The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,a...The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.展开更多
In this work we analyze the supply of biomass from field to an in-land or port destination. The biomass is pelletized to increase its bulk density to extend its storage period and for ease of its transport. The pellet...In this work we analyze the supply of biomass from field to an in-land or port destination. The biomass is pelletized to increase its bulk density to extend its storage period and for ease of its transport. The pellet may be used for conversion to chemicals and animal bedding or for straight combustion. We analyzed supply chain in Saskatchewan where there are plenty of crop residues but widely dispersed and harvest seasons are short. We envisioned that the farmer collects bales from field and transports the bales to farmstead during the harvest season. The bales are then processed into pellets using small scale pellet equipment. A custom operator with expertise in pelletization may engage in handling and densifying the biomass. The business case for the mobile mill will be similar to the well established custom grain and forage harvesting operations. The pellets are stored in hopper bottom grain bins at the farmstead. From this point, the handling of pellets would be similar to the handling and marketing of grain. The farmer trucks a specified volume of pellets from farmstead to the nearest elevator where the pellets are transferred to larger bins or silos. Pellets are extracted from silos and loaded onto the rail cars. The Canadian freight rail companies (mainly CN) currently transport over 3 million dry tonne (dt) of wood pellets in rail cars. The pellets are hauled to marine ports on the West Coast or East Coast for export. The cost of delivering ag pellets to biorefinery or to the shipping port is $86.09/dt. This cost does not include the equivalent value of removing biomass from the farm (e.g. fertilizer replacement) and return on investment. The GHG emissions to produce and transport ag pellets add up to 185.9 kg of CO<sub>2</sub> per dt of biomass. The cost of producing pellets without drying feedstock is $35.05/dt and the corresponding GHG for palletization amounts $146.30/dt.展开更多
Effectively managing complex logistics data is essential for development sustainability and growth,especially in optimizing distribution routes.This article addresses the limitations of current logistics path optimiza...Effectively managing complex logistics data is essential for development sustainability and growth,especially in optimizing distribution routes.This article addresses the limitations of current logistics path optimization methods,such as inefficiencies and high operational costs.To overcome these drawbacks,we introduce the Hybrid Firefly-Spotted Hyena Optimization(HFSHO)algorithm,a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm(FO)with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm(SHO).HFSHO aims to improve logistics path optimization and reduce operational costs.The algorithm’s effectiveness is systematically assessed through rigorous comparative analyses with established algorithms like the Ant Colony Algorithm(ACO),Cuckoo Search Algorithm(CSA)and Jaya Algo-rithm(JA).The evaluation also employs benchmarking methodologies using standardized function sets covering diverse objective functions,including Schwefel’s,Rastrigin,Ackley,Sphere and the ZDT and DTLZ Function suite.HFSHO outperforms these algorithms,achieving a minimum path distance of 546 units,highlighting its prowess in logistics path optimization.This comprehensive evaluation authenticates HFSHO’s exceptional performance across various logistic optimization scenarios.These findings emphasize the critical significance of selecting an appropriate algorithm for logistics path navigation,with HFSHO emerging as an efficient choice.Through the synergistic use of FO and SHO,HFSHO achieves a 15%improvement in convergence,heightened operational efficiency and substantial cost reductions in logistics operations.It presents a promising solution for optimizing logistics paths,offering logistics planners and decision-makers valuable insights and contributing substantively to sustainable sectoral growth.展开更多
This research,from the theories of management science,supply chain management and logistics engineering,on the basis of extensive investigations,and using the method of analytic hierarchy process(AHP),evaluates the pr...This research,from the theories of management science,supply chain management and logistics engineering,on the basis of extensive investigations,and using the method of analytic hierarchy process(AHP),evaluates the present situation of logistics service of agricultural products.Taking Nanping City(Nanping)as a case,it explores the obstacles existing in current logistics service system and the factors limiting the development of agricultural product logistics service.Combining with the theory of modern logistics system,it reveals the problems in the logistics system and the causes,and then constructs the strategy of optimization for agricultural product logistics service in Nanping.The conclusion of the study can be references for the government to make scientific strategies for the development of the agricultural product logistics service and help logistics enterprises improve their service level.展开更多
As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of c...As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.展开更多
The objective of this study is to develop a model that determines the optimal points for investment in green management by defining a mathematical relationship between carbon trading profits and investments in green m...The objective of this study is to develop a model that determines the optimal points for investment in green management by defining a mathematical relationship between carbon trading profits and investments in green management using a company’s supply chain information. To formulate this model, we first define and analyze a green supply chain in a multi-dimensional and quantitative manner. The green investment alternatives considering in our model are as follows: 1) purchasing eco-friendly raw materials that cost more than conventional raw materials but whose use in production results in lower CO2 emissions;2) replacing current facilities with new eco-friendly facilities that have the capability to reduce CO2 emissions;and 3) changing modes of transport from less eco-friendly to more eco-friendly modes. We propose a green investment cost optimization (GICO) model that enables us to determine the optimal investment points. The proposed GICO model can support decision-making processes in green supply chain management environments.展开更多
基金funded by the University of Jeddah,Jeddah,Saudi Arabia,under Grant No.(UJ-23-DR-26)。
文摘The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.
文摘In this work we analyze the supply of biomass from field to an in-land or port destination. The biomass is pelletized to increase its bulk density to extend its storage period and for ease of its transport. The pellet may be used for conversion to chemicals and animal bedding or for straight combustion. We analyzed supply chain in Saskatchewan where there are plenty of crop residues but widely dispersed and harvest seasons are short. We envisioned that the farmer collects bales from field and transports the bales to farmstead during the harvest season. The bales are then processed into pellets using small scale pellet equipment. A custom operator with expertise in pelletization may engage in handling and densifying the biomass. The business case for the mobile mill will be similar to the well established custom grain and forage harvesting operations. The pellets are stored in hopper bottom grain bins at the farmstead. From this point, the handling of pellets would be similar to the handling and marketing of grain. The farmer trucks a specified volume of pellets from farmstead to the nearest elevator where the pellets are transferred to larger bins or silos. Pellets are extracted from silos and loaded onto the rail cars. The Canadian freight rail companies (mainly CN) currently transport over 3 million dry tonne (dt) of wood pellets in rail cars. The pellets are hauled to marine ports on the West Coast or East Coast for export. The cost of delivering ag pellets to biorefinery or to the shipping port is $86.09/dt. This cost does not include the equivalent value of removing biomass from the farm (e.g. fertilizer replacement) and return on investment. The GHG emissions to produce and transport ag pellets add up to 185.9 kg of CO<sub>2</sub> per dt of biomass. The cost of producing pellets without drying feedstock is $35.05/dt and the corresponding GHG for palletization amounts $146.30/dt.
基金funded by the University of Jeddah,Jeddah,Saudi Arabia,under Grant No.(UJ-22-DR-61).
文摘Effectively managing complex logistics data is essential for development sustainability and growth,especially in optimizing distribution routes.This article addresses the limitations of current logistics path optimization methods,such as inefficiencies and high operational costs.To overcome these drawbacks,we introduce the Hybrid Firefly-Spotted Hyena Optimization(HFSHO)algorithm,a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm(FO)with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm(SHO).HFSHO aims to improve logistics path optimization and reduce operational costs.The algorithm’s effectiveness is systematically assessed through rigorous comparative analyses with established algorithms like the Ant Colony Algorithm(ACO),Cuckoo Search Algorithm(CSA)and Jaya Algo-rithm(JA).The evaluation also employs benchmarking methodologies using standardized function sets covering diverse objective functions,including Schwefel’s,Rastrigin,Ackley,Sphere and the ZDT and DTLZ Function suite.HFSHO outperforms these algorithms,achieving a minimum path distance of 546 units,highlighting its prowess in logistics path optimization.This comprehensive evaluation authenticates HFSHO’s exceptional performance across various logistic optimization scenarios.These findings emphasize the critical significance of selecting an appropriate algorithm for logistics path navigation,with HFSHO emerging as an efficient choice.Through the synergistic use of FO and SHO,HFSHO achieves a 15%improvement in convergence,heightened operational efficiency and substantial cost reductions in logistics operations.It presents a promising solution for optimizing logistics paths,offering logistics planners and decision-makers valuable insights and contributing substantively to sustainable sectoral growth.
基金National Social Science Foundation of China(No.17CGJ002)Key Project of Education and Teaching Reform of Undergraduate Universities in Fujian Province,China(No.FBJG20190130)Educational and Scientific Research Project for Young and Middle-aged Teachers in Fujian Province,China(No.JAS19371)
文摘This research,from the theories of management science,supply chain management and logistics engineering,on the basis of extensive investigations,and using the method of analytic hierarchy process(AHP),evaluates the present situation of logistics service of agricultural products.Taking Nanping City(Nanping)as a case,it explores the obstacles existing in current logistics service system and the factors limiting the development of agricultural product logistics service.Combining with the theory of modern logistics system,it reveals the problems in the logistics system and the causes,and then constructs the strategy of optimization for agricultural product logistics service in Nanping.The conclusion of the study can be references for the government to make scientific strategies for the development of the agricultural product logistics service and help logistics enterprises improve their service level.
基金Project(2011ZK2030)supported by the Soft Science Research Plan of Hunan Province,ChinaProject(2010ZDB42)supported by the Social Science Foundation of Hunan Province,China+1 种基金Projects(09A048,11B070)supported by the Science Research Foundation of Education Bureau of Hunan Province,ChinaProjects(2010GK3036,2011FJ6049)supported by the Science and Technology Plan of Hunan Province,China
文摘As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.
文摘The objective of this study is to develop a model that determines the optimal points for investment in green management by defining a mathematical relationship between carbon trading profits and investments in green management using a company’s supply chain information. To formulate this model, we first define and analyze a green supply chain in a multi-dimensional and quantitative manner. The green investment alternatives considering in our model are as follows: 1) purchasing eco-friendly raw materials that cost more than conventional raw materials but whose use in production results in lower CO2 emissions;2) replacing current facilities with new eco-friendly facilities that have the capability to reduce CO2 emissions;and 3) changing modes of transport from less eco-friendly to more eco-friendly modes. We propose a green investment cost optimization (GICO) model that enables us to determine the optimal investment points. The proposed GICO model can support decision-making processes in green supply chain management environments.
