Multi-objective Optimization Approach Research Articles

A bi-objective location routing optimization with fuzzy time-dependent societal risks for enhancing urban medical waste management system

This paper aims to enhance medical waste management by adding new recycling centers or upgrading existing facilities, properly planning vehicle acquisition and routing under the consideration of both societal and economic impacts. Specifically, we focus on the threats posed to the surrounding population during collection and recycling by formulating a fuzzy time-dependent societal risk assessment that integrates the exposure distance estimated in terms of fuzzy vehicle speed into the traditional risk model. Then, considering multiple types of medical wastes and compatible vehicles, a bi-objective location-routing model is developed to make location-routing decisions simultaneously minimizing societal risk and system cost. The complexity of the resulting mathematical model motivates the adoption of three multi-objective optimization approaches, which are used to test our proposed model using a real-life network in Shenzhen, China. This research suggests an affordable opportunity to upgrade the current waste management system to align with the post-pandemic “new normal” by adapting existing facilities for medical waste recycling. The proposed risk measure results in better-controlled total and transportation risks, as well as a more equitable distribution of risk. Compared to the current policy, our recommended plan can reduce the system risk by more than 50% with only a 22% increase in cost.

A multi-objective reinforcement learning-based velocity optimization approach for electric trucks considering battery degradation mitigation

A multi-objective reinforcement learning-based velocity optimization approach for electric trucks considering battery degradation mitigation

Perturbation-Theory Machine Learning for Multi-Objective Antibacterial Discovery: Current Status and Future Perspectives

Antibacterial drugs (commonly known as antibiotics) are essential for eradicating bacterial infections. Nowadays, antibacterial discovery has become an imperative need due to the lack of efficacious antibiotics, the ever-increasing development of multi-drug resistance (MDR), and the withdrawal of many pharmaceutical industries from antibacterial discovery programs. Currently, drug discovery is widely recognized as a multi-objective optimization problem where computational approaches could play a pivotal role, enabling the identification of novel and versatile antibacterial agents. Yet, tackling complex phenomena such as the multi-genic nature of bacterial infections and MDR is a major disadvantage of most of the modern computational methods. To the best of our knowledge, perturbation-theory machine learning (PTML) appears to be the only computational approach capable of overcoming the aforementioned limitation. The present review discusses PTML modeling as the most suitable cutting-edge computational approach for multi-objective optimization in antibacterial discovery. In this sense, we focus our attention on the development and application of PTML models for the prediction and/or design of multi-target (multi-protein or multi-strain) antibacterial inhibitors in the context of small organic molecules, peptide design, and metal-containing nanoparticles. Additionally, we highlight future applications of PTML modeling in the context of novel drug-like chemotypes with multi-protein and/or multi-strain antibacterial activity.

Multi-objective optimization of hybrid energy systems using gravitational search algorithm

The depletion of fossil fuel reserves, increasing environmental concerns, and energy demands of remote communities have increased the acceptance of using hybrid renewable energy systems (HRES). However, choosing an optimal HRES from economic, environmental, reliability, and sustainability aspects is still challenging. To solve this challenge, this study introduces a novel multi-objective optimization approach using the Gravitational Search Algorithm (GSA) and non-dominated sorting techniques. The proposed framework addresses four objectives: minimizing the loss of power supply probability, reducing total costs, increasing renewable energy fraction, and lowering CO2 emissions. A carbon tax sensitivity analysis evaluates the system’s economic performance under varying scenarios. Also, the amount of damage caused by the release of carbon dioxide on human health and the ecosystem is examined. In this way, an optimal configuration consisting of wind turbines, photovoltaic panels, and diesel generators is introduced to satisfy the above objectives. Results demonstrate that the GSA outperforms established methods, such as multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II in Pareto front diversity and convergence. In this work, the optimal system achieves an 18.4% increase in renewable energy share, reducing ecosystem and human health damage by 14.2%. Notably, with a 20% increase in the carbon tax, system costs increased by 3%. These findings underscore the potential of multi-objective optimization combined with carbon tax policies to enhance energy system sustainability and affordability.

Resource-constrained project scheduling problem: Review of recent developments

The Resource-Constrained Project Scheduling Problem (RCPSP) remains a critical area of study in project management, focusing on optimizing project schedules under constraints such as limited resources and task interdependencies. This review synthesizes advancements from 2016 to 2024, encompassing problem variants, optimization techniques, objectives, and real-world applications. Key developments include the evolution of hybrid metaheuristics, multi-objective optimization approaches, and the integration of stochastic models to enhance robustness against uncertainties. Furthermore, the application of machine learning and sustainability-driven models has expanded the practical scope of RCPSP in dynamic and complex environments. Challenges such as scalability, uncertainty management, and the need for practical implementations are addressed, with future directions emphasizing AI integration, decentralized scheduling, and real-time adaptive solutions. This study provides a comprehensive perspective on RCPSP, bridging theoretical research with practical implications for diverse industries.

A multi-objective optimization approach for interprovincial carbon emission reduction in China: Considering industrial structure and ownership attributes.

Balancing economic growth with carbon emission control requires the coordination of policy preferences for economic and carbon reduction goals, as well as the low-carbon transformation of industrial and ownership structures. This study employs a multi-objective input-output optimization model to investigate the trade-offs among maximizing GDP growth, minimizing carbon emissions, and minimizing energy consumption within the planning period of 2017-2035 in China. The model accounts for the heterogeneity of industrial structures and ownership attributes, examining both national aggregates and the adjustment paths and ownership trends across different provinces. The findings reveal: (1) Sectors with high output and low energy consumption are growing, while high pollution and high energy-consuming sectors are declining, driven primarily by technological levels, followed by policy and development preferences. (2) Domestic capital expanding into high technology and capital-intensive sectors, HMT capital favoring the economically developed high technology regions, and foreign investment declining. (3) Regional adjustment in high-value or high-energy-consumption sectors are more pronounced in central and western provinces and less economically developed regions, while industries that are neither high-energy-consumption nor high-value show greater increases in resource-rich provinces with better infrastructure.

Solving Minimum Cost Flow Problem under Neutrosophic Environment Using the Lexicographic Approach

In decision-making, linear programming is one of the most useful models for obtaining the optimal solution. A crucial element of the linear programming (LP) model is the minimum cost flow (MCF). The objective of the MCF is to reduce the transportation cost of a single product across a network with capacity constraints. Recently, neutrosophic set theory has become a strong way to deal with the uncertainty that often comes with trying to optimize things. This manuscript explores how neutrosophic set theory can be applied to the MCF problem which has caught the interest of some researchers. The primary objective of this study is threefold: firstly, to tackle the MCF problem considering the uncertainty of the neutrosophic set, focusing especially on the cost. Secondly, to introduce an innovative lexicographical method tailored for the MCF problem, marking a first in the field of neutrosphic sets. Lastly, to combine this new method with a multi-objective optimization approach, improving the way we solve the MCF problem in various ways at once. This thorough method is meant to lead to more detailed and effective ways of solving optimization problems when there is uncertainty. To show how our method works, we will go through some numerical examples related to the MCF problem with cost defined by neutrosophic numbers.

Experimental investigation, modeling and optimization of wire EDM process parameters for machining AA2024-B4C self-lubricating composite

Abstract Metal matrix composites (MMCs) are increasingly used across various manufacturing sectors, including automotive, defense, and aerospace, due to their exceptional strength-to-weight ratio, lightweight properties, high strength, and appreciable hardness when combined with suitable reinforcing materials. MMCs reinforced with carbide particles not only enhance the mechanical properties, but also exhibit self-lubricating characteristics, providing exceptional wear resistance. The self-lubricating properties of MMCs contribute significantly to minimizing the maintenance requirements, reducing operational costs, and advancing sustainability goals, rendering them indispensable for sectors such as aerospace, automotive, medical equipment, and energy. The present work addresses the challenges associated with machining advanced composite materials and proposes optimal machining parameters to overcome these difficulties. Here in the current investigation, aluminium alloy (AA2024) + 10 wt% B4C composite was selected as the workpiece material, and it was machined using a wire electric discharge machine. Response surface methodology was employed to develop predictive models for the output responses, namely surface roughness (R a ) and material removal rate (MRR). The accuracy of the predictive models was found to be 98.78% for R a and 93.54% for MRR, demonstrating their reliability. To optimize the machining performance, both single-objective and multi-objective optimization approaches were used. Taguchi’s signal-to-noise (S/N) ratio analysis was applied for single-objective optimization, while Pareto optimal fronts generated using the genetic algorithm facilitated the multi-objective optimization to maximize MRR and minimize R a effectively.

Integration of earth-air heat exchangers in sustainable construction: a hybrid NSGA-III/Dual simplex approach for multi-objective optimization

Integration of earth-air heat exchangers in sustainable construction: a hybrid NSGA-III/Dual simplex approach for multi-objective optimization

Multi-Objective Optimization and Multi-Criteria Decision-Making Approach to Design a Multi-Tubular Packed-Bed Membrane Reactor in Oxidative Dehydrogenation of Ethane

Multi-Objective Optimization and Multi-Criteria Decision-Making Approach to Design a Multi-Tubular Packed-Bed Membrane Reactor in Oxidative Dehydrogenation of Ethane

Optimizing microgrid integration of renewable energy for sustainable solutions in off/on-grid communities

Rising energy costs, climate change impacts, and transmission losses have increased demand for renewable energy sources and decentralized solutions. As more people seek smart living and working environments, integrated smart microgrids powered by hybrid renewable systems have become attractive solutions for off-grid and on-grid communities. This study proposes designing a solar-wind-battery hybrid microgrid supplying a medical load et al.-Ain Al-Sokhna, Egypt. The optimization objectives aim to minimize the loss of power supply probability (LPSP %) and the levelized cost of energy (LCOE, $/kWh). A key consideration when designing and optimizing hybrid microgrids is the energy management strategy, which coordinates different generation sources and fluctuating load demand. Therefore, optimization algorithms were applied to balance energy flows while meeting loads, mitigating weather impacts, and preventing overcharging/deep discharge of battery storage. Models of wind turbines, photovoltaic panels, and battery storage were developed to simulate and analyze proposed microgrid operations. A multi-objective optimization approach evaluated LPSP and LCOE metrics using transit search, grey wolf, and particle swarm algorithms to find optimal system configurations. The optimization algorithms demonstrated varying performances in minimizing the multi-objective functions for the on-grid and off-grid microgrids. The particle-swarm optimization technique is the best solution for the off-grid system, which contains PV, wind, and battery storage, with a minimum LCOE of 0.3435 $/kWh and an LPSP of 4.5334%. Meanwhile, the transit-search optimization algorithm found the optimal solution for the on-grid configuration according to the objective function, yielding an LCOE of 0.116 $/kWh and an LPSP value of 3.0639 × 10−16. Statistical analysis confirmed that the algorithms generally exhibited stable and robust optimization capabilities. Of the methods, transit search was the most effective overall optimization approach.

Solving a Fully Intuitionistic Fuzzy Transportation Problem Using a Hybrid Multi-Objective Optimization Approach

In this study, a typical transportation problem involving intuitionistic fuzzy-type variables and parameters is focused on. The approaches proposed in the literature for such transportation problems have many shortcomings, such as the use of ranking functions and obtaining an infeasible solution with negative values for variables and objective functions in the presence of non-negative unit transportation charges. To overcome such weaknesses, a new approach without a ranking function is introduced in this paper. The proposed approach first constructs an equivalent crisp multi-objective form of the intuitionistic fuzzy transportation problem and then proposes a new hybrid multi-objective solution procedure to tackle the obtained crisp multi-objective problem. The conducted computer experiments with benchmark problems from the existing studies of the literature reflect the effectiveness of the proposed solution approach of this study in terms of the quality of the results when compared to the available approaches of the literature.

Integration of Slow and Fast Charging Modes in the Optimized Planning of Electric Vehicle Charging Station Using Multi-objective Optimization Approach

Integration of Slow and Fast Charging Modes in the Optimized Planning of Electric Vehicle Charging Station Using Multi-objective Optimization Approach

Multi-Objective Optimization of the Surface Grinding Process for Heat-Treated Steel

This paper effectively integrates Taguchi, Response Surface Methodology (RSM), and Genetic Algorithm (GA) approaches for both single and multi-objective optimization, delivering low-cost, high-effectiveness solutions for grinding. It examines the effects of three factors—depth of cut in coarse grinding, depth of cut in fine grinding, and the number of spark-outs—on three objectives: surface roughness, grinding time, and the deviation between the desired and actual grinding depth for SKD61 steel. Through in-depth analysis, the paper describes the impact of these factors and their interactions with the responses. It also proposes the optimal parameter setup for each objective. The optimal grinding time is achieved at 950 seconds with a coarse grinding depth of 0.007 mm, fine grinding depth of 0.004 mm, and zero spark-out. The minimal deviation and surface roughness were obtained at 0 mm and 0.144 µm, respectively, using the optimal setup of a coarse grinding depth of 0.004 mm, fine grinding depth of 0.001 mm, and 10 spark-outs. By applying GA, the paper provides a Pareto solution set, offering multiple combinations of optimal factors for minimizing grinding time, deviation, and surface roughness. These solutions serve as useful references for users seeking the best trade-offs in multi-objective scenarios. This paper contributes to improving customer satisfaction by enhancing the quality and efficiency of the machining process while reducing production costs for grinding machines. Its methodology can also be applied to other optimization fields.

A bi-level multi-objective optimization approach for carbon policy formulation towards food waste resource treatment from environmental, energy and economic perspectives

Excessive food waste contributes to the greenhouse effect. Although low-carbon technology has the potential to reduce carbon emissions and generate renewable energy, the high costs may deter the adoption. To promote low-carbon food waste treatment, local authorities could develop carbon reduction policies targeted at the food waste treatment industry, which could lead to a game between local authorities and food waste treatment enterprises. Hence, a bi-level multi-objective optimization model is put forward to balance the decisions of local authorities and those of food waste treatment enterprises. Enterprises can choose which carbon reduction policies to implement, and local authorities determine the intensity of specific policies. After testing this model on Shenzhen’s food waste treatment system, the model’s validity and feasibility were confirmed. It was discovered that by identifying suitable carbon reduction policies, the model can help enterprises achieve more than 50% carbon reduction. Moreover, different enterprises may choose different carbon reduction policies to implement. Optimal food waste disposal arrangements need flexible adjustments. The constructed model can serve as a decision-making tool for city managers to reduce carbon emissions in food waste treatment.

Managing agricultural crop and livestock land use for synergistic energy-economy-environment development: A hybrid multi-objective optimization approach under a waste-to-energy nexus

Managing agricultural crop and livestock land use for synergistic energy-economy-environment development: A hybrid multi-objective optimization approach under a waste-to-energy nexus

Optimizing the utilization of harvested wood products for maximum greenhouse gas emission reduction in a bioeconomy: A multi-objective optimization approach

Climate change mitigation in a bioeconomy can be attained by increased use of harvested wood products. Thereby, substitution effects can contribute to reducing the Global Warming Potential, and storage effects can prevent direct carbon emissions to the atmosphere. Substitution and storage effects are often only considered as marginal changes. However, an absolute upper limit exists for these effects due to limited harvesting of forests. The maximum emission reduction potential of these two effects depends on the distribution of wood across wood value chains. This study investigates how wood can be distributed optimally across value chains to achieve the maximum climate change mitigation potential by substitution and storage effects, taking a multi-objective optimization approach. Results indicate that sawable wood should be utilized in sawnwood applications to achieve the highest GHG emission reduction, while non-sawable wood contributes to maximal storage effects when used in material applications. The highest substitution effects occur when non-sawable wood is used for energy production. Both substitution and storage effects lead to a substantial mitigation effect, whereby storage effects yield a higher total reduction. The presented optimization method overcomes the limitations of marginal considerations and highlights opportunities for climate change mitigation within these limits. This study emphasizes the importance of taking a systems perspective on climate change mitigation in a bioeconomy and of understanding the limits of these effects regarding environmental policies and management.

Multi-objective maintenance strategy for complex systems considering the maintenance uncertain impact by adaptive multi-strategy particle swarm optimization

Effective maintenance optimization strategies are crucial for improving the complex equipment reliability and reducing the maintenance costs. However, the effectiveness of the maintenance procedures applied to industrial equipment is affected by uncertainty, e.g. due to the professional skills of maintenance personnel, the actual condition of the equipment being maintained. The quantification of the uncertainty on the effect of maintenance has practical significance and must be accounted in the development of maintenance strategies for reducing equipment probability of failure. This paper proposes a multi-objective maintenance strategy considering the uncertain impact of the maintenance actions. The impact of maintenance actions on the reliability of components is first studied and a reliability assessment model is developed, which considers the skill of maintenance personnel and the actual condition of the equipment. To optimize the multi-objective maintenance strategy, a multi-strategy particle swarm optimization (MS-PSO) algorithm is proposed. Two case studies are considered to verify the effectiveness of the proposed approach for multi-objective maintenance strategy optimization. In the case studies considered, it turns out that the maintenance cost rate (MCR) is reduced throughout the system life cycle and the cumulative availability is improved.

Multi-condition and multi-objective conceptual optimization design of automotive front subframe

The lightweight design of the automotive front subframe was performed by combining multi-condition topology optimization and multi-objective optimization approaches. Firstly, to avoid the singularity of the research condition, a rigid-flexible coupled multibody dynamic model of the front suspension was established to extract the loads at articulation points of the subframe under typical working conditions. Then, a finite element model of the front subframe was constructed for strength and modal analysis. Multi-condition topology optimization of the front subframe envelope was performed utilizing the compromise programming approach. The subframe was redesigned according to the optimization results to establish an explicit parametric model, which avoided the blindness of optimization. Three approximate models were established through the optimal Latin hypercube test method to improve the efficiency of subsequent optimization. Furthermore, three algorithms were employed for multi-objective optimization based on the approximate model with the highest accuracy. To enhance the reliability of the optimization results, a comparison of the lightweighting effect of the three algorithms was performed, and the NSGA-II algorithm was determined as the final algorithm due to its greater effectiveness in lightweighting. The optimized front subframe has met various performance specifications while increasing the first-order frequency by 10 Hz and reducing weight by 3.27 kg.

Multi-objective optimization approach for permanent magnet machine via improved soft actor–critic based on deep reinforcement learning

As awareness of environmental protection grows, the development of electric vehicles has emerged as a prominent area of research. Naturally, optimizing Permanent Magnet (PM) machines is critical for enhancing the power performance of electric vehicles, as they are essential components of these vehicles. Benefitting from the advantage of Deep Neural Network (DNN), optimizing the PM machine by leveraging DNN has drawn great attention from industry and academia. To increase the torque of the PM machine and reduce torque ripple from a structural design perspective, this paper proposes a Multi-objective Optimization approach for PM machine via improved Soft Actor Critic (called MOPM-SAC) based on Deep Reinforcement Learning (DRL). MOPM-SAC consists of two core components: a surrogate model based on DNN and a SAC algorithm. Specifically, the SAC algorithm based on DNN is used to optimize the PM machine. The surrogate model is established by training the DNN to use Finite Element (FE) simulation samples, which possesses higher accuracy than other methods, such as Support Vector Machine (SVM), Random Forest (RF), and so on. Moreover, the SAC algorithm integrates a trained DNN as the state transition function within the DRL environment, which enhances the optimization algorithm’s generalization ability in the context of PM machines. In addition, the reward function is designed based on optimization requirements to guide the agent in the SAC algorithm toward learning the optimal strategy. Finally, a 75 kW prototype is manufactured and tested. The effectiveness of the proposed method is validated through FE simulations and prototype experiments.