The electric power generation system has always the significant location in the power system, and it should have an efficient and economic operation. This consists of the generating unit’s allocation with minimum fue...The electric power generation system has always the significant location in the power system, and it should have an efficient and economic operation. This consists of the generating unit’s allocation with minimum fuel cost and also considers the emission cost. In this paper we have intended to propose a hybrid technique to optimize the economic and emission dispatch problem in power system. The hybrid technique is used to minimize the cost function of generating units and emission cost by balancing the total load demand and to decrease the power loss. This proposed technique employs Particle Swarm Optimization (PSO) and Neural Network (NN). PSO is one of the computational techniques that use a searching process to obtain an optimal solution and neural network is used to predict the load demand. Prior to performing this, the neural network training method is used to train all the generating power with respect to the load demand. The economic and emission dispatch problem will be solved by the optimized generating power and predicted load demand. The proposed hybrid intelligent technique is implemented in MATLAB platform and its performance is evaluated.展开更多
Efficient warehouse management is critical for modern supply chain systems,particularly in the era of e-commerce and automation.The Multi-Picker Robot Routing Problem(MPRRP)presents a complex challenge involving the o...Efficient warehouse management is critical for modern supply chain systems,particularly in the era of e-commerce and automation.The Multi-Picker Robot Routing Problem(MPRRP)presents a complex challenge involving the optimization of routes for multiple robots assigned to retrieve items from distinct locations within a warehouse.This study introduces optimized metaheuristic strategies to address MPRRP,with the aim of minimizing travel distances,energy consumption,and order fulfillment time while ensuring operational efficiency.Advanced algorithms,including an enhanced Particle Swarm Optimization(PSO-MPRRP)and a tailored Genetic Algorithm(GA-MPRRP),are specifically designed with customized evolutionary operators to effectively solve the MPRRP.Comparative experiments are conducted to evaluate the proposed strategies against benchmark approaches,demonstrating significant improvements in solution quality and computational efficiency.The findings contribute to the development of intelligent,scalable,and environmentally friendly warehouse systems,paving the way for future advances in robotics and automated logistics management.展开更多
This paper presents a comparative study of evolutionary algorithms which are considered to be effective in solving the multilevel lot-sizing problem in material requirement planning(MRP)systems.Three evolutionary algo...This paper presents a comparative study of evolutionary algorithms which are considered to be effective in solving the multilevel lot-sizing problem in material requirement planning(MRP)systems.Three evolutionary algorithms(simulated annealing(SA),particle swarm optimization(PSO)and genetic algorithm(GA))are provided.For evaluating the performances of algorithms,the distribution of total cost(objective function)and the average computational time are compared.As a result,both GA and PSO have better cost performances with lower average total costs and smaller standard deviations.When the scale of the multilevel lot-sizing problem becomes larger,PSO is of a shorter computational time.展开更多
文摘The electric power generation system has always the significant location in the power system, and it should have an efficient and economic operation. This consists of the generating unit’s allocation with minimum fuel cost and also considers the emission cost. In this paper we have intended to propose a hybrid technique to optimize the economic and emission dispatch problem in power system. The hybrid technique is used to minimize the cost function of generating units and emission cost by balancing the total load demand and to decrease the power loss. This proposed technique employs Particle Swarm Optimization (PSO) and Neural Network (NN). PSO is one of the computational techniques that use a searching process to obtain an optimal solution and neural network is used to predict the load demand. Prior to performing this, the neural network training method is used to train all the generating power with respect to the load demand. The economic and emission dispatch problem will be solved by the optimized generating power and predicted load demand. The proposed hybrid intelligent technique is implemented in MATLAB platform and its performance is evaluated.
基金funded by Hanoi University of Industry,Hanoi,Vietnam,under contract number 25−2024−RD/HD−DHCN.
文摘Efficient warehouse management is critical for modern supply chain systems,particularly in the era of e-commerce and automation.The Multi-Picker Robot Routing Problem(MPRRP)presents a complex challenge involving the optimization of routes for multiple robots assigned to retrieve items from distinct locations within a warehouse.This study introduces optimized metaheuristic strategies to address MPRRP,with the aim of minimizing travel distances,energy consumption,and order fulfillment time while ensuring operational efficiency.Advanced algorithms,including an enhanced Particle Swarm Optimization(PSO-MPRRP)and a tailored Genetic Algorithm(GA-MPRRP),are specifically designed with customized evolutionary operators to effectively solve the MPRRP.Comparative experiments are conducted to evaluate the proposed strategies against benchmark approaches,demonstrating significant improvements in solution quality and computational efficiency.The findings contribute to the development of intelligent,scalable,and environmentally friendly warehouse systems,paving the way for future advances in robotics and automated logistics management.
基金the National Natural Science Foundation of China(No.70971017)the Humanities and Social Sciences Project of Ministry of Education(No.10YJC630009)+1 种基金the Social Science Fund of Zhejiang Province(No.10CGGL21YBQ)the Natural Science Foundation of Zhejiang Province(No.Y1100854)
文摘This paper presents a comparative study of evolutionary algorithms which are considered to be effective in solving the multilevel lot-sizing problem in material requirement planning(MRP)systems.Three evolutionary algorithms(simulated annealing(SA),particle swarm optimization(PSO)and genetic algorithm(GA))are provided.For evaluating the performances of algorithms,the distribution of total cost(objective function)and the average computational time are compared.As a result,both GA and PSO have better cost performances with lower average total costs and smaller standard deviations.When the scale of the multilevel lot-sizing problem becomes larger,PSO is of a shorter computational time.
