (susceptible, exposed, infectious, asymptomatic and removed) model evaluates the impacts of variables on different trends using scenario base analysis. This model considers different subsystems including healthcare systems, transportation, contacts and capacities of food and pharmaceutical networks for sensitivity analysis. Besides, an adaptive neuro-fuzzy inference system (ANFIS) is designed to predict the GDP of countries and determine the economic impacts of infectious diseases. These proposed models can predict the future socioeconomic trends of infectious diseases in each country based on the available information to guide the decisions of government planners and policymakers.
Findings
The proposed uncertain SEIAR model predicts social impacts according to uncertain parameters and different coefficients appropriate to the scenarios. It analyzes the sensitivity and the effects of various parameters. A case study is designed in this paper about COVID-19 in a country. Its results show that the effect of transportation on COVID-19 is most sensitive and the contacts have a significant effect on infection. Besides, the future annual costs of COVID-19 are evaluated in different situations. Private transportation, contact behaviors and public transportation have significant impacts on infection, especially in the determined case study, due to its circumstance. Therefore, it is necessary to consider changes in society using flexible behaviors and laws based on the latest status in facing the COVID-19 epidemic.
Practical implications
The proposed methods can be applied to conduct infectious diseases impacts analysis.
Originality/value
In this paper, a proposed uncertain SEIAR system dynamics model, related sensitivity analysis and ANFIS model are utilized to support different programs regarding policymaking and economic issues to face infectious diseases. The results could support the analysis of sensitivities, policies and economic activities.
Highlights:
A new system dynamics model is proposed in this paper based on an uncertain SEIAR model (Susceptible, Exposed, Infectious, Asymptomatic, and Removed) to model population behaviors;
Different subsystems including healthcare systems, transportation, contacts, and capacities of food and pharmaceutical networks are defined in the proposed system dynamics model to find related sensitivities;
Different scenarios are analyzed using the proposed system dynamics model to predict the effects of policies and related costs. The results guide lawmakers and governments' actions for future years;
An adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the gross domestic product (GDP) in future years and analyze effects of COVID-19 based on them;
A real case study is considered to evaluate the performances of the proposed models.
A new system dynamics model is proposed in this paper based on an uncertain SEIAR model (Susceptible, Exposed, Infectious, Asymptomatic, and Removed) to model population behaviors;
Different subsystems including healthcare systems, transportation, contacts, and capacities of food and pharmaceutical networks are defined in the proposed system dynamics model to find related sensitivities;
Different scenarios are analyzed using the proposed system dynamics model to predict the effects of policies and related costs. The results guide lawmakers and governments' actions for future years;
An adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the gross domestic product (GDP) in future years and analyze effects of COVID-19 based on them;
A real case study is considered to evaluate the performances of the proposed models.
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Shadi Ahmadi and Mohammad Javad Ershadi
The current extensive business ecosystem, characterized by technological advances and development, impressive customers, and increasing social concerns, has exerted great pressure…
Abstract
Purpose
The current extensive business ecosystem, characterized by technological advances and development, impressive customers, and increasing social concerns, has exerted great pressure on business organizations. Among different business values for affording this pressure, organizational agility is a critical factor that should be carefully incorporated in business processes. The main purpose of the present study is to investigate the role of social networking technology, as a crucial collaborative tool, on organizational agility.
Design/methodology/approach
A model based on structural equations was designed in this regard. The constructs of this model are quality of service, varieties of services, costs and speed of service as independent variables and also agility management as a dependent variable. Based on the conceptual model, a questionnaire was prepared and distributed among the experts of social networking technology and agility management. Based on Cochran's formula the sample size was 384. The response rate was 100%. The main statistical measures such as Chi-square ratio to the degree of freedom, Non-soft Fitness Index (RMSEA), Goodness of Fit Index (GFI) and Modified fitness index (AGFI) were employed for analyzing the model.
Findings
Results of obtained data indicated that a variety of services as the main factor of social networking technology has the most impact on the agility of a company. Then, the speed of service, service quality and costs were ranked respectively in second to fourth. Providing information technology (IT) service perceptions, promoting the service climate and thorough identification of IT requirements are the main critical success factors for maintaining a robust impact of social networking technology on organizational agility. Moreover, a well-designed enterprise structure alongside employing newly developed IT infrastructures such as cloud computing certainly improves the capabilities of organizations to improve their agility.
Originality/value
Although the literature suggests a positive impact among IT or social networks on organizational agility, it is deficient in relation to considering the impact of social networking. Furthermore, a structural equation model (SEM) is used for assessing unobservable latent constructs and their related interrelationship.
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Reza Edris Abadi, Mohammad Javad Ershadi and Seyed Taghi Akhavan Niaki
The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large volumes of…
Abstract
Purpose
The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large volumes of unstructured data in research information systems, it is necessary to divide the information into logical groupings after examining their quality before attempting to analyze it. On the other hand, data quality results are valuable resources for defining quality excellence programs of any information system. Hence, the purpose of this study is to discover and extract knowledge to evaluate and improve data quality in research information systems.
Design/methodology/approach
Clustering in data analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found. In this study, data extracted from an information system are used in the first stage. Then, the data quality results are classified into an organized structure based on data quality dimension standards. Next, clustering algorithms (K-Means), density-based clustering (density-based spatial clustering of applications with noise [DBSCAN]) and hierarchical clustering (balanced iterative reducing and clustering using hierarchies [BIRCH]) are applied to compare and find the most appropriate clustering algorithms in the research information system.
Findings
This paper showed that quality control results of an information system could be categorized through well-known data quality dimensions, including precision, accuracy, completeness, consistency, reputation and timeliness. Furthermore, among different well-known clustering approaches, the BIRCH algorithm of hierarchical clustering methods performs better in data clustering and gives the highest silhouette coefficient value. Next in line is the DBSCAN method, which performs better than the K-Means method.
Research limitations/implications
In the data quality assessment process, the discrepancies identified and the lack of proper classification for inconsistent data have led to unstructured reports, making the statistical analysis of qualitative metadata problems difficult and thus impossible to root out the observed errors. Therefore, in this study, the evaluation results of data quality have been categorized into various data quality dimensions, based on which multiple analyses have been performed in the form of data mining methods.
Originality/value
Although several pieces of research have been conducted to assess data quality results of research information systems, knowledge extraction from obtained data quality scores is a crucial work that has rarely been studied in the literature. Besides, clustering in data quality analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found.
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Mohammad Mahdi Ershadi and Abbas Seifi
This study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment. For this purpose, different classification methods…
Abstract
Purpose
This study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment. For this purpose, different classification methods based on data, experts’ knowledge and both are considered in some cases. Besides, feature reduction and some clustering methods are used to improve their performance.
Design/methodology/approach
First, the performances of classification methods are evaluated for differential diagnosis of different diseases. Then, experts' knowledge is utilized to modify the Bayesian networks' structures. Analyses of the results show that using experts' knowledge is more effective than other algorithms for increasing the accuracy of Bayesian network classification. A total of ten different diseases are used for testing, taken from the Machine Learning Repository datasets of the University of California at Irvine (UCI).
Findings
The proposed method improves both the computation time and accuracy of the classification methods used in this paper. Bayesian networks based on experts' knowledge achieve a maximum average accuracy of 87 percent, with a minimum standard deviation average of 0.04 over the sample datasets among all classification methods.
Practical implications
The proposed methodology can be applied to perform disease differential diagnosis analysis.
Originality/value
This study presents the usefulness of experts' knowledge in the diagnosis while proposing an adopted improvement method for classifications. Besides, the Bayesian network based on experts' knowledge is useful for different diseases neglected by previous papers.
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Innocent Chigozie Osuizugbo and Olumide Afolarin Adenuga
This study aimed at determining the decisive factors for achieving sustainable procurement in construction projects.
Abstract
Purpose
This study aimed at determining the decisive factors for achieving sustainable procurement in construction projects.
Design/methodology/approach
Questionnaire survey of principal stakeholders involved in construction project delivery within client, consulting and contracting organisations in Nigeria were conducted to assess stakeholders' perspectives on the decisive factors for achieving sustainable procurement in construction projects using importance weights. A total of 243 questionnaires were distributed and a response rate of 51% (123 questionnaires were adequately filled and returned) was achieved. Descriptive and inferential statistics were utilised in analysing elicited data.
Findings
The results from data analysis showed that “satisfaction – including workforce satisfaction and user satisfaction”, “value for money” and “creating a healthy, nontoxic environment – including high indoor air quality” were the top most three decisive factors for achieving sustainable procurement in construction projects in Nigeria.
Originality/value
An understanding of these decisive factors can help principal stakeholders in the construction industry of developing countries to facilitate the development of methods required in supporting the adoption of sustainable procurement practice.