Using a modified subgradient extragradient algorithm, this paper proposed a novel approach to solving a supply chain network equilibrium model. The method extends the scope of optimisation and improves the accuracy at...Using a modified subgradient extragradient algorithm, this paper proposed a novel approach to solving a supply chain network equilibrium model. The method extends the scope of optimisation and improves the accuracy at each iteration by incorporating adaptive parameter selection and a more general subgradient projection operator. The advantages of the proposed method are highlighted by the proof of strong convergence presented in the paper. Several concrete examples are given to demonstrate the effectiveness of the algorithm, with comparisons illustrating its superior CPU running time compared to alternative techniques. The practical applicability of the algorithm is also demonstrated by applying it to a realistic supply chain network model.展开更多
A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is eq...A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.展开更多
In this paper we investigated the stability of konjac glucomnnan（KGM） chain hydrogen networks based on the quantum spin model. Dissipative particle dynamics method was applied in the structure simulation of KGM. The...In this paper we investigated the stability of konjac glucomnnan（KGM） chain hydrogen networks based on the quantum spin model. Dissipative particle dynamics method was applied in the structure simulation of KGM. The results reveled that acetyl residues of KGM were bonded with water molecules in aqueous solutions. Increasing the hydrogen bond formation decreases the energy in acetyl system. The expect-valuation of the thermal state with respect to the Hamiltonian is negative. Hence, the total energy of konjac glucomnnan chain with the acetyl groups decreases, which indicates the increasing stability of konjac glucomnnan chain. Our approach could provide a new insight into the investigation on the stability of konjac glucomnnan chain.展开更多
As a representative of chain-based protocol in Wireless Sensor Networks (WSNs), EEPB is an elegant solution on energy efficiency. However, in the latter part of the operation of the network, there is still a big probl...As a representative of chain-based protocol in Wireless Sensor Networks (WSNs), EEPB is an elegant solution on energy efficiency. However, in the latter part of the operation of the network, there is still a big problem: reserving energy of the node frequently presents the incapacity of directly communicating with the base station, at the same time capacity of data acquisition and transmission as normal nodes. If these nodes were selected as LEADER nodes, that will accelerate the death process and unevenness of energy consumption distribution among nodes.This paper proposed a chain routing algorithm based ontraffic prediction model (CRTP).The novel algorithmdesigns a threshold judgment method through introducing the traffic prediction model in the process of election of LEADER node. The process can be dynamically adjusted according to the flow forecasting. Therefore, this algorithm lets the energy consumption tend-ing to keep at same level. Simulation results show that CRTP has superior performance over EEPB in terms of balanced network energy consumption and the prolonged network life.展开更多
The productivity of an organization is very much affected by non-value adding activity like logistics, which moves the resources from suppliers to factory, raw materials/semi-finished items within the factory and fini...The productivity of an organization is very much affected by non-value adding activity like logistics, which moves the resources from suppliers to factory, raw materials/semi-finished items within the factory and finished goods from factory to customers via a designated distribution channel called as forward logistics. In some cases, parts of the products such as automobiles, computers, cameras, mobile phones, washing machines, refrigerators, garments, footwear and empty glass bottles of beverages, etc. will be brought back to the factories as a product recovery strategy through reverse logistics network which is integrated in a sustainable closed loop supply chain network. So, it is highly essential to optimize the movement of the items in the reverse logistics network. This paper gives a comprehensive review of literature of the design of networks for the reverse logistics as well as for the reverse logistics coupled with forward logistics. The contributions of the researchers are classified into nine categories based on the methods used to design the logistics network.展开更多
This paper presents four different hybrid genetic algorithms for network design problem in closed loop supply chain. They are compared using a complete factorial experiment with two factors, viz. problem size and algo...This paper presents four different hybrid genetic algorithms for network design problem in closed loop supply chain. They are compared using a complete factorial experiment with two factors, viz. problem size and algorithm. Based on the significance of the factor “algorithm”, the best algorithm is identified using Duncan’s multiple range test. Then it is compared with a mathematical model in terms of total cost. It is found that the best hybrid genetic algorithm identified gives results on par with the mathematical model in statistical terms. So, the best algorithm out of four algorithm proposed in this paper is proved to be superior to all other algorithms for all sizes of problems and its performance is equal to that of the mathematical model for small size and medium size problems.展开更多
This research develops a novel cross-disciplinary framework that bridges financial systemic risk modeling with supply chain network analysis to advance resilience assessment and policy guidance.The approach integrates...This research develops a novel cross-disciplinary framework that bridges financial systemic risk modeling with supply chain network analysis to advance resilience assessment and policy guidance.The approach integrates established financial contagion frameworks with the topology of the supply chain network,introducing the concept of“too central to fail”suppliers through systematic importance scoring methodologies.The framework reveals striking asymmetries in supply chain vulnerability patterns.While the majority of suppliers demonstrate systemic importance within network structures,financial fragility analysis indicates remarkable overall network robustness,with minimal nodes exhibiting high vulnerability thresholds.Most significantly,comprehensive stress testing exposes a critical paradox:networks demonstrate moderate resilience to random disruptions yet remain substantially vulnerable to strategic targeting of central nodes.Cascade failure analysis through multiple simulation approaches unveils the dual nature of supply chain risk propagation.Random shock scenarios generate manageable failure rates,while targeted attacks on high-centrality suppliers achieve disproportionate network impact.Most alarmingly,liquidity crisis simulations demonstrate how financial contagion mechanisms can affect nearly half of all network participants,highlighting the interconnected nature of operational and financial vulnerabilities.These findings establish quantitative foundations for the assessment of systemic risk in supply chains,with immediate implications for regulatory frameworks,early warning systems,and resilience enhancement strategies.The integrated financial-operational risk framework advances the theoretical understanding of the propagation of cross-sector vulnerability while providing systematic methodologies for identifying critical suppliers whose failure could trigger systemic collapse.展开更多
One of the main characteristics of Ad hoc networks is node mobility, which results in constantly changing in network topologies. Consequently, the ability to forecast the future status of mobility nodes plays a key ro...One of the main characteristics of Ad hoc networks is node mobility, which results in constantly changing in network topologies. Consequently, the ability to forecast the future status of mobility nodes plays a key role in QOS routing. We propose a random mobility model based on discretetime Markov chain, called ODM. ODM provides a mathematical framework for calculating some parameters to show the future status of mobility nodes, for instance, the state transition probability matrix of nodes, the probability that an edge is valid, the average number of valid-edges and the probability of a request packet found a valid route. Furthermore, ODM can account for obstacle environment. The state transition probability matrix of nodes can quantify the impact of obstacles. Several theorems are given and proved by using the ODM. Simulation results show that the calculated value can forecast the future status of mobility nodes.展开更多
Top-down environmental policies aim to mitigate environmental risks but inevitably lead to economic losses due to the market entry or exit of enterprises.This study developed a universal dynamic agent-based supply cha...Top-down environmental policies aim to mitigate environmental risks but inevitably lead to economic losses due to the market entry or exit of enterprises.This study developed a universal dynamic agent-based supply chain model to achieve tradeoffs between environmental risk reduction and economic sus-tainability.The model was used to conduct high-resolution daily simulations of the dynamic shifts in enterprise operations and their cascading effects on supply chain networks.It includes production,con-sumption,and transportation agents,attributing economic features to supply chain components and cap-turing their interactions.It also accounts for adaptive responses to daily external shocks and replicates realistic firm behaviors.By coupling high spatial-temporal resolution firm-level data from 18916 chemical enterprises,this study investigates the economic and environmental impacts of an environmen-tal policy resulting in the closure of 1800 chemical enterprises over three years.The results revealed a significant economic loss of 25.8 billion USD,ranging from 23.8 billion to 31.8 billion USD.Notably,over 80%of this loss was attributed to supply chain propagation.Counterfactual analyses indicated that imple-menting a staggered shutdown strategy prevented 18.8%of supply chain losses,highlighting the impor-tance of a gradual policy implementation to prevent abrupt supply chain disruptions.Furthermore,the study highlights the effectiveness of a multi-objective policy design in reducing economic losses(about 29%)and environmental risks(about 40%),substantially enhancing the efficiency of the environmental policy.The high-resolution simulations provide valuable insights for policy designers to formulate strategies with staggered implementation and multiple objectives to mitigate supply chain losses and environmental risks and ensure a sustainable future.展开更多
文摘Using a modified subgradient extragradient algorithm, this paper proposed a novel approach to solving a supply chain network equilibrium model. The method extends the scope of optimisation and improves the accuracy at each iteration by incorporating adaptive parameter selection and a more general subgradient projection operator. The advantages of the proposed method are highlighted by the proof of strong convergence presented in the paper. Several concrete examples are given to demonstrate the effectiveness of the algorithm, with comparisons illustrating its superior CPU running time compared to alternative techniques. The practical applicability of the algorithm is also demonstrated by applying it to a realistic supply chain network model.
基金Project(70572090) supported by the National Natural Science Foundation of China
文摘A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.
基金supported by the Natural Science Foundation of China(31271837 and 31471704)
文摘In this paper we investigated the stability of konjac glucomnnan（KGM） chain hydrogen networks based on the quantum spin model. Dissipative particle dynamics method was applied in the structure simulation of KGM. The results reveled that acetyl residues of KGM were bonded with water molecules in aqueous solutions. Increasing the hydrogen bond formation decreases the energy in acetyl system. The expect-valuation of the thermal state with respect to the Hamiltonian is negative. Hence, the total energy of konjac glucomnnan chain with the acetyl groups decreases, which indicates the increasing stability of konjac glucomnnan chain. Our approach could provide a new insight into the investigation on the stability of konjac glucomnnan chain.
文摘As a representative of chain-based protocol in Wireless Sensor Networks (WSNs), EEPB is an elegant solution on energy efficiency. However, in the latter part of the operation of the network, there is still a big problem: reserving energy of the node frequently presents the incapacity of directly communicating with the base station, at the same time capacity of data acquisition and transmission as normal nodes. If these nodes were selected as LEADER nodes, that will accelerate the death process and unevenness of energy consumption distribution among nodes.This paper proposed a chain routing algorithm based ontraffic prediction model (CRTP).The novel algorithmdesigns a threshold judgment method through introducing the traffic prediction model in the process of election of LEADER node. The process can be dynamically adjusted according to the flow forecasting. Therefore, this algorithm lets the energy consumption tend-ing to keep at same level. Simulation results show that CRTP has superior performance over EEPB in terms of balanced network energy consumption and the prolonged network life.
文摘The productivity of an organization is very much affected by non-value adding activity like logistics, which moves the resources from suppliers to factory, raw materials/semi-finished items within the factory and finished goods from factory to customers via a designated distribution channel called as forward logistics. In some cases, parts of the products such as automobiles, computers, cameras, mobile phones, washing machines, refrigerators, garments, footwear and empty glass bottles of beverages, etc. will be brought back to the factories as a product recovery strategy through reverse logistics network which is integrated in a sustainable closed loop supply chain network. So, it is highly essential to optimize the movement of the items in the reverse logistics network. This paper gives a comprehensive review of literature of the design of networks for the reverse logistics as well as for the reverse logistics coupled with forward logistics. The contributions of the researchers are classified into nine categories based on the methods used to design the logistics network.
文摘This paper presents four different hybrid genetic algorithms for network design problem in closed loop supply chain. They are compared using a complete factorial experiment with two factors, viz. problem size and algorithm. Based on the significance of the factor “algorithm”, the best algorithm is identified using Duncan’s multiple range test. Then it is compared with a mathematical model in terms of total cost. It is found that the best hybrid genetic algorithm identified gives results on par with the mathematical model in statistical terms. So, the best algorithm out of four algorithm proposed in this paper is proved to be superior to all other algorithms for all sizes of problems and its performance is equal to that of the mathematical model for small size and medium size problems.
文摘This research develops a novel cross-disciplinary framework that bridges financial systemic risk modeling with supply chain network analysis to advance resilience assessment and policy guidance.The approach integrates established financial contagion frameworks with the topology of the supply chain network,introducing the concept of“too central to fail”suppliers through systematic importance scoring methodologies.The framework reveals striking asymmetries in supply chain vulnerability patterns.While the majority of suppliers demonstrate systemic importance within network structures,financial fragility analysis indicates remarkable overall network robustness,with minimal nodes exhibiting high vulnerability thresholds.Most significantly,comprehensive stress testing exposes a critical paradox:networks demonstrate moderate resilience to random disruptions yet remain substantially vulnerable to strategic targeting of central nodes.Cascade failure analysis through multiple simulation approaches unveils the dual nature of supply chain risk propagation.Random shock scenarios generate manageable failure rates,while targeted attacks on high-centrality suppliers achieve disproportionate network impact.Most alarmingly,liquidity crisis simulations demonstrate how financial contagion mechanisms can affect nearly half of all network participants,highlighting the interconnected nature of operational and financial vulnerabilities.These findings establish quantitative foundations for the assessment of systemic risk in supply chains,with immediate implications for regulatory frameworks,early warning systems,and resilience enhancement strategies.The integrated financial-operational risk framework advances the theoretical understanding of the propagation of cross-sector vulnerability while providing systematic methodologies for identifying critical suppliers whose failure could trigger systemic collapse.
基金Acknowledgements This work is supported by the Postdoctoral Science Foundation of China under Grant No.20080431142.
文摘One of the main characteristics of Ad hoc networks is node mobility, which results in constantly changing in network topologies. Consequently, the ability to forecast the future status of mobility nodes plays a key role in QOS routing. We propose a random mobility model based on discretetime Markov chain, called ODM. ODM provides a mathematical framework for calculating some parameters to show the future status of mobility nodes, for instance, the state transition probability matrix of nodes, the probability that an edge is valid, the average number of valid-edges and the probability of a request packet found a valid route. Furthermore, ODM can account for obstacle environment. The state transition probability matrix of nodes can quantify the impact of obstacles. Several theorems are given and proved by using the ODM. Simulation results show that the calculated value can forecast the future status of mobility nodes.
基金supported by the National Natural Science Foundation of China(52200228 and 72022004)the China Postdoctoral Science Foundation(2022M721817)the National Key Scientific Research Project(2021YFC3200200).
文摘Top-down environmental policies aim to mitigate environmental risks but inevitably lead to economic losses due to the market entry or exit of enterprises.This study developed a universal dynamic agent-based supply chain model to achieve tradeoffs between environmental risk reduction and economic sus-tainability.The model was used to conduct high-resolution daily simulations of the dynamic shifts in enterprise operations and their cascading effects on supply chain networks.It includes production,con-sumption,and transportation agents,attributing economic features to supply chain components and cap-turing their interactions.It also accounts for adaptive responses to daily external shocks and replicates realistic firm behaviors.By coupling high spatial-temporal resolution firm-level data from 18916 chemical enterprises,this study investigates the economic and environmental impacts of an environmen-tal policy resulting in the closure of 1800 chemical enterprises over three years.The results revealed a significant economic loss of 25.8 billion USD,ranging from 23.8 billion to 31.8 billion USD.Notably,over 80%of this loss was attributed to supply chain propagation.Counterfactual analyses indicated that imple-menting a staggered shutdown strategy prevented 18.8%of supply chain losses,highlighting the impor-tance of a gradual policy implementation to prevent abrupt supply chain disruptions.Furthermore,the study highlights the effectiveness of a multi-objective policy design in reducing economic losses(about 29%)and environmental risks(about 40%),substantially enhancing the efficiency of the environmental policy.The high-resolution simulations provide valuable insights for policy designers to formulate strategies with staggered implementation and multiple objectives to mitigate supply chain losses and environmental risks and ensure a sustainable future.
