Morteza Khorram; Mahmood Eghtesadifard; Sadegh Niroomand
Abstract
This paper focuses on a novel model of the U-shaped assembly line balancing problem, in which the objective functions include cost, capacity, and quality. It is assumed that each task requires a set of equipment. In addition, the quality of tasks performed by each worker varies. Hence, the purpose of ...
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This paper focuses on a novel model of the U-shaped assembly line balancing problem, in which the objective functions include cost, capacity, and quality. It is assumed that each task requires a set of equipment. In addition, the quality of tasks performed by each worker varies. Hence, the purpose of the model is that the total cost of the equipment is minimized and the quality of the work is maximized. Additionally, the number of workstations is minimized. At first, a multi-objective non-linear mixed-integer programming model is provided. Then, the model is linearized, and simulated annealing (SA) algorithm and two of its modified modes have been proposed to solve the problem. The proposed algorithm includes a new encoding/decoding scheme, as well as a local search for assigning the worker to each station. To determine the parameters in three algorithms, the experimental design has been used and various modes have been created by combining the parameters. Moreover, numerical examples were established based on the graphs found in the literature and the solution is compared with three algorithms, revealing the efficiency of each algorithm. Additionally, a case study on the nozzle assembly line in oil refineries was conducted to evaluate the efficiency of the proposed model and algorithm. Results from the case study show that the modified SA algorithms performed better.IntroductionNowadays, assembly lines play a crucial role in the production of standardized and high-volume products. If task allocation to workstations is done without considering the balance of the assembly line, it can lead to high levels of idle time in some workstations and decreased line efficiency. Therefore, assembly line balancing is an important stage in the production process to enhance production line productivity. This study focuses on the single-model U-shaped assembly line balancing problem. Assembly lines can be divided into four categories based on their layout, and in this research, the U-shaped assembly lines are specifically considered. The objectives of this problem include minimizing the number of workstations, minimizing equipment costs, and minimizing the level of work quality deviation at each workstation (equivalent to maximizing work quality). Additionally, constraints related to occurrence, precedence, and capacity, as well as limitations on tool and worker allocations, have been considered in the problem model. In terms of research gaps in this field, it should be noted that in previous studies on U-shaped assembly line balancing problems, objective functions combining cost, capacity, and quality have not been simultaneously addressed within a single problem. Furthermore, simultaneous allocation of workers (based on skill levels) and tools has not been studied in the context of U-shaped assembly line balancing problems.Materials and MethodsIn this study, a nonlinear mixed-integer multi-objective programming model is proposed for balancing a single-model U-shaped assembly line. The problem modeling assumes realistic conditions where each task requires a set of tools, and in this regard, the quality of task execution by workers is considered to be different. The modeling of quality in the assembly line balancing problem (as one of the objective functions) is approached differently compared to previous studies in this field, aiming to minimize the level of work quality deviation in all workstations. Additionally, for solving the problem, the allocation of workers and tools to the workstations is performed based on a neighborhood algorithm, which is a notable innovation in the research. In this study, a modified simulated annealing metaheuristic algorithm is developed with innovations in encoding and decoding procedures to solve the proposed model in three optimization scenarios. To compare the results of these algorithms, numerical examples based on graphs available in the research literature are solved using the three algorithms. Furthermore, a case study is conducted on the assembly line of component δ, which is used in oil refineries, to evaluate the efficiency of the proposed model and algorithm in real assembly lines.Discussion and ResultsIn this study, to validate the proposed algorithms, 10 numerical examples of different sizes (small, medium, and large) were designed based on valid graphs available in the research literature. Then, for various parameter values, each problem was solved 10 times using each algorithm, and the results of each algorithm were analyzed. In these examples, the costs of tools and the data related to task quality were randomly generated. Additionally, workers with different skills were defined to perform the tasks. Furthermore, the cycle time proportional to the activity durations was considered. It is observed that in all solved examples, the values of the third objective function (quality objective function) obtained from the third algorithm are better than the values obtained from the first and second algorithms. These results are not unexpected because in the third algorithm, due to the presence of an improvement loop for the third objective function, its value decreases compared to the other two algorithms, resulting in a reduction in the overall objective function and its improvement compared to the other two algorithms. For the cost minimization objective function (first objective function) and the number of workstations minimization objective function (second objective function), the values obtained from the three algorithms are approximately the same, and the difference in the obtained values for the overall objective function is primarily dependent on the value of the quality objective function (third objective function). Additionally, the results of solving the numerical examples show that the third algorithm achieves the best values for the overall objective function (compared to the other two algorithms) on examples with more than 25 activities, indicating that employing a local search for worker allocation in the modified simulated annealing algorithm makes the algorithm stronger and more efficient compared to its classical form.ConclusionIn this research, the modeling and problem-solving of the U-shaped assembly line balancing problem were investigated considering tool allocation constraints and quality conditions. To this end, a mixed integer nonlinear programming model was presented for the problem, where equipment and workers were simultaneously considered as two objectives in terms of minimizing equipment cost and the level of task quality. In addition to these two objectives, the number of workstations was also minimized. To solve the problem, a metaheuristic algorithm called simulated annealing was employed, as well as two improved versions of it (by introducing innovations in the random allocation of workers to workstations and applying a local search for improving worker allocation). The proposed model was solved using well-known graphs in the literature of assembly line balancing problems (as numerical examples) with the proposed algorithms, and the results obtained from the algorithms were compared and the performance of these algorithms was analyzed and examined.
Mohsen Jami; Hamidreza Izadbakhsh; Alireza Arshadi Khamseh
Abstract
In the management of the blood supply chain network, the existence of a coherent and accurate program can help increase the efficiency and effectiveness of the network. This research presents an integrated mathematical model to minimize network costs and blood delivery time, especially in crisis conditions. ...
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In the management of the blood supply chain network, the existence of a coherent and accurate program can help increase the efficiency and effectiveness of the network. This research presents an integrated mathematical model to minimize network costs and blood delivery time, especially in crisis conditions. The model incorporates various factors such as the concentration of blood collection, processing, and distribution sites in facilities, emergency transportation, pollution, route traffic (which can cause delivery delays), blood type substitution, and supporter facilities to ensure timely and sufficient blood supply. Additionally, the model considers decisions related to the location of permanent and temporary facilities at three blood collection, processing, and distribution sites, as well as addressing blood shortages. The proposed model was solved for several problems using the Augmented epsilon-constraint method. The results demonstrate that deploying advanced processing equipment in field hospitals, concentrating sites in facilities, and implementing blood type substitution significantly improve network efficiency. Therefore, managers and decision-makers can utilize these proposed approaches to optimize the blood supply chain network, resulting in minimized network costs and blood delivery time.IntroductionOne of the most important aspects of human life is health, which has a significant impact on other aspects of life. In this study, a two-objective mathematical programming model is proposed to integrate the blood supply chain network for both normal and crisis conditions at three levels: blood collection, processing and storage, and blood distribution. The proposed two-objective mathematical model simultaneously minimizes network costs and response time. The model is solved using the augmented epsilon-constraint method. To enhance the responsiveness to patient demand in healthcare facilities and address shortages, the model considers the concentration of levels (collection, processing and storage, and distribution of blood to patients) in facilities, blood type substitution, and supporter facilities. In blood type substitution, not every blood type can be used for every patient. Among several compatible blood groups, there is a prioritization for blood type substitution, allowing for an optimal allocation of blood groups based on the specific needs.Materials and MethodsIn this research, a two-objective mathematical programming model is proposed to design an integrated blood supply chain network at three levels: collection, processing, and distribution of blood in crisis conditions. The proposed model determines decisions related to the number and location of all permanent and temporary facilities at the three levels of blood collection, processing, and distribution, the quantity of blood collection, processing, and distribution, inventory levels and allocation, amount of blood substitution, and transportation method considering traffic conditions. Achieving an optimal solution for the developed two-objective model, which minimizes both objective functions simultaneously while considering the trade-off between the objective functions, is not feasible. Therefore, multi-objective solution methods can be used to solve problems considering the trade-off between objectives. In this research, the augmented epsilon-constraint method is employed to solve the proposed two-objective mathematical model. In this method, all objective functions, except one, are transformed into constraints and assigned weights. By defining an upper bound for the transformed objective functions, they are transformed into constraints and solved.Discussion and ResultsAlthough the two-objective mathematical model is transformed into a single-objective model using the augmented epsilon-constraint method, this approach can still yield Pareto optimal points. Therefore, managers and decision-makers can create a balanced blood supply chain network considering the importance of costs and blood delivery time. Sensitivity analysis was conducted to examine the effect of changes in the weights of the objective functions and the blood referral rate (RD parameter) on the values of the objective functions for three numerical examples. With changes in the weights of the objective functions relative to each other, the trend of changes in the values of the first and second objective functions for all three solved problems is similar. Specifically, when reducing the weight of the first objective function from 0.9 to 0.1, the values of the first objective function increase, while the values of the second objective function decrease when the weight of the second objective function increases from 0.1 to 0.9. The total amount of processed blood in field hospitals and main blood centers was compared for equal weights and time periods for the three problems. Additionally, the amount of processed blood in field hospitals is significantly higher than in main blood centers. This indicates that eliminating the cost and time of blood transfer in field hospitals (due to the concentration of blood collection, processing, and distribution levels) results in an increased amount of processed blood compared to main blood centers (single-level facilities), ultimately leading to a reduction in network costs.ConclusionThis study presents a two-objective mathematical model for the blood supply chain network, integrating pre- and post-crisis conditions. Decisions are proposed for the deployment of four types of facilities, including temporary blood collection centers, field hospitals, main blood centers, and treatment centers, at three levels of blood collection, processing, and distribution. Additionally, inventory, allocation, blood group substitution, blood shortage, transportation mode, and route traffic (delivery delays) are considered for four 24-hour periods in the model. For the first time in this field, knowledge of concentration levels in facilities is utilized, with simultaneous existence of the three levels of blood collection, processing, and distribution in field hospitals. This problem is formulated in a mixed-integer linear programming model with two objective functions aiming to minimize system costs and blood delivery time. The proposed model is solved using the augmented epsilon-constraint evolution method. Sensitivity analysis is conducted for the weights of the objective functions, and additional experiments (RD parameter) are performed. The sensitivity analysis on the weights of the objective functions reveals that reducing the weight of the first objective function leads to a decrease in blood delivery time, while increasing the weight of the second objective function results in an increase in network costs. The investigation of the impact of reducing the amount of additional testing (RD parameter) on the values of the objective functions confirms that advanced equipment at the processing sites of field hospitals reduces network costs and blood delivery time.
project management
Yahya Dorfeshan; Seyed Meysam Mousavi; Behnam Vahdani
Abstract
Critical path method is one of the most widely used approaches in planning and project control. Time is considered a determinative criterion for the critical path. But it seems necessary to regard other criteria in addition to time. Besides time criterion, effective criteria such as quality, cost, risk ...
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Critical path method is one of the most widely used approaches in planning and project control. Time is considered a determinative criterion for the critical path. But it seems necessary to regard other criteria in addition to time. Besides time criterion, effective criteria such as quality, cost, risk and safety are considered in this paper. Then, the developed problem is solved as a multi-attribute decision making problem by a new extension of MULTIMOORA method. Moreover, type-2 fuzzy sets are utilized for considering uncertainties. Type-2 fuzzy sets are more flexible and capable than type-1 fuzzy sets in reflecting uncertainties. Eventually, SWARA method is developed for determining the weights of efficient criteria such as time, cost, quality, risk and safety under type-2 fuzzy environment. Finally, an applied example has been solved to illustrate the calculations and the ability of the proposed approach. Based on the example, it is clear that the longest path in terms of time criterion is not a critical path, and other influential criteria are involved in determining the critical path. IntroductionToday, in the competitive business environment, project management, planning, scheduling, and project control hold significant importance. One of the widely used and common methods in the field of project planning and control is undoubtedly the Critical Path Method (CPM). In the Critical Path Method, activity durations are predetermined. However, in the real world, many projects and activities are executed for the first time and have considerable uncertainties. Therefore, obtaining an accurate estimate of the time and resources required for activities is challenging. However, considering a single criterion, such as time, will not yield fruitful results, and other influential parameters such as risk should also be taken into account. For example, a path that carries a high level of risk may not be the critical path at present, but it may become critical in the future due to the high risk involved. For this reason, this research explores other influential criteria besides time and considers them in determining the critical path.Materials and MethodsIn this study, the problem under investigation is the determination of the critical path while considering other influential criteria in addition to the time criterion. To achieve this, multiple criteria decision-making methods are used to consider criteria such as time, cost, quality, risk, and safety in determining the critical path. Furthermore, to account for the uncertainties of the real world and incorporate expert opinions, type-2 fuzzy sets are utilized. It should be noted that the MULTIMOORA method is employed for ranking the critical paths, while the SWARA method is used to determine the weights of the influential criteria in determining the critical path. Both methods have been extended and developed in a type-2 fuzzy environment.Discussion and Results Initially, the proposed method is solved considering only the time criterion. As observed, the critical path has changed, indicating the importance of other criteria in determining the critical path. Then, the proposed method is solved considering pairwise combinations of the criteria, where the time criterion is treated as a fixed criterion due to its high importance. In fact, the problem is solved considering time and cost, time and risk, time and quality, and time and risk. By increasing or decreasing each criterion, the critical path changes, demonstrating the significance of all criteria in determining the project's critical path. To determine the critical path, it is necessary to consider all criteria together. These variations in the criteria and the resulting change in the critical path clearly indicate the importance and influence of other criteria in determining the critical path.ConclusionIn this article, an extension of the MULTIMOORA multi-criteria decision-making method is presented in the reference section. Additionally, Type-2 fuzzy numbers, which offer more flexibility and better representation of uncertainties compared to Type-1 fuzzy numbers, are utilized. The MULTIMOORA multi-criteria decision-making method is developed to incorporate these Type-2 fuzzy numbers. The opinions of three experts are used numerically for the time and cost criteria and linguistically as linguistic variables for the quality, risk, and safety criteria. Ultimately, the weights of the influential criteria of time, cost, risk, quality, and safety are determined using the developed SWARA method under Type-2 fuzzy environment. Finally, the most critical path is determined by considering not only the time criterion but also the influential criteria of cost, quality, risk, and safety. Based on the conducted research, a set of criteria including time, cost, quality, risk, and safety are used in this article, and additional criteria can also be added to this set.
Mohamad javad Ershadi; Amir Azizi; Majid Mohajeri
Abstract
One of the major challenges in the automotive industry is facing different risks, especially when introducing new products to meet customer needs. This often leads to difficulties in accurately identifying and adapting to changing methods, designs, new machinery and materials, demand fluctuations, production ...
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One of the major challenges in the automotive industry is facing different risks, especially when introducing new products to meet customer needs. This often leads to difficulties in accurately identifying and adapting to changing methods, designs, new machinery and materials, demand fluctuations, production speed, and more. These factors can result in serious injuries and risks. In order to address these risks, it is crucial to employ effective risk identification methods and prioritize them to exert control over critical risks. Therefore, this paper focuses on identifying the main areas of risks in the automotive industry, specifically within the production line. The identified risks are then categorized and graded. Based on this assessment, a fuzzy cognitive maps approach is developed to analyze 13 risks, which are further divided into three groups: technical, strategic, and operational risks. Furthermore, an interpretive structural modeling approach is used to evaluate the interrelationships among these risks, allowing for a comprehensive understanding of their correlations. Through the network analysis process, the most significant risks are identified. The findings reveal that design errors, low motivation, lack of financial resources, lack of parts, and low productivity are among the top five risks in the ISACO auto parts supply chain. IntroductionThe increasing complexity of industrial systems and the incorporation of new technologies, processes, machinery, and materials have highlighted the importance of considering environmental and safety aspects in risk assessment. Evaluating the impact of failures and their effects is a critical task in industries, particularly in the automotive sector. Among the various risk assessment techniques, failure mode and effects analysis (FMEA) has been widely recognized as a reliable method. Despite the extensive application of FMEA, there are limitations associated with this approach. One of the significant drawbacks is that it considers the SOD factors independently without considering the interdependencies among failures. In reality, production stages are not executed simultaneously, and potential failures do not occur concurrently. Some failures are influenced by previous stage failures and, in turn, affect subsequent stages. On the other hand, interpretive structural modeling (ISM) allows for the comprehensive structuring of a set of interconnected factors in an organized model. By utilizing fundamental concepts of graph theory, ISM describes the intricate pattern of conceptual relationships among variables. In this way, it overcomes the limitations of independent consideration of failures in FMEA. Therefore, this paper employs ISM as an approach to assess the impact of failures. It provides a comprehensive and structured model that captures the interrelationships among various factors. By using this approach, the evaluation of failures becomes more accurate and reliable, considering the interdependencies among different stages and failures.Materials and MethodsThis research is categorized as applied research in terms of its objective and descriptive-qualitative in terms of its method. Field studies were conducted as the data collection tools for this research. The scoring method (utilizing experts) was used for data analysis, and a case study of the ISACO company was employed to test the model. The required data for this research, aimed at presenting a model for identifying production risks in the first stage, were collected through a literature review. Relevant English and Persian books, student theses, related websites, journal articles, conferences, and seminars focusing on the identification of multi-stage production risks were used to gather research literature. Existing documentation from various industries was also utilized in the field of risk assessment and identification. In the initial stage, the main risks of the automotive parts supplier company are identified. In this phase, risks identified in existing scientific research sources were finalized through interviews with experts. The extracted risks are evaluated and ranked based on the failure mode and effects analysis method in the second step. In the third step, the interactions among various risks are examined using the fuzzy cognitive map approach. The results obtained from the second step are utilized in this phase through normalization. In the fourth step, the final ranking of risks is determined based on the static analysis conducted in the third step. In the fifth step, an interpretive structural model is used to determine the interdependence and susceptibility of risks to each other.Discussion and ResultsBased on the research objectives, the risks in the production line domain were first identified using the FMEA (Failure Mode and Effects Analysis) approach. Then, the FCM (Fuzzy Cognitive Mapping) method was employed to design a fuzzy network, and ultimately, the ISM (Interpretive Structural Modeling) approach was used to analyze the penetration and interdependence of risks. The ranking of risks using the FMEA approach is as follows: lack of motivation, parts shortage, low productivity, rework in execution, and weak supervision are ranked from 1 to 5, respectively. After considering the interactions among risks in the dynamic analysis of FCM, the factor of lack of motivation descends from rank 1 to 7. Furthermore, the factors of low productivity and lack of financial resources rank first and second, respectively.ConclusionDecision-making in the field of risk management involves considering various factors that are subject to change over time. The dynamic nature of these factors can influence the effectiveness of risk management decisions, and their impact on the desired outcomes needs to be carefully assessed. Proper risk management requires a comprehensive understanding of potential failures and the ability to predict and mitigate their consequences. Analyzing risks, employing effective mitigation strategies, and conducting thorough evaluations are essential for ensuring the success of a project or business venture. Professional risk management involves identifying and addressing potential vulnerabilities, evaluating their impact on the desired objectives, and devising appropriate strategies to prevent or mitigate their occurrence. The use of risk assessment methodologies, such as Failure Mode and Effects Analysis (FMEA), allows for systematic identification and prediction of potential failures, while incorporating flexibility and adaptability in risk mitigation approaches. These methodologies offer advantages such as scalability, speed, high accuracy in predicting failures, enhanced understanding of complex systems, and facilitation of decision-making processes. By employing fuzzy cognitive mapping (FCM) in FMEA, the prioritization and prediction of potential risks can be effectively performed. This approach provides a more flexible and comprehensive understanding of risks, enabling easier decision-making and utilization of valuable feedback from domain experts. Following the identification of primary risk areas, the risks associated with production lines were classified, and a fuzzy cognitive mapping approach was developed based on this classification. Thirteen identified risks were then analyzed using interpretive structural modeling (ISM) to assess the interrelationships among the risks and provide further insights for decision-making.
Hasan Rabiee; Farhad Etebari
Abstract
In this study, a location routing model has been considered for the distribution network of multiple perishable food products in a cold supply chain in which the vehicles can fuel at filling stations. Here, the fuel consumption is supposed to vary depending on the loading amount transported between the ...
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In this study, a location routing model has been considered for the distribution network of multiple perishable food products in a cold supply chain in which the vehicles can fuel at filling stations. Here, the fuel consumption is supposed to vary depending on the loading amount transported between the nodes using a fleet that uses unusual fuels. The problem has been formulated as an integer linear programming model to reduce the production of Carbon Dioxide. The model was validated using several numerical examples solved in GAMS software. Results show that in this case the fuel consumption in average decreases 14 percent. Due to the problem complexity, genetic simulated annealing algorithms were developed for solving the problems in real size and their performance has been also evaluated.
Ali Akbar Mohamadian; Masoud Simkhah
Abstract
Supplier selection is one of the most important problems in the field of management and optimization. It aims to optimize the cost of supply, quality of products and services, and the risk of non-supply, among others. However, existing models often overlook the risk of non-supply and the brand effect ...
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Supplier selection is one of the most important problems in the field of management and optimization. It aims to optimize the cost of supply, quality of products and services, and the risk of non-supply, among others. However, existing models often overlook the risk of non-supply and the brand effect on demand. In this research, a supplier selection integer model is developed that considers lead time and the risk of non-supply. To solve this model, the LOKAD benchmark database is utilized, and a new adaptive variable neighborhood search algorithm is introduced, incorporating a scoring strategy to handle the model's complexity and obtain optimal or near-optimal solutions. The obtained Pareto solutions outperform traditional results, as confirmed by the Wilcoxon test. Sensitivity analysis of the solutions on the budget demonstrates that the final profit is more sensitive compared to lead time. Furthermore, distance from the ideal and diversity measures are used to quantitatively compare the results. IntroductionSupplier selection involves identifying, evaluating, and contracting with suppliers to meet an organization's requirements for raw materials and related infrastructure. It plays a crucial role in financial resource allocation and product/service quality. The main objectives of supplier selection include reducing purchasing risks, minimizing lead time, and enhancing quality. Many organizations face multiple criteria for selecting suppliers, such as receipt risk, green criteria, and lead time. Mathematical modeling and optimization techniques are commonly employed to achieve these objectives. However, existing models often lack real-case assumptions, motivating researchers to extend models in this environment. This research addresses these gaps by developing a mathematical model for supplier selection that considers the risk of non-supply and the substitution rate of inventory-less products. Materials and MethodsTechnically, the conducted modeling is a quantitative research in which data has been collected using library-based tools. Additionally, the statistical population corresponds to the LOKAD company, and a sample of its information is publicly available and will be utilized in the numerical computations phase of this research. This research develops a novel supplier selection multi-objective mathematical model that considers the risk of non-supply and substitution rate of demand when a product is inventory-less. Additionally, a modified adaptive variable neighborhood search (AVNS) algorithm is proposed to solve the model. While the focus of this paper is on the retail industry, the proposed model can be adapted to any industry. Discussion and ResultsThe developed model will be solved using data related to the LOKAD company, which was collected during a one-year period in 2018. This dataset includes sales and purchasing information of products related to the LOKAD company, encompassing details about the suppliers of the products and their brands. The utilization of this dataset is due to the fact that many articles have made use of it for its detailed information provided by the LOKAD company. In order to compare the method, the Wilcoxon test is used to compare two groups of variables, which can determine the presence of a difference between them. The observations reveal that the substitution rate of demand significantly affects the results and can alter the selection of final suppliers. Budget limitations are another important factor, where increasing the budget leads to higher profits by enabling the selection of more competent suppliers and high-quality products for customers. ConclusionsSupplier selection is a challenging problem in various industries. The experiments conducted in this research demonstrate that increasing the budget limitation results in higher profits, as customers prefer products or services with higher levels of quality. The developed mathematical multi-objective model incorporates real-case assumptions such as the risk of non-supply and substitution rate of demand. The model is solved using the proposed modified AVNS algorithm. The solutions obtained are analyzed using mean ideal distance and diversification metric measures to ensure their reliability. The analysis highlights the significance of budget limitations, which outweighs the impact of lead time in supplier selection problems. Ultimately, the model provides an optimal combination of suppliers. Additionally, the sensitivity analysis performed on the budget constraint reveals that changes in the budget have a greater impact on the final profit compared to lead time. The proposed model effectively determines the allocation of purchases from each supplier to enhance the final profit. In this regard, an initial estimation of future demand is considered as deterministic, although transforming this parameter into a probabilistic form can make the model more robust.
Azam Tariyan; Hessam ZandHessami; Abbas khamseh
Abstract
The construction industry has always had significant destructive effects on the environment. Utilizing green supply chain management strategies to achieve sustainable construction is an effective approach to reducing environmental damage. In this research, the qualitative approach and interpretive paradigm, ...
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The construction industry has always had significant destructive effects on the environment. Utilizing green supply chain management strategies to achieve sustainable construction is an effective approach to reducing environmental damage. In this research, the qualitative approach and interpretive paradigm, along with the seven-step meta-synthesis method, were employed to explain the influential components in establishing green supply chain management in the construction industry. A total of 728 relevant studies in the field of supply chain management, particularly with a focus on sustainability, green practices, and reverse logistics, were identified, reviewed, evaluated, and screened. Based on the entry and exit protocol, 37 studies were included in the research portfolio, resulting in the extraction of 73 primary codes that were categorized into 13 concepts and 4 categories. The reliability of the classification was confirmed using the Kappa coefficient. The research findings revealed that the components influencing green supply chain management were identified across four main categories: main factors of green supply chain management, facilitating factors, internal and external drivers and barriers. Finally, suggestions based on the extracted results from this research were presented for future researchers. IntroductionThe construction industry is the primary consumer of resources and energy worldwide and has significant detrimental effects on the environment. Environmental protection and sustainability have become global concerns across various industries in the past two decades. Companies and governments in many countries are recognizing the importance of green supply chains, and this awareness is rapidly growing within the construction industry. Currently, competition between companies has shifted towards competition between their supply chains. Therefore, expanding the concept of sustainability in supply chain management is considered a strategy to enhance performance and improve a company's competitiveness. Green supply chain management is highly regarded among academics and industry professionals as it aims to preserve product quality, conserve resources, and minimize production waste. In the construction industry, green supply chain management involves managing all activities throughout the supply chain that contribute to the final product (building) to minimize environmental impacts. Hence, a holistic approach is required to manage all construction project activities and ensure sustainability across social, economic, and environmental dimensions. While extensive studies have been conducted on green supply chain management in various industries, the number of studies in the construction industry is relatively limited and fragmented, lacking a cohesive understanding of scientific findings in this field. To address this research gap and considering the importance of the topic and the lack of a comprehensive method in this field, the current research aims to present a model and identify the factors influencing green supply chain management in the construction industry, particularly in Iran. Materials and MethodsAccording to the nature of the research, the current study adopts a meta-synthesis approach, which is qualitative in nature with an interpretive paradigm. Meta-synthesis is a type of systematic qualitative approach that combines and integrates qualitative findings from different but related studies. By providing a systematic perspective to researchers, meta-synthesis allows for the discovery of new and fundamental themes and insights by synthesizing various qualitative studies. This approach enhances the existing knowledge and provides a broader understanding of the issues at hand. The seven-step method developed by Sandelowski has been employed to conduct the meta-synthesis. The data collected for this research have been coded and summarized using content analysis with the assistance of the MAXQDA qualitative data analysis software.ConclusionsThis research aimed to identify the factors influencing green supply chain management in the construction industry. After conducting keyword searches, the identified documents were screened and evaluated, resulting in a set of documents that were coded and summarized using content analysis. These codes were then evaluated by experts. The findings were categorized and presented in a table, and a visual model was proposed for better comprehension. The research findings revealed four categories of factors influencing green supply chain management: main factors, facilitating factors, internal and external drivers, and barriers. The main factors identified in this research include green purchasing, green design, green construction, and reverse logistics. Facilitating factors include human resource management, green technology, internal environmental management, and green marketing. Additionally, the research identified important drivers and barriers for the implementation of green supply chain management. Internal drivers include the desire to enhance reputation, credibility, and business image; cost reduction; the desire to enter foreign markets; and societal pressure
