Optimization Model Research Articles

Dynamic Collaborative Optimization Strategy for Multiple Area Clusters in Distribution Networks Considering Topology Change

To tackle the challenges arising from missing real-time measurement data and dynamic changes in network topology in optimizing and controlling distribution networks, this study proposes a data-driven collaborative optimization strategy tailored for multi-area clusters. Firstly, the distribution network is clustered based on electrical distance modularity and power balance indicators. Next, a collaborative optimization model for multiple area clusters is constructed with the objectives of minimizing node voltage deviations and active power losses. Then, a locally observable Markov decision model within the clusters is developed to characterize the relationship between the temporal operating states of the distribution network and the decision-making instructions. Using the Actor–Critic framework, the cluster agents are trained while considering the changes in cluster boundaries due to topology variations. A Critic network based on an attention encoder is designed to map the dynamically changing cluster observations to a fixed-dimensional space, enabling agents to learn control strategies under topology changes. Finally, case studies show the effectiveness and superiority of the proposed method.

Hybrid genetic algorithm and Q-learning-based solution for the time-variant berth and quay crane allocation problem

IntroductionThis study addresses the joint scheduling optimization of continuous berths and quay cranes by proposing a time-variant quay crane allocation method.MethodsA coordinated optimization model is constructed that considers the temporal dimension of quay crane scheduling and equipment collision factors to reduce overall port operational costs. A hybrid intelligent algorithm integrating Q-learning is innovatively designed, using a genetic algorithm as the main framework while embedding a quay crane allocation module and dynamically selecting genetic operators through Q-learning to achieve adaptive optimization of the evolutionary mechanism.ResultsThe module with Q-learning optimization is compared to the module without Q-learning optimization, demonstrating that the Q-learning module can accelerate the convergence of the algorithm and has a better ability to find the optimal solution in large-scale cases, proving the effectiveness of the module.DiscussionThe results show that the proposed algorithm and CPLEX perform similarly in small-scale cases, while the solution speed and capability are better than the genetic algorithm in large-scale problems and superior to the CPLEX algorithm with time constraints in some cases, proving the effectiveness and superiority of the proposed algorithm.

Integrated dynamic scheduling of shipyard blocks considering adaptive adjustment in stockyard layout

The scarcity of space resources and backward scheduling management in block assembly yards have become constraints on shipyard logistics owing to demands in shipbuilding technology and scheduling efficiency. This article proposes a novel integrated dynamic scheduling method for shipyard blocks considering adaptive adjustment in stockyard layout. First, an optimization model is established to minimize the interference block moves. Then, dynamic adaptive layout prioritization rules and multi-strategy block movement policies are introduced for different cases. A heap field dynamic layout method based on a binary search tree and deep reinforcement learning algorithms is integrated to seek optimal scheduling. The proposed algorithm is validated with 50 sets of actual block data, four road types and four stockyard types. The results indicate that the proposed method effectively utilizes stockyard layout while reducing interference blocks during the movement process by 56%. A stockyard aspect ratio of approximately 3.2 yields optimal block scheduling effectiveness.

Delay-tolerated vehicle positioning and scheduling problem at terminal aprons: a target-oriented robust optimisation approach

The terminal apron, a critical component of container terminals, handles the majority of container flow but faces challenges due to limited maneuvering space and high traffic volume, necessitating improved vehicle positioning and scheduling. Operational uncertainties, such as unpredictable container transit times, compound these challenges by potentially causing delays in arrivals at the terminal apron. These types of delays are particularly problematic for container loading operations with tight schedules, making it difficult to estimate vessel turnaround time. This study addresses the integrated vehicle positioning and scheduling problem at terminal aprons while considering vehicle transit time uncertainties. The aim is to increase the resilience of the terminal apron system against delays, thereby enhancing the efficiency and robustness of loading operations under uncertain conditions. To achieve this, a target-oriented robust optimisation (RO) model is proposed, developing an uncertainty set to characterise transit time variability. A solution approach includes model reformulation, a customised bisection method, and the logic-based Benders decomposition method to efficiently solve the proposed models. Numerical experiments using data from Tianjin Port demonstrate that the target-oriented RO is suitable for managing terminal apron operations in uncertain environments, especially true during peak operating times or in scenarios characterised by significant variability.

Dynamic scheduling optimization model and algorithm for linear projects considering local rescheduling

PurposeLinear projects often involve lengthy construction periods, necessitating dynamic adjustments to the plan. Completely rescheduling remaining activities every time can lead to unnecessary time and cost wastage and significant deviations in resource supply. To address these issues, this paper proposes a dynamic scheduling method designed to effectively manage both time and cost during construction projects.Design/methodology/approachDetermining the rescheduling frequency through a hybrid driving strategy and buffer mechanism, introducing rolling window technology to determine the scope of local rescheduling and constructing a local rescheduling model under the constraints of time and cost deviation with the objective of minimizing the cost. Combined decision-making for construction and rushing modes constrained by multiple construction scenarios. Opposite learning is introduced to optimize the hybrid algorithm solution.FindingsArithmetic examples and cases confirm the model’s feasibility and applicability. The results indicate that (1) continuous rescheduling throughout project construction is essential and effective and (2) a well-structured buffer mechanism can prevent redundant rescheduling and enhance overall control of cost and schedule deviations.Originality/valueThis study introduces an innovative dynamic scheduling framework for linear engineering, offering a method for effectively controlling schedule deviations during construction. The developed model enhances rescheduling efficiency and introduces a combined quantization strategy to increase the model’s applicability to linear engineering. This model emerges as a promising decision support tool, facilitating the implementation of sustainable construction scheduling practices.

Considering the Comprehensive Energy System Capacity Optimization Configuration of Electric to Gas Conversion and Compressed Liquid Carbon Dioxide Energy Storage

In view of the carbon emission reduction and new energy consumption problems in the integrated energy system (IES), this paper, for the first time, combines power to gas (P2G) with liquid carbon dioxide energy storage (LCES) and takes demand response (DR) into account simultaneously to construct a new type of IES capacity configuration optimization model. Firstly, based on the operation characteristics and coupling features of various devices within the system, the IES model was constructed. Meanwhile, the electricity, heat, and cold DR models were, respectively, established according to price-based and incentive-based methods. Finally, a two-layer collaborative optimization configuration model was built, with the upper layer aiming to minimize the annual total cost of the system and the lower layer aiming to minimize the annual operation cost of the system. Through case studies, the effectiveness of the established model was verified, and the impacts of DR, P2G, and LCES on the system capacity configuration results, economic efficiency, and environmental friendliness were investigated. Additionally, the impact of natural gas prices on the system optimization results was studied. The results showed that considering LCES and P2G could reduce the cost of the IES by 7.26% and the carbon emissions of the system by 31.03%, verifying the effectiveness of the proposed method and providing a feasible solution for IES capacity configuration.

Optimal Operation of Renewable Energy Bases Considering Short-Circuit Ratio and Transient Overvoltage Constraints

The increasing integration of renewable energy bases into power systems poses significant challenges for voltage stability and operational optimization. This paper develops an optimization model to maximize the total generation output in a renewable energy base while satisfying short-circuit ratio (SCR), transient overvoltage (TOV), and conventional operational constraints. To address the inherent nonlinearity of power systems, the trajectory sensitivities of SCR and TOV with respect to the decision variables are calculated, allowing the original nonlinear optimization problem to be transformed into a linear programming (LP) problem for efficient solving. Recognizing that the LP-based solution may not strictly satisfy all constraints during time-domain simulation verification due to system nonlinearity, the continuation method is introduced to ensure that a refined solution that satisfies all constraints is obtained. Case studies conducted on a real-world renewable energy base demonstrate the effectiveness and feasibility of the proposed approach.

Support vector machine (SVM) algorithm optimization and innovative talent matching model for employment needs

Support vector machine (SVM) algorithm optimization and innovative talent matching model for employment needs

Tunable Dielectric Carbon Materials from Hydrothermally Nanostructured Organic Carbon Sources.

This work presents a systematic study of the electronic response and physico-chemical characteristics from hydrothermally treated organic carbon sources (banana peels and cocoa husks). Both samples are exposed to 150 °C and 210 °C for 2, 4, and 6 hours. X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and conductivity measurements are used to describe the electronic properties for each organic carbon source. A multicategorical statistical optimization model let us to identify the best dielectric performance considering: a) temperature treatment, b) exposure time, c) frequency, and d) the organic carbon source. Our results indicate that cocoa husk hydrothermally treated samples (CHH) exhibited the best dielectric response, originating from high carboxyl concentrations or diamond-like carbon structures at 150°C for 6 and 2 hours. In contrast, banana peel hydrothermally treated samples (BPH) are good conductors in comparison to CHH, due to low carboxylation or highly graphitization. This study provides valuable insights into the fundamental structure of lignocellulosic carbon sources that can aid in the development of energy storage and microwave technologies by transforming agricultural residues into high-value electronic materials.

A Bionic-Based Multi-Objective Optimization for a Compact HVAC System with Integrated Air Conditioning, Purification, and Humidification

This study is dedicated to the development of a multifunctional device that integrates air conditioning, humidification, and air purification functions, aimed at meeting the demands for energy efficiency, space-saving, and comfortable indoor environments in modern residential and commercial settings. The research focuses on achieving a balance between performance, energy consumption, and noise levels by combining bionic design principles with advanced optimization algorithms to propose innovative design and optimization methods. Specific methods include the establishment and optimization of mathematical models for air conditioning, air purification, and humidification functions. The air conditioning module employs a nonlinear programming model optimized through the Parrot Optimizer (PO) Algorithm to achieve uniform temperature distribution and minimal energy consumption. The air purification function is based on a bionic model and optimized using the Deep ACO Algorithm to ensure high efficiency and low noise levels. The humidification function utilizes a mist diffusion model optimized through the Slime Mold Algorithm (SMA) to enhance performance. Ultimately, a multi-objective optimization model is constructed using the Beluga Whale Optimization (BWO), successfully integrating the three main functions and designing a compact segmented cylindrical device that achieves a balance of high efficiency and multifunctionality. The optimization results indicate that the device exhibits superior performance, with a Clean Air Delivery Rate (CADR) of 400 m3/h, a humidification rate of 1.2 kg/h, a temperature uniformity index of 0.08, and a total power consumption controlled within 1600 W. This study demonstrates the significant potential of bionic design and optimization technology in the development of multifunctional indoor environment control devices, enhancing not only the overall performance of the device but also the comfort and sustainability of the indoor environment. Future work will focus on system scalability, experimental validation, and further optimization of bionic characteristics to expand the device’s applicability and enhance its environmental adaptability.

Long-Term Stochastic Co-Scheduling of Hydro–Wind–PV Systems Using Enhanced Evolutionary Multi-Objective Optimization

With the increasing presence of large-scale new energy sources, such as wind and photovoltaic (PV) systems, integrating traditional hydropower with wind and PV power into a hydro–wind–PV complementary system in economic dispatch can effectively mitigate wind and PV fluctuations. In this study, Markov chains and the Copula joint distribution function were adopted to quantize the spatiotemporal relationships among hydro, wind and PV, whereby runoff, wind, and PV output scenarios were generated to simulate their uncertainties. A dual-objective optimization model is proposed for the long-term hydro–wind–PV co-scheduling (LHWP-CS) problem. To solve the model, a well-tailored evolutionary multi-objective optimization method was developed, which combines multiple recombination operators and two different dominance rules for basic and elite populations. The proposed model and algorithm were tested on three annual reservoirs with large wind and PV farms in the Hongshui River Basin. The proposed algorithm demonstrates superior performance, with average improvements of 2.90% and 2.63% in total power generation, and 1.23% and 0.96% in minimum output expectation compared to BORG and NSGA-II, respectively. The results also infer that the number of scenarios is a key parameter in achieving a tradeoff between economics and risk.

Survival Prediction of Agricultural Trees by Optimizing Long and Short Term Memory Networks Based on Multi-head Attention Mechanism

In this study, long short-term memory network (LSTM) model was optimized based on multi-head attention mechanism to effectively predict the survival of agricultural trees. Based on the in-depth analysis of a large number of agricultural tree survival data, the corresponding prediction model was constructed. In the training phase, we analyzed the confusion matrix of the training set, and the results revealed that the accuracy of the model in predicting the survival of agricultural trees was as high as 99.74%. In addition, the performance on the independent test set is also very good, with an accuracy of 99.40%, although there is a slight decrease compared to the training set (0.34%), but both maintain a high accuracy level of more than 99%. This shows that the model has very high prediction accuracy and maintains good generalization ability across different data sets. Further, by drawing the ROC curve and calculating the AUC (area under the curve), the result is 0.9857, which fully reflects the strong ability of the model in the tree survival prediction task. An AUC value higher than 0.9 indicates that the model has a very low error rate on the classification task, which ensures the reliability of the prediction results. This study shows that the LSTM optimization model based on multi-head attention mechanism can provide a high-precision tree survival prediction tool for agricultural management, so as to help farmers make more effective decisions. This result not only has theoretical value, but also has important significance for the growth management and sustainable development of trees in practical application. Through this research, we expect to be able to contribute to the development of smart agriculture.

Low-Altitude and High-Speed Transportation Approach for Improved Logistics Efficiency and Carbon Reduction in Urban Networks

The new framework that combines the low-altitude economy with high-speed transportation offers a practical and effective solution for modern city logistics systems. It shows strong potential in boosting delivery efficiency, cutting costs, and reducing carbon emissions. This study focuses on Suzhou's logistics network, using a multi-goal optimization model and a smart scheduling system to assess how well the low-altitude economy works in different logistics situations. By combining an ARIMA time series prediction model with a Long Short-Term Memory (LSTM) network, the study looks at how logistics needs change during busy times, bad weather, and emergency situations. The research uses different data sources to help drones and ground vehicles work together smoothly. The model includes three main measures: delivery efficiency (T), transportation costs (C), and carbon emissions (E), and shows the real benefits of the low-altitude economy through clear data analysis. The results show that in normal conditions, delivery efficiency increased to 97.9, transportation costs dropped to 65.4, and carbon emissions fell to 58.2. During peak traffic and bad weather, delivery efficiency stayed strong at 85.5 and 80.3. The smart scheduling system managed resources well, keeping costs and emissions within safe limits (cost: 70.1-73.5; emissions: 61.8-67.9). Scheduling efficiency went up from 0.85 to 0.93, and resource use improved from 74.6% to 88.1%. The analysis showed a clear negative link between delivery efficiency and carbon emissions (-0.85) and a positive link between costs and emissions (0.78). This suggests the need to balance cost savings with environmental benefits. The suggested approach, combining the multi-goal optimization model with a smart scheduling system, not only helps Suzhou handle complex logistics challenges but also provides a useful model for other large cities, supporting the move towards faster, greener, and smarter city logistics systems.

Multi-Objective Structural Optimization of Hydraulically Controllable Reciprocating Seals With Comprehensive Static and Dynamic Performance

Abstract The hydraulically controllable reciprocating seal (HCRS) is a novel intelligent sealing technology capable of online performance regulation, making it one of the preferred solutions for highly reliable seals in the industrial field. However, the impact of structural parameters on the performance of it remains unclear, complicating efforts to provide precise guidance for structural design. To address these issues, this article first employs a mixed thermoelastohydrodynamic lubrication (TEHL) model to perform a parametric analysis of how structural parameters affect the static and dynamic performance of seals, identifying key influencing factors. Second, a multiobjective optimization model was established to derive the optimal structural design using a comprehensive balance method. Finally, the performance results before and after optimization were compared. The results indicate that the width of the slide ring, the air side angle of the slide ring, the inner diameter of the slide ring, and the length of the internal pressure cavity are critical factors influencing sealing performance. Following optimization, the maximum von Mises stress in the seal was reduced by 51.5%, net leakage decreased by 12.9%, and friction power loss reduced by 2.25%. This demonstrates that the optimization method is effective for designing seals with low leakage, low friction, and high reliability.

Multi-Objective Optimization for Artificial Island Construction Scheduling Using Cooperative Differential Evolution

The construction of artificial islands is a complex engineering challenge requiring precise scheduling to optimize resource utilization, manage costs, ensure safety, and minimize environmental impacts in dynamic marine settings. In this paper, we present a multi-objective artificial island construction scheduling optimization model. This model considers many crucial factors that influence artificial island construction from 5 aspects: construction time, construction cost, project quality, resource utilization efficiency, and environmental impact. To optimize the proposed model, we propose an algorithm called Multi-objective Cooperative Differential Evolution (MOCDE). MOCDE integrates Cooperative Co-evolutionary Algorithms, and Differential Evolution to efficiently obtain the optimal schedules. To explore the performance of this model and the algorithm, extensive experiments are conducted based on real-world project data. Comparing MOCDE with established algorithms, results indicate that MOCDE improvements over previous SOTA models, achieving a reduction of 0.56% in Total Time, a decrease of 0.43% in Total Cost, and an enhancement of 7.38% in Total Quality. Besides, it also could adhere to ensure the environmental requirements.

Optimal Scheduling Model of Load Aggregator on Demand Side Under Demand Response

ABSTRACTThe reaction capacity of the demand side would influence the price volatility in the electricity supply and demand scenario in the spot market because of the differences in user involvement in the market. To enhance the power market's stability and dependability, a demand‐side resource aggregation response optimization model is designed considering the load adjustment ability of users. First of all, a demand‐side response architecture covering various forms of user participation is established, and the demand response mechanism of users in different forms of participation under this architecture is analyzed. To guarantee the sustainability of the demand response mechanism in this architecture, the market clearing model taking into account the active response of indirect consumers is created, and the appropriate settlement method is suggested. Then, the revenue problem of demand‐side resource aggregators in the power market is mapped to the bidding decision problem, and the bidding optimization model of demand response under two contract modes of load reduction and load transfer is proposed. The findings indicate that demand response can improve the reliability of the distribution network and user benefit under the selected index and verify the rationality and effectiveness of the proposed method.

Sustainable e-grocery home delivery: An optimization model considering on-demand vehicles

Sustainable e-grocery home delivery: An optimization model considering on-demand vehicles

A robotic process automation model for order-handling optimization in supply chain management

A robotic process automation model for order-handling optimization in supply chain management

ANN-assisted comprehensive screening of silica gel-alunite composite sorbent system for efficient adsorption of toxic nickel ions: Batch and continuous mode water treatment applications.

ANN-assisted comprehensive screening of silica gel-alunite composite sorbent system for efficient adsorption of toxic nickel ions: Batch and continuous mode water treatment applications.

Digital mapping of soil salinity with time-windows features optimization and ensemble learning model

Digital mapping of soil salinity with time-windows features optimization and ensemble learning model