Multi-objective Programming Optimization Research Articles

Multi-Scenario Land Use/Cover Change and Its Impact on Carbon Storage Based on the Coupled GMOP-PLUS-InVEST Model in the Hexi Corridor, China

Understanding the relationship between land use and carbon storage is vital for achieving sustainable development goals. However, our understanding of how carbon storage develops under land policy planning is still incomplete. In this study, a comprehensive framework that integrates Gray Multi-objective Optimization Programming (GMOP), the Patch-generating Land Use Simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models is introduced to evaluate land use dynamics and ecosystem services. Two scenarios have been established to estimate Land Use and Land Cover Change (LUCC) patterns in the Hexi Corridor by 2035: the business-as-usual (BAU) scenario, developed based on historical trends, and the ecological conservation scenario (ECS), optimized with multiple policy objectives. The results show the following: (1) From 2000 to 2020, the predominant land use type in the Hexi Corridor was unutilized land, with LUCC mainly involving the transformation of unutilized land to grass land. (2) Carbon storage in the Hexi Corridor increased by approximately 9.05 × 106 t from 2000 to 2020 due to LUCC, characterized by higher levels in the south and lower levels in the north. (3) The areas of grass land and arable land are expected to continue increasing until 2035, while the extent of unutilized land is projected to decrease. The ECS is poised to create a balance between ecological protection and economic development. (4) By 2035, both the BAU scenario and ECS estimate an increase in the carbon storage of the Hexi Corridor, with the ECS expected to result in the most significant gains. These research findings provide valuable insights for administrators and researchers, guiding more rational land use planning and ecological restoration policies to achieve carbon peaking and neutrality.

Development of ventilation and sound insulation materials by the multi-objective optimization

Natural ventilation-based houses in tropical countries often suffer from poor acoustic performance. To address this issue, we developed ventilation and sound-absorbing materials using a multi-objective optimization program. The program was developed to design test specimens that optimize the sum of the flow velocities of all elements of the divided cylindrical 3D model for sound-absorbing ability and the sum of the flow velocities in the bottom elements of the model for ventilation ability, with the objective of maximizing both. By outputting the specimens using a 3D printer, we were able to select materials suitable for the installation environment without requiring individual design changes. In this report, we focus on improving the sound insulation rate by refining the objective functions used to create the specimens. We measured the transmission loss using a self-made impedance tube and the 4mic method and verified the validity of our programming results by comparing them with those of laboratory experiments. Our approach has the potential to improve the acoustic performance of natural ventilation-based houses in tropical countries, making them more comfortable and conducive to living and working.

Optimal Cable Force Adjustment for Long-Span Concrete-Filled Steel Tube Arch Bridges: Real-Time Correction and Reliable Results

For complex structures, the existing optimization method for suspender cable forces involves extensive matrix operations during the solution process, requiring high computational power and time. As a result, obtaining a more accurate solution becomes challenging. To address this issue and improve the stress distribution of suspenders in the completed state, while minimizing the need for frequent cable force adjustments and grid beam elevation changes during construction, a novel method for cable force optimization is proposed. In this study, the Pingnan Third Bridge, which is the world’s longest large span arch bridge with a span of 575 m, is taken as the engineering background. This study combines finite element analysis and multi-objective optimization methods to develop a cable force optimization approach for real-time correction during the panel girder lifting of long-span concrete-filled steel tube (CFST) arch bridges. The optimization method involves treating the panel girder weight and displacement during construction as parameter variables, and considering the displacement and unevenness of the panel girder in the completed state as constraint conditions. The objective equation is defined based on the displacement and cable force during the lifting construction process and, through optimization, the cable forces and displacements of each lifting section are calculated. The results demonstrate the feasibility of integrating optimization theory into the cable force optimization process during panel girder lifting. In this study, we have taken into account the characteristics of real-world engineering and focused on specific key points to reduce the order of the influence matrix. Consequently, the computational costs are reduced, facilitating the development of a multi-objective tension optimization program. By minimizing segment displacement variations and ensuring even cable force distribution in the completed state, the method ensures that the bridge meets the required completion requirements without the need for repetitive iterations or cumbersome calculations. It provides higher optimization efficiency and superior outcomes, offering significant value for cable force calculations during suspender construction of similar bridge types and guiding construction processes.

Multi-objective rapid optimization method for a variable-stroke valve train device

Raising the maximum valve lift in two-stroke drive mode is the key to increasing the power output of the variable-stroke engine, so the limit design of the variable-stroke valve train device is critical. The multi-objective genetic optimization algorithm NSGA-2 was coupled with the single-freedom dynamics model of the variable-stroke valve train device. A multi-objective rapid optimization program for the valve train device was constructed in Matlab. Two optimization methods explored the maximum valve lift limit in two-stroke drive mode. The first was to optimize the rocker structure, and the second was to optimize the cam position and the rocker structure together. The results show that the single-freedom dynamics model has sufficient computational accuracy. The collaborative optimization results of the cam position-rocker arm structure are more optimal. Under the same maximum lift of the valve, compared to the results of individual optimization of the rocker arm structure, the maximum contact force is reduced by up to 285 N. The maximum contact stress is reduced by up to 63 MPa through collaborative optimization. Compared with the original case, the maximum increase rate of the maximum valve lift obtained through optimizing the rocker structure is 12.45%, while that obtained through collaborative optimization is 12.85%.

A two-stage stochastic multi-objective resilience optimization model for network expansion of interdependent power–water networks under disruption

Network expansion of interdependent critical infrastructures under disruption uncertainty is modeled as a mixed-integer two-stage stochastic multi-objective optimization program. In this model, expected total cost and expected post-disaster resilience are considered competing objectives. Network resilience is quantified through network complexity and unmet demand. Functional relationships between critical infrastructures are modeled using a network-based approach with the physical interdependency enforced through demand constraints. Uncertainty is introduced as a set of random parameters corresponding to disruption scenarios. The proposed model is demonstrated in a case study of coupled power–water networks with the power flow in the grid modeled using linear DC power flow approximation equations. The deterministic-equivalent multi-objective model is solved using the augmented ϵ-constraint method. Solutions from stochastic and deterministic models are compared and the value of stochastic optimization is discussed.

Multi-objective optimization of fuel injection performance of a common rail injector

The improvement of injector performance is important for the operation of common rail (CR) fuel systems. In this study, the optimization work has been carried out on a large-flow common rail injector. First, to simulate the injection process, a one-dimensional (1-D) model was developed and validated against the experimental data. The mathematical approximation model was also established to correlate the injection performance with the factors. Then, a multi-objective model by taking three design objectives into account was established and solved. Finally, Pareto front was obtained and the final optimization solution was determined. Furthermore, an extensive validation and analysis for the optimized results under various operating conditions was also carried out. Results shows that the three objectives, total response time, injection pressure fluctuation and injection pressure drop respectively, are improved by 11.75%, 39.12%, and 10.03% under the typical condition. And the proposed multi-objective optimization program based on design of experiment (DOE) technique, response surface methodology (RSM) and Elitist Non-dominated sorting genetic algorithm (NSGA-II) in present work can exhibit a good capability to optimize the problem of the injection performance in a CR fuel system.

Research on Partner Selection of High-Speed Railway Dynamic Logistics Alliance Based on the Dynamic Programming Model

Traditional railway transportation can no longer meet people’s demand for logistics services. This paper takes advantage of high speed to propose a high-speed railway dynamic logistics alliance based on a cloud platform to make up for the lack of transport capacity at both ends of the high-speed railway logistics trunk line. Selecting partners is crucial to the high-speed rail logistics alliance. This paper uses the methods of multiobjective fuzzy optimization and dynamic programming to conduct multistage optimization of high-speed railway dynamic logistics alliance partners. When the market demand changes, in order to optimize the overall interests of the alliance, this paper uses the efficiency profit field method to achieve the dynamic selection of alliance partners or potential partners. The case study shows that the establishment of the high-speed railway dynamic logistics alliance can optimize the interests of the members of the alliance, verify the effectiveness of the method, and provide a reference for the better development of high-speed railway logistics.

Optimal facility combination set of integrated energy system based on consensus point between independent system operator and independent power producer

In recent years, the frequency of power demand imbalance and negative price phenomenon has risen due to the rapid expansion of renewable energy sources (RES). Because of this, a means to reduce the curtailment of RES by utilizing surplus energy is essential. This paper focuses on reducing the curtailment of wind turbines (WT) with high output intermittency and minimizing the investment cost of IES via an integrated energy system (IES). The IES operation seeks to improve the acceptability and efficiency of the RES as it supports the integration of various energies mix, such as electricity, heat, hydrogen. This paper proposes an optimal facility combination set (FCS) of IES that satisfies the requirements of ISO and IPP using Multi-Objective Optimization Programming (MOP). The case study is based on a wind farm in South Korea, set in Aewol-eup, Jeju-Island. The case study results provide the best configuration of the IES energy mix with the best economic value and efficiency while satisfying ISO and IPP perspectives.

Intelligent System Application in Clinical Management of Medical Teaching Based on Deep Reinforcement Learning

When dealing with engineering projects, there are many kinds of planning schemes. There are many problems to be solved in the performance and economic conditions of these indicators and engineering contents we study. Recently, many researchers have made great achievements in optimizing various subject projects. One of its purposes is to optimize and improve intelligent multi-objectives to make it more effective. Therefore, it is of great significance to develop intelligent multi-objective projects in academia and more engineering fields. The purpose of this study is to make efforts to strengthen the learning and construction of rapidly developing multi-objective optimization programs, and closely link these programs with neural network, fuzzy technology, interactive technology, and give a lot of examples of multi-objective optimization improvement program methods, which can eliminate the uncertainty in the multi-objective project plan, The purpose is to solve some difficult problems of multi-objective optimization and give the best suggestions. In this study, the strengthening of learning construction is combined with the clinical experiment, and most of this teaching method is put in the clinical management experiment of medical teaching. At the end of this study, we compared the performance of deep reinforcement learning with its dual structure version and interleaved structure version in different medical teaching clinical management environments and make a detailed analysis. Deep reinforcement learning provides a new idea for the improvement of intelligent multi-objective optimization and clinical management of medical teaching.

Approximate solutions for robust multiobjective optimization programming in Asplund spaces

In this paper, we study a nonsmooth/nonconvex multiobjective optimization problem with uncertain constraints in arbitrary Asplund spaces. We first provide necessary optimality condition in a fuzzy form for approximate weakly robust efficient solutions and then establish necessary optimality theorem for approximate weakly robust quasi-efficient solutions of the problem in the sense of the limiting subdifferential by exploiting a fuzzy optimality condition in terms of the Fréchet subdifferential. Sufficient conditions for approximate (weakly) robust quasi-efficient solutions to such a problem are also driven under the new concept of generalized pseudo convex functions. Finally, we address an approximate Mond-Weir-type dual robust problem to the reference problem and explore weak, strong, and converse duality properties under assumptions of pseudo convexity.

An efficient Bi-level hybrid multi-objective reliability-based design optimization of composite structures

This paper presents computationally an efficient multi-objective reliability-based design optimization (RBDO) method for composite structures with uncertain design parameters. The approach adopted is based on a novel hybrid decomposing-based multi-objective particle swarm optimization and sequential quadratic programming algorithms, which is coupled with bi-level modelling and optimization strategy of composite structures and RBDO using Cross entropy method. The reliability analysis considers the rare-event corresponding to the limit states, which is based on Tsai-Wu failure index. In addition, the growing requirements for composite structural design with a minimum weight, introduces significant research challenges in the RBDO approach as the weight and reliability are the two important opposing design requirements. To address this research challenges, the proposed approach involves both reliability and weight as objective functions. A laminated composite plate and benchmark problems were used to demonstrate the efficiency and accuracy of the proposed method. Results show that the proposed method provides computationally efficient and accurate evaluations of the performance of the design of composite structures in comparison to the conventional Monte Carlo simulation method and can provide a potential for solving large-scale multi-objective RBDO of composite structures design problems.

Mean-line model development for off-design performance prediction of transonic axial compressor of an industrial gas turbine based on computational fluid dynamics database

In this work, a mean-line model with the row-by-row stacking scheme is developed to simulate the off-design operation of a multi-stage transonic axial compressor of an industrial gas turbine. A database of computational fluid dynamics (CFD) simulation results validated with the field data is generated at different rotating speeds and pressure ratios by using CFD simulation tools and available detail geometry of the studied compressor. The compressor characteristic parameters including inlet/exit blockage factor, deviation angle parameter, rotor temperature and pressure coefficients, and the pressure loss of the stator for all stages, plus variable inlet guide vane, are extracted from CFD simulation results. Based on analytical/empirical relations in the literature, a generic formulation for the characteristic parameters is defined as a function of airflow parameters such as inlet Mach number, inlet flow angle, and velocity ratio. Since the accuracy of this type of model depends on the formulated characteristic parameters, and due to the error propagation potential of the sequential stacking solutions, a multi-objective optimization program is developed to specify the coefficients and exponent values of the generic formulations. The functionality of characteristic curves versus airflow parameters for different compressor rows is comprehensively investigated. The comparison between the results of the mean-line model and CFD simulations shows the high accuracy of the model. The developed model can be used in applications such as condition monitoring of in-service gas turbines, studying the effects of rescheduling the variable stator vanes, and investigating the wet compression process.

Augmented ε-Constraint Algorithm Applied to Multi-objective Optimization Programs of Residential Micro-CHP Systems

Micro-combined heat and power systems (Micro-CHP) are expected to play a major role in reducing carbon dioxide emission, increasing the primary energy and economic saving in the future. In this paper, the optimal planning of a residential Micro-CHP system for a single-family house situated in Iran is investigated. In order to achieve this target, a new mixed-integer linear programming model is developed. Mathematical modeling and optimization were carried out for the Micro-CHP system using natural gas, at the residential building level and for three different operating strategies, cost-driven, primary energy driven, and carbon emission driven. Three competing objective functions are simultaneously minimized using an augmented ε-constraint optimization algorithm. This work has multiple novelties, containing the consideration of some economic and technical constraints previously neglected, such as the energy, economic, and environmental effects of replacing conventional energy systems with a residential Micro-CHP system and the effects of integrating an electrical heating element for storing thermal energy (electrical heating element can act as a power sink when required). A detailed case study on a residential building situated in Tabriz city, Iran, was carried out by applying the developed model and the optimum strategies and optimal sizes for Micro-CHP, axillary boiler, and other equipment are obtained. Results have shown that the optimal values of CSR, PESR, and ERR, in the augmented ε-constraint method, were 12.46%, 1.19%, and 88.38%, respectively. In this case, the obtained result for a nominal capacity of Micro-CHP and boiler was 3.6 kW and 1.05 kW, respectively. Finally, the payback period was obtained for 2 years. Furthermore, to understand the influence of key parameters on the planning of the Micro-CHP system, the sensitivity analysis has been performed on energy prices. Sensitivity analysis of electricity price indicated that, by increasing the electricity price, the overall annual cost-saving value clearly increases, while increasing gas prices significantly reduces the profitability index.

An integrated generalized TODIM model for portfolio selection based on financial performance of firms

Multi-criteria decision-making (MCDM) models are well-suited for solving portfolio selection problems. Diversified financial indices and complex subjective preferences are important factors affecting investment decisions within the MCDM framework. Therefore, this study adopts a well-known behavioral MCDM model called generalized TODIM (TOmada de Decisão Iterativa Multicritério) for portfolio selection based on the financial performance of firms. First, a multidimensional financial evaluation index system is proposed for financial performance evaluation, which is the most appropriate approach for stock investment within a long-term horizon. Second, features are selected from the financial ratios using affinity propagation clustering (APC). Through the APC algorithm, financial ratios with a strong influence on stock evaluation can be obtained. Third, a generalized TODIM method with entropy weight is used to calculate the dominance relation between stocks and reflect investors’ bounded rational behaviors. Fourth, an extended mean–variance multi-objective portfolio selection model that considers financial and stock market performance is constructed. The compromise solution is used when solving the multi-objective optimization programming. Finally, a case study on medical stock investment in the Chinese stock market is examined to recommend the optimal portfolio allocation for investors. Sensitivity and comparative analyses are performed to demonstrate the robustness, effectiveness, and superiority of the proposed methodology.

A time dimension-added multiple obstacles avoidance approach for unmanned surface vehicles

The capability of avoiding collisions in an environment with obstacles is a necessary function for an autonomous unmanned surface vehicle (USV). This paper reports the preliminary research results of a novel multiple obstacles avoidance approach for USVs. This method adds the time dimension in a time-varying environment containing multiple obstacles with arbitrary known motion patterns. The approach presented is a path searching-based algorithm, the three-dimensional multiple obstacle avoidance (TDMOA) algorithm, which applies a novel multi-objective optimization and interval programming method, and can plan a suboptimal collision-free path in seconds. A unique property of the approach is the compliance with existing marine collision regulations in the planning process. The approach first incorporates navigational safety and maritime operational rules to establish an obstacle risk domain model. The time dimension is then introduced to a two-dimensional plane to establish the three-dimensional risk model, and then the improved D* Lite algorithm searches a collision-free path in compliance with marine collision regulations. Several complex multi-obstacle collision scenarios are simulated in experiments to verify the validity of the new TDMOA algorithm and results indicate good real-time performance and safe navigation paths for USVs.

Energy, exergy, and exergoeconomic analyses and optimization of a novel thermal and compressed air energy storage integrated with a dual-pressure organic Rankine cycle and ejector refrigeration cycle

The current work analyzes an innovative thermal and compressed air energy storage cycle integrated with a dual-pressure organic Rankine cycle combined with an ejector refrigeration cycle for power and cooling cogeneration. The bottoming cycle employs zeotropic mixtures, including Pentane (0.5)/Butane (0.5) and Pentane (0.5)/Trans-2-butene (0.5), as working fluids for performance improvement. The system assessment includes three crucial viewpoints: energy, exergy, and exergoeconomic. A parametric study is performed to examine the effects of some key parameters on the performance of the devised system. Also, a multi-objective optimization program based on the MOPSO algorithm is conducted to find an optimal trade-off between thermodynamic performance and economic attractiveness of the system considering two decision-making techniques of TOPSIS and LINMAP. The results showed that the sum unit cost of the product has less value for Pentane (0.5)/Trans-2-butene (0.5). Also, it is found that the performance of Pentane (0.5)/Trans-2-butene (0.5) as the zeotropic working fluid is better. Therefore, this fluid is considered as the working fluid for the optimization procedure. Besides, based on the achieved results, the optimum round trip efficiency and sum unit cost of the product are 54.25% and 13.51 $.GJ−1, respectively.

P2G 기술 연계형 풍력발전 기반 독립형 마이크로그리드 최적 운영 방안

The purpose of this study is to optimize the operation of wind power generation-based standalone microgrid linking P2G technology. Renewable energy sources, such as wind turbine, photovoltaic, etc., use natural energy as a power source, so the system operator could not control the power of the renewable energy sources. Failure may occur and damage such as power failure may occur if the facilities of the RES that cannot be controlled output increase and the balance of power supply and demand in the power system is not matched. In this study, insufficient power is supplemented by power supply using diesel generator, and excess power is supplemented by Battery Energy Storage System (BESS) and Power-to-Gas (P2G) technologies. In particular, P2G technology using hydrogen energy is mainly dealt with, and multi-objective optimization programming that satisfies both objective functions is interpreted based on pareto-front optimization by deriving the power limit of wind power generators and system costs incurred when building and operating microgrids.

Optimal location of accident relief facilities in a railway network

We study the strategic problem of optimally locating different types of relief facilities across the railway network that could respond to an accident-related emergency. A multi-objective optimization program that aims to maximize network coverage, prioritizes important rail links, optimizes redundancy, and minimizes overall response times is proposed. The concept of cooperative coverage is adopted where a decaying function is used to ascertain the coverage, and the importance of rail-links is determined based on inputs from the experts via a questionnaire-based survey. The multi-objective optimization program is solved using an interactive augmented ε-constraint method that makes use of intervention from the decision-maker. The proposed optimization program is used to study the North Central Railway zone of Indian Railways, and the computational experiments demonstrate that reconfiguring the existing network of relief facilities will increase the coverage of rail nodes by 7.86% and that of rail-links by 10.78%.

Staggered work schedules for congestion mitigation: A morning commute problem

In urban networks, traffic congestion can be curbed by deconcentrating the temporal distribution of the travel demand. In this paper, we propose an optimal staggered work schedules problem to minimize the network total travel time and prevent the schedule delay in the trips of commuters over morning peaks in a bicentric network. The objective is to optimize the work start times of individual firms with minimum deviations from their initial schedules while taking into account that commuters choose their departure time selfishly to minimize their travel cost. We formulate the optimal work schedule problem in a bicentric network as a multi-objective optimization program that simultaneously minimizes the total travel time and the schedule deviation for the firms while satisfying near-equilibrium temporal conditions. The time-varying congestion dynamics are modeled using macroscopic fundamental diagrams. We solve the optimization problem for a test network and analyze the sensitivity of the Pareto solution to the policy parameters of the model. We assess the accuracy and effectiveness of the proposed method using an individual-level trip-based macroscopic simulation model. The numerical results demonstrate that implementing the proposed optimal staggered work schedules strategy accounting for commuters’ departure trip time choice can significantly reduce the traffic congestion in urban networks.

Optimal design of integrated urban energy systems under uncertainty and sustainability requirements

Large amounts of urban energy consumption and consequent environmental impacts are posing great pressure on sustainable development. Urban energy systems of the future should be designed in an integrated approach featuring enhanced economic and ecological performances. This work aims to develop a generic tool to support the optimal design of integrated urban energy systems. Methods of energy systems engineering are utilized to compose the tool, including superstructure-based modeling, mixed-integer linear programming, multi-objective optimization, and stochastic programming. The model is applied to facilitate the design of a newly planned urban region in North China. Results reveal trade-offs between system cost, carbon emission, and water consumption. Capability of integrated design is illustrated via multi-energy synthesis, process integration, and multi-regional interconnection. Considering uncertainties of renewables, more energy generation, conversion, and storage capacity are needed. The total cost expectation also increases. Ignoring uncertainties rooted in integrated urban energy systems might result in sub-optimal design.