Industrial management
elham aghazadeh; Akbar Alem Tabriz
Abstract
In today's industrial units, operators monitor equipment performance, and the challenging coordination between units in vast operating environments with high volumes of equipment can lead to irreparable damage. Despite considerable technological advancements in inspection and surveillance, this responsibility ...
Read More
In today's industrial units, operators monitor equipment performance, and the challenging coordination between units in vast operating environments with high volumes of equipment can lead to irreparable damage. Despite considerable technological advancements in inspection and surveillance, this responsibility can be effectively delegated to smart devices and the Internet of Things (IoT). Furthermore, the emergence of "edge computing" technology has prompted researchers to explore edge-based computing designs due to their numerous benefits. This study presents a combined model of IoT and civilian drones for intelligent monitoring of industrial equipment performance, employing an edge computing approach. The model is specifically investigated through a case study involving wind turbines. The model evaluates the performance of drones for intelligent monitoring of wind turbines in three stages: 1) Detection process, 2) UAV computational evacuation process, and 3) UAV local computation process. Given the dual purpose of the final model, which involves a combination of the aforementioned three steps, a genetic method was employed for problem-solving with negligible sorting. The amplified epsilon restriction method, utilizing random numbers, was also considered, but the combination of genetic and negligible sorting methods outperformed it, particularly in large problems where the enhanced epsilon restriction method struggled to provide timely responses due to the inherent complexity of the problem. IntroductionToday, in various industries, the productivity and efficiency of equipment contribute to the advancement of production and the profitability of production units. Beyond repair costs, equipment breakdowns also result in the expense of lost opportunities for the production unit. Without a solution to prevent these costs, bankruptcy for production units becomes a real possibility. Therefore, consideration should be given to a solution for the optimal monitoring of equipment. Clearly, swift action is crucial when any equipment is damaged, and such rapid response is unattainable through human effort alone. Despite significant technological advances in inspection and monitoring, this task can be delegated to smart tools and the Internet of Things (IoT). The IoT is regarded as one of the most crucial factors for the prosperity and progress of today's and future industrial businesses. Modernizing equipment is a priority for today's industries to quickly adapt to the evolving market changes and harness existing technologies. Businesses incorporating IoT into their infrastructure experience substantial growth in areas such as security, productivity, and profitability. As the use of industrial IoT increases, productivity levels in industries are naturally expected to rise. The IoT can accumulate massive amounts of information and data, enabling factories and companies to optimize their systems and equipment without being hindered by technological and economic limitations. However, a challenge arises from the substantial volume of data generated by the IoT, which is sent to cloud computing centers for processing. Centralized (cloud) processing results in high communication delays and lowers the data transfer rate between IoT devices and potential users, creating operational challenges in the network. To address this issue, the concept of edge computing has been proposed. Edge computing allows IoT services to process data near their own data sources and data sinks instead of relying on the cloud environment. This approach leads to reduced communication delays and more efficient utilization of computing, storage, and network resources. It also minimizes execution time and energy consumption, proving to be highly beneficial for IoT applications. Consequently, with the advent of "edge computing" technology, many researchers have embraced edge computing-based designs due to its numerous advantages.Materials and Methods In this research, a combined model of the Internet of Things and civilian drones was presented for the intelligent monitoring of industrial equipment, utilizing an edge computing approach. The model was investigated through a case study involving wind turbines. The performance of UAVs for intelligent monitoring of wind turbines was examined in three stages: 1) Detection process, 2) UAV computational evacuation process, and 3) UAV local computing process. Given the dual purpose of the final model, which involved a combination of the aforementioned three steps, the model was addressed using genetic methods with sparse sorting and the enhanced epsilon constraint method employing random numbers. The genetic method with sparse sorting outperformed the enhanced epsilon limit method, particularly in problems with large dimensions. The complexity of the problem made it challenging for the enhanced epsilon constraint method to provide timely responses in such cases.ResultsThe findings of this research offer valuable insights for the effective and accurate management and monitoring of industrial equipment across various industrial units, aiming to optimize costs, quality, and inspection time. Additionally, this research can provide guidance in considering regulatory restrictions in equipment placement before constructing an industrial unit. During the equipment arrangement phase, the model presented in this research can be utilized for optimal energy consumption and time management. As the combined model of the Internet of Things and civilian drones for intelligent monitoring of industrial equipment is a novel concept in the literature, there exist numerous opportunities for further development in this field. This may include the application of the model in additional case studies, such as enhancing the intelligent monitoring of power supply systems, fire services, etc. Moreover, there is potential for refining the mentioned model under conditions where drones operate simultaneously without a specific sequence.ConclusionFailure to monitor industrial equipment properly can result in substantial financial losses for factories and production units. The improper operation of equipment may lead to complete failure, necessitating the need for replacement. Additionally, increased equipment downtime, quality issues, reduced production speed, safety hazards, and environmental pollution can be consequences of equipment failure, ultimately diminishing the profitability of the production unit. Considering factors such as embargoes, emphasis on domestic production, and self-sufficiency, accurate supervision becomes economically crucial for factories.Effective management of the proper operation of industrial equipment is a fundamental requirement for every production unit, given that industrial equipment represents a significant investment for the unit. If device maintenance is limited to repairs only after breakdowns occur, production devices will consistently face unexpected halts, preventing production productivity from reaching its predetermined goals. Therefore, designing a framework for the "intelligent monitoring of the performance of all relevant industrial equipment" stands as one of the most crucial actions for any production unit. Depending on the type of equipment, monitoring the performance of industrial equipment may encompass periodic inspections, maintenance and repair planning, and scheduling the optimal operational time for the equipment
production and operations management
Morteza Saeidi; Mostafa Ebrahimpour Azbari; MohammadRahim Ramazanian
Abstract
The purpose of this article is to design a model for improving the sustainable performance of small and medium food companies in Guilan Province. The aim is to provide managers and decision-makers with insights into the factors, challenges, and consequences associated with enhancing the sustainable performance ...
Read More
The purpose of this article is to design a model for improving the sustainable performance of small and medium food companies in Guilan Province. The aim is to provide managers and decision-makers with insights into the factors, challenges, and consequences associated with enhancing the sustainable performance of these companies in the current business environment. The article falls under the category of applied research in terms of its purpose, exploratory-descriptive research in nature, and field studies based on data collection. It is classified as qualitative research. The Grounded Theory method was employed to create a paradigm model, progressing through three stages of open, axial, and selective coding. The results of data analysis using Grounded Theory led to the identification of 13 main floors, 29 main categories, and 248 concepts. The paradigm model, developed through theoretical coding, encompasses six main classes: pivotal phenomenon (sustainable performance improvement), causal conditions (organizational survival, social and environmental obligation, gaining competitive advantage), contextual conditions (environmental factors, organizational factors), interfering conditions (environmental factors, organizational factors), and strategies (macro level, organizational level). Finally, the economic, social, and environmental consequences of achieving sustainable performance improvement were derived through the implementation of these strategies.
Introduction
The increasing societal focus on sustainability necessitates attention to improving sustainable performance in small and medium-sized companies. Today, achieving a competitive edge and attracting customers goes beyond operational or financial superiority. In the contemporary business landscape, companies are expected to demonstrate responsibility and consider future generations in their activities. SMEs play a pivotal role in the industrial growth of developing economies globally. Enhancing communication and information flow in small and medium-sized companies can lead to more efficient processes and cost reduction. To thrive in the current business environment, SMEs must adopt emerging technologies to improve their sustainable performance. Industry 4.0 and the circular economy, which involve the use of advanced technologies like artificial intelligence and the Internet of Things, can be instrumental in achieving sustainable performance in these companies. The synergy between Industry 4.0 technologies and circular economy methods positively contributes to enhancing sustainable performance. Given that small and medium industries are significant drivers of economic growth in regions like Guilan province, improving the sustainable performance of companies in this sector can have a substantial impact on preserving and maintaining environmental aspects. With the rapid growth of technology and the advancement of Industry 4.0, SMEs are recognized as vital economic foundations contributing to the development and economic growth of societies. It is crucial to investigate and conduct research in the field of Industry 4.0 and the circular economy to improve the sustainable performance of small and medium-sized companies. A conscious analysis of the benefits and challenges of Industry 4.0 on SMEs allows for a better understanding of the needs and opportunities within this sector. Examining the effectiveness and utilization of new technologies in the processes and organizational structures of small and medium companies can lead to increased efficiency, cost reduction, improved quality, enhanced decision-making processes, and expanded market presence.
Literature Review
Performance, in a broad sense, can be defined in accordance with the concept of quality and an organization's ability to achieve internal and external goals. It is important to note that performance encompasses multiple dimensions. Sustainability, on the other hand, is a multidimensional concept that presents a significant challenge in our time. It involves the understanding and management of economic, social, and environmental performance simultaneously. Researchers argue that if the current population and economic growth rates persist, the utilization of the planet's natural resources will surpass its capacity. This issue gives rise to environmental protection concerns, which are addressed under the umbrella of sustainable development. Sustainable development is defined as development that meets the needs of the present without compromising the ability of future generations to meet their own needs. In a broader sense, a sustainable development strategy entails creating harmony among humans and between humans and nature. It implies that sustainability requires a societal approach that manages the environmental perspective alongside the economic perspective in the progress of development and performance improvement. In the present era, there is an expectation from customers and members of society for companies and organizations to be responsible and consider future generations in their activities and operations. Prioritizing the future generation in activities signifies a positive step towards sustainable performance and demonstrates the organization's commitment to a sustainable global economy. While the concept of sustainability is easily understood, operationalizing and concretely expressing it poses challenges. Decision-makers in industries need to evaluate and review their operations considering both internal and external effects. Optimal decisions can only be made when the social, economic, and environmental consequences are taken into account.
Methodology
The Research falls under the category of applied research in terms of its purpose, exploratory-descriptive research in nature, and field studies based on data collection. Grounded theory, a qualitative research approach, is particularly well-suited for exploratory research seeking to theorize or identify patterns. It employs open, axial, and selective coding methods for data analysis. This approach, introduced by Strauss and Corbin in 1990, is based on the three-stage coding process of open, axial, and selective (or theoretical) coding. In this regard, it utilizes the logical paradigm or diagrammatic representation of the theory created. Research based on the grounded theory approach, employing a systematic strategy, culminates in the development of hypotheses and statements that may specify the relationships between classes in the axial coding paradigm.
Discussion and Results
The central category identified is Sustainable Performance Improvement. Two dimensions associated with this central category are Industry 4.0 and the circular economy. The most crucial factors influencing sustainable performance improvement are categorized into three groups: organizational survival (including profitability and maintaining market share), social and environmental requirements (such as attracting public support, considering future generations, and managing limited resources), and gaining competitive advantage (encompassing both temporary and sustainable competitive advantages). These factors contributing to sustainable performance improvement are further classified into two categories: environmental factors and organizational factors. Environmental factors involve challenges such as environmental instability, legal factors, and political factors, while organizational factors encompass financial and cost considerations, along with human resources. Strategies for achieving desirable outcomes are outlined at both the macro level (involving government and industrial estate company activities related to consortium formation, culture, and education) and the organizational level. Small and medium-sized food industry companies are encouraged to pursue innovation, creativity, value chain knowledge, and professional structure to create favorable benefits for themselves. The final segment of the sustainable performance improvement model outlines economic, social, and environmental consequences. Key consequences at the economic level include company development, sustainable profitability, and sustainable productivity. At the social level, consequences encompass the welfare of workers and the social responsibility of the organization. The most critical consequence at the environmental level is the achievement of a sustainable environment.
Conclusion
To design a model for enhancing the sustainable performance of small and medium-sized companies, the grounded theory methodology with the systematic approach of Strauss and Corbin was employed. This involved conducting interviews with experts to gather the necessary conceptual codes. Following the systematic approach, data analysis utilized three stages: open coding, axial coding, and selective coding. The design of the model adhered to the paradigm model of this approach, incorporating six dimensions: the central category, causal conditions, contextual conditions, intervening conditions, strategies, and consequences.
safety,risk and reliability
Seyedeh Sara Khorashadizadeh; Jalal Haghighat Monfared; Mohammadali Afshar Kazemi; Shahram Yazdani
Abstract
In this study, a comprehensive classification for supply chain risks in the pharmaceutical industry is presented using the Bailey’s classical strategy method and the four-stage Collier method. Initially, through the examination of texts related to the main hazard groups, supply chain elements, ...
Read More
In this study, a comprehensive classification for supply chain risks in the pharmaceutical industry is presented using the Bailey’s classical strategy method and the four-stage Collier method. Initially, through the examination of texts related to the main hazard groups, supply chain elements, considering resources and functions, and categorizing upstream supply chain organizations, primary industry, and downstream supply chain organizations within the industrial and market environment, infrastructural environment, and external macro environment were modeled. In the next stage, criteria related to the security and safety of the supply chain were identified. In the final stage, a two-dimensional matrix classification for the identification of supply chain risk factors was proposed through the cross-tabulation of supply chain elements with security and safety criteria. Based on this classification and utilizing the exemplification method through a synthetic framework, a detailed list of risk factors was compiled. The aim of this study is to propose a comprehensive risk classification for pharmaceutical industries.MethodBailey’s classical strategy method has been used to develop a comprehensive classification of supply chain risks in pharmaceutical industries. In order to review the existing knowledge about supply chain risk groups, a systematic review of literature was performed. In the first stage, to find articles related to supply chain risks in the pharmaceutical industry, different combinations of related keywords have been used to search for articles in relevant databases. The selected articles were examined in three stages: extracting and classifying the main risk groups of the supply chain (the first dimension of the conceptual framework of classification), extracting and classifying criteria for a low-risk supply chain (the second dimension of the conceptual framework of classification), and applying the two-dimensional framework of classification to identify and classify risk factors of the supply chain.ResultsA total of 77 articles were selected for review. Based on the analysis of these articles, 83 risk groups were identified. These risk groups were arranged into a model including upstream supply organizations, the main industry, and downstream supply organizations, considering the relationships between supply chain’s resources, functions, and outcomes in the industry and market environment, infrastructural environment, and external macro environment. In the next step, 30 criteria for a safe and secure supply chain were identified. These criteria are divided into two general categories: criteria for the security of the internal supply chain environment (criteria of resistant supply chain resources and criteria of resilient supply chain functions) and criteria for the safety of the external supply chain environment (criteria of safety of market and industry, criteria of safety of infrastructural environment, and criteria of safety of external macro environment). In the last stage, through cross-tabulation of resource groups with resource resistance criteria, function groups with function resilience criteria, and peripheral environment elements with peripheral environment safety criteria, a model for identifying risk factors in the industrial environment was proposed. Based on this model, 372 risk factors of the supply chain of the pharmaceutical industry were identified.ConclusionIn this study, a new classification for supply chain risks of the pharmaceutical industry has been presented. The proposed classification is highly comprehensive, and the number of risk groups counted in this study is more than all the studies that have been done in this field so far. Most existing risk taxonomies are incomplete and do not follow a specific theoretical model. The classification of risk groups identified in this study has been done based on a model that considers the relationship between assets, functions, and outcomes of the supply chain. The risk groups identified in this study cover from the upstream of the supply chain to the main industry and the downstream of the supply chain. Many risk taxonomies focus on the pharmaceutical industry and do not cover the entire supply chain from raw material production to customers. In this study, cross-tabulation of resource groups with resource resistance criteria, function groups with function resilience criteria, and peripheral environment elements with peripheral safety criteria create an ideal model for identifying risk factors in the industrial environment. The classification proposed in this study can be used to evaluate the resistance and resilience of the supply chain. This model can also provide a suitable basis for identifying and evaluating risks in the supply chain environment. In addition, results of this study provide a very practical guide for choosing supply chain risk management strategies.
supply chain management
Allahyar Beigi Firoozi; Mohammad Bashokouh Ajirlou; Naser Seffollahi; Ghasem Zarei
Abstract
The current study aimed to cluster the application of digital technologies from Industry 4.0 in the agricultural food distribution network. To achieve this goal, a bibliometric technique was employed to identify prominent trends and themes in this field through the analysis of articles, authors, countries, ...
Read More
The current study aimed to cluster the application of digital technologies from Industry 4.0 in the agricultural food distribution network. To achieve this goal, a bibliometric technique was employed to identify prominent trends and themes in this field through the analysis of articles, authors, countries, and co-citations of authors and bibliographic pairs. Through an extensive search in the Scopus scientific database, bibliographic information for 331 valid and relevant scientific articles was acquired. This information was inputted into the bibliometric package in R software, and the most influential journal, author, university, country, and most cited authors were determined. To visualize the information, Vosviewer software was utilized for co-citation analysis of authors, cited references, and bibliographic pairs. The findings from the network analysis revealed that the studies on the application of digital technologies in the agricultural food distribution network can be categorized into five main clusters.IntroductionIndustry 4.0, viewed as a new industrial stage, has introduced complex information and communication technologies that facilitate comprehensive connections across different parts of the supply chain. The digital technologies associated with Industry 4.0 allow production lines, business processes, and teams within a supply chain to collaborate seamlessly, irrespective of location, time zone, network constraints, or any other factors. Researchers highlight that the advent of digital technologies from the fourth industrial revolution, including radio frequency identification, big data, cloud computing, smart sensors, machine learning, robotics, augmented production, artificial intelligence, augmented reality, the Internet of Things, blockchain, and similar technologies, holds immense potential for significantly enhancing production productivity. These technologies could lead to substantial innovation, competitive growth, and may contribute to improving the sustainability of the current industrial system. To meet the escalating demand for food, agricultural marketing professionals and managers globally must maximize the efficiency of the agricultural distribution network, given the widespread adoption of digital technologies. The increasing significance of this goal has prompted marketing researchers to explore the use of digital technologies in the agricultural food distribution network, leading to a substantial number of studies in this research field since 2011. In this context, the present study aimed to cluster the utilization of digital technologies in Industry 4.0 within the agricultural food distribution network. A bibliometric study was conducted to identify existing gaps in research and propose future directions. The research focuses on the application of digital technologies in the distribution network.Aligned with the research objective, fundamental questions are posed: Which publications, authors, and countries are most influential in the application of Industry 4.0 digital technologies in the agricultural food distribution network? Additionally, what scientific clusters exist in this domain?MethodologyThe objective of the current research is to conduct a bibliographic analysis of studies related to the application of digital technologies in Industry 4.0 within the agricultural food distribution network. Utilizing bibliometric techniques, a crucial measure for evaluating scientific output, a comprehensive examination of scientific literature was carried out concerning the application of digital technologies in Industry 4.0 within the agricultural food distribution network.The search was conducted within the Scopus scientific database, which encompasses a significant array of diverse journals and authoritative articles globally. The search covered three sections: title, abstract, and keywords, yielding a list of studies that exclusively included English-language articles from journals (excluding conference studies and book chapters) published between 2011 (the inception year of Industry 4.0) and 2023. By imposing these criteria, 352 original pieces of data containing bibliographic information were obtained. Subsequently, the title and abstract of each article were meticulously scrutinized to identify information relevant to the agricultural food distribution networks. Among these, 6 articles pertaining to the halal supply chain and 15 articles conducted as systematic reviews were excluded from the bibliographic information collection. The final portfolio for analysis consisted of bibliographic information from 331 articles, which was then entered into the bibliometric software package. This analysis was carried out using R software and VOSviewer software. The bibliometric software package facilitated quantitative bibliographic analysis, while the VOSviewer software was employed for visualizing and analyzing citation networks.ResultsThe quantitative findings indicate a significant increase in studies related to the adoption of digital technologies in the agricultural food distribution network, particularly after 2017. The most widely utilized digital technologies in the food distribution network include blockchain, the Internet of Things, simulation, artificial intelligence, big data, machine learning, 3D printers, sensors, and digital twins.Through the analysis of bibliographic pairs, five primary clusters were identified concerning the application of digital technologies in the agricultural food distribution network. These clusters are associated with the use of digital technologies in ensuring food quality, enhancing distribution network flexibility, establishing modular architecture within the distribution network, implementing intelligent logistics systems, and promoting sustainable distribution networks.ConclusionBased on the themes of the clusters identified in Table 7, it can be concluded that the Internet of Things and blockchain play crucial roles in real-time tracking, tracing, and monitoring of food throughout the supply chain, thereby reducing wastage. RFID technologies and digital twins are highly effective in ensuring food safety and facilitating delivery to consumers, especially in the face of environmental changes and crises such as epidemics. Another application of digital technologies lies in the modular architecture of the food distribution network. Through the use of modular architecture, various technologies can modularize tasks and extensive operations within the food distribution network. Ultimately, all these components can be centralized under blockchain technology, with diverse data stored in a vast cloud space. Consistent implementation of digital technologies in the food distribution network has the potential to establish regional warehouses, resulting in reduced distribution and delivery costs, enhanced food safety and sustainability, and the possibility of customizing food for end consumers. This, in turn, will contribute to the stability of the food network.
uncertainty
Hossein Firouzi; Javad Rezaeian; Mohammad Mehdi Movahedi; Alireza Rashidi Komijan
Abstract
This paper presents a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic, incorporating dimensions of sustainability. The objectives of the model are as follows: 1) Minimizing the costs associated with building facilities ...
Read More
This paper presents a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic, incorporating dimensions of sustainability. The objectives of the model are as follows: 1) Minimizing the costs associated with building facilities and waste treatment centers, vehicle fuel costs, and environmental costs due to pollutant emissions; 2) Maximizing the energy generated from the waste combustion process; 3) Minimizing the risk of virus transmission resulting from inadequate waste management; and 4) Maximizing the number of job opportunities in the established centers. It is important to note that existing uncertainties are addressed through the application of fuzzy set theory. Given the multi-objective nature of the model, two multi-objective algorithms, namely the Pareto archive-based Krill Herd Algorithm and Non-dominated Sorting Genetic Algorithm II (NSGA-II), are employed to solve the defined problem. The results indicate that the proposed Krill Herd Algorithm converges to a solution with higher quality and dispersion compared to NSGA-II. Additionally, through a comparison of the spacing index and running time of the two algorithms, it is observed that NSGA-II explores the solution space with higher uniformity and solves the model in less time.IntroductionHospital waste encompasses a broad spectrum of both hazardous and non-hazardous materials. The management of hospital waste involves the development of a suitable supply chain network for handling waste generated in the healthcare sector. Improper disposal or mishandling of contaminated waste not only contributes to environmental pollution but also poses a risk of transferring viral pathogens to healthcare and recycling personnel. Research has shown that inadequate disposal of medical waste can lead to the transmission of up to 30% of hepatitis B, 1-3% of hepatitis C, and 0.3% of HIV infections from patients to healthcare workers. This paper aims to design a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic while considering the dimensions of sustainability.Literatur ReviewIn recent years, various studies have delved into the complexities of medical and hospital waste management, proposing mathematical models to address this intricate issue. The current study is built upon the work of Valizadeh et al. (2021). In their paper, a hybrid mathematical modeling approach was introduced, featuring a Bi-level programming model specifically tailored for infectious waste management during the COVID-19 pandemic. The outcomes revealed that, at the higher level of the model, governmental decisions aiming to minimize total costs associated with infectious waste management were crucial. This involved the conversion of collected infectious waste into energy, with the generated revenue being reinvested back into the system. The findings indicated that, through energy production from waste during the COVID-19 pandemic, approximately 34% of the total costs related to waste collection and transportation could be offset. The uniqueness of this study lies in its consideration of three sustainability dimensions: risk, vehicle routing, energy production, employment, and emission of polluting gases. Consequently, the novelty of this research, when compared to previous studies and the article by Valizadeh et al. (2021), is evident in several aspects. It introduces an integrated multi-objective positioning-routing model for the supply chain of waste management under pandemic conditions, taking into account sustainability dimensions, notably the economic aspect, and employs meta-heuristic algorithms for model resolution.MethdologyTo ensure the proper management of hospital waste, the waste is categorized into two groups: infectious and non-infectious waste. It is assumed that waste in hospitals and health centers is segregated and placed in infectious and non-infectious waste bins. The collected waste undergoes further processing: infectious waste is transported to incineration centers, where it is burned and converted into electrical energy, while non-infectious waste is sent to waste recycling centers, where it is reprocessed and returned to the production cycle in the industry. A multi-objective mathematical model is presented to integrate location-routing decisions in the supply chain of hospital waste management, with the following modeling assumptions:Waste segregation at the source helps prevent all waste from becoming viral, reducing the spread of viruses through waste.The risk of spreading viruses is assumed to be relatively equal for each type of waste.Two types of vehicles are considered for transporting waste: the first type carries non-infectious waste, while the second type carries infectious waste.The number of cars, waste collectors, and the capacity of waste incinerators are considered constant in this study.The mathematical model is multi-objective, with the objectives being to optimize the three dimensions of sustainability (economic, social, and environmental).The economic goal is to minimize system costs, including the cost of site location, recycling, collection, segregation of non-infectious waste, and incineration.The environmental goal is to minimize the emission of pollutants in the transportation and processing system in various facilities, as well as to maximize the production of electrical energy.The social goal is to minimize the risk of virus transmission and maximize the employment rate.Results and DiscussionThis research presents a multi-objective mathematical model for the reverse supply chain of hospital waste management during the COVID-19 pandemic in Iran and solves it. The pandemic period is considered a time of maximum utilization of health centers and waste disposal. In this context, a three-objective mathematical model was initially introduced. To solve the model, the krill herd optimization algorithm was employed. The performance of the krill herd optimization algorithm was scientifically and practically evaluated by comparing it with the well-known NSGA-II algorithm. After designing the model, both the multi-objective krill herd algorithm based on Pareto Archive and the NSGA-II algorithm were utilized to solve the model. The results of solving the model demonstrated that the proposed krill herd algorithm, designed in combination with VNS, effectively solved the model and determined the optimal solution within a boundary. Comparing the results of this algorithm with those obtained by the renowned NSGA-II algorithm revealed that the krill herd algorithm produced solutions of much higher quality.ConclusionThe comparison of the Index of dispersion between the two algorithms indicates that the krill herd optimization algorithm explores more points in the solution space, leading to a lower probability of getting stuck in local optima compared to the NSGA-II algorithm. On the other hand, the index of uniformity for the NSGA-II algorithm is lower than that of the krill herd algorithm (lower values are better), suggesting that the multi-objective genetic algorithm explores the solution space more uniformly. Considering the execution time of the two algorithms, it was observed that the NSGA-II algorithm solved the model in less time. Additionally, the increasing trend of execution time in both algorithms confirms the NP-HARD nature of the hospital waste management problem. According to the output of the MATLAB software, considering the presented model, the results affirm the capability to optimally select hospital waste recycling centers.
project management
Ali Namazian; Somayeh Behboodian
Abstract
Projects, during their execution, face various risks that can impact the achievement of project objectives. Therefore, the need for extensive project risk management is widely recognized. In a systematic risk management process, after risk evaluation, risk analysts are confronted with the risk response ...
Read More
Projects, during their execution, face various risks that can impact the achievement of project objectives. Therefore, the need for extensive project risk management is widely recognized. In a systematic risk management process, after risk evaluation, risk analysts are confronted with the risk response phase, where they decide on the actions to be taken regarding identified risks. Hence, designing and implementing a structured approach to manage and mitigate risks will yield beneficial outcomes for successful completion within the desired budget, time, and quality. In conducted studies, a comprehensive approach that integrates the time and cost implications of risks and response strategies has been lacking. In this article, an optimization model of zero-one programming has been employed to select the most suitable risk response strategies for the project. In the developed framework, the modeling of the impact of risks on the time and cost of activities, as well as the effect of implementing risk response strategies on reducing the undesirable time and cost implications of risks, has been utilized to select optimal strategies. Finally, to evaluate the efficiency of the model, an industrial case study was utilized, which confirmed the favorable performance of this framework.IntroductionEvery project throughout its lifespan faces opportunities and risks. Risks are uncertain outcomes or consequences of activities or decisions. Therefore, in the project planning process, it is necessary to identify potential risks and then consider appropriate strategies to deal with various risks. In this article, a mathematical programming model is used to evaluate and analyze project risks and to select project risk responses. This model considers the probabilistic nature of risk events and develops an index for evaluating the time and cost impacts of risks, as well as response strategies. The proposed approach can be used to select the best combination of risk response strategies that have the most impact on the time and cost of implementing activities, resulting in completing the project with minimum time and cost.Literature ReviewDifferent models have been developed for project risk management to enhance success in development projects. These approaches utilize various structures and tools to quantitatively or qualitatively model the selection of risk response strategies for the project. In recent years, due to unexpected events such as financial crises, significant delays have occurred in projects worldwide (Motaleb, 2021). Thus, researchers have attempted to propose various methods to mitigate the effects of risks in recent years.In the Zonal-based approach, two selected criteria based on risks are plotted on the horizontal and vertical axes, respectively. The two chosen criteria are the weighted probability of immediate project risk and external project risk, and the controllability and specificity of the risks related to the project. Based on the different values of these two criteria, a two-dimensional chart consisting of multiple regions is formed. Different strategies are placed in the corresponding regions. Therefore, suitable strategies can be selected based on the regions formed by the coordinates of the two criterion values.In the Trade-off-based approach, in order to identify the selected risk for formulating response strategies, exchanges are conducted considering the project's goals, requirements, and managers' mental settings among risk-related criteria such as cost, success probability, percentage of work losses, duration, quality, etc. Then, desirable strategies can be selected from the options based on the efficiency frontier rule.The approach based on WBS is considered a risk management and project management method. This choice aligns the risk response strategy with the work activities based on WBS analysis of the project. (Guan et al., 2023) developed an integrated approach based on an optimization model and fault tree analysis for budget allocation in response to risk from safety and prevention perspectives.The optimization approach involves creating a mathematical model to solve the problem of selecting risk response strategies. In general, the objective function aims to minimize the cost of implementing strategies, and the constraints include combinations of strategies, an acceptable level of risk loss, budget for implementing strategies, etc.MethodologyIn this study, a set of work activities is considered, and for each work activity, there may be associated risks that can have an impact. Then, risk response strategies are modeled to determine the most desirable strategy. The zero-one programming technique is used to solve the model. By solving the model, strategies are selected that maximize the estimated impact of risk response after implementation and minimize the cost of implementation. In the proposed model, a set of actions is selected in a way that satisfies the system constraints and optimizes the corresponding objective function. The objective function can be related to time or cost, and the goal of the model is to minimize project completion time or project cost. The model constraints are related to time and cost. The time constraint means that selected strategies should not exceed the specified time frame for their execution and impact on time. The cost constraint means that selected strategies should not exceed the budget and predefined cost in terms of their cost and impact on cost. ResultsThe model presented in this study has an objective function and nine constraints. The purpose of this model is to determine strategies that minimize project completion delay and help achieve and improve project goals. Due to the structure of the modeling, including the objective function and problem constraints, the complexity of the model will change polynomially based on the number of risks, response strategies, and project activities. If simulation-based approaches are used to solve the model, considering the binary nature of project risks and replacing it with the expected value, the complexity of the solution approach will be exponential. Therefore, using the logic of expected value to calculate the duration of activities and project completion time will accelerate the solution process.Discussion and conclusionsIn a systematic project risk management process, after assessing the risks, the implementation of project risk response strategies takes place. The conducted research has generally provided general solutions, and there is no comprehensive model for evaluating project risk reduction measures. In this article, a mathematical optimization model has been developed by considering the risks and response strategies as independent variables for each work activity. Essentially, based on the potential risks that may occur for each work activity, strategies are chosen to minimize project completion delay and reduce the incurred costs, ultimately achieving the project's completion with the least delay and cost. Implementing risk response strategies to mitigate the time and cost impacts of risks requires time and investment. Therefore, selecting these strategies will be justifiable when the time and cost benefits derived from their implementation are greater than the time and cost spent.
supply chain management
Mahsa Pishdar; Atefeh Habibi
Abstract
To explain a framework for managing the risk of transportation in the food industry supply chain, the initial step involves identifying 12 risks that can potentially lead to transportation disruptions, based on the research background. Utilizing the Delphi method and gathering opinions from 15 academic ...
Read More
To explain a framework for managing the risk of transportation in the food industry supply chain, the initial step involves identifying 12 risks that can potentially lead to transportation disruptions, based on the research background. Utilizing the Delphi method and gathering opinions from 15 academic and industrial experts across 3 stages, the risks were ultimately defined. Furthermore, expert opinions were sought to determine solutions to address the identified risks. The grey DEMATEL method was employed to investigate the interaction of risks. The findings revealed that weather problems, natural disasters, insufficient skilled labor/labor strikes, infrastructure capacity, and inflation and exchange rate changes are among the risks that exert a more significant influence on other risks than they are influenced by them. Subsequently, using the grey COPRAS method, the prioritization of solutions to mitigate the identified risks, based on expert opinions, was undertaken. The results indicated that the top-ranked solution is the definition of key performance indicators. Therefore, it is recommended to managers that, in order to establish a robust supply chain and proactively manage risks, they should identify stakeholders and critical processes. Afterward, an agreement on the financial flow in each situation should be obtained, and a value flow map drawn. This approach enables the implementation of preventive measures to reduce supply chain risk and facilitates the preparation of an emergency plan for unforeseen conditions, thereby enhancing resilience.IntroductionDisruption in transportation stands as the pivotal factor undermining the efficiency of the supply chain. Any significant interruption can result in delays or business flow cessation, leading to consequential impacts (Ali et al., 2021). The transportation supply network is susceptible to various technical, economic, and environmental factors (Tan et al., 2023). Simultaneously, research indicates that factors such as a workforce lacking sufficient skills, suboptimal selection of service providers, traffic accidents, and the inability to predict the systemic impact of these risks play a crucial role in transportation, causing disruptions in the flow. In addition to these factors, it is noteworthy that supply chain management in the food industry introduces its own complexities. Unlike other industries, the quality of products in this type of supply chain consistently diminishes during product movement and this issue of perishability intensifying the need for transportation risk management (Hosseini-Motlagh et al., 2019; Choe et al., 2021). Given this context, emphasis should be placed on establishing distribution channels with lower costs and implementing change management to enhance efficiency. However, existing studies have predominantly focused solely on analyzing transportation disruption within companies' supply chains. Clearly, it is insufficient to only address disorders or the risks that may lead to their occurrence. A comprehensive examination of the cause-and-effect relationships among these risks facilitates a systematic understanding of the risk network. This approach enables the development of a more effective program to enhance the resilience of the transportation system. Even in the face of risks and disruptions, this ensures minimal damage, a swift return to normal operational levels in the supply chain, or the application of knowledge management to learn from experiences and prevent the recurrence of disruptions through appropriate implementation solutions. Consequently, the overall performance of the supply chain can be improved. In consideration of these elements, this study aims to address the following main questions:- What are the risks associated with transportation in the food supply chain?- What are the pertinent solutions, according to expert opinions, and what is their prioritization?MethodologyIn terms of its objective, the current study falls within the domain of applied research as it aims to discover practical solutions to address a real-world problem. Regarding information collection, the study is categorized as survey research, wherein data is gathered based on the opinions of 15 experts. Among these experts, 9 are drawn from the industrial community, each possessing over 10 years of experience in the commercial sector and raw material procurement within the food industry. The remaining 6 experts are affiliated with the academic community and have published numerous articles in the field. The study unfolds in several stages. Initially, a compilation of transportation risks causing disruptions in the food supply chain is accomplished through a literature review. Subsequently, the Delphi method is employed to screen and refine these disruptions. The cause-and-effect relationships among the identified disruptions are then scrutinized using the Grey DEMATEL method. Experts are engaged to contribute not only by assessing the risks but also by providing their insights into coping strategies based on their experience. Finally, the coping strategies are prioritized using the Grey COPRAS method.Results and DiscussionAccording to the obtained results, it is evident that the climate problems risks of failure to choose logistics service providers that care about sustainability principles (C6) and frequent change of product delivery time (C9) exhibit the highest degree of interconnectedness with other risks. The weights of these risks have also been determined. Notably, the failure to choose logistics service providers committed to sustainability principles has secured the top rank with a weight of 0.1443. Following closely, the frequent change of product delivery time holds the second position with a weight of 0.1384, while natural disasters rank third with a weight of 0.1039. Turning to coping strategies, it is noteworthy that the solution of "Definition of Key Performance Indicators (KPI)" has claimed the top position. In today's business landscape dominated by Logistics 4.0 and Omnichannel, simplifying processes can create significant added value for any business, particularly in minimizing transfer time. Concurrently, many manufacturing companies are leveraging various logistics transportation modes as a critical factor for promptly responding to demands, thereby enhancing service reliability and minimizing travel time (Foroozesh et al., 2022). The adoption of modern technologies such as the Internet of Things facilitates real-time inventory monitoring, contributing to dynamic pricing policies. As product quality diminishes along the chain, electronic labels enable adjusting product prices based on features (Kumar & Agrawal, 2023).ConclusionStudies indicate that supply chain managers, particularly in food supply chains, have demonstrated significant commitments to sustainability goals, leading to the pursuit of a diverse array of performance improvement projects. This study identifies various risks and corresponding coping strategies. Outsourcing logistics activities to 3PL allows leveraging their expertise in supply chain management, thereby enhancing stability and efficiency. This approach can contribute to reducing the carbon footprint, increasing order fulfillment, and lowering energy consumption throughout the supply chain.For future research endeavors, it is recommended to prioritize strategies related to realizing the circular economy within the logistics system of the food industry. Providing a roadmap for the sustainable development of logistics clusters can enhance supply chain performance, minimize waste, and boost the social credibility of the supply chain. Additionally, attention to the concept of greenwashing in sustainable logistics, particularly concerning the fulfillment of social responsibility, can prove beneficial in improving overall supply chain performance
