Cost Reduction of Inventory-Production-System in Multi-echelon Supply Chain Using Game Theory and Fuzzy Demand Forecasting

Лю, Пэйдэ; Hendalianpour, Ayad; Hamzehlou, Mohammad; Feylizadeh, Mohammad Reza

doi:10.1007/s40815-021-01240-5

Cited by 22 publications

(7 citation statements)

References 79 publications

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“…Bloetscher et al identified beverage manufacturing, papermaking and paper products, food manufacturing, and agricultural and sideline food processing industries as typical industries to reduce oxygen demand pollution through the summary, analysis, and research on the census data of pollution sources and focused on the generation, emission, and treatment of five-day biochemical oxygen demand in four industries [8]. Liu et al analyzed the production, emission characteristics, and pollution status of pollutants in Jingjiang City, Jiangsu Province, by using the census data of pollution sources and social statistical data and pointed out that while industrial pollution is paid attention to, domestic pollution and agricultural pollution cannot be ignored [9]. Wen et al analyzed the main environmental problems within the jurisdiction according to the results of the general survey of pollution sources in a city and put forward corresponding countermeasures and suggestions in combination with local conditions [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Liu

Sun

Jing

et al. 2022

Wireless Communications and Mobile Computing

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In order to strengthen the supervision and management of environmental pollution and timely understand and record the basic information of potential environmental pollution of enterprises and institutions, this paper proposes a general survey and inventory method of pollution sources based on big data technology under machine learning. Firstly, this paper evaluates and screens the data provided by government departments, constructs a machine learning classification model, and uses a variety of classification algorithms to compare and analyze according to the basic idea of machine learning to deal with practical problems. Then, the calibration data set constructed is used as the training set to predict and classify the national industrial and commercial data, provincial industrial and commercial data, and municipal industrial and commercial data provided by government departments. The experimental results show that the naive Bayesian classification algorithm is the best algorithm, and the F1 values of each data set are increased by 32.92%, 21.42%, and 14.91%, respectively. The classified prediction of the screened Internet data shows that the accuracy of the final Internet supplementary data is 17.26%, which is similar to the industrial and commercial data of the city. The availability of the machine learning model established in this paper is proven.

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Section: Literature Reviewmentioning

confidence: 99%

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Liu

Sun

Jing

et al. 2022

Wireless Communications and Mobile Computing

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“…Furthermore, game theory can be applied to analyze strategic decisions regarding inventory management, production planning, and capacity allocation in supply chains (Liu et al, 2022). By modeling these decisions as games, businesses can analyze the strategic interactions between stakeholders and develop optimal strategies that balance competing objectives such as cost minimization, service level maximization, and risk mitigation.…”

Section: Game Theory Applications For Strategic Decision Making In Su...mentioning

confidence: 99%

Theoretical approaches to AI in supply chain optimization: Pathways to efficiency and resilience

Emmanuel Adeyemi Abaku,

Tolulope Esther Edunjobi,

Agnes Clare Odimarha

2024

Int. J. Sci. Technol. Res. Arch

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The integration of Artificial Intelligence (AI) into supply chain management has emerged as a pivotal avenue for enhancing efficiency and resilience in contemporary business operations. This paper explores various theoretical approaches to AI within the context of supply chain optimization, delineating pathways to achieve heightened performance and adaptability. Commencing with a historical overview, the paper delves into the evolution of AI techniques in supply chain management, elucidating how these methodologies have transformed the landscape of logistics and operations. Fundamental to this exploration is the discussion on mathematical modeling and algorithmic frameworks that underpin supply chain optimization, providing the theoretical foundation for subsequent AI applications. A key focus of the paper lies in the application of machine learning techniques for demand forecasting and inventory management, which leverage data-driven insights to optimize resource allocation and mitigate risks associated with supply-demand fluctuations. Additionally, network theory and graph algorithms play a crucial role in optimizing the structure and dynamics of supply chain networks, enabling efficient transportation, distribution, and inventory routing. Strategic decision-making in supply chains is addressed through the lens of game theory, which offers theoretical frameworks to model interactions among multiple stakeholders and optimize outcomes in competitive environments. Moreover, swarm intelligence and multi-agent systems provide innovative solutions for coordination and collaboration within complex supply chain ecosystems. Evolutionary algorithms and artificial neural networks are discussed as powerful tools for supply chain design, predictive analytics, and risk management, offering capabilities for optimizing decision-making processes across various operational domains. Furthermore, reinforcement learning techniques empower dynamic decision-making in real-time operational settings, fostering adaptive and resilient supply chain management practices. By integrating multiple AI techniques, hybrid approaches offer synergistic solutions that capitalize on the strengths of diverse methodologies to address multifaceted challenges in supply chain optimization. Through a synthesis of theoretical insights and practical case studies, this paper provides valuable insights into the current state and future directions of AI-driven supply chain optimization.

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“…In recent years, domestic and foreign scholars' research in evolutionary game theory and application has shown diversified characteristics. The evolutionary game model has been widely applied in product quality regulation (Liu et al, 2022) [48], environmental governance (Aghmashhadi et al, 2022) [49], collaborative innovation (Hosseini-Motlagh et al, 2022) [50], and so on.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Game Mechanism of Governmental Cross-Regional Cooperation in AirPollution Management

et al. 2023

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Intergovernmental collaboration is essential for regionally coordinated air pollution prevention and control. An evolutionary game model of local government behavior strategy selection taking into account the fixed cost of the local government, the total amount of network production factors, and the total amount of total network factors of production that can be moved is built based on the social capital theory in order to realize the cross-border collaborative control of regional air pollution. The issue of an intergovernmental cooperation framework for collaborative prevention and control of large-scale air pollution is addressed from the standpoint of the multi-stakeholder “benefit–cost” drive. Additionally, the major variables influencing the behavioral approach selection for intergovernmental cooperation are considered. The initial sensitivity of the evolution path of the local government behavioral strategy is also analyzed. The results of this study are: (1) The primary elements impacting intergovernmental cooperation on joint prevention and control of air pollution are fixed costs and fixed benefits, and reducing the fixed costs of such cooperation in an appropriate manner without compromising local governments’ pollution control can do so. (2) Under the assumption that local governments have fixed expenses, the total amount of network factors of production and total network factors of production that can be moved factors of production have a direct impact on intergovernmental cooperation. When local governments’ fixed costs are constant, they are more likely to choose the cooperative behavior option if the sum of their network production factors and total network factors of production that can be moved is higher. (3) The initial probability of cooperation among the three parties and the total amount of production factors have an impact on the system’s ESS when local governments in the area have equal total production factors. The study’s findings can offer theoretical justification for the “profit-driven” intergovernmental coordination of joint prevention and management of air pollution.

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Cost Reduction of Inventory-Production-System in Multi-echelon Supply Chain Using Game Theory and Fuzzy Demand Forecasting

Cited by 22 publications

References 79 publications

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Theoretical approaches to AI in supply chain optimization: Pathways to efficiency and resilience

Evolutionary Game Mechanism of Governmental Cross-Regional Cooperation in AirPollution Management

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