Multi-objective Model Research Articles

Multi-objective model predictive control for ship roll motion with gyrostabilizers

Ships are prone to significant roll motion while sailing in adverse conditions, posing a serious threat to ship safety and maneuverability. Therefore, effective ship motion control is crucial. Integrating the MPC control algorithm with a gyrostabilizer can effectively achieve this goal. To evaluate and compare control strategies, a multi-objective model predictive control is proposed that integrates considerations of ship motion, safety, and energy consumption to conduct the operation concurrently. By assigning different weights to these factors, the study aims to discern the varying impacts on control effectiveness. The response of ship roll motion in beam waves is evaluated through roll hydrodynamic modelling, accounting for wave memory effects. A state-space model of ship and gyrostabilizers is proposed to represent their dynamic interaction and response to external moment. Subsequently, the influence of different weightings in the multi-objective model predictive control is compared, and the control performances of a frigate under different wave conditions are analyzed respectively. The multi-objective model predictive control, with varied weight assignments, leads to distinct reductions in roll motions. This investigation offers valuable insights into controlling roll motion in beam wave conditions, effectively reducing motion under varying sea conditions, and providing alternative guidance tailored to user preferences.

Multi-objective optimal allocation of construction project risks, ant colony optimization algorithm

PurposeThe purpose of this paper is to introduce a new approach for finding the most appropriate risk allocation among construction contract parties. Although risk allocation is a strategic decision that can greatly affect the cost and time of the project, it is often made based on the personal judgments of employers. That is why owners usually find it a very time-consuming and expensive decision process, while most contractors feel that risk sharing is not done fairly. In this paper, a quantitative model for risk allocation is introduced to fulfill clients’ conflicting expectations in the risk allocation process.Design/methodology/approachBy defining conflicting expectation of owners in the form of three quantitative objectives (lowest cost, maximum reliability and minimum risk exposure), a multi-objective optimization algorithm was developed to select the most appropriate risk allocation. Using experts’ knowledge through fuzzy set theory, a multi-objective decision-making model is developed based on an ant colony optimization algorithm. The proposed model is able to find the optimum risk allocation at the lowest cost and highest reliability while protecting the client against risk exposure within multiple parties projects.FindingsThe proposed model has the ability to select the most appropriate risk allocation in multi-parties projects (such as public–private partnerships) and quantitatively measure the impact of each employer’s choice on project results in the form of cost and time. The results of implementing the proposed model in a case study project revealed that optimum risk allocation requires a balanced attitude, and the transfer of all risks to the other parties will not necessarily lead to the lowest cost. The client should bear more responsibilities in risk management to avoid extreme time delay and cost overrun.Research limitations/implicationsThe proposed model can be implemented in multi-parties projects (such as public–private partnership), while other methods introduced in previous studies can only be used for projects with two party (client and contractor).Practical implicationsBy implementing the proposed model in a real project in this article and comparing its results with previous works, it has been shown that the proposed model has a good performance. Using this model can help clients drastically reduce cost and time and ultimately successfully conclude risk allocation negotiations (which is the most difficult part of any contract negotiation).Originality/valueIn this paper, the risk allocation process is modeled in the form of a multi-objective decision problem. This method helps employers to measure their conflicting goals and choose the most appropriate risk allocation according to their objectives. Also, in this article, the conflicting expectations of the employer in the process of contract negotiations are introduced as three measurable goals, which gives the decision-maker the ability to balance his expectations and not miss an objective. In the final step, an optimization model is developed to select the best option, which can choose the most appropriate party to bear the risk in a short time and among an unlimited number of participants.

An improved multi-objective method for the selection of driverless taxi site locations

To expedite the large-scale deployment of driverless taxis and advance the autonomous driving industry, research on the location of integrated parking and charging facilities for driverless taxis has emerged as a significant issue in urban traffic. This study employs a progressive “preliminary selection-screening-optimal selection” approach for site selection. First, the preliminary selection of parking sites is conducted by clustering various Points of Interest (POI) types. Subsequently, a multi-objective site selection model is developed to maximize the coverage of demand points, minimize construction costs, address the largest population demands, and minimize the distance between demand points and candidate sites. The improved genetic algorithm NSGA-II is adopted to obtain several Pareto optimal solutions. The evaluation indexes are selected according to operators, users, and the public transport system to estimate the Pareto optimal solutions, and then the final location solution can be obtained. The calculation methods of several key parameters are improved during the modeling process. Location potential and location influence coefficient are selected to adjust the number of driverless taxi parking spaces. Additionally, isochrones drawn based on the actual road network and path planning represent the service range of candidate points. Meanwhile, distance based on actual road network rather than Euclidean distance is introduced to calculate the distance between candidate points. Finally, a case study shows that the method proposed in this study could reduce the total initial travel time to reach the demand points by 64%, which is independent of operational scheduling.

Multi-objective parametric modelling during wire-cut electric discharge machining of Incoloy 800H

Multi-objective parametric modelling during wire-cut electric discharge machining of Incoloy 800H

Multi-objective parametric modelling during minimum quantity lubrication machining of Incoloy 800H

Multi-objective parametric modelling during minimum quantity lubrication machining of Incoloy 800H

An investigation of mixed-model assembly line balancing problem with uncertain assembly time in remanufacturing

In recent decades, remanufacturing has emerged as an effective way to address resource crises and environmental pollution issues. Unlike traditional manufacturing, remanufacturing production is filled with various variable factors, especially in the assembly phase. Due to changes in part types, quality conditions, and assembly methods, the assembly time becomes highly uncertain. Assembly line balancing is a key challenge to achieve the stable operation of remanufacturing system. This study proposes an evaluation method for remanufacturing assembly time and establishes a multi-objective mathematical model for balancing remanufacturing mixed-model assembly (RMMA) line. The evaluation method utilizes the Fuzzy Graphical Evaluation Review Technique (FGERT) network to predict expected assembly time for each operation. The balancing model aims to optimize remanufacturing takt time and comprehensive balance rate (CBR). To effectively solve this model, an adaptive double-layer genetic algorithm (ADGA) is designed, where layer I ensures production efficiency and layer II optimizes assembly line balance. Finally, an assemble example of high-pressure common rail fuel pumps (HCRFP) is used to validate the effectiveness of the proposed method. The results demonstrate notable improvements compared to traditional single-product assembly (TSPA) line in scenarios with workstation numbers 4, 5, 6, and 7. Specifically, the production takt time is reduced by 4.19% to 9.56%, and CBR is enhanced by approximately 50%. Further comparison with three other classic algorithms confirms the superiority of ADGA. Additionally, it is observed that remanufacturability (proportion of remanufactured parts) has a significant impact on assembly performance. As remanufacturability increases, both takt time and CBR increase, reaching their maximum values when remanufacturability is around 0.5.

Design of high-temperature sodium heat pipe with composite wick based on non-dominated sorting genetic algorithm (NSGA)

As small modular reactors continue to advance, heat pipe reactors (HPRs) are being developed by multiple organizations. Designing heat pipes with optimal heat transfer capability is crucial for the effective performance of HPRs. Based on NSGA-III genetic algorithm, this paper presented the design method of a high-temperature heat pipe featuring a composite wick. The study examined the influence of heat pipe structure parameters on performance parameters and heat transfer limits. Based on the analysis, we built multi-objective modeling of heat pipe and obtained the optimal structural parameters by NSGA-III. Verification confirmed that the heat pipe met the Mach number and effective capillary radius requirements. This study provided valuable insights for improving heat pipe manufacturing techniques and reactor miniaturization.

Energy and temperature management in buildings through Multi-Objective Model Predictive Control on a chip

Energy and temperature management in buildings through Multi-Objective Model Predictive Control on a chip

Collaboration and resource sharing in the multidepot time-dependent vehicle routing problem with time windows

Concerns about energy conservation and emission reduction have highlighted the importance of environmentally sound logistics networks in urban areas. These networks are deeply intertwined with urban traffic systems, where variations in transit speeds can significantly increase the energy consumption and carbon emissions of delivery vehicles, compromising the environmental sustainability of urban deliveries. To address this, we propose a multidepot time-dependent vehicle routing problem with time windows, enhancing route planning flexibility and resource configuration. Our approach begins with a route spatiotemporal decomposition method to estimate vehicle travel times and emissions based on varying vehicle speeds. We then develop a multiobjective mixed integer linear programming model that aims to minimize total operating costs, the number of vehicles, and carbon dioxide emissions. A hybrid heuristic algorithm combining spectral clustering, multiobjective ant colony optimization, and variable neighborhood search is proposed to solve the model. This algorithm incorporates collaboration and resource sharing strategies, a pheromone initialization mechanism, a novel heuristic operator that accounts for time dependency, and a self-adaptive update mechanism, enhancing both solution quality and algorithm convergence. We compare the performance of our algorithm with that of the CPLEX solver, multiobjective ant colony optimization, non-dominated sorting genetic algorithm-Ⅲ, and multiobjective particle swarm optimization. The results demonstrate the superior convergence, uniformity, and spread of our proposed algorithm. Furthermore, we apply our model and algorithm to a real-world case in Chongqing, China, analyzing optimized results for different time intervals and vehicle speeds. This study offers robust methodologies for theoretically and practically addressing the multidepot time-dependent vehicle routing problem with time windows, contributing to the development of economical, efficient, collaborative, and sustainable urban logistics networks.

BIG DATA, BUSINESS PROCESSES, AND MATHEMATICAL ALGORITHMS FOR MULTI-OBJECTIVE MANAGERIAL DECISION OPTIMIZATION

The use of Big Data and the engineering of business processes are becoming increasingly important for managing the business of companies and large international corporate groups. For more successful outcomes, even for complex interventions such as those of the PNRR (National Recovery and Resilience Plan), the use of mathematical models to assist management decisions represents a significant lever to optimize time, costs, and planned overall performance. This analytical approach strengthens traditional qualitative techniques like SWOT analysis and finds useful application in the optimal definition of any form of PPP (Public-Private Partnership), such as the recent “Partnership for Innovation” (PPI) included in the new procurement code. The scientific work developed from the systematic analysis of processes proposes a particular multi-objective mathematical model that uses a system of vectors in an m-dimensional reference space.

Optimizing Higher Education for Entrepreneurship Digital economy and Innovation: A Novel Spatial Analysis Approach

The paper extensively examined the intricate components underpinning innovation ability, culminating in the construction of a linear spatial model delineating innovation and entrepreneurship prowess. This paper analyzed the components of the connotation of innovation ability, then constructs a linear spatial model of innovation and entrepreneurship ability, proposes a multi-objective function model of the utilization efficiency and allocation efficiency of education resources, and uses the grey correlation algorithm The experimental simulation and model solution are carried out. The simulation results show that, through the optimization, the utilization efficiency and allocation efficiency of the educational resources for innovation and entrepreneurship for all are increased by 18.72\% and 20.98\% respectively, and tend to be in equilibrium, which can achieve the optimization of educational resources allocation. Among all people, the correlation value with ideal entrepreneurship is 0.3177, achieving the most excellent innovation and entrepreneurship education.

A multi-objective mathematical programming model for location-routing of COVID-19 healthcare waste in Tunisia

A multi-objective mathematical programming model for location-routing of COVID-19 healthcare waste in Tunisia

Multi-objective intermodal transportation planning with real-life application

ABSTRACT This paper explores the optimal route selection problem within an intermodal transportation network that comprises road, rail, sea, and inland waterways. Initially, we propose a multi-objective integer linear programming model that aims to minimize total cost, transport time, and carbon emissions. Subsequently, we apply two multi-objective programming approaches: Fuzzy Goal Programming and Compromise Programming. These approaches are implemented in a real-life case study, aiming to determine the optimal intermodal transportation route from Türkiye (Denizli) to Germany (Duisburg). Four out of the six methods suggest the same road and maritime pathway. Furthermore, this model has the potential to aid decision-makers in selecting routes based on customer requirements and serve as a valuable tool for policymakers in establishing an efficient intermodal transport network.

Multi-Objective Optimization and Reconstruction of Distribution Networks with Distributed Power Sources Based on an Improved BPSO Algorithm

The continuous integration of distributed power into the distribution network has increased the complexity of the distribution network and created challenges in distribution-network reconfiguration. In order to make the distribution network operate in the optimal mode, this paper establishes a multi-objective reconfiguration-optimization model that takes into account active network loss, voltage offset, number of switching actions and distributed power output. For a distribution network with a distributed power supply, it is easy for the traditional binary particle swarm optimization algorithm to fall into a local optimum. In order to improve the convergence speed of the algorithm and avoid premature convergence, this paper adopts an improved binary particle swarm optimization algorithm to solve the problem. The IEEE33 node system is used as an example for simulation verification. The experimental results show that the algorithm improves the convergence speed and global search ability, effectively reduces the system network loss, and greatly improves the voltage level of each node. It improves the stability and economy of distribution-network operation and can effectively solve the problem of multi-objective reconfiguration.

A dynamic multi-objective optimization model for inner-industry carbon responsibility allocation based on carbon tax and carbon offsets accounting

The setting of Intergovernmental Panel on Climate Change (IPCC) raises concerns on curbing the excessive carbon emissions, and European Union (EU) has been proactively attempting “green” initiatives striving to achieve carbon neutrality. The current Carbon Border Adjustment Mechanism (CBAM) only works on short-term carbon reduction without much “greenness” investment incentives so far, and few studies focus on the long-standing inner-industrial carbon reduction coordination. To address these issues, this paper creatively presents a dynamic multi-objective carbon responsibility allocation model (CRAM) for EU carbon market, allows the carbon trade with a taxation based on carbon offsets accounting, and considers the final targets of sustainable “greenness” investment incentives. To find the optimal results of the designed dynamic CRAM, an improved KT-NSGA-II algorithm is proposed to detect the Pareto frontiers of each successive periods. Data from EU Cement and Aluminium industries are then selected for empirical analysis to compare the proposed CRAM to conventional CBAM model. The findings demonstrate that the superiority of CRAM in carbon emissions reduction, economic benefits improvement and green invests encouragement. With the adjustment of the inner-industrial carbon allowance trade, total carbon emissions decreased by 28.03% and the “greenness” investment initiative increased by 39.24% in contrast to the CBAM. The sensitivity analysis of the model also provides suggestions on the settings of carbon quota and tax with different industrial production process, and proposes policy recommendations for CRAM implementation.

Rescue guiders layout study based on a two-layer optimization framework

In an emergency evacuation, the free evacuation of pedestrians can make the entire evacuation process slow and dangerous. To limit the free behavior of pedestrians and reduce the interaction between pedestrians, a reasonable layout of the guider can improve the efficiency and safety of evacuation. How to set the number, location, and exit allocation of guiders requires further investigation. In the current study, we transform the evacuation into a multi-objective optimization problem. A two-layer optimization framework is developed. In the upper level, the improved NSGA-II multi-objective algorithm is introduced to generate the favorable guider layout, and a chromosome fragment deletion operator is added to improve the optimization efficiency. In the lower layer, the agent movement simulation model is used to simulate the evacuation dynamic of crowd under the favorable guider layout. The variables of this multi-objective solution model in the upper layer are the number and location of the guiders. The evacuation time and agent movement cost are calculated by the lower layer simulation as the objective values of the solution sample, and guide the iterative search process to obtain more reasonable optimization results. The developed model is verified and then applied to a fictional scenario. The number, initial position and exit allocation of guiders are obtained by optimizing the iterative process. The results show that the near optimal solution can be applied in various visibility conditions, and the evacuation efficiency is much higher than that of unguided evacuation. This optimization framework can provide theoretical and methodological support for emergency evacuation planning.

Assessing airline efficiency with a network DEA model: A Z-number approach with shared resources, undesirable outputs, and negative data

This study measures the efficiency of airlines using a novel fuzzy common weight additive network data envelopment analysis (NDEA) with shared resources, negative data, and undesirable outputs. First, an appropriate two-stage network is designed for each airline so that stages 1 and 2 are called the Production and Service stages, respectively. The proposed model adopts a top-down approach and calculates the efficiency of the system first and then estimates the efficiency of stages 1 and 2. To evaluate and predict the airlines’ efficiency considering fuzzy data and the reliability of the information, the values of input/intermediate/output variables are predicted as the Z-number and the appropriate Z-number version of NDEA (ZNDEA) models is proposed. To develop the proposed ZNDEA models and find common weights for the variables, three multi-objective ZNDEA models for the system, stage 1 and stage 2 are presented. The multi-objective common weight ZNDEA models are solved using the min-max Chebyshev goal programming technique and the final efficiencies are calculated. To illustrate the capability of the proposed approach, real-life data from Iranian airlines in 2022 are collected, and the efficiencies are analyzed.

A Multi-Objective Prediction XGBoost Model for Predicting Ground Settlement, Station Settlement, and Pit Deformation Induced by Ultra-Deep Foundation Construction

Building a deep foundation pit in urban centers frequently confronts issues such as closeness to structures, high excavation depths, and extended exposure durations, making monitoring and prediction of the settlement and deformation of neighboring buildings critical. Machine learning and deep learning models are more popular than physical models because they can handle dynamic process data. However, these models frequently fail to establish an appropriate balance between accuracy and generalization capacity when dealing with multi-objective prediction. This work proposes a multi-objective prediction model based on the XGBoost algorithm and introduces the Random Forest Bayesian Optimization method for hyperparameter self-optimization and self-adaptation in the prediction process. This model was trained with monitoring data from a deep foundation pit at Luomashi Station of Chengdu Metro Line 18, which are characterized by a sand and pebble stratum, cut-and-cover construction, and a depth of 45.5 m. Input data of the model included excavation rate, excavation depth, construction time, shutdown time, and dewatering; output data included settlement, ground settlement, and pit deformation at an operating metro station only 5.7 m adjacent to the ongoing pits. The training effectiveness of the model was validated through its high R2 scores in both training and test sets, and its generalization ability and transferability were evaluated through the R2 calculated by deploying it on adjacent monitoring data (new data). The multi-objective prediction model proposed in this paper will be promising for monitoring the data processing and prediction of settlement of surrounding buildings for ultra-deep foundation pit engineering.

Multi-Objective Short-Term Operation of Hydro–Wind–Photovoltaic–Thermal Hybrid System Considering Power Peak Shaving, the Economy and the Environment

In recent years, renewable, clean energy options such as hydropower, wind energy and solar energy have been attracting more and more attention as high-quality alternatives to fossil fuels, due to the depletion of fossil fuels and environmental pollution. Multi-energy power systems have replaced traditional thermal power systems. However, the output of solar and wind power is highly variable, random and intermittent, making it difficult to integrate it directly into the grid. In this context, a multi-objective model for the short-term operation of wind–solar–hydro–thermal hybrid systems is developed in this paper. The model considers the stability of the system operation, the operating costs and the impact in terms of environmental pollution. To solve the model, an evolutionary cost value region search algorithm is also proposed. The algorithm is applied to a hydro–thermal hybrid system, a multi-energy hybrid system and a realistic model of the wind–solar–hydro experimental base of the Yalong River Basin in China. The experimental results demonstrate that the proposed algorithm exhibits superior performance in terms of both convergence and diversity when compared to the reference algorithm. The integration of wind and solar energy into the power system can enhance the economic efficiency and mitigate the environment impact from thermal power generation. Furthermore, the inherent unpredictability of wind and solar energy sources introduces operational inconsistencies into the system loads. Conversely, the adaptable operational capacity of hydroelectric power plants enables them to effectively mitigate peak loads, thereby enhancing the stability of the power system. The findings of this research can inform decision-making regarding the economic, ecological and stable operation of hybrid energy systems.

Multi-objective predictive maintenance scheduling models integrating remaining useful life prediction and maintenance decisions

This paper presents two novel data-driven multi-objective predictive maintenance scheduling models that integrate remaining useful life (RUL) prediction into maintenance planning. First, we propose a deep learning ensemble model that includes a convolutional neural network and bidirectional long short-term memory network with a temporal self-attentional mechanism and Bayesian optimization method to predict system RUL effectively. Then, two novel multi-objective mixed-integer linear programming (MILP) models are developed based on the system-predicted RUL to deal with the system predictive maintenance problem, which aims to minimize the total maintenance completion time and maintenance-related costs simultaneously. To solve this problem, we design an iteration ϵ-constraint method to achieve Pareto solutions. Meanwhile, a fuzzy logic method is proposed to recommend a preferred Pareto solution for decision-makers. Finally, the aircraft engine C-MAPSS dataset from NASA validates that the effectiveness of the proposed data-driven multi-objective predictive maintenance scheduling models are effective. For the predictive maintenance of 20 aircraft engines, both the maintenance completion time and maintenance cost are reduced by more than 40%.