Big Data-driven FMCG Terminal Visits

doi:10.3901/JME.2024.06.058

Abstract

Abstract: With the rapid growth of consumer market of FMCG(Fast moving consumer goods), companys' terminal visit cost have increased significantly. At the same time, the terminal visit scene is faced with the problems of obtaining terminal information in real time, making dynamic decisions and responding to customer demand dynamically. The "dynamic cloud-side linkage information architecture" and "dynamic cloud-side linkage mechanism" for FMCG terminal visit problem is proposed. At the same time, the deep reinforcement learning algorithm is used to optimize the system decision. The simulation results show that it can effectively reduce the traveling mileage of terminal visitors. For the FMCG terminal visit scenario, it can effectively improve the terminal service level of enterprises, maintain customer relationship, so as to achieve the purpose of increasing sales volume and order conversion rate.

Key words: terminal visits, linkage decision, reinforcement learning

CLC Number:

TP399

ZHAO Kuo, WANG Zhaoqi, PAN Zhenxin, PAN Yanghua, ZHANG Zhongfei, QU Ting. Big Data-driven FMCG Terminal Visits "Cloud-side" Based Decision-making and Optimization[J]. Journal of Mechanical Engineering, 2024, 60(6): 58-68.

References

[1] 王爱玲，李春玲. 快速消费品终端拜访研究[J]. 企业活力，2008，23(10)：52-53. WANG Ailing，LI Chunling. FMCG terminal visit research[J]. Corporate vitality，2008，23(10)：52-53.
[2] 宣陈林. 深圳市烟草坪山公司零售客户拜访与服务流程的优化研究[D]. 兰州：兰州大学，2020. XUAN Chenlin. Optimization research on retail customer visit and service process of shenzhen tobacco pingshan company[D]. Lanzhou：Lanzhou University，2020.
[3] 徐波，陈欢，田定胜，等. 基于的智能巡检机器人避障算法[J]. 供用电，2020，37(12)：74-80 XU Bo，CHEN Huan，TIAN Dingsheng，et al. Obstacle avoidance algorithm of intelligent inspection robot based on dijkstra[J]. Distribution & Utilization，2020，37(12)：74-80.
[4] 程鹏举，孟凡坤，李爽，等. A^*与蚁群算法在火灾逃生中的应用分析[J]. 通信技术，2020，53(11)：2680-2686. CHENG Pengju，MENG Fankun，LI Shuang，et al. Application analysis of A* and ant colony algorithm in fire escape[J]. Communication Technology，2020，53(11)：2680-2686.
[5] 易小泉. 出租车最优路径算法求解[J]. 计算机产品与流通，2020，36(6)：134-136 YI Xiaoquan. Floyd algorithm to solve the optimal taxi route[J]. Computer Products and Circulation，2020，36(6)：134-136.
[6] 龚铭凡，徐海祥，冯辉，等. 基于改进蚁群算法的智能船舶路径规划[J]. 武汉理工大学学报，2020，44(6)：1072-1076 GONG Mingfan，XU Haixiang，FENG Hui，et al. Intelligent ship path planning based on improved ant colony algorithm[J]. Journal of Wuhan University of Technology，2020，44(6)：1072-1076.
[7] 徐美清，刘洞波. 基于神经网络和遗传算法的移动机器人路径规划[J]. 南方农机，2020，51(21)：13-14，19. XU Meiqing，LIU Dongbo. Mobile robot path planning based on neural network and genetic algorithm[J]. China Southern Agricultural Machinery，2020，51(21)：13-14，19.
[8] RAO T，KHAN A，MASCHENDRA M，et al. A paradigm shift from cloud to fog computing[J]. International Journal of Science，Engineering and Computer Technology，2015，5(11)：385-385.
[9] SHI W，CAO J，ZHANG Q，et al. Edge computing：Vision and challenges[J]. IEEE Internet Things，2016，3(5)：637-646
[10] DOLUI K，DATTA S. Comparison of edge computing implementations：Fog computing，cloudlet and mobile edge computing[C]//2017 Global Internet of Things Summit (GIoTS). IEEE，2017：1-6.
[11] HONG H J. From cloud computing to fog computing: unleash the power of edge and end devices[C]//2017 IEEE international conference on cloud computing technology and science (CloudCom). IEEE，2017：331-334.
[12] QU T，PAN Y，LIU X，et al. Internet of things-based real-time production logistics synchronization mechanism and method toward customer order dynamics[J]. Transactions of the Institute of Measurement and Control，2017，39(4)：429-445.
[13] 雷水平. 物联制造系统中考虑云资源的“生产—物流”两级联动控制方法[D]. 广州：广东工业大学，2016. LEI Shuiping. Two-level control method for an IoT-based production logistics synchronization system with cloud resources[D]. Guangzhou：Guangdong University of Technology，2016.
[14] 张航. 面向物联网的RFID技术研究[D]. 上海：东华大学，2011. ZHANG Hang. Research on IoT-Oriented RFID[D]. Shanghai：Donghua University，2011.
[15] 陈奕东. 物联网环境下的动态JIT循环取货物流两阶段规划方法与研究[D]. 广州：广东工业大学，2015. CHEN Yidong. Research on a two-stage planning method for dynamic Just-in-Time Milk-run logistics under internet of things environment[D]. Guangzhou：Guangdong University of Technology，2015.
[16] MOHAMMADREZA N，AFSHIN O，et al. Deep reinforcement learning for solving the vehicle routing problem[J]. arXiv preprint arXiv：1802.04240，2018.
[17] 董冬艳. 基于生鲜农产品的冷链物流配送路径优化研究[D]．沈阳：沈阳大学，2017． DONG Dongyan. Study on route optimization of cold chain logistics based on fresh agricultural products[D]. Shenyang：Shenyang University，2017.

Big Data-driven FMCG Terminal Visits "Cloud-side" Based Decision-making and Optimization

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	WANG Wuyin, HUANG Zizhao, ZHUANG Zilong, FANG Huaijin, QIN Wei. Yard Crane Scheduling Method Based on Deep Reinforcement Learning for the Automated Container Terminal [J]. Journal of Mechanical Engineering, 2024, 60(6): 44-57.
[2]	GUO Jinghua, LI Wenchang, WANG Ban, WANG Jingyao. Deep Reinforcement Learning-based Control Strategy of Connected Hybrid Electric Vehicles Platooning [J]. Journal of Mechanical Engineering, 2024, 60(2): 262-271.
[3]	LI Wenli, ZHANG Yinan, SHI Xiaohui, WANG Mengxin. Game Theory-based Simulation of Pedestrian Behavior at Right-turn Unsignalized Intersections [J]. Journal of Mechanical Engineering, 2024, 60(10): 86-101.
[4]	ZENG Di, ZHENG Ling, LI Yinong, YANG Xiantong. Bayesian Inverse Reinforcement Learning-based Reward Learning for Automated Driving [J]. Journal of Mechanical Engineering, 2024, 60(10): 245-260.
[5]	YANG Shuo, LI Shizhen, ZHAO Zhongyuan, HUANG Xiaopeng, HUANG Yanjun. Integrated Autonomous Driving Lane Change Policy Based on Temporal Difference Learning Model Predictive Control [J]. Journal of Mechanical Engineering, 2024, 60(10): 329-338.
[6]	ZHANG Zhi-yong, HUANG Da-yang, HUANG Cai-xia, HU Lin, DU Rong-hua. TD3 Algorithm Improving and Lane-merging Strategy Learning for Autonomous Vehicles [J]. Journal of Mechanical Engineering, 2023, 59(8): 224-234.
[7]	GUO Hongfei, LU Xinyu, REN Yaping, ZHANG Chaoyong, LI Jianqing. Reinforcement Learning-based Swarm Evolutionary Algorithm To Solve Two-sided Multi-objective Synchronous Parallel Disassembly Line Balancing Problem [J]. Journal of Mechanical Engineering, 2023, 59(7): 355-366.
[8]	ZHENG Pai, LI Chengxi, YIN Yue, ZHANG Rong, BAO Jinsong, WANG Baicun, XIE Haibo, WANG Lihui. Augmented Reality-assisted Mutual Cognitive System for Human-Robot Interaction Safety Concerns [J]. Journal of Mechanical Engineering, 2023, 59(6): 173-184.
[9]	LOU Shanhe, FENG Yixiong, ZHENG Hao, HU Bingtao, HONG Zhaoxi, TAN Jianrong. Brain-like Behavior Effect-inspiring and Principle Structure-combining Method for Mechanical Product Conceptual Design [J]. Journal of Mechanical Engineering, 2023, 59(24): 344-358.
[10]	MA Liying, WEI Yunbing. Research on Bidding Strategy of Power Generation Enterprise Based on DDPG Algorithm [J]. Journal of Electrical Engineering, 2023, 18(2): 192-200.
[11]	LIN Xinyou, YE Zhuoming, ZHOU Binhao. DQN Reinforcement Learning-based Steering Control Strategy for Autonomous Driving [J]. Journal of Mechanical Engineering, 2023, 59(16): 315-324.
[12]	ZHONG Peicheng, LUO Deyuan, PANG Mingjun. Research and System Implementation of Quadruped Robot Following Strategy Based on Deep Reinforcement Learning [J]. Journal of Mechanical Engineering, 2023, 59(13): 79-88.
[13]	GU Wenbin, LI Yuxin, LIU Siqi, YUAN Minghai, PEI Fengque. Research on Data-driven Real-time Scheduling Method of Smart Workshop [J]. Journal of Mechanical Engineering, 2023, 59(12): 47-61.
[14]	CHEN Ruiqi, LI Wenxin, WANG Chuanyang, YANG Hongbing. Interactive Operation Agent Scheduling Method for Job Shop Based on Deep Reinforcement Learning [J]. Journal of Mechanical Engineering, 2023, 59(12): 78-88.
[15]	TANG Xiaolin, CHEN Jiaxin, GAO Bolin, YANG Kai, HU Xiaosong, LI Keqiang. Deep Reinforcement Learning-based Integrated Control of Hybrid Electric Vehicles Driven by High Definition Map in Cloud Control System [J]. Journal of Mechanical Engineering, 2022, 58(24): 163-177.