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Ren A, Yu L, Zhao X, Jia F, Han F, Hou H, Liu Y. A multi-objective optimization approach for green supply chain network design for the sea cucumber (Apostichopus japonicus) industry. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172050. [PMID: 38565356 DOI: 10.1016/j.scitotenv.2024.172050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/28/2024] [Accepted: 03/26/2024] [Indexed: 04/04/2024]
Abstract
In China, aquatic supply chain network design does not include the green concept or the coordination of environmental and economic performance. Sea cucumber (Apostichopus japonicus) is an aquatic product of high economic value; however, studies on sea cucumber supply chain network optimization are lacking. This study is the first to design the sea cucumber supply chain and construct an optimization model. Considering the characteristics of the sea cucumber industry, LCA for Experts software and the CML-IA-Aug. 2016-world method were used to assess each aquaculture model's global warming potential (GWP), as the environmental performance indicator. In addition, multi-objective genetic algorithm (MOGA) coupled with Modified Technique for Order of Preference by Similarity to Ideal Solution (M-TOPSIS) integrates yield production, economic benefits, and environmental performance. The results demonstrated that cage seed rearing (CSR) combined bottom sowing aquaculture (BSA) represents the best production strategy upstream of the sea cucumber supply chain. In the downstream, the best proportion of sales channels in supermarkets, boutique stores and online shops accounted for 14.79 %, 58.02 % and 27.19 % of the production, respectively. The proposed optimization scenario 4 (S4) can increase product profit by 27.88 % and reduce GWP by 56.89 %. The following improvement measures are proposed: using sea cucumber aquaculture industry standards (cleaner production and green supplier selection) to regulate the behavior of enterprises, adopting an ecological and green production strategy, eliminating high-energy consumption and high emission production practices, and promoting widespread adoption of green consumption concepts. Finally, these measures may improve the sea cucumber supply chain, achieve coordinated environmental and economic performance development in the sea cucumber industry, and provide guidance for green optimization of other aquatic product supply chains in China.
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Affiliation(s)
- Anqi Ren
- Key Laboratory of Environment Controlled Aquaculture (Dalian Ocean University) Ministry of Education, 52 Heishijiao Street, Dalian 116023, China; College of Marine Technology and Environment, Dalian Ocean University, 52 Heishijiao Street, Dalian 116023, China
| | - Lixingbo Yu
- Key Laboratory of Environment Controlled Aquaculture (Dalian Ocean University) Ministry of Education, 52 Heishijiao Street, Dalian 116023, China; College of Marine Technology and Environment, Dalian Ocean University, 52 Heishijiao Street, Dalian 116023, China
| | - Xintao Zhao
- Dalian Bangchui Island Sea Cucumber Development Co. Ltd, 987 Wuyi Road, Dalian 116100, China
| | - Fei Jia
- Key Laboratory of Environment Controlled Aquaculture (Dalian Ocean University) Ministry of Education, 52 Heishijiao Street, Dalian 116023, China; College of Marine Technology and Environment, Dalian Ocean University, 52 Heishijiao Street, Dalian 116023, China
| | - Fengfan Han
- Key Laboratory of Environment Controlled Aquaculture (Dalian Ocean University) Ministry of Education, 52 Heishijiao Street, Dalian 116023, China; College of Marine Technology and Environment, Dalian Ocean University, 52 Heishijiao Street, Dalian 116023, China
| | - Haochen Hou
- Key Laboratory of Environment Controlled Aquaculture (Dalian Ocean University) Ministry of Education, 52 Heishijiao Street, Dalian 116023, China; College of Marine Technology and Environment, Dalian Ocean University, 52 Heishijiao Street, Dalian 116023, China.
| | - Ying Liu
- Key Laboratory of Environment Controlled Aquaculture (Dalian Ocean University) Ministry of Education, 52 Heishijiao Street, Dalian 116023, China; College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
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Multi-UAV autonomous collision avoidance based on PPO-GIC algorithm with CNN-LSTM fusion network. Neural Netw 2023; 162:21-33. [PMID: 36878168 DOI: 10.1016/j.neunet.2023.02.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/23/2022] [Accepted: 02/15/2023] [Indexed: 03/07/2023]
Abstract
This paper is concerned with the autonomous effective collision avoidance strategy for multiple unmanned aerial vehicles (multi-UAV) in limited airspace under the framework of proximal policy optimization (PPO) algorithm. An end-to-end deep reinforcement learning (DRL) control strategy and a potential-based reward function are designed. Next, the CNN-LSTM (CL) fusion network is constructed by fusing the convolutional neural network (CNN) and the long short-term memory network (LSTM), which realizes the feature interaction among the information of multi-UAV. Then, a generalized integral compensator (GIC) is introduced into the actor-critic structure, and the CLPPO-GIC algorithm is proposed by combining CL and GIC. Finally, we validate the learned policy in various simulation environments by performance evaluation. The simulation results show that the introduction of the LSTM network and GIC can further improve the efficiency of collision avoidance, and the robustness and accuracy of the algorithm are verified in different environments.
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Niu B, Xue B, Zhou T, Kustudic M. Aviation maintenance technician scheduling with personnel satisfaction based on interactive multi‐swarm bacterial foraging optimization. INT J INTELL SYST 2022. [DOI: 10.1002/int.22645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Ben Niu
- College of Management Shenzhen University Shenzhen China
- Greater Bay Area International Institute for Innovation Shenzhen University Shenzhen China
- Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen China
| | - Bowen Xue
- College of Management Shenzhen University Shenzhen China
- Greater Bay Area International Institute for Innovation Shenzhen University Shenzhen China
| | - Tianwei Zhou
- College of Management Shenzhen University Shenzhen China
- Greater Bay Area International Institute for Innovation Shenzhen University Shenzhen China
| | - Mijat Kustudic
- College of Management Shenzhen University Shenzhen China
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Identifying Dynamic Changes in Water Surface Using Sentinel-1 Data Based on Genetic Algorithm and Machine Learning Techniques. REMOTE SENSING 2021. [DOI: 10.3390/rs13183745] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The knowledge of water surface changes provides invaluable information for water resources management and flood monitoring. However, the accurate identification of water bodies is a long-term challenge due to human activities and climate change. Sentinel-1 synthetic aperture radar (SAR) data have been drawn, increasing attention to water extraction due to the availability of weather conditions, water sensitivity and high spatial and temporal resolutions. This study investigated the abilities of random forest (RF), Extreme Gradient Boosting (XGB) and support vector machine (SVM) methods to identify water bodies using Sentinel-1 imageries in the upper stream of the Yangtze River, China. Three sets of hyper-parameters including default values, optimized by grid searches and genetic algorithms, were examined for each model. Model performances were evaluated using a Sentinel-1 image of the developed site and the transfer site. The results showed that SVM outperformed RF and XGB under the three scenarios on both the validated and transfer sites. Among them, SVM optimized by genetic algorithm obtained the best accuracy with precisions of 0.9917 and 0.985, kappa statistics of 0.9833 and 0.97, F1-scores of 0.9919 and 0.9848 on validated and transfer sites, respectively. The best model was then used to identify the dynamic changes in water surfaces during the 2020 flood season in the study area. Overall, the study further demonstrated that SVM optimized using a genetic algorithm was a suitable method for monitoring water surface changes with a Sentinel-1 dataset.
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Yang Q, Chen WN, Gu T, Zhang H, Yuan H, Kwong S, Zhang J. A Distributed Swarm Optimizer With Adaptive Communication for Large-Scale Optimization. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:3393-3408. [PMID: 30969936 DOI: 10.1109/tcyb.2019.2904543] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Large-scale optimization with high dimensionality and high computational cost becomes ubiquitous nowadays. To tackle such challenging problems efficiently, devising distributed evolutionary computation algorithms is imperative. To this end, this paper proposes a distributed swarm optimizer based on a special master-slave model. Specifically, in this distributed optimizer, the master is mainly responsible for communication with slaves, while each slave iterates a swarm to traverse the solution space. An asynchronous and adaptive communication strategy based on the request-response mechanism is especially devised to let the slaves communicate with the master efficiently. Particularly, the communication between the master and each slave is adaptively triggered during the iteration. To aid the slaves to search the space efficiently, an elite-guided learning strategy is especially designed via utilizing elite particles in the current swarm and historically best solutions found by different slaves to guide the update of particles. Together, this distributed optimizer asynchronously iterates multiple swarms to collaboratively seek the optimum in parallel. Extensive experiments on a widely used large-scale benchmark set substantiate that the distributed optimizer could: 1) achieve competitive effectiveness in terms of solution quality as compared to the state-of-the-art large-scale methods; 2) accelerate the execution of the algorithm in comparison with the sequential one and obtain almost linear speedup as the number of cores increases; and 3) preserve a good scalability to solve higher dimensional problems.
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Gong YJ, Chen WN, Zhan ZH, Zhang J, Li Y, Zhang Q, Li JJ. Distributed evolutionary algorithms and their models: A survey of the state-of-the-art. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.04.061] [Citation(s) in RCA: 247] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Masegosa AD, Pelta DA, Verdegay JL. A centralised cooperative strategy for continuous optimisation: The influence of cooperation in performance and behaviour. Inf Sci (N Y) 2013. [DOI: 10.1016/j.ins.2012.07.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Kampolis IC, Giannakoglou KC. Synergetic use of different evaluation, parameterization and search tools within a multilevel optimization platform. Appl Soft Comput 2011. [DOI: 10.1016/j.asoc.2009.12.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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