Multi-objective Optimization Model Research Articles

Designing a multi-purpose network of sustainable and closed-loop renewable energy supply chain, considering reliability and circular economy

Since renewable energy systems have a limited lifespan and cannot be used after a while, it seems imperative to pay attention to recycling and sustainability in the supply chain network design of these systems. Also, modern power systems have high complexity and a large number of problems that affect the reliability of electricity supply to consumers. Therefore, organizations should consider broader goals, including the maximum use of resources and the minimization of waste, which is known as lean economy. In this research, a multi-objective non-linear forward optimization model has been considered for the design of the supply chain network of photovoltaic and wind systems based on the aspects of stability and taking into account the reliability. Also, adding the reverse flow, in addition to the forward flow, is also considered, which, in addition to economic advantages, also pays attention to environmental and social issues. The objective function includes minimizing all costs and negative environmental effects as well as increasing reliability. In order to convert the multi-objective planning model into a single-objective one, the epsilon method of the enhanced constraint is used. Uncertainty has been considered for all variables (except binary variables) and parameters of demand, intensity of sun radiation and amount of wind speed. Then, the efficiency of the model has been investigated through its implementation on a case study and the results have been obtained. Eventually, the model's efficiency is illustrated by discussing a real-world case, through which prominent managerial results are acquired.

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.

A bi-level cooperating optimization for AC/DC power systems considering renewable energy integration

The integration of renewable energy on a large scale into the power system presents a significant challenge in ensuring the secure and cost-efficient operation of the system. Considering the uncertainty associated with wind and photovoltaic power, this paper develops a bi-level cooperating optimization strategy for AC-DC hybrid systems integrated with renewable energy generation. Firstly, to characterize the uncertainty of wind and photovoltaic power, the Latin hypercube sampling and the Kantorovich distance-based scenario reduction methods are employed to obtain the typical daily outputs of wind and photovoltaic power. Next, a bi-level multi-objective comprehensive optimization model is established to minimize the active power network loss and voltage deviation during system operation. To solve the bi-level optimization model, the proposed approach presents a multi-objective hybrid algorithm that combines the traditional particle swarm and whale algorithms, thereby enhancing the optimization capability of the algorithm through the incorporation of a whale enveloping search strategy into particles. Finally, the simulation results explain the effectiveness and practicability of the proposed strategy and methods, as well as the superiority of the proposed algorithm.

A Web Data Mining Algorithm Based on Manifold Distance for Mixed Data in Cloud Service Architecture

Due to the complex distribution of web data and frequent updates under the cloud service architecture, the existing methods for global consistency of data ignore the global consistency of distance measurement and the inability to obtain neighborhood information of data. To overcome these problems, we transform the multi-information goal and multi-user demand (constraint conditions) in web data mining into a constrained multi-objective optimization model and solve it by a constrained particle swarm multi-objective optimization algorithm. While we measure the distance between data by manifold distance. In order to make it easier for the constrained multi-objective particle swarm algorithm to solve different types of problems to find an effective solution set closer to the real Pareto front, a new manifold learning algorithm based on the constrained multi-objective particle swarm algorithm is built and used to solve this problem. Experiments results demonstrate that this can improve the service efficiency of cloud computing.

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.

Collaborative optimization decision making of cement grinding process operational indicators considering dual dynamic problem

The cement grinding process requires monitoring of unit power consumption and specific surface area indicators to improve production efficiency and product quality. The strong coupling between operational variables necessitates careful adjustment of these variables to prevent production from becoming instability. Furthermore, the continuous and time-dependent nature of the process makes this adjustment particularly challenging. To address this dual dynamic problem, we have developed a multi-objective optimization model, with global optimization as the desired outcome, to optimize unit power consumption and specific surface area. Our optimization algorithm, termed “Optimization Algorithm - Dynamic Search Space and Rolling Time Domain” (OA-DSTR), considers the problem of dynamic production process. First, the algorithm uses a dynamic search space strategy, allowing for changes in the constraint range of the operational variables and introducing a fluctuation coefficient (FC) to measure solution rationality. Second, to deal with the time-dependent problem, we have added a rolling time domain strategy, enabling real-time monitoring of the cement grinding process. The experimental results show that OA-DSTR not only ensures global optimization, but also realizes the tracking of dynamic conditions, improving the stability of the cement grinding process and achieving a lower FC value.

Realizing low-carbon development of industrial parks in China: Model construction and its application

As the main energy consumption and emission area, carbon emission reduction for industrial parks is a pivotal target for China. In this study, a multi-objective optimization model was established to quantitatively develop low-carbon development strategies for industrial parks that simultaneously considers land productivity, energy structure and efficiency, carbon emissions, and industrial structure optimization. A Monte Carlo method was adopted to investigate the uncertainties associated with emission-reduction technologies and economic development. A study on an industrial park showed that with the implementation of a series of fossil energy-saving measures, the percentage of clean energy in the park is projected to reach 62.6–72.2 %, while the percentage of output from energy-intensive enterprises relative to the total output of the industrial park decreases from 3.78 % in the baseline year to 1.74–3.29 %, and carbon emissions are projected to decrease by 69–85.4 % relative to the benchmarking scenario. This implies that the model developed in this study is effective and can serve as a roadmap for designing industrial parks to realize low-carbon development.

A two-step post-optimality approach for a multi-objective railway maintenance planning problem

Maintenance planning for railway networks is an exacting task, impacting numerous stakeholders with diverse objectives. While a multi-objective optimization model addresses the problem, it often results in numerous solutions on a Pareto front. Past researchers have introduced various Pareto pruning methods to autonomously create a shortlist of promising solutions. Nonetheless, solely relying on data-driven approaches to finalize solutions can lead to undesirable outcomes for the decision maker (DM). To strike a balance between a human-involved and a data-centric decision, this article proposes a two-step approach. In the first step, Latent Profile Analysis (LPA) is utilized to group similar solutions on the Pareto front together, and a representative solution from each group is presented to the DM. After the DM identifies a focus region, Data Envelopment Analysis (DEA), including CCR and BCC models, is employed in the second step to rank the solutions in a selected group by their relative efficiency. Applying the proposed approach to a real-world case study demonstrates its capability in the railway asset management context. Key advantages of LPA lie in its ability to provide the appropriate number of solutions in the pruned set and its flexibility in grouping solutions. DEA enhances decision-making by ranking solutions within the focus region. Ultimately, by integrating automated algorithms with human insight, the two-step approach successfully identifies an efficient solution and mitigates the risk of obtaining undesirable outcomes on the Pareto front.

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.

Withdrawn: Optimization of water supply reservoir scheduling with insufficient regulating capacity

ABSTRACT At the request of the authors, the article ‘Optimization of water supply reservoir scheduling with insufficient regulating capacity’ has been withdrawn from Water Supply.

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.