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Multi-objective optimization algorithms for freight allocation in a food grain supply chain

APPLIED SOFT COMPUTING 2025年 171卷

作者： Anoop, K. P. Panicker, Vinay V. Siby, Jerin Aryadutt, C. S. Natl Inst Technol Calicut Dept Mech Engn Supply Chain & Syst Simulat Lab Kozhikode 673601 India

This article addresses multi-objective optimization of a freight allocation problem and presents the case of a food grain organization in India (FOI). The inventory and warehouse parameters that are relevant in the regional level allocation of food grains (using freight trains) are represented using three penalty factors, namely rake penalty factor, capacity utilization penalty factor, and weekly penalty factor. The article formulates a tri-objective optimization model to minimize each of the three penalty factors. Two customized multi-objective optimization algorithms are developed based on Multi-Objective Simulated Annealing (MOSA) and Elitist non-dominated sorting genetic algorithm ii (NSGA ii) to solve the formulated model. The algorithms are tested and validated via computational experiments designed using historical data collected from the FOI. The algorithms help the transportation managers at the FOI to generate improved and balanced transportation plans (with respect to the three objectives) in a quick time. Further, the performance of the algorithms is compared based on seven different performance metrics reported in literature. The MOSA-based algorithm performs equally or better than the NSGA ii-based algorithm with respect to four performance metrics.

关键词： Food grain supply chain Multi-objective optimization Freight allocation Multi-objective simulated annealing non-dominated sorting genetic algorithm ii

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Model-agnostic local explanation: Multi-objective genetic algorithm explainer

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 139卷

作者： Nematzadeh, Hossein Garcia-Nieto, Jose Hurtado, Sandro Aldana-Montes, Jose F. Navas-Delgado, Ismael Univ Malaga ITIS Software Arquitecto Francisco Penalosa 18 Malaga 29071 Spain Univ Malaga Dept Lenguajes & Ciencias Comp Malaga Spain

Late detection of plant diseases leads to irreparable losses for farmers, threatening global food security, economic stability, and environmental sustainability. This research introduces the Multi-Objective genetic algorithm Explainer (MOGAE), a novel model-agnostic local explainer for image data aimed at the early detection of citrus diseases. MOGAE enhances eXplainable Artificial Intelligence (XAI) by leveraging the non-dominated sorting genetic algorithm ii (NSGA-ii) with an adaptive Bit Flip Mutation (BFM) incorporating densify and sparsify operators to adjust superpixel granularity automatically. This innovative approach simplifies the explanation process by eliminating several critical hyperparameters required by traditional methods like Local Interpretable Model-Agnostic Explanations (LIME). To develop the citrus disease classification model, we preprocess the leaf dataset through stratified data splitting, oversampling, and augmentation techniques, then fine-tuning a pre-trained Residual Network 50 layers (ResNet50) model. MOGAE's effectiveness is demonstrated through comparative analyses with the Ensemble-based genetic algorithm Explainer (EGAE) and LIME, showing superior accuracy and interpretability using criteria such as numeric accuracy of explanation and Number of Function Evaluations (NFE). We assess accuracy both intuitively and numerically by measuring the Euclidean distance between expert-provided explanations and those generated by the explainer. The appendix also includes an extensive evaluation of MOGAE on the melanoma dataset, highlighting its versatility and robustness in other domains. The related implementation code for the fine-tuned ResNet50 and MOGAE is available at https://***/KhaosResearch/Plant-disease-explanation.

关键词： Citrus diseases Multi-objective genetic algorithm explainer non-dominated sorting genetic algorithm ii Residual network 50 layers Local Interpretable Model-agnostic Explanations Melanoma detection

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A bi-objective optimisation model for the drone scheduling problem in island delivery

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年

作者： Yang, Ying Liu, Jiaxin Wang, Shuaian Hong Kong Polytech Univ Dept Logist & Maritime Studies Kowloon Hong Kong 999077 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Dept Logist & Transportat Shenzhen Peoples R China

Drone-assisted parcel delivery to remote islands is increasingly replacing traditional methods, offering improved efficiency and enhanced service reliability. This paper addresses the drone scheduling problem in island delivery (DSP-ID) by optimising drone delivery routes. In particular, we first introduce a bi-objective mixed-integer linear programming model that concurrently optimises delivery time and energy consumption. To address the model, both a heuristic non-dominated sorting genetic algorithm ii (NSGA-ii) and an exact augmented epsilon-constraint method are developed. The efficacy and robustness of the proposed model and algorithms are evaluated through experiments across various scales. Results indicate that both algorithms yield high-quality solutions for DSP-ID in small-scale scenarios. However, as the problem size expands, the performance of the augmented epsilon-constraint method wanes under time constraints, whereas the NSGA-ii consistently delivers high-quality solutions. Additionally, we provide decision-makers with actionable insights for selecting the most effective drone delivery routes.

关键词： Island delivery drone scheduling problem bi-objective optimisation model energy consumption non-dominated sorting genetic algorithm ii augmented epsilon-constraint

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Metaheuristic approach to multi-objective ergonomic workforce scheduling for Bi-hazard industrial tasks

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2025年第1期20卷 32-41页

作者： Rattanamanee, Tarit Nanthavanij, Suebsak King Mongkuts Univ Technol North Bangkok Fac Engn Dept Mat Handling & Logist Engn Bangkok 10800 Thailand Log Plus Min & Serv Co Ltd Suksawad Rd Phra Pradaeng Sumut Prakarn Thailand

This paper considers the daily scheduling of workers who are simultaneously exposed to two ergonomic hazards when performing their assigned tasks. Safe daily rotating work schedules are generated to minimize the maximum exposure amounts of both hazards simultaneously. A non-dominated sorting genetic algorithm ii (NSGA-ii) method is employed to generate near-optimal workforce scheduling solutions. A numerical example shows that for a small multi-objective problem, the NSGA-ii is able to yield the same set of non-dominated Pareto points as those obtained from a multi-objective optimization model with epsilon-constraint method. A computational experiment further demonstrates that the NSGA-ii can yield acceptable non-dominated Pareto points in reasonable computation times irrespective of the problem size.

关键词： Workforce scheduling problem ergonomic hazard reduction multi-objective optimization non-dominated sorting genetic algorithm ii

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An improved particle swarm optimization algorithm for berth allocation and time-variant quay crane scheduling problem during an emergency

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 269卷

作者： Ran, Lingyu Liu, Boning Zhang, Guiqing Cheng, Yongxi Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Econ & Finance Xian 710049 Peoples R China State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

This study investigated the combined continuous berth allocation and time-variant quay crane-scheduling problem in container ports during emergencies (BACASP_TVE), which is a new extension of the classical berth allocation and quay crane assignment and scheduling problem (BACASP). We proposed a bi-objective mixedinteger linear programming formulation for the problem, to minimize the maximum makespan of all ships and total energy consumption. In particular, the energy consumption caused by movement of quay-cranes are first considered in our model. Owing to the complexity of the model, we designed an extended non-dominated sorting genetic algorithm (ENSGA-ii), and an improved particle swarm optimization algorithm (IPSO) integrated with a two-dimensional bin-packing strategy and a feasibility re-check strategy (IPSO-BP), to obtain near-optimal solutions. The formulation of the time-space constraints for allocating berth to ships as a two-dimensional binpacking problem is also first proposed in our paper. A Taguchi experiment was performed to set the parameter combination of the ENSGA-ii and IPSO-BP. A total number of 191 randomly generated instances are tested for the validation and robustness of the approaches (ENSGA-ii, IPSO-BP, and Gurobi) and the mathematical model proposed. The experimental results confirm the effectiveness of IPSO-BP, which outperforms ENSGA-ii and Gurobi in both solution quality and computation time. For an instance with 100 ships, the model is solved for five different combinations of the two objectives, and discussions on the results for the five scenarios and guidance for the port operators are given. The conclusions drawn from this study can be regarded as an operational guide for container operators for making trade-off decisions between the makespan of all ships and energy consumption, when encountered with emergencies.

关键词： OR in maritime Berth allocation and time-variant quay crane scheduling Multi-object optimization Two-dimensional Bin-packing Particle swarm optimization non-dominated sorting genetic algorithm ii

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The heating effect analysis of electromagnetic induction heating system based on the multi objective optimization strategy

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JOURNAL OF ENERGY STORAGE 2025年 105卷

作者： Wang, Borui Yan, Mingyin Shenyang Univ Technol Sch Mech Engn Shenyang 110000 Peoples R China

Lithium-ion battery heating in cold weather is necessary to ensure its low-temperature performance and lifetime, so the multi objective optimization heating strategy based on the non-dominated sorting genetic algorithm ii is introduced to improve the heating effect of electromagnetic induction heating system, in which the generated Pareto frontier as the research sample is input in K-medoid algorithm in order to reduce the feasible range of solution sets and then get the optimal induction coil parameters combination, and the temperature rise and terminal voltage output performance of battery are characterized by the electrochemical-thermal coupling model. Firstly, under the optimal parameters combination (N = 180, f = 9 kHZ, IA = 6 A), the battery can be heated from 243.15 K to 293.15 K in 1430 s with the temperature difference of 2.74 K, and the temperature field is mainly generated by the heat conduction from active material to mandrel during the heating process due to skin effect and material property. Secondly, the internal resistance is decreased by more than 3 times after the heating, giving rise to the substantial improvement of energy output ability. Finally, neither noticeable discharging capacity retention loss nor apparent voltage output difference is founded after the 200 repeated heating cycles, as compared with the normal charging-discharging cycle at 293.15 K, so the heating strategy has almost no effect on the battery aging and lifetime deterioration. Therefore, the proposed heating strategy can realize a suitable tradeoff among temperature-rising effect, lifespan and low-temperature operating performance, which has substantial potential to be a feasible method for improving the applicability of electric vehicles in a cold weather.

关键词： Multi objective optimization non-dominated sorting genetic algorithm ii Electromagnetic induction heating system Electrochemical-thermal coupling model Optimal parameters combination

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Optimization of industrial waste composition for eco-cement production using machine learning with meta-heuristic method

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Resources, Conservation and Recycling 2025年 220卷

作者： Luo, Wei Liu, Longming Hu, Jingping Hou, Huijie Hu, Hongyun Yang, Jiakuan Hubei Wuhan430074 China Hubei Key Laboratory of Multi-media Pollution Cooperative Control in Yangtze Basin Hubei Wuhan430074 China Engineering Laboratory of Solid Waste Treatment Disposal and Recycling Hubei Wuhan430074 China State Key Laboratory of Coal Combustion Hubei Wuhan430074 China

Industrial solid waste utilization in ecological cement production reduces carbon emissions and resource consumption. However, waste composition variability challenges product consistency, hindering widespread implementation. This study presents a novel artificial intelligence driven optimization framework that combines triple back-propagation neural network (BPNN), non-dominated sorting genetic algorithm-ii (NSGA-ii) and super-efficiency data envelopment analysis (SE-DEA), to optimize raw material proportions. The triple BPNN architecture, which separately models raw meal grinding, clinker calcination, and clinker homogenization, demonstrates superior prediction accuracy. By integrating SE-DEA within NSGA-ii, the algorithm achieves balanced optimization across multiple performance metrics. Cement produced using this optimization framework demonstrates improved long-term mechanical properties, with 28-day compressive strength ranking in the top 4.11 % and 28-day flexural strength in the top 7.41 % of the historical production data from the same facility. The proposed framework exhibits significant potential for the development and promotion of ecological cement technology while promoting industrial waste utilization at large scale. © 2025 Elsevier B.V.

关键词： Backpropagation neural networks Ecological cement Industrial solid waste non-dominated sorting genetic algorithm ii Super-efficiency data envelopment analysis

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Simulated fire observables as indicators for optimizing wireless sensor networks in wildfire risk monitoring

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Ecological Indicators 2025年 175卷

作者： Juan Luis Gómez-González Effie Marcoulaki Alexis Cantizano Myrto Konstantinidou Raquel Caro Mario Castro Institute for Research in Technology (IIT) ICAI School of Engineering Comillas Pontifical University c/ Rey Francisco 4 28008 Madrid Spain System Reliability and Industrial Safety Laboratory Institute of Nuclear & Radiological Sciences & Technology Energy & Safety National Centre for Scientific Research “Demokritos” 15310 Ag. Paraskevi Attiki Athens 60037 Greece University Institute of Studies on Migration (IUEM) Chair in Disasters Fundación AON España c/ Rey Francisco 4 28008 Madrid Spain Grupo Interdisciplinar de Sistemas Complejos (GISC) Comillas Pontifical University c/ Alberto Aguilera 25 28008 Madrid Spain

Uncontrolled wildfires cause significant damage and economic costs. Wireless Sensor Networks (WSNs) can mitigate these impacts by detecting fires early across extensive wildland areas. This work presents a simulation-driven optimization framework for localizing WSNs to enhance early wildfire detection and minimize potential damage. Formulated as a Multi-Objective Optimization Problem (MOOP) and solved using the non-dominated sorting genetic algorithm ii (NSGA-ii), the method utilizes dynamic wildfire simulations and considers stochastic variables such as ignition likelihood and weather conditions. The methodology is general and independent of the simulation model or the studied region. The framework supports decision-making under uncertainty, ensuring the designed networks remain effective across varying conditions. A practical case study with validated fire behaviour demonstrates the robustness of the approach to identify the most efficient and cost-effective sensor locations. Results show significantly better performance compared to uniform sensor grids and WSNs designed for fixed-weather scenarios, highlighting the benefits of this approach for wildfire management.

关键词： AT Arrival Time BA Burnt Area CA Cellular Automata DBA Detected Burnt Area ECMWF European Centre for Medium-Range Weather Forecasts EFFIS European Forest Fire Information System ETRS89 European Terrestrial Reference System 1989 ERA5 Fifth Generation ECMWF Reanalysis for the Global Climate and Weather EWDS Early Wildfire Detection System FSF Free-Spreading Fire FWI Fire Weather Index GA genetic algorithms IoT Internet of Things LBPM Landscape Burnt Probability Map MOOP Multi-Objective Optimization Problem NSGA-ii non-dominated sorting genetic algorithm ii SIGIF Integrated Forest Fire Management System of Generalitat Valenciana Wii Wildland Industrial Interface WSN Wireless Sensor Network WUI Wildland Urban Interface. Wildfire simulation Optimization Early wildfire detection system Wireless sensor network NSGA-ii

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Multi-objective optimization of proton exchange membrane fuel cells by RSM and NSGA-ii

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ENERGY CONVERSION AND MANAGEMENT 2023年第1期277卷

作者： Chen, Zhijie Zuo, Wei Zhou, Kun Li, Qingqing Huang, Yuhan Jiaqiang, E. Wuhan Univ Sci & Technol State Key Lab Refractories & Met Wuhan 430081 Peoples R China Univ Technol Sydney Ctr Green Technol Sch Civil & Environm Engn Sydney NSW 2007 Australia Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China Wuhan Univ Sci & Technol Natl Prov Joint Engn Res Ctr High Temp Mat & Linin Wuhan 430081 Peoples R China

This study optimized the performance of a proton exchange membrane fuel cell by combining the response surface methodology and non-dominated ranking genetic algorithm. Firstly, the design variables are determined, including operating pressure (p), operating temperature (T), Anode stoichiometry ratio (lambda(a)), thickness of the proton exchange membrane (H-mem) and gas diffusion layer (GDL) porosity (epsilon(GDL)). The objective functions are also identified, including power density (P), system efficiency (eta) and exergy efficiency. Then, the Box-Behnken design is employed to arrange the numerical investigations. Analysis of variance is used to verify the appropriateness and reliability of the constructed regression models. Response surface analysis is used to show the interaction between each pair of design parameters. Finally, the Pareto optimal frontier is obtained by non-dominated ranking genetic algorithm ii and the regression models constructed by response surface methodology. The Pareto optimal solution offers a power density of 0.6327 W center dot cm(-2), a system efficiency of 26.16% and an exergy efficiency of 43.94 %, which is 13.18 %, 7.06 % and 20.29 % better than the initial direct current channel, respectively. The corresponding design variables is p = 2.6498 atm, T = 341.621 K, lambda(a) = 1.1808, H-mem = 0.0577 mm and epsilon(GDL) = 0.4908. This work provides a new multi-objective optimization method for designing more efficient proton exchange membrane fuel cells.

关键词： Proton exchange membrane fuel cell Multi-objective optimization Response surface methodology non-dominated sorting genetic algorithm ii

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Multi-objective optimization of turbulent heat transfer flow in novel outward helically corrugated tubes

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APPLIED THERMAL ENGINEERING 2018年 138卷 795-806页

作者： Wang, Wei Zhang, Yaning Li, Yongji Han, Huaizhi Li, Bingxi Harbin Inst Technol Sch Energy Sci & Engn Harbin 150001 Heilongjiang Peoples R China Harbin Engn Univ Coll Power & Energy Engn Harbin 150001 Heilongjiang Peoples R China

This paper reports a triple-objective optimization of a novel outward helically corrugated tube to obtain the equilibrium performance for heat transfer, pressure drop, and energy benefit. The response surface method is employed to design the numerical work with three objective functions and three factors. Variance analysis and sensitivity analysis are performed to ensure the regression model and clarify the effect of each factor on the responses. The results show that the regression model obtained after removing the insignificant terms shows good agreement with the numerical data and the error is within +/- 10%. The Reynolds number (Re) has the most effect on Nusselt number (Nu), and its sensitivity coefficient is five times higher than that for corrugation pitch-to-diameter ratio (pl/D) and corrugation height-to-diameter ratio (HUD). The Pareto optimal solution is obtained by the multi-objective genetic algorithm (MOGA), which can be selected by the designer according to the operation conditions and the emphases target values. From a balanced consideration, the variable range of Re = 18,375-23,858, pl/D = 0.92-0.75 and Hl/D = 0.11-0.13 is better for decision-making. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Outward helically corrugated tube Triple-objective optimization non-dominated sorting genetic algorithm ii Heat transfer enhancement Response surface method

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