원문정보
보안공학연구지원센터(IJMUE)
International Journal of Multimedia and Ubiquitous Engineering
Vol.10 No.8
2015.08
pp.367-378
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
To effectively optimize multi-objective logistics distribution path, the distance and distance related customer satisfaction factor are used as the objective function, a novel kruskal crossover genetic algorithm (KCGA) for multi-objective logistics distribution path optimization is proposed. To test the optimization results, the terminal distribution model and the virtual logistics system operating model are built. Experiment results show that, compared with basic genetic algorithm (GA), the run time of KCGA takes a slightly higher. But the average distribution distance and the best distribution distance are reduced by 6%-8%. Achieve the goal of multi-objective logistics distribution path optimization.
목차
Abstract
1. Introduction
2. Multi-Objective Logistics Distribution Path Optimization
2.1. Objective Function
2.2. Kruskal Algorithm
2.3. Terminal Distribution Model
2.4. Kruskal Crossover Genetic Algorithm (KCGA)
3. Experimental Results and Analysis
3.1. Comparative Experiment of Using Basic GA and KCGA to Solve the Order Terminal Distribution Model, Which Makes the Distribution Distance as the Objective Function
3.2. Experiment of Using Basic GA and KCGA to Solve the Order TerminalDistribution Model, which Makes the Distribution Distance and CustomerSatisfaction as the Objective Function
3.3. Distribution Path Optimization Experiment Under Virtual Logistics System
4. Conclusion
Acknowledgments
References
1. Introduction
2. Multi-Objective Logistics Distribution Path Optimization
2.1. Objective Function
2.2. Kruskal Algorithm
2.3. Terminal Distribution Model
2.4. Kruskal Crossover Genetic Algorithm (KCGA)
3. Experimental Results and Analysis
3.1. Comparative Experiment of Using Basic GA and KCGA to Solve the Order Terminal Distribution Model, Which Makes the Distribution Distance as the Objective Function
3.2. Experiment of Using Basic GA and KCGA to Solve the Order TerminalDistribution Model, which Makes the Distribution Distance and CustomerSatisfaction as the Objective Function
3.3. Distribution Path Optimization Experiment Under Virtual Logistics System
4. Conclusion
Acknowledgments
References
저자정보
참고문헌
자료제공 : 네이버학술정보