We study the fabric spreading and cutting problem in apparel factories.For the sake of saving the material costs,the cutting requirement should be met exactly without producing additional garment components.For reduci...We study the fabric spreading and cutting problem in apparel factories.For the sake of saving the material costs,the cutting requirement should be met exactly without producing additional garment components.For reducing the production costs,the number of lays that corresponds to the frequency of using the cutting beds should be minimized.We propose an iterated greedy algorithm for solving the fabric spreading and cutting problem.This algorithm contains a constructive procedure and an improving loop.Firstly the constructive procedure creates a set of lays in sequence,and then the improving loop tries to pick each lay from the lay set and rearrange the remaining lays into a smaller lay set.The improving loop will run until it cannot obtain any smaller lay set or the time limit is due.The experiment results on 500 cases show that the proposed algorithm is effective and efficient.展开更多
To address indeterminism in the bilevel knapsack problem,an uncertain bilevel knapsack problem(UBKP)model is proposed.Then,an uncertain solution for UBKP is proposed by defining thePE Nash equilibrium andPE Stackelber...To address indeterminism in the bilevel knapsack problem,an uncertain bilevel knapsack problem(UBKP)model is proposed.Then,an uncertain solution for UBKP is proposed by defining thePE Nash equilibrium andPE Stackelberg-Nash equilibrium.To improve the computational efficiency of the uncertain solution,an evolutionary algorithm,the improved binary wolf pack algorithm,is constructed with one rule(wolf leader regulation),two operators(invert operator and move operator),and three intelligent behaviors(scouting behavior,intelligent hunting behavior,and upgrading).The UBKP model and thePE uncertain solution are applied to an armament transportation problem as a case study.展开更多
基金supported in part by the National Key Research and Development Program of China(2018YFB1702701)the National Natural Science Foundation of China(61773381,61773382,61533019,61702519)+3 种基金Dongguan’s Innovation Talents Project(Gang Xiong)Guangdong’s Science and Technology Project(2017B090912001)Beijing Natural Science Foundation(4182065)Chinese Hunan’s Science and Technology Project(20181040)
文摘We study the fabric spreading and cutting problem in apparel factories.For the sake of saving the material costs,the cutting requirement should be met exactly without producing additional garment components.For reducing the production costs,the number of lays that corresponds to the frequency of using the cutting beds should be minimized.We propose an iterated greedy algorithm for solving the fabric spreading and cutting problem.This algorithm contains a constructive procedure and an improving loop.Firstly the constructive procedure creates a set of lays in sequence,and then the improving loop tries to pick each lay from the lay set and rearrange the remaining lays into a smaller lay set.The improving loop will run until it cannot obtain any smaller lay set or the time limit is due.The experiment results on 500 cases show that the proposed algorithm is effective and efficient.
基金Project supported by the National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China(No.2018AAA0101200)the National Natural Science Foundation of China(No.61502534)+1 种基金the Natural Science Foundation of Shaanxi Province,China(No.2020JQ-493)and the Domain Foundation of China(No.61400010304)。
文摘To address indeterminism in the bilevel knapsack problem,an uncertain bilevel knapsack problem(UBKP)model is proposed.Then,an uncertain solution for UBKP is proposed by defining thePE Nash equilibrium andPE Stackelberg-Nash equilibrium.To improve the computational efficiency of the uncertain solution,an evolutionary algorithm,the improved binary wolf pack algorithm,is constructed with one rule(wolf leader regulation),two operators(invert operator and move operator),and three intelligent behaviors(scouting behavior,intelligent hunting behavior,and upgrading).The UBKP model and thePE uncertain solution are applied to an armament transportation problem as a case study.
