In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location...In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem.展开更多
Purpose–The purpose of this paper is to solve the capacitated location routing problem(CLRP),which is an NP-hard problem that involves making strategic decisions as well as tactical and operational decisions,using a ...Purpose–The purpose of this paper is to solve the capacitated location routing problem(CLRP),which is an NP-hard problem that involves making strategic decisions as well as tactical and operational decisions,using a hybrid particle swarm optimization(PSO)algorithm.Design/methodology/approach–PSO,which is a population-based metaheuristic,is combined with a variable neighborhood strategy variable neighborhood search to solve the CLRP.Findings–The algorithm is tested on a set of instances available in the literature and gave good quality solutions,results are compared to those obtained by other metaheuristic,evolutionary and PSO algorithms.Originality/value–Local search is a time consuming phase in hybrid PSO algorithms,a set of neighborhood structures suitable for the solution representation used in the PSO algorithm is proposed in the VNS phase,moves are applied directly to particles,a clear decoding method is adopted to evaluate a particle(solution)and there is no need to re-encode solutions in the form of particles after applying local search.展开更多
基金Natural Science Foundation of Shanghai,China(No.15ZR1401600)the Fundamental Research Funds for the Central Universities,China(No.CUSF-DH-D-2015096)
文摘In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem.
文摘Purpose–The purpose of this paper is to solve the capacitated location routing problem(CLRP),which is an NP-hard problem that involves making strategic decisions as well as tactical and operational decisions,using a hybrid particle swarm optimization(PSO)algorithm.Design/methodology/approach–PSO,which is a population-based metaheuristic,is combined with a variable neighborhood strategy variable neighborhood search to solve the CLRP.Findings–The algorithm is tested on a set of instances available in the literature and gave good quality solutions,results are compared to those obtained by other metaheuristic,evolutionary and PSO algorithms.Originality/value–Local search is a time consuming phase in hybrid PSO algorithms,a set of neighborhood structures suitable for the solution representation used in the PSO algorithm is proposed in the VNS phase,moves are applied directly to particles,a clear decoding method is adopted to evaluate a particle(solution)and there is no need to re-encode solutions in the form of particles after applying local search.
