Optimization of Fresh Food Logistics Routes for Heterogeneous Fleets in Segmented Transshipment Mode by Haoqing Sun, Manhui He, Yanbing Gai, Jinghao Cao

“…The k-means++ clustering algorithm is used to determine transshipment points, while an improved adaptive multi-objective ant colony optimization algorithm (IAMACO) is employed to optimize the delivery routes for the heterogeneous fleet. …”
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A Fast Recognition Method for Dynamic Blasting Fragmentation Based on YOLOv8 and Binocular Vision by Ming Tao, Ziheng Xiao, Yulong Liu, Lei Huang, Gongliang Xiang, Yuanquan Xu

“…As the primary method used in open-pit mining, blasting has a direct impact on the efficiency and cost of subsequent operations. Therefore, dynamic identification of rock fragment size after blasting is essential for evaluating blasting quality and optimizing mining plans. …”
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Implementing an Industry 4.0 UWB-Based Real-Time Locating System for Optimized Tracking by Athanasios Sidiropoulos, Dimitrios Bechtsis, Dimitrios Vlachos

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Enhancing Route Optimization in Road Transport Systems Through Machine Learning: A Case Study of the Dakhla-Paris Corridor by Najib El Karkouri, Lahcen Hassine, Younes Ledmaoui, Hasna Chaibi, Rachid Saadane, Nourddine Enneya, Mohamed El Aroussi

“…However, these systems face numerous challenges, particularly regarding safety, cost, and route optimization, requiring innovative and practical solutions to improve their overall performance. …”
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Integrating AI Deep Reinforcement Learning With Evolutionary Algorithms for Advanced Threat Detection in Smart City Energy Management by Fenghua Liu, Xiaoming Li

“…The integration of Deep Reinforcement Learning (DRL) with Evolutionary Algorithms (EAs) represents a significant advancement in optimizing smart city energy operations, addressing the inherent uncertainties and dynamic conditions of urban environments. …”
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Multi-energy System Planning and Configuration Study for Low-Carbon Parks Based on Comprehensive Optimization Objectives by Yang WANG, Fei LU, Ji LI, Zhukui TAN, Zongyu SUN, Wei XU, Zihong SONG, Zhenpeng LIU

“…Secondly, by taking a multi-energy system with triple supply of cooling, heating and power system coupled with ground source heat pump, energy storage, and gas boiler in a typical low-carbon park as an example, a comparative analysis was made on the configuration results and optimization speed with different optimization algorithms, and a study was conducted on the impacts of different optimization objectives such as optimal comprehensive optimization objective and lowest whole life-cycle cost and energy consumption on the capacity optimization configuration results and typical daily operation situation. …”
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A Theoretical Bound Which Improves the Performance of Compilation-Based Multi-Agent Path Finding by Rodrigo Lopez, Roberto Asin-Acha, Jorge A. Baier

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A genetic algorithm-based solution for multi-type maximal covering location problem (MMCLP): application to defense and deterrence by J.D. Lessan, Geoff Pond

“…Design/methodology/approach – In our case study, we use open source geographic and demographic data from Canadian sources as inputs to our optimization problem. Due to the complexity of the MIP formulation, we propose a hybrid metaheuristic solution approach, for which a genetic algorithm (GA) is proposed and integrated with local and large neighborhood search operators. …”
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Improved A* Algorithm Based on Artificial Potential Field Method for Global Path Planning of Unmanned Surface Vessels in Complex Environments by Shuxuan Liu, Zhijia Qian, Sunwei Jiang, Quanming Miao

“…In the improved A* algorithm, the search direction was first optimized, simplifying the traditional eight direction search to a “target point attracts four directions” search, significantly reducing the ineffective expansion nodes of the traditional algorithm, reducing algorithm running time, and improving search efficiency; Secondly, on the basis of improving the cost function, the idea of artificial potential field algorithm is integrated, and the repulsive function is introduced. …”
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Optimization of vehicle routing problems combining the demand urgency and road damage for multiple disasters by Ran Li, Xiaofei Ye, Shuyi Pei, Xingchen Yan, Tao Wang, Jun Chen, Pengjun Zheng

“…A set of evaluation index systems for multiple disasters was established to quantify the urgency of demand. The routing optimization model of emergency vehicles for multiple disasters was proposed by combining demand urgency and road damage, and the non-dominated sorting genetic algorithm II (NSGA-II) was used to simulate and validate the model. …”
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Optimization of the heat recovery performance of enhanced geothermal system based on PSO-GA-BP neural networks and analytic hierarchy process by Ling Zhou, Jingchao Sun, Yanjun Zhang, Yunjuan Chen, Honglei Lei

“…Based on the numerical simulation data, optimized Back-Propagation Neural Network (BPNN) prediction models combining the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA) were developed to investigate the impact of various factors on the heat recovery performance of a three-horizontal-well EGS in the Zhacang geothermal field. …”
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Optimization of Home Energy Management Systems in Smart Cities Using Bacterial Foraging Algorithm and Deep Reinforcement Learning for Enhanced Renewable Energy Integration by Mohammed Naif Alatawi

“…Significant reductions in total energy consumption and cost, accompanied by improved peak demand management, exemplify the algorithms’ impact. …”
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A novel two stage neighborhood search for flexible job shop scheduling problem considering reconfigurable machine tools by Yanjun Shi, Chengjia Yu, Shiduo Ning

“…This paper focuses on the flexible job shop scheduling problem with machine reconfigurations (FJSP-MR) and proposes an improved genetic algorithm with a two-stage neighborhood search (IGA-TNS) to minimize total weighted tardiness (TWT). …”
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Microservice Workflow Scheduling with a Resource Configuration Model Under Deadline and Reliability Constraints by Wenzheng Li, Xiaoping Li, Long Chen, Mingjing Wang

“…Experiments on four scientific workflow datasets show that the proposed approach achieves an average cost reduction of 44.59% compared to existing reliability scheduling algorithms, with improvements of 26.63% in the worst case and 73.72% in the best case.…”
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A new stochastic multi-objective model for the optimal management of a PV/wind integrated energy system with demand response, P2G, and energy storage devices by Hossein Faramarzi, Navid Ghaffarzadeh, Farhad Shahnia

“…Optimal energy hub scheduling (EHS) has emerged as a promising strategy for improving the efficiency and flexibility of power systems. …”
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Energy Management of a Semi-Autonomous Truck Using a Blended Multiple Model Controller Based on Particle Swarm Optimization by Mohammad Ghazali, Ishaan Gupta, Kemal Buyukkabasakal, Mohamed Amine Ben Abdallah, Caner Harman, Berfin Kahraman, Ahu Ece Hartavi

“…The approach combines a particle swarm optimization algorithm to determine optimal controller gains and a multiple model controller to adapt these gains dynamically based on real-time vehicle mass. …”
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Optimization for Express/Local Train Stop Plans on City Rapid Rail Transit Lines by GUO Jingfan

“…Although the algorithm slightly increases operational costs for enterprises, it significantly reduces passenger travel time costs and improves overall passenger travel accessibility.…”
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Maximizing efficiency and performance of water distribution systems through the implementation of optimization algorithms: A comprehensive analysis of valve and chlorine booster pl... by Mohamadreza Najarzadegan, Mehrtash Eskandaripour

“…By employing advanced optimization algorithms, specifically the Genetic Algorithm (GA) and Slime Mould Algorithm (SMA), the research identifies optimal configurations across two benchmark networks, Jowitt and Xu and GoYang. …”
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Autonomous UAV Path Optimization Using Genetic and Multiobjective Evolutionary Algorithms for Effective Data Retrieval in Cache-Enabled Mobile Ad-Hoc WSNs by Umair B. Chaudhry, Chris Ian Phillips

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Enhanced Reinforcement Learning Algorithm Based-Transmission Parameter Selection for Optimization of Energy Consumption and Packet Delivery Ratio in LoRa Wireless Networks by Batyrbek Zholamanov, Askhat Bolatbek, Ahmet Saymbetov, Madiyar Nurgaliyev, Evan Yershov, Kymbat Kopbay, Sayat Orynbassar, Gulbakhar Dosymbetova, Ainur Kapparova, Nurzhigit Kuttybay, Nursultan Koshkarbay

“…The proposed approach demonstrates the best performance, achieving a 17.2% increase in the packet delivery ratio compared to the traditional Adaptive Data Rate (ADR) algorithm. The proposed DDQN-PER algorithm showed PDR improvement in the range of 6.2–8.11% compared to other existing RL and machine-learning-based works.…”
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