Multi-objective Optimization Model Research Articles

Optimization strategy of computer numerical control machining process parameters in biomanufacturing mold

With the rapid development of biomanufacturing technology in the medical and pharmaceutical fields, the demand for high-precision and high-quality molds has surged. When Computer numerical control (CNC) machining biomanufacturing molds, the optimization of process parameters becomes the key to improve efficiency and quality. The purpose of this study is to explore the optimization strategy of CNC machining process parameters to achieve the best surface quality, dimensional accuracy and machining efficiency. Through literature review, the spindle speed, feed speed and cutting depth are selected as the key parameters, and the multi-objective optimization model is constructed by response surface method, which is solved by genetic algorithm. The experiment shows that the process parameters of CNC system in mold manufacturing are cutting speed 100 (m/min), feed rate 0.2 (mm/rev) and cutting depth 0.5 (mm), which will effectively reduce the manufacturing cost, and effectively control the alarm times within 35 times in different processing equipment, greatly reduce the risk. The optimization strategy can significantly improve the surface quality and productivity of the mold and reduce the cost. The comparative analysis verifies the effectiveness of the method, which provides new theoretical and technical support for CNC machining in the field of biomanufacturing.

Evaluate Multi-Objective Optimization Model for Product Supply Chain Inventory Control Based on Grey Wolf Algorithm

Enterprise supply chain inventory control plays a critical role in modern enterprise management. Yet achieving multi-objective optimization remains challenging. This paper proposes a deep learning integrated model based on grey wolf optimization (GWO-DLIM) aimed at optimizing inventory holding costs, stockout costs, and order costs. The model integrates multi-layer perceptron (MLP), DeBERTa, and GWO, leveraging the powerful predictive and feature extraction capabilities of deep learning models, combined with the global optimization ability of the grey wolf algorithm, to achieve multi-objective optimization. These results demonstrate that the GWO-DLIM model significantly outperforms other comparative models across all metrics, exhibiting lower prediction errors and root mean square errors, as well as higher service levels. This study provides an effective multi-objective optimization solution for supply chain inventory management, holding significant theoretical and practical implications.

Multiobjective Route Optimization for Multimodal Cold Chain Networks Considering Carbon Emissions and Food Waste

The cold chain logistics industry faces significant challenges in terms of transportation costs and carbon emissions. It is imperative to plan multimodal transportation routes efficiently to address these issues, minimize food waste, and reduce carbon emissions. This paper focuses on four key optimization objectives for multimodal cold chain transport: minimizing total transportation time, costs, carbon emissions, and food waste. To tackle these objectives, we propose a high-dimensional multiobjective route optimization model for multimodal cold chain networks. Our approach involves the development of a multiobjective evolutionary algorithm, utilizing Monte Carlo simulation and a one-by-one selection strategy. We evaluate the proposed algorithm’s performance by analyzing various convergence and distribution indicators. The average values for the minimum total transportation time, transportation cost, carbon emission cost, and cargo loss rate derived from the proposed algorithm ultimately converge to 6721.7, 5184.4, 301.5, and 0.21, respectively, demonstrating the effectiveness of the algorithmic solution. Additionally, we benchmark our algorithm against the existing literature to showcase its efficiency in solving high-dimensional multi-objective route optimization problems. Furthermore, we investigate the impact of different parameters, such as carbon tax rates, temperature, and cargo activation energy, on carbon emissions, and food waste. Moreover, we conduct a real-world case study to apply our approach to solving a practical business problem related to multimodal cold chain transportation. The insights gained from this research offer valuable decision-making support for multimodal carriers in developing low-carbon and environmentally friendly transportation strategies to efficiently transport perishable goods.

Enhancing end-of-life product recyclability through modular design and social engineering optimiser

Amidst the thriving landscape of manufacturing, the vision of sustainability in Industry 5.0 is becoming increasingly significant. The implementation of recycling represents a crucial step in the pursuit of sustainability, particularly in light of the mounting challenge posed by the proliferation of end-of-life (EOL) products. Addressing this challenge, we propose a novel Design for Modular Recyclability (DFMR) approach aimed at facilitating the recycling of EOL products. Our study develops a multi-objective optimisation model with a focus on maximising green recyclability and independence while minimising aggregation. We introduce an innovative Social Engineering Optimiser (SEO) to simulate behavioural patterns in complex environments, aiding in identifying and implementing effective strategies for optimal or near-optimal results in diverse scenarios. The practical effectiveness of the proposed models and algorithms is demonstrated by applying them to a real-life case study in an internal combustion engine, followed by performance comparisons with existing well-established multi-objective optimisation algorithms. The findings of our study demonstrate the efficacy of the proposed DFMR model, offering a novel approach for decision-makers to undertake EOL product recovery. This contributes to the further exploration of complex and promising paths towards sustainable manufacturing and green production that are more in line with Industry 5.0.

Multi-Objective Optimization of Steel Pipe Pile Cofferdam Construction Based on Improved Sparrow Search Algorithm

This paper develops a multi-objective optimization model to address the absence of systematic and practical evaluation methods for selecting construction schemes for steel pipe pile cofferdams. The model aims to minimize duration and cost while maximizing quality. Additionally, it proposes an improved sparrow search algorithm (ISSA) to solve this problem. First, a tent chaotic map is introduced to initialize the sparrow population, enhancing the diversity of the initial population. Second, the principle of non-dominated ordering is introduced to sort the parent and offspring populations during the iteration process, and the appropriate individuals are selected to form the offspring population. Finally, gray correlation analysis is applied to optimize the Pareto solution set and determine the final construction scheme. The effectiveness and superiority of the ISSA is verified by using the Changsha Jinan Avenue project as a case study. The results indicate that the quality of the optimized construction scheme remains at a high level of 0.90 or more; the duration is shortened by 18 days, a reduction of 21%; and the total cost is reduced by CNY 220,000, saving 3% of the cost.

Multi-Objective Optimization Design of Traditional Soil Dwelling Renovation Based on Analytic Hierarchy Process—Quality Function Deployment—Non-Dominated Sorting Genetic Algorithm II: Case Study in Tuyugou Village in Turpan, Xinjiang

As the socio-economic landscape expands and tourism flourishes, the traditional earthen dwellings of Tuyugou Village, Turpan, Xinjiang, face significant challenges, including low energy efficiency and suboptimal living comfort, necessitating data-driven and scientifically robust renovation strategies. Existing renovation methods, however, often lack empirical support and rely heavily on the subjective judgments of architects, thus hindering the effective preservation and transmission of cultural heritage. This research addresses the renovation of these traditional dwellings by employing the AHP method to systematically evaluate user requirements, with input from diverse stakeholders, including homeowners, tourists, experts, and government authorities. The study then applies the QFD method to construct the House of Quality, translating user needs into specific design attributes; this is followed by a comprehensive quantitative analysis for optimization. A novel multi-objective optimization model (MOP) is introduced, with materials as the central focus, addressing key aspects of engineering, culture, and energy conservation. The NSGA-II algorithm is utilized to generate optimal Pareto solutions, which are then further refined using the entropy-weighted VIKOR method. Among the ten pre-selected renovation solutions, the sixth design plan was identified as the optimal choice, excelling in cost control, cultural integration, and energy performance. Specifically, it achieved a unit construction cost of RMB 340.566/m2, a cultural adaptability score of 1.5364, and an energy cost of RMB 352.793/kWh, thereby demonstrating an effective balance between traditional architectural elements and modern requirements. The objective decision making enabled by the VIKOR method successfully balances cultural preservation with contemporary needs, enhancing both living standards and tourism appeal. This study offers innovative and empirically grounded renovation strategies for traditional dwellings in arid and semi-arid climates, providing a framework that effectively balances cultural preservation and modernization.

Transit passenger-oriented optimisation of arrival aircraft sequencing

Abstract This study presents a model that aims to optimise the sequencing arrival aircraft around the terminal manoeuvring area (TMA). The model considers the transit passenger counts of these aircraft and employs the point merge at Sabiha Gokcen Airport. In this study, aircraft were categorised into two groups, namely ‘High or Low Transit Passenger (HTP/LTP)’. Subsequently, multi-objective models were employed to solve the test problems. Weighted sum scalarisation (WSS), conic scalarization (CS), and epsilon constraint (EC) models were utilised to increase robustness and their results were compared with a single-objective optimisation model. This approach aims to provide decision-makers with a variety of outcomes, thus expanding their options. Simultaneously, efforts are made within the model to allow aircraft with HTP counts to have minimal delays. Additionally, emission calculations were conducted to offer a critical perspective on the environmental implications, and the delay results of the multi-objective optimisation (MOO) models underwent statistical analysis.

A Multi-Objective Energy Optimization Model for Renewable Energy Systems with Hybrid Energy Storage

Building a Renewable Energy System (RES) is a viable way to address resource depletion and accomplish decarbonization. This study presents a hybrid power system that combines wind, solar, battery, and thermal energy storage. It also examines the co-optimization of scheduling and operation for multiple objectives. The hybrid system combines the affordability of thermal energy storage (TES) with the adaptability of batteries to effectively address the issue of intermittent RES. A new approach for integrated operation is offered, which relies on the electrical block's operation limit. The planning-operation co-optimization system considers minimizing the Net Present Cost (NPC) and reducing power supply likelihood todetermine the best operation limit and sizing decision factors. The co-optimization issue is addressed using novel multi-objective decision-making (MO-DM). This approach incorporates the Decision-Maker (DM) preferences data to direct the evolutionary process toward the desired area. In addition, a data-driven prediction model captures the risks and losses associated with wind generation. The case study's findings indicate the following: (1) The data-driven system has a higher level of precision in wind power projection when compared with commonly used physical simulations. (2) The suggested MO-DM exhibits superior integration, variety, and robustness achievement in the DMchosen area compared to others. (3) The combined battery-thermal energy storing structure achieves improved economy and dependability through the optimum organized operation approach, in contrast to using a single energy storage system under various testing constraints.

Optimization of process parameters for polishing aero-engine blade with abrasive cloth wheel considering spindle vibration and polished roughness

The vibration of machine tool spindle is very important for machining. Firstly, in order to explore the relationship between spindle vibration and process parameters, the spindle vibration acceleration when polishing aero-engine blade was measured, and the quadratic polynomial models between the spindle vibration acceleration in X, Y and Z direction and the process parameters were established. Secondly, a quadratic polynomial model for the influence of process parameters on polished surface roughness was also determined. Thirdly, through the Analysis of Variance (ANOVA) and main effect analysis, it can be seen that the spindle speed has the greatest impact on the vibration. Finally, a multi-objective optimization model was established with the optimization objective of minimizing spindle vibration acceleration and polished surface roughness, and the optimal process parameters were solved using genetic algorithm. The optimal process parameters were verified and the results show that the polished surface roughness with the optimal process parameters are all less than 0.4 μm, and the deviation rates between the theoretical optimization results of spindle vibration acceleration and the experimental results are all less than 10%, indicating that the optimization results are good.

A multiobjective optimization model for allocating water quantity and quality in the Yangtze and Yellow River basins

Rapid urbanization and industrial growth have led to water quantity and quality problems. Most current water allocation models focus solely on physical water and neglect the optimization of the water quantity and quality considering the water footprint. Therefore, this study aimed to establish an optimal water resource allocation model for industrial sectors based on the water footprint to identify the most effective strategies for determining the water quantity and quality distribution in river basins. First, we constructed an improved water footprint accounting system using input‒output tables to quantify the total water footprint, grey water footprint, and virtual water trade. Second, we constructed a multiobjective optimal water resource allocation model by considering the water footprint as a decision variable and combining regional economic development and productivity differences. Finally, we compared the water allocation results obtained for the Yellow River Basin, a typical “water-quantity water-scarce region” in China, with those of the Yangtze River Basin, a “water-quality water-scarce region.” The results indicate that the total water footprints of the Yellow and Yangtze River Basins were 211.37 and 317.83 billion m³, respectively, prior to optimization. After optimization, the footprints were 199.54 and 306.03 billion m³, respectively. Water resources have been reallocated through virtual water trade strategies to effectively alleviate both water quantity and quality problems. Virtual water trade strategies have emerged as policy tools capable of mitigating mismatches between industrial systems and regional water resource allocation and providing a solution to physical–virtual water system challenges within river basins.

Techno-economic assessment of surfactant Huff-n-Puff EOR in shale plays via multi-objective optimization

Surfactant Huff-n-Puff (HnP) has proven effective for enhanced oil recovery (EOR) in unconventional petroleum reservoirs. Due to the high cost of surfactant injectants, a comprehensive techno-economic analysis is crucial for optimizing development strategies. This study focuses on the Jimusar J305 shale oil reservoir, employing a Pareto-optimal multi-objective optimization (MOO) model to identify the best injection-production strategies for surfactant HnP. Using crude oil recovery and economic performance as objectives, the model combines numerical simulations with the multi-objective particle swarm optimization (MOPSO) algorithm. A decision-making protocol based on Pareto solutions is proposed to balance objectives, alongside an EOR mechanism analysis. Results show the net present value (NPV) improved from −2.98 million to +3.48 million USD without reducing oil recovery, indicating that different surfactant HnP strategies can achieve similar recovery outcomes but vary significantly in profitability. Optimal project design involves adjusting surfactant volume to reverse rock wettability and balancing injection rates and concentrations to enhance production while controlling costs.

Truck Transportation Scheduling for a New Transport Mode of Battery-Swapping Trucks in Open-Pit Mines

To address the scheduling challenges associated with the increasing deployment of battery-swapping trucks in open-pit mines, this study proposes a multi-objective scheduling optimization model. This model accounts for the unique characteristics of battery-swapping trucks by incorporating constraints related to battery swapping alerts, the selection of battery-swapping stations, and the impact of ambient temperature on battery capacity. The primary objective is to minimize the total haulage cost and total waiting time. Both a genetic algorithm and an adaptive genetic algorithm are applied to solve the proposed multi-objective scheduling optimization model. The aim is to identify an optimal scheduling solution without violating any model constraints. Results demonstrate that both the basic genetic algorithm and the adaptive genetic algorithm effectively achieve truck transportation scheduling. However, the adaptive genetic algorithm surpasses the basic genetic algorithm, reducing the total transportation costs by 5.6% and total waiting time by 17.4%. It also reduces the number of battery swaps and transportation distance by 15.8% and 1.2%, respectively. The proposed multi-objective scheduling optimization model successfully minimizes the waiting time and transportation costs of battery-swapping trucks while ensuring the completion of production tasks. This approach provides valuable technical support for improving the production and transportation efficiency of open-pit mining operations.

LCL APF control strategy based on model predictive control

In order to further control harmonic current in distribution network, a LCL APF control strategy based on model predictive control is proposed. Firstly, APF mathematical model is established. Then, a multi-objective optimization model predictive control strategy (MPC) is proposed, which comprehensively considers the performance of AC side and DC side of APF. This strategy solves the delay problem existing in the existing MPC strategy: while improving the performance of APF, there is no need to set weight factor; Then, Kalman filter is used to predict the inverter current, thus simplifying the APF measurement system. Finally, the simulation results show that the proposed control strategy can simplify the measurement system and make APF have stronger harmonic suppression ability when it only participates in power conversion and better active filter performance when it participates in harmonic control.

Review of Uncertainty, Carbon Emissions, Greenness Index, and Quality Issues in Green Supply Chains

The ability of closed-loop supply chains (CLSC) and reverse logistics (RL) to improve the triple bottom line (economic, social and environmental values) has increased the development of design and management models for CLSCs and RL. Consequently, there exists an extensive body of literature dedicated to exploring these supply and logistics issues. This paper reviews recent and relevant literature on CLSC and RL with an emphasis on uncertainty, carbon emissions, greenness index, return product quality and reliability considerations. The selected references are organized, reviewed, and analyzed to establish valuable mapping to highlight major findings. Finally, the outcomes are synthesized, and the primary research gaps are emphasized, pointing toward potential avenues for future investigation. These findings reveal that research efforts must be directed towards the development of multi-criteria greenness indices and multi-objective robust optimization models for uncertain quality and reliability of returns.

Optimization of heterogeneous continuous flow hydrogenation using FTIR inline analysis: a comparative study of multi-objective Bayesian optimization and kinetic modeling

Heterogeneous continuous flow hydrogenation is important in the chemical industry, yet the simultaneous optimization of yield and productivity has historically been complex. This study introduces a heterogeneous continuous flow hydrogenation system specifically designed for preparing 2-amino-3-methylbenzoic acid (AMA), employing FTIR inline analysis coupled with an artificial neural network model for monitoring. We explored two distinct reaction optimization strategies: multi-objective Bayesian optimization (MOBO) and mechanism-based intrinsic kinetic modeling, executed in parallel to optimize reaction conditions. Remarkably, the MOBO approach achieved an optimal AMA yield (99%) and productivity (0.64 g/hour) within a limited number of iterations. In comparison, despite requiring extensive experimental data collection and equation fitting, the intrinsic kinetic modeling approach yielded a similar optimal result. Thus, while MOBO offers a more efficient route with fewer required experiments, kinetic modeling provides deeper insights into the optimization landscape but may be impacted by non-chemical kinetic phenomena and requires significant time and resources.

A Multi-Objective Simulation-Optimization framework for water resources management in canal-well conjunctive irrigation area based on nexus perspective

Water scarcity drives the nexus of water-food-energy-ecosystem (WFEE) in arid and semi-arid agricultural irrigation areas, while the impacts of different irrigation strategies can propagate from water sector to other sectors through groundwater system. In this work, a Multi-Objective Simulation Optimization (MOSO) framework that couples the numerical groundwater model (MODFLOW) with the multi-objective optimization model (NSGA-III) is proposed to coordinate multiple resource systems with conflicting interests based on nexus perspective by reasonably allocating irrigation water resources from surface water diversion and groundwater abstraction. The management objectives include maximization of water use efficiency and food economic efficiency, as well as minimization of energy consumption efficiency and the impact of irrigation on ecosystem subject to groundwater levels, surface water supply and irrigation water demand. To demonstrate the feasibility of the MOSO framework, a water resources management problem in typical canal-well irrigation area − Baojixia Irrigation Area (BIA), located in Shaanxi province in northwest China, has been solved. The major findings are: (a) for every 10 % increase in the proportion of groundwater usage across the total irrigation area, water use efficiency decreases by 0.05 kg/m3, energy consumption efficiency increases by 900 CNY/ha, the ecosystem impact index decreases by 0.1, and food economic efficiency remains unchanged; (b) the four-dimensional Pareto-optimal solutions comprehensively consider all the impacts of the WFEE nexus system, avoiding decision shortsightedness caused by a low-dimensional perspective; (c) spatial differences in irrigation strategies across different zones are primarily influenced by crop structure, hydrogeological parameters, and surface elevation, while temporal variations are mainly concentrated during the maize irrigation period. The framework based on water resources management and groundwater dynamics can efficiently evaluate trade-offs and synergies among nexus sectors, providing far-sighted suggestions for policy makers to improve resource-use efficiency, while mitigating the impact of irrigation on ecosystem.

Reliability-based multi-objective optimization design of composite patch repair structure using artificial neural networks

Due to the parameter differences and the existence of random factors, the performance of composite repair structure shows a large dispersion. In order to obtain stronger, lighter and more reliable repair structure, multi-objective optimization design of carbon fiber reinforced polymers (CFRP) laminate repair structure is investigated by combining the reliability theory, artificial neural networks and the genetic algorithm. First, a 3D simulation model of the composite laminate patch repair structures is established using the 3D Hashin criterion and the cohesive region model. Then, the Latin Hypercube sampling (LHS) method is used to realize the random sampling, and the strength proxy model of the repair structure is established by using Back-propagation artificial neural network, further a multi-objective optimization model with tensile strength, reliability and weight as objective functions is built considering the design parameters and the random parameters. Finally, NSGAⅡalgorithm is used to solve the multi-objective optimization problem, and a set of solutions on the Pareto front surface are obtained, also the optimal design parameters of the composite repair structure meets the requirements is obtained.

Hierarchical Federated Deep Reinforcement Learning based Joint Communication and Computation for UAV Situation Awareness

The computation-intensive situational awareness (SA) task of unmanned aerial vehicle (UAV) is greatly affected by its limited power and computing capability. To solve this challenge, we consider the joint communication and computation (JCC) design for UAV network in this paper. Firstly, a multi-objective optimization (MOO) model, which can optimize UAV computation offloading, transmit power, and local computation resources simultaneously, is built to minimize energy consumption and task execution delay. Then, we develop Thompson sampling based double-DQN (TDDQN) learning algorithm which allows the agent to explore more deeply and effectively, and propose a joint optimization algorithm that combines TDDQN and sequential least squares quadratic programming (SLSQP) to handle the MOO problem. Finally, to enhance the training speed and quality, we incorporate federated learning (FL) into the presented joint optimization algorithm and propose hierarchical federated TDDQN with SLSQP (HF TDDQN-S) to implement the JCC design. Simulation results show that the introduced HF TDDQN-S can efficiently learn the best JCC strategy and minimize the average cost contrasted with the DDQN with SLSQP (DDQN-S) and TDDQN with SLSPQ (TDDQN-S) approach, and achieve the low average delay SA with power efficient.

Design and optimization of a planar anti-buckling compliant rotational joint with a remote center of motion

Compliant mechanisms (CMs) exhibit some excellent mechanical properties and provide numerous innovative solutions for many existing mechanical applications. Among them, planar compliant rotational joints have made substantial contributions to precision engineering. To achieve high-precision positioning with a compliant rotational joint, it is essential to study characteristics such as anti-buckling and constant stiffness. The remote center of motion (RCM) mechanism, with its compact structure and ease of precise control, is expected to enhance overall rigidity and reduce parasitic displacements. Here, we propose a planar anti-buckling compliant rotational joint with a RCM. Static modeling and model validation are conducted for different geometric configurations, including single and combined structures. A distributed configuration with bidirectional anti-buckling properties is selected for optimization. Using a global parameter optimization model, single-objective optimization studies are conducted for three distinct characteristics. Subsequently, a multi-objective optimization model for constant stiffness and high precision is established. Based on the optimization results, a compliant rotational joint with bidirectional anti-buckling, constant rotational stiffness, and high precision is designed. Finally, the effectiveness of the optimization method is validated through physical experiments.

Deep reinforcement learning-based multi-objective optimization for electricity–gas–heat integrated energy systems

With the increasing global attention on energy efficiency and carbon emissions, the optimization of integrated energy systems (IES) has become the key to improve energy efficiency and reduce pollution emissions. However, most of the existing optimization methods cannot effectively deal with the complexity of high dimensional continuous action space. Therefore, this paper focuses on a novel multi-objective optimization strategy for the electricity–gas–heat integrated energy systems (EGH-IES). Firstly, considering the absorption capacity of wind power and the emission of pollutant gases, a multi-objective optimization model is constructed based on the mechanism model and operation constraints of each device in EGH-IES, in which the integrated operation cost and the environmental factors are taken as optimization objectives. Then, the multi-objective optimization problem is designed as the optimal strategy of interaction learning between agent and environment in reinforcement learning, and the output power of the devices constitutes the action of reinforcement learning. Additionally, the Ornstein–Uhlenbeck process is introduced to enhance the training efficiency and exploration performance of the agent, and the deep deterministic policy gradients (DDPG) algorithm is employed to optimize the action, thus the output power of the appliances could be obtained. Finally, the simulation results show that compared with deep Q network (DQN) method and proximal policy optimization (PPO) method, the reward function value of the proposed method increases by 2.43% and 6.09%, respectively, which represents a reduction in economic cost and pollutant emissions. These verify the effectiveness and superiority of the proposed multi-objective optimization scheme in cost reduction and benefit improvement for the EGH-IES.