TY - GEN
T1 - Ant Colony Optimization solutions for logistic route planning with pick-up and delivery
AU - Lu, Eric Hsueh Chan
AU - Yang, Ya Wen
AU - Su, Zeal Li Tse
N1 - Funding Information:
This research was supported by Ministry of Science and Technology, Taiwan, R.O.C. under grant no. MOST 104-2221-E-006-205 -; and Ministry of Education, Taiwan, R.O.C. The Aim for the Top University Project to the National Cheng Kung University (NCKU).
Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/6
Y1 - 2017/2/6
N2 - Online shopping behaviors lead to a large number of goods need to be transported in the real world. Researches on logistics have attracted extensive attentions. One of popular topics is logistic route planning. Although various previous studies have discussed some classical routing problems, real logistic constraints are not considered such as the vehicle capacity, various logistic requirements, etc. Thus, these solutions may not be directly applied to the logistic route planning. In this paper, we propose a novel solution based on Ant Colony Optimization (ACO) to find high quality logistic routes not only meeting real logistic constraints but also taking pick-up and delivery requirements into account. To the best of our knowledge, this is the first work using ACO to plan the logistic routs that considers various logistic requirements, simultaneously. Through extensive experimental evaluations by a semi-real logistic dataset, the proposed ACO-based solution was shown to deliver excellent performance.
AB - Online shopping behaviors lead to a large number of goods need to be transported in the real world. Researches on logistics have attracted extensive attentions. One of popular topics is logistic route planning. Although various previous studies have discussed some classical routing problems, real logistic constraints are not considered such as the vehicle capacity, various logistic requirements, etc. Thus, these solutions may not be directly applied to the logistic route planning. In this paper, we propose a novel solution based on Ant Colony Optimization (ACO) to find high quality logistic routes not only meeting real logistic constraints but also taking pick-up and delivery requirements into account. To the best of our knowledge, this is the first work using ACO to plan the logistic routs that considers various logistic requirements, simultaneously. Through extensive experimental evaluations by a semi-real logistic dataset, the proposed ACO-based solution was shown to deliver excellent performance.
UR - http://www.scopus.com/inward/record.url?scp=85015754361&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015754361&partnerID=8YFLogxK
U2 - 10.1109/SMC.2016.7844340
DO - 10.1109/SMC.2016.7844340
M3 - Conference contribution
AN - SCOPUS:85015754361
T3 - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
SP - 808
EP - 813
BT - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
Y2 - 9 October 2016 through 12 October 2016
ER -