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Published in last 1 year |  In last 2 years |  In last 3 years |  All Please wait a minute... For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails Select Memetic Algorithm Based on Deep Reinforcement Learning for Vehicle Routing Problem with Pickup-Delivery ZHOU Yalan, LIAO Yitian, SU Xiao, WANG Jiahai Journal of Frontiers of Computer Science and Technology    2024, 18 (3): 818-830.   DOI: 10.3778/j.issn.1673-9418.2302072 The vehicle routing problem with simultaneous pickup-delivery and time windows (VRPSPDTW) is a NP hard problem, which has a wide application in modern logistics. Memetic algorithm based on deep reinforcement learning is proposed to solve the problem. The large neighborhood search process of Memetic algorithm for VRPSPDTW is modeled into a Markov decision process. An encoder-decoder neural network architecture is designed for the removal operation in large neighborhood search. The extracted individual characteristics and location characteristics of all nodes in the current solution are input into the encoder for information interaction. The decoder outputs the nodes to be removed. Two kinds of decoders are designed including non-autoregressive and autoregressive structures. The neural network architecture uses reinforcement learning for training. A hybrid strategy is also designed, combining manually designed heuristic strategies with strategies learned through deep reinforcement learning to improve the optimization ability. Experimental results show that the proposed algorithm has a stronger ability to jump out of the local optimum, and can provide better solutions than the comparison algorithms in an effective time, especially in solving large-scale problems. In addition, ablation experiments are conducted on the new components of the proposed algorithm to show the effectiveness. Reference | Related Articles | Metrics Abstract （122） PDF （158） Select Green Supply Chain Emission Reduction Strategies and Smart Contracts Under Blockchain Technology WANG Xin, WANG Yasheng, ZHANG Shuhua, WANG Xinyu, XU Shuai Journal of Frontiers of Computer Science and Technology    2024, 18 (1): 265-278.   DOI: 10.3778/j.issn.1673-9418.2302003 Under the background of “double carbon”, the concept of green consumption has been deeply rooted in the hearts of people. However, consumers do not fully trust the greenness of products. The information transparency and traceability mechanism of blockchain technology can well dispel consumers?? doubts. Introducing blockchain technology into traditional green supply chains, considering consumer green preferences and green trust, a game model is constructed among members of the green supply chain before and after the application of blockchain technology, as well as under different power structures, to quantitatively study their emission reduction and pricing strategies, and to explore how to achieve optimal consumer surplus and total social welfare. On the basis of adopting blockchain technology, smart wholesale price contracts and cost sharing smart contracts are designed, and the reasonable range of smart wholesale prices and the optimal cost sharing ratio are calculated, to improve enterprise operational efficiency and achieve supply chain coordination. The results show that when consumers?? green preference is high, the use of blockchain can bring more benefits to all participants in the supply chain. At the same time, the higher the willingness of consumers to buy green products, the greater the benefits. Through numerical analysis, it is found that the smart contracts can better coordinate the supply chain in the case of retailers leading the supply chain. Finally, the validity of the relevant conclusions is verified through empirical cases. Reference | Related Articles | Metrics Abstract （216） PDF （230）