TY - GEN
T1 - Transformer Surrogate Genetic Programming for Dynamic Container Port Truck Dispatching
AU - Chen, Xinan
AU - Dong, Jing
AU - Qu, Rong
AU - Bai, Ruibin
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - In the wake of burgeoning demands on port logistics, optimizing the operational efficiency of container ports has become a compelling necessity. A critical facet of this efficiency lies in practical truck dispatching systems. Although effective, traditional Genetic Programming (GP) techniques suffer from computational inefficiencies, particularly during the fitness evaluation stage. This inefficiency arises from the need to simulate each new individual in the population, a process that neither fully leverages the computational resources nor utilizes the acquired knowledge about the evolving GP structures and their corresponding fitness values. This paper introduces a novel Transformer-Surrogate Genetic Programming (TSGP) approach to address these limitations. The methodology harnesses the accumulated knowledge during fitness calculations to train a transformer model as a surrogate evaluator. This surrogate model obviates the need for individual simulations, thereby substantially reducing the algorithmic training time. Furthermore, the trained transformer model can be repurposed to generate superior initial populations for GPs, leading to enhanced performance. Our approach synergizes the computational advantages of transformer models with the search capabilities of GPs, presenting a significant advance in the quest for optimized truck dispatching in dynamic container port settings. This work improves the efficiency of Genetic Programming and opens new avenues for leveraging GP in scenarios with substantial computational constraints.
AB - In the wake of burgeoning demands on port logistics, optimizing the operational efficiency of container ports has become a compelling necessity. A critical facet of this efficiency lies in practical truck dispatching systems. Although effective, traditional Genetic Programming (GP) techniques suffer from computational inefficiencies, particularly during the fitness evaluation stage. This inefficiency arises from the need to simulate each new individual in the population, a process that neither fully leverages the computational resources nor utilizes the acquired knowledge about the evolving GP structures and their corresponding fitness values. This paper introduces a novel Transformer-Surrogate Genetic Programming (TSGP) approach to address these limitations. The methodology harnesses the accumulated knowledge during fitness calculations to train a transformer model as a surrogate evaluator. This surrogate model obviates the need for individual simulations, thereby substantially reducing the algorithmic training time. Furthermore, the trained transformer model can be repurposed to generate superior initial populations for GPs, leading to enhanced performance. Our approach synergizes the computational advantages of transformer models with the search capabilities of GPs, presenting a significant advance in the quest for optimized truck dispatching in dynamic container port settings. This work improves the efficiency of Genetic Programming and opens new avenues for leveraging GP in scenarios with substantial computational constraints.
KW - Deep Neural Network
KW - Dynamic Optimization
KW - Evolutionary Algorithm
KW - Machine Learning
KW - Truck Dispatching
UR - http://www.scopus.com/inward/record.url?scp=85192486266&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-2272-3_21
DO - 10.1007/978-981-97-2272-3_21
M3 - Conference Proceeding
AN - SCOPUS:85192486266
SN - 9789819722716
T3 - Communications in Computer and Information Science
SP - 276
EP - 290
BT - Bio-Inspired Computing
A2 - Pan, Linqiang
A2 - Lin, Jianqing
A2 - Wang, Yong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2023
Y2 - 15 December 2023 through 17 December 2023
ER -