Multi-objective Optimization Model Research Articles

Environmental impact assessment for a meta-model-based food-energy-water-nexus system

A growing global population uses essential resources such as food, energy, and water to drive economic growth. However, population growth, urbanization, and human activities strain these limited resources, resulting in serious harm, particularly from global warming and climate change. Sustainable resource allocation in the Nexus system is required for improved resource security. This issue is essential since it impacts resource management, sustainability, and the environment. This research proposes developing an integrated assessment model (IAM) for the FEW-N system. The system undergoes a comprehensive evaluation that considers sustainability, economics, social aspects, and the environment. This evaluation combines an environmental impact assessment with a meta-model-based approach to the FEW-N system. A multi-objective optimization model is utilized to optimize the system with three key targets: maximum economic benefit, positive environmental impact, and least negative environmental impact. The findings show significant improvements in resource security within a community-level FEW-N system, with the Nexus system sectors contributing to both food and energy security. Greenhouse farming, rainfed farming, and irrigated farming supply 55.6%, 11.9%, and 32.5% of food security, respectively. Similarly, bioenergy, solar/wind hybrid renewable energy, and hydropower contribute 52.7%, 40.1%, and 7.2% of total energy security, respectively. The FEW-N system optimizes resource allocation for sustainability, reliability, and efficiency while emphasizing the specific contributions of diverse sectors to food and energy security. These findings provide critical insights for making educated resource allocation policy decisions in South Africa, emphasizing balancing societal, economic, and environmental factors. The study has increased the evidence base for the FEW-N environmental impact assessment framework to address resource allocation issues.

Hydrostatic bearing groove multi-objective optimization of the gear ring housing interface in a straight-line conjugate internal meshing gear pump

The lubrication performance of a straight-line conjugate internal meshing gear pump is poor under the low-speed, high-pressure operating conditions of the volumetric servo speed control system, and it is difficult to establish a full fluid lubricating oil film between the gear ring and the housing. This leads to significant wear and severe heating between the gear ring and the housing. The lubrication performance of the interface moving pair of the electro-hydraulic actuator pump gear ring housing can be improved by designing a reasonable lubrication bearing structure for the gear ring housing. In this study, a multi-field coupling multi-objective optimization model was established to improve lubrication performance and volumetric efficiency. The whole model consists of the dynamic model of the gear ring components, the fluid lubrication model of the gear ring housing interface, the oil film formation and sealing model considering the influence of temperature, and the multi-objective optimization model. The comprehensive performance of the straight-line conjugate internal meshing gear pump was verified experimentally using a test bench. The results show that the lubrication performance is improved, the mechanical loss is reduced by 31.52%, and the volumetric efficiency is increased by 4.91%.

A multi-objective optimization design method for underwater composite sandwich structures with high acoustic transmission and low vibration performance

ABSTRACT To address the issue of reduced structural integrity and optimality resulting from the conventional serial design of sonar dome structures, this paper establishes a multi-objective optimal design model for composite sandwich structures, utilising the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The model's stability and reliability are subsequently validated. Upon this model, a structural optimization design technique is postulated for composite sandwich structures within the realm of Fluid-Structure Interaction (FSI) multi-physics coupling. This approach aims to optimize the lightweight, high-rigidity composite sandwich structure, enhancing its acoustic transmission and minimizing vibration performance under turbulent pressure pulsations. The suggested method notably enhances the efficiency of structural design while ensuring precision. The proposed method effectively addresses the deficiency of serially designed sonar domes by neglecting the synergistic effects among multiple disciplines. In summary, this approach offers technical support and new research perspectives for the design of sonar domes under the coupling of multiple physical fields.

Research on multi-objective optimisation of product form design based on kansei engineering

It is crucial to understand and meet the multi-dimensional affective image needs of users for product form in a user demand-oriented product development model. Multi-objective evolutionary algorithms based on decomposition will be introduced into the field of kansei engineering to carry out research on product form optimisation design based on multi-objective evolutionary algorithms. A constrained multi-objective discrete optimisation model was established using the kansei engineering prediction model constructed through machine learning techniques as the objective function, and a reference vector guided evolutionary algorithm was used to solve it. The superiority of this method was verified by comparing it with other commonly used solving methods in this field. Combining entropy weight method and TOPSIS, select the optimisation design scheme that best meets the multi-dimensional affective needs of users from the obtained pareto set. Taking the train as an example, the proposed method was explained. The results indicate that the optimisation scheme obtained by this method can achieve the improvement and optimisation of product form in multiple affective dimensions. Meanwhile, a comparative study on the applicability of multi-objective evolutionary algorithms in the form optimisation problem of different affective dimensions is carried out to provide reference and suggestions for subsequent product design research.

Multi-objective Vehicle Routing Optimization Model for Emergency Logistics Considering Aftershock Effects

Earthquake is a kind of common and terrible sudden natural disaster. Frequent earthquake disasters bring inestimable loss of people and property to human society. Based on the earthquake background and considering the impact of aftershocks on emergency rescue activities, this paper constructs a multi-objective vehicle routing optimization model with the objectives of time, risk, fairness of material distribution and minimum cost. Through simulation examples, conclusions are drawn: Decision makers should first consider risk and time, when considering time, focus on considering the time window to meet the fairness of material distribution, and finally consider the cost.

Optimal configuration of acquisition terminals in regional distribution grids considering dynamic observability

Optimizing the configuration of acquisition terminals can maximize the observability and state estimation accuracy of the distribution grid achieving comprehensive perception of the distribution grid. However, the existing optimization configuration methods typically target a single topology structure. For regional distribution grids with dynamic topology changes, it cannot meet the observability requirements of all their topologies. In this regard, this paper proposes an optimal configuration scheme for regional distribution grid acquisition terminals considering dynamic observability. Firstly, the regional distribution grid considering the change of contact switch is modeled. Based on the observation redundancy and state estimation accuracy, the dynamic observability index of regional distribution grid is proposed. Then, a multi-objective optimal configuration model of acquisition terminal is constructed with the objective function of maximizing dynamic observability and minimizing configuration cost. Finally, the effectiveness of the proposed approach is validated with the simulation model.

Optimal operation of flexible interconnected distribution grids based on improved virtual synchronous control techniques

With distributed energy sources connected to the distribution grid on a large scale for distributed photovoltaic power randomness, this paper proposes a flexible interconnection system optimization operation strategy. First, the virtual synchronous control technology is improved to improve the DC bus voltage stability; second, it analyzes the system operation mode to judge the output logic of PV and storage units, takes DC bus power balance as the underlying logic, and puts forward the power coordination optimization strategy and fault power supply restoration strategy with full consideration of factors such as the load balance degree of the distribution station area, the economic operation of the main transformer, and the amount of power lost in the faulty station area. It also establishes a multi-objective optimization model to obtain the power commands of each port and achieves the power flexibility mutualization of the flexible interconnected system through the accurate regulation of the soft normally open point (SNOP). Finally, a simulation model of the flexible interconnection system is built using MATLAB/Simulink to verify the effectiveness of the proposed optimization strategy.

Deployment Method for Aircraft-Based Maritime Emergency Communication Resource Reserve Bases

Maritime emergency communication facilities play a crucial role in establishing communication links between land and sea, serving as essential communication means for maintaining maritime safety, disaster response, and emergency rescue operations. With the increasing frequency of marine activities, the rapid response capability of maritime emergency communication is becoming increasingly critical. With their characteristics of high-speed mobility, broad coverage and flexibility, aircraft serve as carriers for emergency communication facilities. The selection of aircraft bases is paramount in meeting the requirement of rapid response for maritime emergency communication. In this paper, we present a multi-objective optimization model for site selection by considering the coverage capabilities of different carriers. The model incorporates hierarchical coverage of unmanned aerial vehicles (UAVs) and helicopters with a genetic algorithm. Through a case study of the Bohai Sea, this paper verifies the feasibility and effectiveness of the model.

3D Path Planning of the Solar Powered UAV in the Urban-Mountainous Environment with Multi-Objective and Multi-Constraint Based on the Enhanced Sparrow Search Algorithm Incorporating the Levy Flight Strategy

In response to practical application challenges in utilizing solar-powered unmanned aerial vehicle (UAV) for remote sensing, this study presents a three-dimensional path planning method tailored for urban-mountainous environment. Taking into account constraints related to the solar-powered UAV, terrain, and mission objectives, a multi-objective trajectory optimization model is transferred into a single-objective optimization problem with weight factors and multi-constraint and is developed with a focus on three key indicators: minimizing trajectory length, maximizing energy flow efficiency, and minimizing regional risk levels. Additionally, an enhanced sparrow search algorithm incorporating the Levy flight strategy (SSA-Levy) is introduced to address trajectory planning challenges in such complex environments. Through simulation, the proposed algorithm is compared with particle swarm optimization (PSO) and the regular sparrow search algorithm (SSA) across 17 standard test functions and a simplified simulation of urban-mountainous environments. The results of the simulation demonstrate the superior effectiveness of the designed improved SSA based on the Levy flight strategy for solving the established single-objective trajectory optimization model.

Waiting Strategy for the Dynamic Meal Delivery Routing Problem with Time-Sensitive Customers Using a Hybrid Adaptive Genetic Algorithm and Adaptive Large Neighborhood Search Algorithm

In this paper, we study the dynamic meal delivery routing problem (MDRP) with time-sensitive customers. The multi-objective MDRP optimization model is developed to maximize customer satisfaction and minimize delay penalty cost and riding cost. To solve the dynamic MDRP, a novel waiting strategy is proposed to divide the dynamic problem into a series of static subproblems. This waiting strategy utilizes the decision threshold to determine rerouting points based on the number of dynamic meal orders. Meanwhile, time-sensitive priority is introduced to accelerate assignment and routing decisions for orders from customers with high time sensitivity. For each static subproblem, a hybrid AGA–ALNS algorithm that incorporates the adaptive genetic algorithm and adaptive large neighborhood search is developed to improve both the global and local search capabilities of the genetic algorithm. We validate the performance of the proposed waiting strategy and the AGA–ALNS algorithm through numerical instances. In addition, managerial insights are obtained from sensitivity analysis experiments.

Multi-Objective Evcuation Planning Model Considering Post-Earthquake Fire Spread: A Tokyo Case Study

As an integral part of the 2030 Agenda for Sustainable Development, Disaster Risk Reduction (DRR) is essential for human safety and city sustainability. In recent years, natural disasters, which have had a tremendous negative impact on economic and social development, have frequently occurred in cities. As one of these devastating disasters, earthquakes can severely damage the achievements of urban development and impact the sustainable development of cities. To prepare for potential large earthquakes in the future, efficient evacuation plans need to be developed to enhance evacuation efficiency and minimize casualties. Most previous research focuses on minimization of distance or cost while ignoring risk factors. We propose a multi-objective optimization model with the goal of reducing the risk during the evacuation process, which is called the risk reduction model (RRM). Problem-specific indicators for screening optimal solutions are introduced. The research selects the Ogu area in Tokyo as a case study, where there is a relatively high density of wooden structures, increasing the risks of building collapse and fire spread after an earthquake, and is based on a two-phase evacuation flow that considers secondary evacuation for fire response. The results indicate that, in this case, RRM can, in most situations, reduce the risk level during the evacuation process and improve evacuation efficiency and success rate without significantly increasing the total evacuation distance. It proves to be superior to the traditional distance minimization model (DMM), which prioritizes minimizing the total distance as the objective function.

Multi-objective capacity configuration optimization of the combined wind - Storage system considering ELCC and LCOE

The optimal capacity configuration of combined wind-storage systems (CWSSs) serves as a foundation and premise for building new electricity system. This paper proposes a novel capacity configuration model of CWSS. The model simultaneously considers economy, stability and low carbon by respectively minimizing Levelized cost of electricity (LCOE) and maximizing effective load carrying capacity (ELCC). The multi-objective optimization model is solved by Non-dominated Sorting Genetic Algorithm II (NSGA-II). And Multi-Attributive Border Approximation Area Comparison (MABAC) method is used to score the alternative solutions on the Pareto front and make decisions under different scenarios. The results of the case study show that: (1) The evaluation of the ELCC for CWSS can quantify its ability to replace the installed capacity of thermal power, thus greatly reducing the uncertainty of wind power output. (2) Under low-carbon scenario, economic scenario and standard scenario, the results of optimal capacity configuration for the CWSS show that LCOE and ELCC are negatively correlated. When the weights of ELCC and LCOE are equal, the three goals of economy, low carbon and safety and stability are achieved. (3) The results of a single sensitivity analysis on LCOE show that the sensitivity coefficient of electricity generation is the highest, exerting the greatest impact on the capacity configuration decision of the CWSS. A 20 % increase in power generation can reduce LCOE by 0.1203RMB/kWh. This paper provides energy planning recommendations for decision-makers in CWSSs, contributing to the development of a reliable new electricity system.

Optimization of water supply reservoir scheduling with insufficient regulating capacity

ABSTRACT To make use of a limited regulating reservoir capacity to provide a small amount of water supply benefits, a multiobjective optimal model that considers the water demand and regulating capacity of the pumping station was established, by combining the maximum sequence similarity (F1), maximum pumping station suitability (F2), maximum ecological flow satisfaction (F3) and maximum number of continuous water supply periods (F4), which were solved by NSGA-II algorithm, K-means clustering, and EW-TOPSIS method were used to select the optimal scheme. The results showed that the results of the combination of F1 and F2 were better than that of the list method, with a multiannual average water supply of 3.88 × 107 m3; the occupation of ecological flow significantly increased the water supply efficiency, with a multiannual average water supply of 4.49 × 107 m3, with a multiannual average ecological satisfaction degree of 10.84; the interruption periods were gathered between January and May by accepting the interruption periods regularly occurred, the water supply was 1.10 × 107 m3 with 95% guarantee rate by taking June–December as the water supply period. Our data indicated that reservoirs with insufficient regulation capacity can produce certain water supply benefits, and there were competitive relationships between objectives, and competitiveness increases with the increase of pumping station regulation capacity.

An Optimization Strategy for Truncating Ultralong Bus Lines Integrated with Metro Networks

For most ultralong bus lines, low punctuality rates and vehicle bunching are two main challenges. One potential strategy to solve these two problems is to split an ultralong line into two or three shorter lines according to historical passenger flow patterns as well as other presetting objectives and constraints. Thus, in this paper, a multiobjective optimization model is first established in consideration of both passenger flow distribution and coline metro networks. Then, the optimal truncated stop is determined by estimating the model results generated by enumerating each potential bus stop derived from related metro stops. The nondominated sorting genetic algorithm II (NSGA-II) is applied to solve the model efficiently. A case study conducted on a real bus line associated with metro networks in Beijing, China, demonstrates the efficiency of the optimization strategy adoption in terms of operating cost and passenger travel time, even with a slight increase in passenger cost. Besides, the multivehicle type is also referred in the experiments to verify economic efficiency to reduce operational costs.

Multi-objective optimization strategy for regional multi-energy systems integrated with medium-high temperature solar thermal technology

The high proportion and volatility of renewable energy pose a significant challenge to efficient collaboration between photovoltaic/thermal and wind power in multi-energy systems. Therefore, this paper proposes a Regional Multi-Energy System (RMES) based on Medium-High Temperature Solar Thermal (MHTST) technology. The novelty of the solution integrates dispatchable photovoltaic and photothermal conversion pathways, facilitating flexible and controllable utilization of solar energy via installed Thermal Energy Storage (TES). A multi-objective optimization model is developed to ensure the optimal operation of the proposed system under typical seasonal scenarios. The method that combines Branch-and-Reduce with Multi-Learning-Task is used to handle the formulated Mixed Integer Nonlinear Programming (MINLP) and swiftly identify optimal Pareto solutions. Case studies are performed over the improved united communities of the Test Region for Energy Transition and Comprehensive Reform in Taiyuan, China. Compared with the conventional photovoltaic technology, the simulation results show that the proposed solution has superior performance in both winter-heating and summer-cooling operations across 24-hour scheduling periods: operating costs reduced by 12.5% and 18.7%, carbon emissions decreased by 7.4% and 21.4%, with local solar power accommodation increased by 263.89kW and 487.47kW, respectively.

Mathematical Programming Formulations for the Berth Allocation Problems in Container Seaport Terminals

Background: Improving the performance of marine terminals is one of the major concerns of both researchers and decision-makers in the maritime transportation sector. The problem of container storage planning and the berth allocation problem (BAP) are the two mainstays of optimizing port operations. Methods: In this work, we address these two issues, proposing two mathematical models that operate sequentially and are applicable to both static and dynamic cases. The first developed model is a mixed-integer linear problem model aimed at minimizing vessel traffic time in the port. The second model developed is a multi-objective optimization model based on goal programming (GP) to minimize both container transfer time and the number of storage areas (minimizing container dispersion). Results: The robustness of the proposed models has been proven through a benchmark with tests using data from the literature and real port data, based on the IBM ILOG CPLEX 12.5 solver. Conclusions: The two developed mathematical models allowed the both minimization of the transfer time and the number of used storage areas, whatever the number of operations handling companies (OHCs) operating in the seaport and for both static and dynamic cases. We propose, as prospects for this work, the development of a heuristic model to deal with the major instances relating to the case of large ports.

Optimal location and operation of energy storage and transmission switching for minimizing wind power spillage

This paper proposes a bi-level multi-objective optimization model to improve the integration of wind power generators in electrical networks based on the optimal location and operation of the energy storage system and transmission switching strategies. The minimization of wind power spillage, load shedding, power losses, and the improvement of the voltage stability margin are the objectives of the upper-level subproblem. While the lower-level subproblem aims to maximize social welfare under different scenarios and overall system constraints. Under the uncertainty of the wind power and load demand, a collection of lower-level subproblems that represent the market clearing conditions is used to constrain the upper-level subproblem. The effectiveness of the proposed algorithm is examined on a modified IEEE 24-bus test system. The results show that the applicability of the proposed algorithm in aiding power system improvement planning for minimizing wind power spillage to integrate wind energy with maximizing the social welfare and improving the loadability and the voltage stability.

Cooperative management can mitigate trade-offs between livestock production and ecological functions to promote grassland sustainability

An increase in the population in pastoral regions and an improvement in living standards have increased livestock production; however, this has led, at least in part, to global grassland degradation. Consequently, the optimal pathway to mitigate trade-offs between livestock production and ecological functions of grassland is the key to achieve sustainable development in pastoral regions. Transformative adaptation is the recommended and feasible approach. However, most studies are not designed to determine which grassland management system addresses transformative adaptation and, therefore, do not provide options that can resolve the trade-offs. To fill this gap, we compared three grassland management systems, namely single, joint and cooperative. The grassland health index (GHI), data envelopment analysis (DEA), life cycle assessment (LCA), TOPSIS model and the multi-objective optimization model were employed to assess the productive and ecological benefits of the three systems. Cooperative management had the greatest comprehensive benefits when considering ecological functions, livestock production, carbon efficiency utilization and grassland area utilization efficiency, and had the greatest potential to achieve a balance between livestock production and ecological functions in the future.

Noise reduction of axial piston motors using multi-objective optimization of reinforcing ribs based on identification of acoustic sources

The optimization of the housing for reducing vibration and noise in axial piston motors is facing challenges related to inaccurate locations of acoustic sources and inappropriate optimization parameters. In this study, a multi-objective optimization of the thicknesses of the reinforcing ribs for reducing the noise of axial piston motors is investigated by using the compression sensing (CS) method and response surface methodology (RSM). Combined with the CS method, the high-resolution reconstruction of sound-intensity images is used to guide the structural optimization areas through locations of acoustic sources. To reduce vibration and noise, the variable rib structure model (VRSM) is established in structural optimization areas. The thicknesses of reinforcing ribs in different areas are considered optimization variables, while the motor housing's peak vibration acceleration obtained by coupling the motor's dynamic model with the hydraulic model is utilized as the objective function. The mathematical relationship between these variables and the objective function is established through the RSM. Utilizing these functions, a multi-objective optimization model is formulated for discretizing the thicknesses of reinforcing ribs to reduce noise, employing a multi-objective genetic algorithm (MOGA) for optimization calculations. Finally, comparing the sound pressure levels of the axial piston motor with and without the optimized reinforcing ribs, the experimental results demonstrate a notable reduction in noise with the optimized reinforcing ribs. Specifically, there's a reduction of 2.7 dB at the peak frequency of 610 Hz.

A novel solution methodology for electrical vehicles charging schedule in coordinated with full flexible resources in microgrid

This paper proposes a new model for the day-ahead Electric Vehicle charging schedule in Microgrids (MGs) with flexible resources such as Wind Turbine (WT) and Photovoltaic (PV) electrical power, Combined Heat and Power (CHP), Energy Storage Source (ESS), and load Demand Responses (DR). The proposed Mixed Integer Nonlinear Multi-Objective Optimization (MINMO) model considers uncertainty properties with optimal operation objects. The cost function, and power loss minimization are the proposed model's objects for considering optimal operation objects. This model is solved by a three-level decomposition method in master-sub problem presentation and Binary-Continues PSO algorithm (BC PSO) to quickly calculate the optimal solution. In the first layer, the main constraint of electric vehicle aggregators (EVAs) is satisfied by the binary PSO algorithm. According to the top-layer solution, flexible recourses are managed to satisfy the operation objects by the Bi-level Continues PSO algorithm in the second layer. Then, after converging to the optimum solution of these two layers, Electric Vehicles (EVs) charging stations are solved at the last layer by algebraic equations. Finally, the efficiency of the proposed method is illustrated via its application in IEEE 33-bus MG system. The numerical results show that the proposed solution methodology reduces the runtime of the problem up to 95 % compared to intelligence-based and full Newton–Raphson AC power flow method. Also, numerical results illustrate that considering CHP and DR in addition to the other flexible resources reduces the operation cost and power loss up to 21 % and 5 % respectively. In top of that the minimum total cost for day-ahead EV charging schedule by the proposed model and solution methodology is 578,312.7$.