Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertai...Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved Particle Swarm Optimization （PSO） algorithm based on the Differential Evolution （DE） algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.展开更多
The grey fuzzy variable was defined for the two fold uncertain parameters combining grey and fuzziness factors. On the basis of the credibility and chance measure of grey fuzzy variables, the distribution center inven...The grey fuzzy variable was defined for the two fold uncertain parameters combining grey and fuzziness factors. On the basis of the credibility and chance measure of grey fuzzy variables, the distribution center inventory uncertain programming model was presented. The grey fuzzy simulation technology can generate input-output data for the uncertain functions. The neural network trained from the inputoutput data can approximate the uncertain functions. The designed hybrid intelligent algorithm by embedding the trained neural network into genetic algorithm can optimize the general grey fuzzy programming problems. Finally, one numerical example is provided to illustrate the effectiveness of the model and the hybrid intelligent algorithm.展开更多
Bipolar Interval-valued neutrosophic set is another generalization of fuzzy set,neutrosophic set,bipolar fuzzy set and bipolar neutrosophic set and thus when applied to the optimization problem handles uncertain data ...Bipolar Interval-valued neutrosophic set is another generalization of fuzzy set,neutrosophic set,bipolar fuzzy set and bipolar neutrosophic set and thus when applied to the optimization problem handles uncertain data more efficiently and flexibly.Current work is an effort to design a flexible optimization model in the backdrop of interval-valued bipolar neutrosophic sets.Bipolar interval-valued neutrosophic membership grades are picked so that they indicate the restriction of the plausible infringement of the inequalities given in the problem.To prove the adequacy and effectiveness of the method a unified system of sustainable medical healthcare supply chain model with an uncertain figure of product complaints is used.Time,quality and cost are considered as satisfaction level to choose best supplier for medicine procurement.The proposed model ensures 99%satisfaction for cost reduction,63%satisfaction for the quality of product and 64%satisfaction for total time taken in medicine supply chain.展开更多
As Internet of Things(IoT)technology rapidly advances,its application within smart logistics systems has become increasingly widespread,significantly enhancing the efficiency and precision of logistics management.This...As Internet of Things(IoT)technology rapidly advances,its application within smart logistics systems has become increasingly widespread,significantly enhancing the efficiency and precision of logistics management.This paper comprehensively discusses the core components of IoT,its specific applications,the improvements and outcomes it brings,as well as the challenges faced and solutions proposed.Through real-time data collection,efficient information processing,and automation,IoT technology not only optimizes supply chain management but also enhances customer service levels while reducing operational costs.Studies indicate that IoT technology effectively meets modern logistics demands and drives the logistics industry towards smarter,more efficient operations.展开更多
Warehouse automation is no longer an emerging concept-it is a disruptive force actively reshaping logistics and supply chain dynamics.Robotics,AIdriven optimization,and autonomous material handling are revolutionizing...Warehouse automation is no longer an emerging concept-it is a disruptive force actively reshaping logistics and supply chain dynamics.Robotics,AIdriven optimization,and autonomous material handling are revolutionizing how goods are processed,stored,and transported.This paper explores the rapid evolution of warehouse automation,highlighting its role in improving efficiency,reducing costs,and reshaping industry standards.However,this technological shift is not without controversy.Labor unions and industry stakeholders continue to raise concerns about job displacement,economic restructuring,and the unintended consequences of large-scale automation.Does automation represent an existential threat to the workforce,or is it the key to a more resilient and optimized supply chain?By examining industry trends,case studies,and financial data,this study argues that resistance to automation signals its deep market penetration rather than a barrier to its adoption.As investments surge and technological integration accelerates,the debate surrounding automation is no longer about if it will dominate the industry but how businesses will adapt to its inevitable rise.Despite ongoing resistance,warehouse automation is becoming an irreversible cornerstone of modern logistics,pushing companies to redefine their operations or risk obsolescence in an increasingly automated world.展开更多
Decisions in supply chains are hierarchically organized. Strategic decisions involve the long-term planning of the structure of the supply chain network.Tactical decisions are mid-term plans to allocate the production...Decisions in supply chains are hierarchically organized. Strategic decisions involve the long-term planning of the structure of the supply chain network.Tactical decisions are mid-term plans to allocate the production and distribution of materials, while operational decisions are related to the daily planning of the execution of manufacturing operations. These planning processes are conducted independently with minimal exchange of information between them. Achieving a better coordination between these processes allows companies to capture benefits that are currently out of their reach and improve the communication among their functional areas. We propose a network representation for the multilevel decision structure and analyze the components that are involved in finding integrated solutions that maximize the sum of the benefits of all nodes of the decision network.Although such task is very challenging, significant research progress has been made in each component of this structure. An overview of strategic models, mid-term planning models, and scheduling models is presented to address the solution of each node in the decision network.Coordination mechanisms for converging the integrated solutions are also analyzed, including solving large-scale models, multiobjective optimization, bi-level programming, and decomposition. We conclude by summarizing the challenges that hinder the full integration of multilevel decision making in supply chain management.展开更多
基金The Science and Research Foundation of Shanghai Municipal Education Commission (No06DZ033)the Doctoral Science and Research Foundation of Shanghai Nor mal University ( No PL719)the Science and Research Foundation of Shanghai Nor mal University (NoSK200741)
文摘Most supply chain programming problems are restricted to the deterministic situations or stochastic environmcnts. Considering twofold uncertainty combining grey and fuzzy factors, this paper proposes a hybrid uncertain programming model to optimize the supply chain production-distribution cost. The programming parameters of the material suppliers, manufacturer, distribution centers, and the customers are integrated into the presented model. On the basis of the chance measure and the credibility of grey fuzzy variable, the grey fuzzy simulation methodology was proposed to generate input-output data for the uncertain functions. The designed neural network can expedite the simulation process after trained from the generated input-output data. The improved Particle Swarm Optimization （PSO） algorithm based on the Differential Evolution （DE） algorithm can optimize the uncertain programming problems. A numerical example was presented to highlight the significance of the uncertain model and the feasibility of the solution strategy.
基金Supported bythe Science and Research Foundationof Shanghai Municipal Educational Commssion (05DZ33)
文摘The grey fuzzy variable was defined for the two fold uncertain parameters combining grey and fuzziness factors. On the basis of the credibility and chance measure of grey fuzzy variables, the distribution center inventory uncertain programming model was presented. The grey fuzzy simulation technology can generate input-output data for the uncertain functions. The neural network trained from the inputoutput data can approximate the uncertain functions. The designed hybrid intelligent algorithm by embedding the trained neural network into genetic algorithm can optimize the general grey fuzzy programming problems. Finally, one numerical example is provided to illustrate the effectiveness of the model and the hybrid intelligent algorithm.
基金The research has been partially funded by the University of Oradea,within the Grants Competition“Scientific Research of Excellence Related to Priority Areas with Capitalization through Technology Transfer:INO-TRANSFER-UO”,Project No.323/2021.
文摘Bipolar Interval-valued neutrosophic set is another generalization of fuzzy set,neutrosophic set,bipolar fuzzy set and bipolar neutrosophic set and thus when applied to the optimization problem handles uncertain data more efficiently and flexibly.Current work is an effort to design a flexible optimization model in the backdrop of interval-valued bipolar neutrosophic sets.Bipolar interval-valued neutrosophic membership grades are picked so that they indicate the restriction of the plausible infringement of the inequalities given in the problem.To prove the adequacy and effectiveness of the method a unified system of sustainable medical healthcare supply chain model with an uncertain figure of product complaints is used.Time,quality and cost are considered as satisfaction level to choose best supplier for medicine procurement.The proposed model ensures 99%satisfaction for cost reduction,63%satisfaction for the quality of product and 64%satisfaction for total time taken in medicine supply chain.
文摘As Internet of Things(IoT)technology rapidly advances,its application within smart logistics systems has become increasingly widespread,significantly enhancing the efficiency and precision of logistics management.This paper comprehensively discusses the core components of IoT,its specific applications,the improvements and outcomes it brings,as well as the challenges faced and solutions proposed.Through real-time data collection,efficient information processing,and automation,IoT technology not only optimizes supply chain management but also enhances customer service levels while reducing operational costs.Studies indicate that IoT technology effectively meets modern logistics demands and drives the logistics industry towards smarter,more efficient operations.
文摘Warehouse automation is no longer an emerging concept-it is a disruptive force actively reshaping logistics and supply chain dynamics.Robotics,AIdriven optimization,and autonomous material handling are revolutionizing how goods are processed,stored,and transported.This paper explores the rapid evolution of warehouse automation,highlighting its role in improving efficiency,reducing costs,and reshaping industry standards.However,this technological shift is not without controversy.Labor unions and industry stakeholders continue to raise concerns about job displacement,economic restructuring,and the unintended consequences of large-scale automation.Does automation represent an existential threat to the workforce,or is it the key to a more resilient and optimized supply chain?By examining industry trends,case studies,and financial data,this study argues that resistance to automation signals its deep market penetration rather than a barrier to its adoption.As investments surge and technological integration accelerates,the debate surrounding automation is no longer about if it will dominate the industry but how businesses will adapt to its inevitable rise.Despite ongoing resistance,warehouse automation is becoming an irreversible cornerstone of modern logistics,pushing companies to redefine their operations or risk obsolescence in an increasingly automated world.
文摘Decisions in supply chains are hierarchically organized. Strategic decisions involve the long-term planning of the structure of the supply chain network.Tactical decisions are mid-term plans to allocate the production and distribution of materials, while operational decisions are related to the daily planning of the execution of manufacturing operations. These planning processes are conducted independently with minimal exchange of information between them. Achieving a better coordination between these processes allows companies to capture benefits that are currently out of their reach and improve the communication among their functional areas. We propose a network representation for the multilevel decision structure and analyze the components that are involved in finding integrated solutions that maximize the sum of the benefits of all nodes of the decision network.Although such task is very challenging, significant research progress has been made in each component of this structure. An overview of strategic models, mid-term planning models, and scheduling models is presented to address the solution of each node in the decision network.Coordination mechanisms for converging the integrated solutions are also analyzed, including solving large-scale models, multiobjective optimization, bi-level programming, and decomposition. We conclude by summarizing the challenges that hinder the full integration of multilevel decision making in supply chain management.
