Multi-objective Optimization Model Research Articles

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.

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.

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.

Multi-objective optimization of key parameters of stirred tank based on ANN-CFD

In order to balance the maximum mixing efficiency and minimum energy consumption of stirred tanks, this study proposes a four-stage optimization framework, integrating all: CFD model, ANN data-prediction model, multi-objective optimization model, and multi-criteria decision making model. With the stirred tank reactor as the modeling and simulation context, a data-prediction model, GA-GABP, is developed firstly. Second, an optimization model is established targeting energy consumption, fluid mixing degree, and suspension uniformity: the predictions of GA-GABP are optimized using the NSGA II, and finally, based on the Pareto front, weights are determined using the entropy weighting method, and the TOPSIS algorithm is employed to decide the final optimization scheme. Compared to the base case, the optimized scheme Opt1 shows a 52.49% reduction in energy consumption, a 1.35% increase in fluid mixing degree, and a 72.31% improvement in suspension uniformity. This demonstrates the framework's effectiveness in balancing multiple conflicting objectives.

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.

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.

Enhancing agricultural product trade efficiency through machine learning predictions and multi-objective optimization of financial strategies

In the context of global economic development and food safety, agricultural trade plays a vital role in linking agricultural production with market demand, and the efficient formulation of agricultural financial strategies is crucial for enhancing trade efficiency. This study employs advanced machine learning technologies to predict and optimize the efficiency of agricultural trade while seeking to balance the interests of various stakeholders in agricultural finance. By integrating quantile factor models, long short-term memory (LSTM) networks, and attention mechanisms, this paper conducts an in-depth analysis and precise prediction of the key factors affecting trade efficiency. This approach effectively addresses the nonlinearity and long-term dependency issues in time-series data, utilizing attention mechanisms to highlight critical information and improve prediction accuracy. Furthermore, this research establishes a multi-objective optimization model to balance the interests of agricultural finance participants, providing a new quantitative tool for formulating agricultural financial strategies aimed at optimizing decisions to enhance economic and social value. This paper offers new perspectives, methods, and empirical support for improving the efficiency of agricultural trade and the formulation of agricultural financial strategies.

Selection of urban flood shelter locations based on risk assessment

Within the framework of global climate change, there is a recurring occurrence of floods and waterlogging disasters, which pose significant risks to human lives and overall safety. The phenomenon of urbanization has led to an increased vulnerability of cities to flood disasters. Evacuation serves as a viable strategy for adapting to climate change, managing intense precipitation events, and mitigating the impact of flood disasters. Hence, the meticulous choice of shelters has emerged as a crucial element in enhancing the effectiveness of evacuations. Nevertheless, the process of choosing shelters sometimes neglects to consider the potential for flooding and has not undergone thorough testing. Hence, the objective of this research is to systematically choose shelters within the central metropolitan region, incorporate flood risk considerations into the shelter site model, and assist cities in mitigating the consequences of floods resulting from climate change. To begin with, employ a two-dimensional shallow water model in order to replicate instances of intense precipitation and produce data on flood inundation. Subsequently, the simulation results are utilized to assess the flood risk using the entropy weight approach. This risk is then incorporated into the multi-objective optimization model to determine the optimal shelter placement. Ultimately, the ABM model was employed to create a simulation of crowd evacuation in urban areas, confirming the efficacy of shelter selection and thoroughly examining the influence of various shelters on the efficiency of community emergency evacuation. The study's findings indicate that a total of 26 shelters were chosen within the study area using a multi-objective methodology, ensuring the inclusion of all affected populations. The utilization of multi-objective models in the selection of shelter places has been shown to enhance evacuation efficiency, as confirmed by the ABM model. The simulation findings indicate a decline in the population's death rate from 1.94% to 0.98%, an increase in the evacuation rate from 97.4% to 98.4%, and a reduction in the number of casualties by 133 individuals.

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.

Network-level pavement maintenance and rehabilitation planning considering uncertainties using chance-constrained programming

ABSTRACT In practice, given limited funds, to consider multiple strategic goals/objectives that different stakeholders concern, pavement network-level maintenance and rehabilitation (M&R) planning becomes a multi-objective optimisation (MOO) based project selection and budget allocation problem. In an attempt to solve this problem, most agencies established MOO models under the deterministic situation without appropriate consideration of uncertainties. However, ignoring performance uncertainties often leads to unreasonable decisions. To provide more convincing and reliable pavement M&R decisions, this paper proposes a Chance-Constrained Programming (CCP) based MOO method to incorporate performance uncertainties in network-level single period pavement M&R planning. First, a general deterministic MOO model with budget and network performance constraints is established. Then, three commonly-used statistical forms of network-level performance measures are introduced. To incorporate uncertainties, the probability distribution of each form of performance measure is derived. Based on the CCP method, the MOO model is transformed to an equivalent deterministic formulation as a mixed non-linear integer programming (MNLIP) problem. To demonstrate the proposed method, a case study using real data is conducted. The results show that the proposed method can effectively help decision-makers to appropriately incorporate performance uncertainties in conducting network-level pavement M&R planning.

Optimization of Charging or Discharging Strategy of Energy Storage in Multi-Objective Market Transactions Based on Quantum Genetic Algorithm

Multi-objective energy optimization is critical for ensuring stable and power system operation securely. Though, multi-objective energy optimization is difficult because of an interdependence and opposing goals. To address conflicting objectives, a multi-objective optimization model is required, hence Monarch Butterfly African Vulture Optimization Algorithm (MBAVOA) is newly proposed for resolving multi objective issues. MBAVOA is the hybridized of two optimization algorithms, which includes Monarch Butterfly Optimization (MBO), and African Vulture Optimization Algorithm (AVOA). Here, charging cost, distance, and the user convenience are optimized while taking Renewable Energy Sources (RES) into consideration using MBAVOA. In addition, a load is computed using a Quantum Genetic Algorithm (QGA), describes intermittent and uncertain RES, such as wind and solar. The QGA-MBAVOA outperformed with the least charging cost 63%, fitness 0.010, and user convenience 0.819.

A multi-objective optimization model for planning emergency shelters after a tsunami

Vertical evacuation helps people escape tsunami risks by elevating them above the level of tsunami inundation, usually by moving to higher ground or taking refuge in tall buildings or other elevated structures. Unlike horizontal evacuation, which involves moving away from the coast to higher ground, vertical evacuation reduces the demand for horizontal evacuation routes that can become congested and impede evacuation efforts. Therefore, investing in critical infrastructure that enables vertical evacuation is crucial in tsunami-prone areas. This study proposes a multi-objective optimization model to help decision-makers assign critical infrastructure for vertical evacuation in tsunami-prone areas. Critical infrastructure includes buildings that can provide shelter during a tsunami and road networks for rapid access to shelter points. The proposed model balances three objectives: (1) minimizing investment costs in critical infrastructure, (2) maximizing the population covered by shelters, and (3) minimizing the evacuation time for evacuees to reach the shelters. This model is tested on real-world data from the Coquimbo-La Serena coastal conurbation in the Coquimbo region of Chile. The study contributes to the literature on tsunami evacuation modeling and provides valuable information for decision-makers to plan and invest in critical infrastructure for vertical evacuation during tsunamis. A sensitivity analysis of various parameters is conducted, and managerial insights are provided.

A route network resource allocation method based on multi-objective optimization and improved genetic algorithm

In order to solve the problem of imbalance between traffic demand and airspace capacity of high-altitude air route network, reduce unnecessary delay costs, and improve air route operation efficiency, the resource allocation problem of multi-objective air route network for CTOP program is studied. Taking the affected flights in the congested area of air routes as the research object, taking into account the constraints of actual flight operation, FCA time slot resource availability limit, FCA capacity limit, etc., aiming at minimizing the total delay time of each flight and maximizing the fairness of airlines, a multi-objective optimization model for air route network resource allocation is established, and an improved NSGA-II algorithm is designed to solve the model. Based on the actual operation data of air routes in East China, the Pareto optimal solution set is obtained and compared with the traditional RBS algorithm, the average delay time is reduced by 5.49% and the average fair loss degree is reduced by 66.76%. The results show that the proposed multi-objective optimization model and the improved NSGA-II algorithm have better performance, which can take into account the fairness of each airline on the basis of reducing the total delay cost, realize the allocation of optimal flight trajectories and time slot resources, and provide a reference scheme for air traffic control resource scheduling.

Metaheuristic approach to multi-objective ergonomic workforce scheduling for Bi-hazard industrial tasks

ABSTRACT This paper considers the daily scheduling of workers who are simultaneously exposed to two ergonomic hazards when performing their assigned tasks. Safe daily rotating work schedules are generated to minimize the maximum exposure amounts of both hazards simultaneously. A non-dominated sorting genetic algorithm II (NSGA-II) method is employed to generate near-optimal workforce scheduling solutions. A numerical example shows that for a small multi-objective problem, the NSGA-II is able to yield the same set of non-dominated Pareto points as those obtained from a multi-objective optimization model with ε-constraint method. A computational experiment further demonstrates that the NSGA-II can yield acceptable non-dominated Pareto points in reasonable computation times irrespective of the problem size.

Multi-Objective Optimization for Seismic Structural Investigation and Maintenance Strategies of Bridges in Service

With the continuous updating of seismic design codes for Bridges, the new regulations have higher requirements for the seismic structure and mechanical properties of old Bridges. In order to make the decision of seismic structure strengthening more scientifically, with the goal of optimal overall performance, minimum economic cost and minimum environmental impact, the optimal model of seismic strengthening sustainable strategy is established. Firstly, according to the results of the seismic measures of the existing Bridges, the corresponding reinforcement measures are proposed for the structural problems that do not meet the seismic measures. Then the utility evaluation of the three objectives of the existing bridge was carried out. At last, NSGA-Ⅱ algorithm was used to build a multi-objective maintenance decision optimization model of the bridge network. Combined with the weight of the decision-maker's preference, TOPSIS algorithm was used to obtain the optimal solution of the target, and decision-making suggestions were provided for the relevant departments. The results show that there are many structural problems in the new evaluation of the old bridge, and the maintenance of the structural problems are more feasible and effective than the improvement of the traditional mechanical properties. Combined with the comprehensive evaluation utility and reinforcement objective function, the maintenance decision can be better under different objectives. The reinforcement maintenance decision model has the ability to improve the comprehensive performance and sustainability of the bridge to the maximum extent within the limited budget.

An adaptive differential evolution algorithm with multi-strategy for solving complex optimization problem

In this paper, an adaptive differential evolution algorithm with multi-strategy, namely ESADE is proposed to solve the premature convergence and high time complexity for complex optimization problem. In the ESADE, the population is divided into several sub-populations after the fitness value of each individual is sorted. Then different mutation strategies are proposed for different populations to balance the global exploration and local optimization. Next, a new self-adaptive strategy is designed adjust parameters to avoid falling into local optimum while the convergence accuracy has reached its maximum value. And a complex airport gate allocation multi-objective optimization model with the maximum flight allocation rate, the maximum near gate allocation rate, and the maximum passenger rate at near gate is constructed, which is divided into several single-objective optimization model. Finally, the ESADE is applied solve airport gate allocation optimization model. The experiment results show that the proposed ESADE algorithm can effectively solve the complex airport gate allocation problem and achieve ideal airport gate allocation results by comparing with the current common heuristic optimization algorithms.

Multi-objective optimization for sustainable and economical polycarbonate production with reaction kinetics inference for real-world industrial process

Polycarbonate has a wide range of applications in electronic devices and medicine because of its remarkable properties such as transparency and mechanical strength. However, the polycarbonate production process using diphenyl carbonate leads to the inevitable generation of nitrogen oxide (NOx) from byproduct incineration. Therefore, it is essential to optimize the process conditions while simultaneously considering the production costs and NOx emissions. Herein, we propose a multi-objective optimization model for sustainable and economical polycarbonate production using a high-fidelity reaction model for real-world industrial processes. Additionally, a data-driven model was developed using industrial data to identify the operating conditions that minimize NOx emissions and maximize profitability. Four of the ten reaction parameters were selected through variance-based global sensitivity analysis, and the posterior distribution of each parameter was determined through Bayesian inference. In addition, NOx emissions were predicted using the data-driven model developed from industrial data. A process model with coefficients of determination above 0.7 and a data-driven model with R-squared value above 0.99 for pilot-scale experimental and industrial data were generated using our approach. The Pareto optimality, identified based on the developed models, suggested operating conditions that optimize both economic and environmental benefits. Consequently, NOx emissions could be reduced by 27.2%–27.4% and the unit costs of production could be reduced by 0.8%–1.6% compared with the base case.