A novel location-queuing problem for blood collection facilities against MPHEs is studied in this paper.The decision variables to be determined include the opening plan of fixed blood collection rooms,the location of ...A novel location-queuing problem for blood collection facilities against MPHEs is studied in this paper.The decision variables to be determined include the opening plan of fixed blood collection rooms,the location of mobile blood collecting vehicles,and the number of service desks within facilities.This problem is formulated as a bi-objective multi-period integer nonlinear programming model,incorporating unique features that distinguish it from previous studies,such as pandemic risk,blood donation behavior,and the heterogeneity of blood collection facilities.The objectives are to minimize the total system cost and maximize donor satisfaction.To solve this problem,an improved multi-objective grey wolf optimization(IMOGWO)algorithm,which incorporates chaotic mapping and adaptive convergence factors,is proposed.Real data from Chongqing,China,is utilized to demonstrate the applicability of the model and the effectiveness of IMOGWO.Using evaluation metrics such as the C metric(CM),number of Pareto frontier(NPF),maximum spread(MS),spacing(SP),mean ideal distance(MID)and computation time(CPU time),numerical experiments demonstrate that the proposed IMOGWO outperforms non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ),multi-objective particle swarm optimization(MOPSO),multi-objective whale optimization(MOWOA),multi-objective chimp optimization(MOCh OA),and multi-objective grey wolf optimization(MOGWO).展开更多
基金Supported by National Social Science Fund of China(23XGL039)。
文摘A novel location-queuing problem for blood collection facilities against MPHEs is studied in this paper.The decision variables to be determined include the opening plan of fixed blood collection rooms,the location of mobile blood collecting vehicles,and the number of service desks within facilities.This problem is formulated as a bi-objective multi-period integer nonlinear programming model,incorporating unique features that distinguish it from previous studies,such as pandemic risk,blood donation behavior,and the heterogeneity of blood collection facilities.The objectives are to minimize the total system cost and maximize donor satisfaction.To solve this problem,an improved multi-objective grey wolf optimization(IMOGWO)algorithm,which incorporates chaotic mapping and adaptive convergence factors,is proposed.Real data from Chongqing,China,is utilized to demonstrate the applicability of the model and the effectiveness of IMOGWO.Using evaluation metrics such as the C metric(CM),number of Pareto frontier(NPF),maximum spread(MS),spacing(SP),mean ideal distance(MID)and computation time(CPU time),numerical experiments demonstrate that the proposed IMOGWO outperforms non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ),multi-objective particle swarm optimization(MOPSO),multi-objective whale optimization(MOWOA),multi-objective chimp optimization(MOCh OA),and multi-objective grey wolf optimization(MOGWO).
