A. Babaeinesami, P. Ghasemi, Adel Pourghader Chobar, M. R. Sasouli, Masoumeh Lajevardi
Abstract Nowadays, companies need to take responsibility for addressing growing markets and the growing expectations of their customers to survive in a highly competitive context that is progressing on a daily basis. Rapid economic changes and increasing competitive pressure in global markets have led companies to pay special attention to their supply chains. As a result, in this research, a mathematical model is proposed to minimize closed loop supply chain costs taking into account environmental effects. Thus, suppliers first send wood as raw materials from forests to factories. After processing the wood and turning it into products, the factories send the wood to retailers. The retailers then send the products to the customers. Finally, customers send returned products to recovery centers. After processing the products, the recovery centers send their products to the factories. The considered innovations include: designing a supply chain of wood products regarding environmental effects, customizing the genetic solution approach to solve the proposed model 3-Considering the flow of wood products and determining the amount of raw materials and products sent and received.
{"title":"A New Wooden Supply Chain Model for Inventory Management Considering Environmental Pollution: A Genetic algorithm","authors":"A. Babaeinesami, P. Ghasemi, Adel Pourghader Chobar, M. R. Sasouli, Masoumeh Lajevardi","doi":"10.2478/fcds-2022-0021","DOIUrl":"https://doi.org/10.2478/fcds-2022-0021","url":null,"abstract":"Abstract Nowadays, companies need to take responsibility for addressing growing markets and the growing expectations of their customers to survive in a highly competitive context that is progressing on a daily basis. Rapid economic changes and increasing competitive pressure in global markets have led companies to pay special attention to their supply chains. As a result, in this research, a mathematical model is proposed to minimize closed loop supply chain costs taking into account environmental effects. Thus, suppliers first send wood as raw materials from forests to factories. After processing the wood and turning it into products, the factories send the wood to retailers. The retailers then send the products to the customers. Finally, customers send returned products to recovery centers. After processing the products, the recovery centers send their products to the factories. The considered innovations include: designing a supply chain of wood products regarding environmental effects, customizing the genetic solution approach to solve the proposed model 3-Considering the flow of wood products and determining the amount of raw materials and products sent and received.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"47 1","pages":"383 - 408"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46992309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Manufacturing is one of the biggest drivers of a country’s economic growth. Nevertheless, due to globalization and flourishing consumer markets, the technological influx in manufacturing evolution poses a significant threat to climate change. To deal with the situation, green manufacturing came forward to play a vital role in lowering the impact of mass production on the global environment. The qualitative research based on expert opinion is used to have viewpoints for the implementation of green manufacturing based on green supply chain manufacturing (GSCMEs) enablers. The study, in this regard, focuses on exploring the key enablers adopted by the manufacturers to embrace green practices by using framework based on Interpretative Structural Modelling and Cross-Impact Multiplication Applied to Classification (MICMAC) analysis. Results indicate that economic constraints and the regulatory framework have high driving power and less dependency power. Researchers provide managers with a new outlook on the future towards building an eco-friendly supply chain and gaining a competitive edge over their competitors.
{"title":"Green Manufacturing: An Assessment of Enablers’ Framework Using ISM-MICMAC Analysis","authors":"S. Ali","doi":"10.2478/fcds-2022-0015","DOIUrl":"https://doi.org/10.2478/fcds-2022-0015","url":null,"abstract":"Abstract Manufacturing is one of the biggest drivers of a country’s economic growth. Nevertheless, due to globalization and flourishing consumer markets, the technological influx in manufacturing evolution poses a significant threat to climate change. To deal with the situation, green manufacturing came forward to play a vital role in lowering the impact of mass production on the global environment. The qualitative research based on expert opinion is used to have viewpoints for the implementation of green manufacturing based on green supply chain manufacturing (GSCMEs) enablers. The study, in this regard, focuses on exploring the key enablers adopted by the manufacturers to embrace green practices by using framework based on Interpretative Structural Modelling and Cross-Impact Multiplication Applied to Classification (MICMAC) analysis. Results indicate that economic constraints and the regulatory framework have high driving power and less dependency power. Researchers provide managers with a new outlook on the future towards building an eco-friendly supply chain and gaining a competitive edge over their competitors.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"47 1","pages":"271 - 290"},"PeriodicalIF":1.1,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41498714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The emergence of an infectious disease pandemic may result in the introduction of restrictions in the distance and number of employees, as was the case of COVID-19 in 2020/2021. In the face of fluctuating restrictions, the process of determining seating plans in office space requires repetitive execution of seat assignments, and manual planning becomes a time-consuming and error-prone task. In this paper, we introduce the Epidemiology-constrained Seating Plan problem (ESP), and we show that it, in general, belongs to the NP-complete class. However, due to some regularities in input data that could a affect computational complexity for practical cases, we conduct experiments for generated test cases. For that reason, we developed a computational environment, including the test case generator, and we published generated benchmarking test cases. Our results show that the problem can be solved to optimality by CPLEX solver only for specific settings, even in regular cases. Therefore, there is a need for new algorithms that could optimize seating plans in more general cases.
{"title":"Epidemiology-constrained Seating Plan Problem","authors":"J. Da̧bkowski, Przemysław Kacperski, M. Kaleta","doi":"10.2478/fcds-2022-0013","DOIUrl":"https://doi.org/10.2478/fcds-2022-0013","url":null,"abstract":"Abstract The emergence of an infectious disease pandemic may result in the introduction of restrictions in the distance and number of employees, as was the case of COVID-19 in 2020/2021. In the face of fluctuating restrictions, the process of determining seating plans in office space requires repetitive execution of seat assignments, and manual planning becomes a time-consuming and error-prone task. In this paper, we introduce the Epidemiology-constrained Seating Plan problem (ESP), and we show that it, in general, belongs to the NP-complete class. However, due to some regularities in input data that could a affect computational complexity for practical cases, we conduct experiments for generated test cases. For that reason, we developed a computational environment, including the test case generator, and we published generated benchmarking test cases. Our results show that the problem can be solved to optimality by CPLEX solver only for specific settings, even in regular cases. Therefore, there is a need for new algorithms that could optimize seating plans in more general cases.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"47 1","pages":"235 - 246"},"PeriodicalIF":1.1,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48566593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Soukaina Laabadi, M. Naimi, H. E. Amri, B. Achchab
Abstract The growing need for profit maximization and cost minimization has made the optimization field very attractive to both researchers and practitioners. In fact, many authors were interested in this field and they have developed a large number of optimization algorithms to solve either academic or real-life problems. Among such algorithms, we cite a well-known metaheuristic called the genetic algorithm. This optimizer tool, as any algorithm, suffers from some drawbacks; like the problem of premature convergence. In this paper, we propose a new selection strategy hoping to avoid such a problem. The proposed selection operator is based on the principle of the k-means clustering method for the purpose of guiding the genetic algorithm to explore different regions of the search space. We have elaborated a genetic algorithm based on this new selection mechanism. We have then tested our algorithm on various data instances of the 0/1 multidimensional knapsack problem. The obtained results are encouraging when compared with those reached by other versions of genetic algorithms and those reached by an adapted version of the particle swarm optimization algorithm.
{"title":"On Solving 0/1 Multidimensional Knapsack Problem with a Genetic Algorithm Using a Selection Operator Based on K-Means Clustering Principle","authors":"Soukaina Laabadi, M. Naimi, H. E. Amri, B. Achchab","doi":"10.2478/fcds-2022-0014","DOIUrl":"https://doi.org/10.2478/fcds-2022-0014","url":null,"abstract":"Abstract The growing need for profit maximization and cost minimization has made the optimization field very attractive to both researchers and practitioners. In fact, many authors were interested in this field and they have developed a large number of optimization algorithms to solve either academic or real-life problems. Among such algorithms, we cite a well-known metaheuristic called the genetic algorithm. This optimizer tool, as any algorithm, suffers from some drawbacks; like the problem of premature convergence. In this paper, we propose a new selection strategy hoping to avoid such a problem. The proposed selection operator is based on the principle of the k-means clustering method for the purpose of guiding the genetic algorithm to explore different regions of the search space. We have elaborated a genetic algorithm based on this new selection mechanism. We have then tested our algorithm on various data instances of the 0/1 multidimensional knapsack problem. The obtained results are encouraging when compared with those reached by other versions of genetic algorithms and those reached by an adapted version of the particle swarm optimization algorithm.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"47 1","pages":"247 - 269"},"PeriodicalIF":1.1,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44009710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Semantic checking of railway infrastructure information support data is one of the ways to improve the consistency of information system data and, as a result, increase the safety of train traffic. Existing ontological developments have demonstrated the applicability of description logic for modelling railway transport, but have not paid enough attention to the data resources structure and the railway regulatory support. In this work, the formalization of the tabular presentation of data and the rules of railway transport regulations is carried out using the example of a connection track passport and temporary speed restrictions using ontological means, data wrangling and extraction tools. Ontologies of the various formats data resources and railway station infrastructure, tools for converting and extracting data have been developed. The semantic checking of the compliance of railway information system data with regulatory documents in terms of the connection track passport is carried out on the basis of a multi-level concretization model and integration of ontologies. The mechanisms for implementing the constituent ontologies and their integration are demonstrated by an example. Further research includes ontological checking of natural language normative documents of railway transport.
{"title":"Ontology-Based Semantic Checking of Data in Railway Infrastructure Information Systems","authors":"V. Shynkarenko, L. Zhuchyi, Oleksandr Ivanov","doi":"10.2478/fcds-2022-0016","DOIUrl":"https://doi.org/10.2478/fcds-2022-0016","url":null,"abstract":"Abstract Semantic checking of railway infrastructure information support data is one of the ways to improve the consistency of information system data and, as a result, increase the safety of train traffic. Existing ontological developments have demonstrated the applicability of description logic for modelling railway transport, but have not paid enough attention to the data resources structure and the railway regulatory support. In this work, the formalization of the tabular presentation of data and the rules of railway transport regulations is carried out using the example of a connection track passport and temporary speed restrictions using ontological means, data wrangling and extraction tools. Ontologies of the various formats data resources and railway station infrastructure, tools for converting and extracting data have been developed. The semantic checking of the compliance of railway information system data with regulatory documents in terms of the connection track passport is carried out on the basis of a multi-level concretization model and integration of ontologies. The mechanisms for implementing the constituent ontologies and their integration are demonstrated by an example. Further research includes ontological checking of natural language normative documents of railway transport.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"47 1","pages":"291 - 319"},"PeriodicalIF":1.1,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44633050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zulhery Noer, M. Elveny, A. Jalil, A. H. Iswanto, Samaher Al-Janabi, A. Alkaim, G. Mullagulova, Natalia Nikolaeva, R. Shichiyakh
Abstract In recent years, the increase in population and the decrease in agricultural lands and water shortages have caused many problems for agriculture and farmers. That is why scheduling is so important for farmers. Therefore, the implementation of an optimal schedule will lead to better use of agricultural land, reduce water consumption in agriculture, increase efficiency and quality of agricultural products. In this research, a scheduling problem for harvesting agricultural products has been investigated. In this problem, there are n number of agricultural lands that in each land m agricultural operations are performed by a number of machines that have different characteristics. This problem is modeled as a scheduling problem in a flexible workshop flow environment that aims to minimize the maximum completion time of agricultural land. The problem is solved by programming an integer linear number using Gams software. The results show that the proposed mathematical model is only capable of solving small and medium-sized problems, and due to the Hard-NP nature of the problem, large-scale software is not able to achieve the optimal solution.
{"title":"A New Model for Scheduling Operations in Modern Agricultural Processes","authors":"Zulhery Noer, M. Elveny, A. Jalil, A. H. Iswanto, Samaher Al-Janabi, A. Alkaim, G. Mullagulova, Natalia Nikolaeva, R. Shichiyakh","doi":"10.2478/fcds-2022-0008","DOIUrl":"https://doi.org/10.2478/fcds-2022-0008","url":null,"abstract":"Abstract In recent years, the increase in population and the decrease in agricultural lands and water shortages have caused many problems for agriculture and farmers. That is why scheduling is so important for farmers. Therefore, the implementation of an optimal schedule will lead to better use of agricultural land, reduce water consumption in agriculture, increase efficiency and quality of agricultural products. In this research, a scheduling problem for harvesting agricultural products has been investigated. In this problem, there are n number of agricultural lands that in each land m agricultural operations are performed by a number of machines that have different characteristics. This problem is modeled as a scheduling problem in a flexible workshop flow environment that aims to minimize the maximum completion time of agricultural land. The problem is solved by programming an integer linear number using Gams software. The results show that the proposed mathematical model is only capable of solving small and medium-sized problems, and due to the Hard-NP nature of the problem, large-scale software is not able to achieve the optimal solution.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"47 1","pages":"151 - 161"},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44130231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Tikhonov, A. Sazonov, V. M. Kraev, I. Kuzmina-Merlino
Abstract The purpose of the research in this article is to investigate the main trends in the development of the different industries during the COVID-19 pandemic, to identify the main problems facing the different industries in the context of the global crisis, as well as to form the basic concepts necessary for a real recovery of the global industry. The authors identify the main problems facing the aviation industry in the developing world crisis and possible ways to solve them. As a working hypothesis, it is proposed to form the basic concepts necessary for preparing and implementing operational measures to restore passenger and cargo aviation. Considering the main threats facing the aviation industry during COVID-19, the article proposes the organizational and economic mechanisms to restore the industry. Furthermore, several recovery scenarios are considered, considering the relevant factors that have a particular impact. Next, a novel mathematical model for pharmaceutical products, which are the most important in COVID-19 pandemics, is proposed. Moreover, the model considers the uncertainty, and a robust optimization approach is applied. The study is based on a comprehensive analysis of documentary data provided by government agencies in several European countries. An analysis of global and Russian passenger traffic for Q1-Q4 (quartile) of 2020 and a development forecast for Q1-Q2 of 2021 is provided. The scenario problems facing the aviation industry in the context of the COVID-19 crisis are identified. There are key concepts necessary to prepare and implement effective measures to restore the aviation industry.
{"title":"The Main Trends and Challenges in The Development of the Different Industries During The COVID-19 Pandemic","authors":"A. Tikhonov, A. Sazonov, V. M. Kraev, I. Kuzmina-Merlino","doi":"10.2478/fcds-2022-0012","DOIUrl":"https://doi.org/10.2478/fcds-2022-0012","url":null,"abstract":"Abstract The purpose of the research in this article is to investigate the main trends in the development of the different industries during the COVID-19 pandemic, to identify the main problems facing the different industries in the context of the global crisis, as well as to form the basic concepts necessary for a real recovery of the global industry. The authors identify the main problems facing the aviation industry in the developing world crisis and possible ways to solve them. As a working hypothesis, it is proposed to form the basic concepts necessary for preparing and implementing operational measures to restore passenger and cargo aviation. Considering the main threats facing the aviation industry during COVID-19, the article proposes the organizational and economic mechanisms to restore the industry. Furthermore, several recovery scenarios are considered, considering the relevant factors that have a particular impact. Next, a novel mathematical model for pharmaceutical products, which are the most important in COVID-19 pandemics, is proposed. Moreover, the model considers the uncertainty, and a robust optimization approach is applied. The study is based on a comprehensive analysis of documentary data provided by government agencies in several European countries. An analysis of global and Russian passenger traffic for Q1-Q4 (quartile) of 2020 and a development forecast for Q1-Q2 of 2021 is provided. The scenario problems facing the aviation industry in the context of the COVID-19 crisis are identified. There are key concepts necessary to prepare and implement effective measures to restore the aviation industry.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"47 1","pages":"209 - 231"},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43483225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Dastani, Sayyed Mohammad Reza Davoodi, Mehdi Karbassian, Shahram Moeini
Abstract Intense competition in today’s market and quick change in customer preferences, along with the rapid development of technology and globalization, have forced companies to work as members of a supply chain instead of individual companies. The success of the supply chain depends on the integration and coordination of all its institutions to form an efficient network structure. An efficient network leads to cost savings throughout the supply chain and helps it respond to customer needs faster. Accordingly, and with respect to the importance of the supply chain, in this study a developed mathematical model for the design of a green closed-loop supply chain is presented. In this mathematical model, the economic and environmental objectives are simultaneously optimized. In order to tackle this mathematical model, two methods of epsilon constraint and multi-objective gray wolf optimization (MOGWO) algorithm have been applied. The results of comparisons between the two mentioned methods show that MOGWO reduce the average solving time from about 1300 seconds to 88 seconds. In the last step of this research, in order to show the application of the proposed mathematical model and the method of solving the research problem, it was implemented in the supply chain of Dalan Kouh diary product and the Pareto optimal solutions were analyzed.
{"title":"Developing a Mathematical Model for a Green Closed-Loop Supply Chain with a Multi-Objective Gray Wolf Optimization Algorithm","authors":"M. Dastani, Sayyed Mohammad Reza Davoodi, Mehdi Karbassian, Shahram Moeini","doi":"10.2478/fcds-2022-0007","DOIUrl":"https://doi.org/10.2478/fcds-2022-0007","url":null,"abstract":"Abstract Intense competition in today’s market and quick change in customer preferences, along with the rapid development of technology and globalization, have forced companies to work as members of a supply chain instead of individual companies. The success of the supply chain depends on the integration and coordination of all its institutions to form an efficient network structure. An efficient network leads to cost savings throughout the supply chain and helps it respond to customer needs faster. Accordingly, and with respect to the importance of the supply chain, in this study a developed mathematical model for the design of a green closed-loop supply chain is presented. In this mathematical model, the economic and environmental objectives are simultaneously optimized. In order to tackle this mathematical model, two methods of epsilon constraint and multi-objective gray wolf optimization (MOGWO) algorithm have been applied. The results of comparisons between the two mentioned methods show that MOGWO reduce the average solving time from about 1300 seconds to 88 seconds. In the last step of this research, in order to show the application of the proposed mathematical model and the method of solving the research problem, it was implemented in the supply chain of Dalan Kouh diary product and the Pareto optimal solutions were analyzed.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"47 1","pages":"127 - 150"},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47943108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This special issue of the Foundations of Computing and Decision Sciences, titled “Computational Performance Analysis based on Novel Intelligent Methods: Exploration and Future Directions in Production and Logistics”, is devoted to the application of Computational Performance Analysis (CPA) for real-life phenomena. The special issue and its editorial present novel intelligent methods as they meet with various research topics in production and logistics, especially in terms of challenges, limitations and future trends. This special issue aims to bring together current progress on the CPA, organization management, and novel models and solution techniques that can contribute to a better understanding of the CPA systems and delineate useful practical strategies. Methodologically interesting and well-documented case studies are highly recommended. Additionally, the special issue covers innovative cutting-edge research methodologies and applications in the related research field.
{"title":"Preface to the Special Issue on Computational Performance Analysis based on Novel Intelligent Methods: Exploration and Future Directions in Production and Logistics","authors":"Alireza Goli, E. B. Tirkolaee, G. Weber","doi":"10.2478/fcds-2022-0005","DOIUrl":"https://doi.org/10.2478/fcds-2022-0005","url":null,"abstract":"Abstract This special issue of the Foundations of Computing and Decision Sciences, titled “Computational Performance Analysis based on Novel Intelligent Methods: Exploration and Future Directions in Production and Logistics”, is devoted to the application of Computational Performance Analysis (CPA) for real-life phenomena. The special issue and its editorial present novel intelligent methods as they meet with various research topics in production and logistics, especially in terms of challenges, limitations and future trends. This special issue aims to bring together current progress on the CPA, organization management, and novel models and solution techniques that can contribute to a better understanding of the CPA systems and delineate useful practical strategies. Methodologically interesting and well-documented case studies are highly recommended. Additionally, the special issue covers innovative cutting-edge research methodologies and applications in the related research field.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"47 1","pages":"107 - 110"},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42106293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rahmad Syah, M. Nasution, Vladimir Vladimirovich Shol, N. Kireeva, A. Jalil, Tzu-Chia Chen, S. Aravindhan, E. Abood, A. Alkaim
Abstract Recently, due to the increasing awareness of communities regarding environmental issues and environmental regulations, companies have evolved to provide products with lower prices and better quality to retain and attract customers. Economics should also pay attention to environmental goals. Therefore, it is essential to provide a supply chain model that can consider both economic and environmental objectives. In this paper, the green direct supply chain network is presented to an automotive company, including five suppliers, primary warehouses, manufacturing plants, distributors, and sales centers. The objectives of this model are to minimize the total cost of construction, transportation, and the amount of carbon dioxide emissions during forwarding network transportation at all levels. The proposed model is also drawn using the weight method, which is one of the methods for solving multi-objective problems, and the solution of the model part. Ultimately, it has been discussed how much the automobile company should focus on reducing carbon dioxide so that managers can determine the best solutions from the Pareto border according to their organization’s priorities, which can be environmental or financial.
{"title":"Designing a Green Supply Chain Transportation System for an Automotive Company Based On Bi-Objective Optimization","authors":"Rahmad Syah, M. Nasution, Vladimir Vladimirovich Shol, N. Kireeva, A. Jalil, Tzu-Chia Chen, S. Aravindhan, E. Abood, A. Alkaim","doi":"10.2478/fcds-2022-0011","DOIUrl":"https://doi.org/10.2478/fcds-2022-0011","url":null,"abstract":"Abstract Recently, due to the increasing awareness of communities regarding environmental issues and environmental regulations, companies have evolved to provide products with lower prices and better quality to retain and attract customers. Economics should also pay attention to environmental goals. Therefore, it is essential to provide a supply chain model that can consider both economic and environmental objectives. In this paper, the green direct supply chain network is presented to an automotive company, including five suppliers, primary warehouses, manufacturing plants, distributors, and sales centers. The objectives of this model are to minimize the total cost of construction, transportation, and the amount of carbon dioxide emissions during forwarding network transportation at all levels. The proposed model is also drawn using the weight method, which is one of the methods for solving multi-objective problems, and the solution of the model part. Ultimately, it has been discussed how much the automobile company should focus on reducing carbon dioxide so that managers can determine the best solutions from the Pareto border according to their organization’s priorities, which can be environmental or financial.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"47 1","pages":"193 - 207"},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47515121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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