| Title | Hybrid Route Recommender System for Smarter Logistics |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Unnikrishnan, Grieshma, Mathew, Deepa, Jose, Bijoy A., Arvind, Raju |
| Conference Name | 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS) |
| Keywords | Accelerometers, collaborative filtering, content based filtering, decision making, fuzzy set theory, Global Positioning System, human factors, hybrid, hybrid route recommender system, Logistics, multiple recommendation techniques, poor road conditions, pothole, pubcrawl, recommender system, recommender systems, Resiliency, road surface conditions, road vehicles, Roads, routing techniques, Scalability, supply chain industry face, Transportation, unmaintained roads |
| Abstract | The condition of road surface has a significant role in land transportation. Due to poor road conditions, the logistics and supply chain industry face a drastic loss in their business. Unmaintained roads can cause damage to goods and accidents. The existing routing techniques do not consider factors like shock, temperature and tilt of goods etc. but these factors have to be considered for the logistics and supply chain industry. This paper proposes a recommender system which target management of goods in logistics. A 3 axis accelerometer is used to measure the road surface conditions. The pothole location is obtained using Global Positioning System (GPS). Using these details a hybrid recommender system is built. Hybrid recommender system combines multiple recommendation techniques to develop an effective recommender system. Here content-based and collaborative-based techniques is combined to build a hybrid recommender system. One of the popular Multiple Criteria Decision Making (MCDM) method, The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used for content based filtering and normalised Euclidean distance and KNN algorithm is used for collaborative filtering. The best route recommended by the system will be displayed to the user using a map application. |
| DOI | 10.1109/BigDataSecurity-HPSC-IDS.2019.00053 |
| Citation Key | unnikrishnan_hybrid_2019 |
Hybrid Route Recommender System for Smarter Logistics