Green cold-chain logistics vehicle distribution path optimization method considering traffic conditions

Green cold-chain logistics vehicle distribution path optimization method considering traffic conditions

The invention discloses a green cold-chain logistics vehicle distribution path optimization method considering traffic conditions. The method specifically comprises the following steps: 1) constructing a multi-objective optimization model; 2) setting algorithm parameters, initializing a population,...

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Bibliographische Detailangaben
Hauptverfasser: ZHOU XUJING, LIU XIANGXIANG, ZHOU YANCONG, ZHANG BO, CHENG GUANGTAO, MA XI, ZHANG XIAOQIN, WANG LELE, GAO HONGCAN, WANG YAN, HU BIN, SUN HAIJUN
Format: Patent
Sprache:chi ; eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Zusammenfassung:The invention discloses a green cold-chain logistics vehicle distribution path optimization method considering traffic conditions. The method specifically comprises the following steps: 1) constructing a multi-objective optimization model; 2) setting algorithm parameters, initializing a population, and calculating the fitness value of each Hallis eagle individual; 3) performing non-dominated sorting, and calculating the individual congestion degree of the Hallis eagle; finding and recording a group of current optimal solutions; (4) externally archiving an Archive set; according to a Pareto dominating principle, performing dominating relation judgment on Harris eagle individuals, and putting non-inferior liberates into a preset Archive set; 5) performing grid division on individuals in the Archive set, and using a roulette method to select Harlins eagle individuals as a historical optimal solution of the population; 6) updating the Harlisia eagle individuals by using the Harlisia eagle algorithm; 7) generating