Deep Learning Approach to Technician Routing and Scheduling Problem
[ X ]
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
EDICIONES UNIV SALAMANCA
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This paper proposes a hybrid algorithm including the Adam algorithm and body change operator (BCO). Feasible solutions to technician routing and scheduling problems (TRSP) are investigated by performing deep learning based on the Adam algorithm and the hybridization of Adam-BCO. TRSP is a problem where all tasks are routed, and technicians are scheduled. In the deep learning method based on the Adam algorithm and Adam-BCO algorithm, the weights of the network are updated, and these weights are evaluated as Greedy approach, and routing and scheduling are performed. The performance of the Adam-BCO algorithm is experimentally compared with the Adam and BCO algorithm by solving the TRSP on the instances developed from the literature. The numerical results evidence that Adam-BCO offers faster and better solutions considering Adam and BCO algorithm. The average solution time increases from 0.14 minutes to 4.03 minutes, but in return, Gap decreases from 9.99% to 5.71%. The hybridization of both algorithms through deep learning provides an effective and feasible solution, as evidenced by the results.
Açıklama
Anahtar Kelimeler
Adam algorithm, Deep learning, Optimization, Technician routing and scheduling
Kaynak
ADCAIJ
WoS Q Değeri
N/A
Scopus Q Değeri
Q4
Cilt
11
Sayı
2
Künye
Pekel, E. (2022). Deep Learning Approach to Technician Routing and Scheduling Problem. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(2), 191-206.
