Front Solutions Research Articles

A Resilient Approach to a Test Rig Setup in the Qualification of a Tilt Rotor Carbon Fiber-Reinforced Polymer (CFRP) Wing

The evolution of aircraft wing development has seen significant progress since the early days of aviation, with static testing emerging as a crucial aspect for ensuring safety and reliability. This study focused specifically on the engineering phase of static testing for the Clean Sky 2 T-WING project, which is dedicated to testing the innovative composite wing of the Next-Generation Civil Tiltrotor Technology Demonstrator. During the design phase, critical load cases were identified through shear force/bending moment (SFBM) and failure mode analyses. To qualify the wing, an engineering team designed a dedicated test rig equipped with hydraulic jacks to mirror the SFBM diagrams. Adhering to specifications and geometric constraints due to several factors, the jacks aimed to minimize the errors (within 5%) in replicating the diagrams. An effective algorithm, spanning five phases, was employed to pinpoint the optimal configuration. This involved analyzing significant components, conducting least square linear optimizations, selecting solutions that met the directional constraints, analyzing the Pareto front solutions, and evaluating the external jack forces. The outcome was a test rig setup with a viable set of hydraulic jack forces, achieving precise SFBM replication on the wing with minimal jacks and overall applied forces.

A multiobjective approach for weekly Green Home Health Care routing and scheduling problem with care continuity and synchronized services

Home Health Care (HHC) services are essential for delivering healthcare programs to patients in their homes, with the goal of reducing hospitalization rates and improving patients’ quality of life. However, HHC organizations face significant challenges in scheduling and routing caregivers for home care visits due to complex criteria and constraints. This paper addresses these challenges by considering both caregiver assignments and transportation logistics. The objective is to minimize the total travel distance and CO2 emissions while ensuring a balanced workload for caregivers, meeting patients’ preferences, synchronization, precedence, and availability constraints. To tackle this problem, we propose a multiperiodic Green Home Health Care (GHHC) framework. In the first stage, we utilize multiobjective programming and the NSGA-II algorithm to generate Pareto front solutions that consider travel distance and CO2 emissions. In the second stage, a Mixed-Integer Linear Programming (MILP) model is proposed to balance caregivers’ workload by assigning them to the patient routes generated in the first stage. The results highlight the trade-off between shorter routes and lower emissions. Furthermore, we examine the impact of prioritizing continuity of care and patient satisfaction. This research provides valuable insights into addressing the scheduling and routing challenges in HHC services, contributing to a more efficient and environmentally friendly healthcare delivery.

A novel feature selection method based on quantum support vector machine

Feature selection is critical in machine learning to reduce dimensionality and improve model accuracy and efficiency. The exponential growth in feature space dimensionality for modern datasets directly results in ambiguous samples and redundant features, which can severely degrade classification accuracy. Quantum machine learning offers potential advantages for addressing this challenge. In this paper, we propose a novel method, quantum support vector machine feature selection (QSVMF), integrating quantum support vector machines with multi-objective genetic algorithm. QSVMF optimizes multiple simultaneous objectives: maximizing classification accuracy, minimizing selected features and quantum circuit costs, and reducing feature covariance. We apply QSVMF for feature selection on a breast cancer dataset, comparing the performance of QSVMF against classical approaches with the selected features. Experimental results show that QSVMF achieves superior performance. Furthermore, the Pareto front solutions of QSVMF enable analysis of accuracy versus feature set size trade-offs, identifying extremely sparse yet accurate feature subsets. We contextualize the biological relevance of the selected features in terms of known breast cancer biomarkers. This work highlights the potential of quantum-based feature selection to enhance machine learning efficiency and performance on complex real-world data.

Phase Saturation Control on Vorticity Enhances Mixing in Porous Media

AbstractMixing controls the fate of any solute entering porous media. Hence, an understanding of the involved processes is essential for assessing subsurface contamination and planning for its protection. However, the three‐dimensional mechanisms dominating solute mixing in the presence of several fluid phases in the pore space, and their dependency on phase saturation degree (fraction of the pore volume occupied by a phase) are unknown. Here, we analyze solute mixing in unsaturated porous media at the pore scale using X‐ray micro‐tomography performed with synchrotron radiation at unprecedented temporal and spatial resolutions for such an investigation. Transport experiments through a synthetic, sand‐like porous medium, followed in 4D using a contrast solution, are performed at different liquid phase saturation degrees. The results reveal larger solute's front deformation at lower saturation, which translates into an enhanced mixing with time. Using different topological indexes, defined based on a description of the liquid phase geometry and of the resulting hydrodynamics, we show an increase in the spatial convergence of flow streamlines at lower saturation, which, in turn, leads to a strengthened helical flow inside the liquid phase. Consequently, this increases the number of shear‐ and vorticity‐dominated deformation regions, as characterized by larger negative and positive Q‐criterion values, respectively. These findings represent a major step toward understanding the control of both saturation and the system's heterogeneity on solute mixing, essential, among others, to assess reactivity in porous media.

Multi-objective size optimization and economic analysis of a hydrogen-based standalone hybrid energy system for a health care center

The main contribution of this study is the multi-objective size optimization of a standalone hybrid energy system (HES) to supply the electricity demand of a health care center in Kerman province, south of Iran, in which fuel cell, photovoltaic, and diesel generator produces electricity, while the combination of electrolyzer and hydrogen tank is the storage system. The non-dominated sorting genetic algorithm II (NSGA-II) is applied to find the Pareto front of optimal solutions, where the loss of power supply probability (LPSP) and total net present cost (TNPC) are the objective functions. The results show that considering operating reserve and uncertainty, the TNPC increases 30.50% compared to the base load. Moreover, the fuel cell has a smaller share of energy production, which is due to the high cost of the storage system, while about 50% of the demand load is provided by DG in the cold seasons. The sensitivity analysis reveals that TNPC decreases by 8.93% for a 50% reduction of the storage system cost, while increases by 24.55% for a 50% increase in the fuel cost. Moreover, AHP/TOPSIS-based multi-criteria decision-making is implemented to find the best solution according to the opinions of three different managers/experts, which shows that the HES with LPSP = 0.2503% and TNPC = 2.1038 × 106 $ is the best solution in which uncertainties and operating reserve are also considered.

Enhancing the vertical downward condensation heat transfer of a plate heat exchanger by electrochemical etching

This research employed electrochemical etching to generate microscale roughness on a plate heat exchanger (PHE) thereby enhancing condensation heat transfer in a vertical downward flow. The effects of the mass flux, vapor quality, heat flux, saturation temperature and microscale surface roughness on the condensation heat transfer coefficient (HTC) and frictional pressure drop (FPD) of R-513a flowing through a PHE was experimentally investigated. Experimental measurements were made under various heat fluxes of 5–15 kW/m2, mass fluxes of 60–80 kg/m2s, saturation temperatures of 40–50 °C, and mean vapor qualities of 0.1–0.7. HTC increased by up to 28, 26, 74, and 32.5 % as the heat flux, mass flux, average quality, and microscale surface roughness were respectively increased, but decreased by 19 % with the saturation temperature. FPD increased as the mass flux and average vapor quality increased, whereas it was reduced as the refrigerant saturation temperature increased. In contrast, the heat flux had little to no influence on FPD. Correlations for predicting the Nusselt number and friction factor were postulated by considering influential dimensionless numbers during heat transfer. Maximizing the Nusselt number and thermal performance factor was ultimately resolved using a multi-objective genetic algorithm to find a Pareto front solution. Consequently, the optimal surface roughness was found to be 9.94 μm.

Multi-objective continuous review inventory policy using MOPSO and TOPSIS methods

In the dynamic business environment of today, decision-making involves considering various conflicting aspects. Inventory planning problems aim to determine how much and when to order products to satisfy customer demand at the lowest possible cost while maintaining a desirable service level. These problems can be formulated as a MOPSO algorithm, which is used to handle multiple objectives in a continuous review stochastic inventory control system (r, Q). Unfortunately, most multi-objective inventory models have been solved by aggregating objectives using specific weights or by optimizing only one objective and treating the others as constraints. Considering the complexity of real-world inventory control problems, which involves conflicting objectives such as minimizing cost and maximizing service level, the need arises to employ more precise optimizers that can generate better and more diverse non-dominated solutions of reorder point and order size system. In this paper multi-criteria decision-making framework that combines MOPSO algorithm and TOPSIS method to generate a Pareto front of non-dominated solutions and rank them based on decision makers' preferences. Initially, the original MOPSO is applied to the multi-objective inventory control problem, and then the mutation operator is integrated into the MOPSO to maintain diversity in the swarm and explore the entire search space. Next, the leader selection strategy called the geographically-based system (Grids) is replaced by the crowding distance factor to choose the global optimal particle as a leader. Additionally, the ε-dominance concept is employed to limit the archive size and maintain more diversity and convergence in the MOPSO for optimizing the inventory control problem. In conclusion, this work not only pioneers a cutting-edge approach to multi objective inventory control but also underscores its practical value. By facilitating the generation of superior solutions that cater to diverse decision-maker preferences, our methodology resonates deeply with real-world challenges and sets a new benchmark for effective inventory planning.

Multi-Objective Robust Optimization of Integrated Energy System with Hydrogen Energy Storage

A novel multi-objective robust optimization model of an integrated energy system with hydrogen storage (HIES) considering source–load uncertainty is proposed to promote the low-carbon economy operation of the integrated energy system of a park. Firstly, the lowest total system cost and carbon emissions are selected as the multi-objective optimization functions. The Pareto front solution set of the objective function is applied by compromise planning, and the optimal solution among them is obtained by the maximum–minimum fuzzy method. Furthermore, the robust optimization (RO) approach is introduced to cope with the source–load uncertainty effectively. Finally, it is demonstrated that the illustrated HIES can significantly reduce the total system cost, carbon emissions, and abandoned wind and solar power. Meanwhile, the effectiveness of the proposed model and solution method is verified by analyzing the influence of multi-objective solutions and a robust coefficient on the Chongli Demonstration Project in Hebei Province.

Investigating Methods for ASPmT-Based Design Space Exploration in Evolutionary Product Design

Nowadays, product development is challenged by increasing system complexity and stringent time-to-market. To handle the demanding market requirements, knowledge from prior product generations is used to derive new, but partially similar product versions. The concept of product generation engineering, hence, allows manufacturers to release high-quality products within short development times. Therefore, in this paper, we propose a novel approach to evaluate the similarity of two product implementations based on the concept of the Hamming distance. This allows the usage of similarity information in various heuristics as well as in strategies and thus, to improve the product design process. In a wide set of cases, we investigate the quality and similarity of design points. In the experiments, the use of strategies leads to significantly short searching times, but also tends to be too restrictive in certain cases. Simultaneously, the quality of the solutions found in the heuristic design space exploration has been shown to be as good or better than for the search from scratch and considerably closer solutions as part of the non-dominated solution front have been found.

A timely efficient and emissions-aware multiobjective truck-sharing integrated scheduling model in container terminals

In line with the development of green port terminals and the necessity of environmentally friendly efficient use of handling and transportation equipment, criteria such as emission reduction, energy minimization, and efficiency in a container terminal are prominent issues, which in recent years, have been the main concern of container terminal managers in developing terminal operating systems. Therefore, in this study, a bi-objective mixed integer mathematical programing model of integrated scheduling for ships’ loading and unloading operation sequencing in container terminals is proposed, which considers the minimization of containers’ flow time, trucks’ emission, and energy consumption as well as sharing trucks among quay cranes and decreasing their empty trips. In this model trucks’ technical specifications and polluting levels are input parameters for estimating emission and energy consumption of operation, which consequently leads to two uniform parallel machine scheduling models. To derive the exact solution of the model, the epsilon-constraint method is applied. Nevertheless, due to the problem’s NP-hardness attribute, two variants of non-dominated sorting genetic algorithm and multiobjective particle swarm optimization metaheuristic algorithms are proposed to identify the approximate Pareto front of solutions. In large-size problems, these algorithms outperform the epsilon-constraint method by finding acceptable results in a reasonable time. This model, which could be embedded in terminal operating software, not only yields a practical optimum operational sequence but also measures the amount of energy and emission from trucks during loading and unloading operations. The results, which utilized the data collected from Shahid Rajaee port in southern Iran, indicate that this practical model as compared to the current procedure in the terminal, results in a significant improvement in energy and emission reduction and terminal flow time.

Simulation of Fountain Flow based on Molecular Dynamics Method

In this paper, numerical simulations of the fountain effect on the flow front of polymer dilute solutions are carried out based on molecular dynamics principles and the FENE dumbbell model. The Euler method is used to solve the constitutive equations of the mechanical model for the simple shear flow field and the dislocation equations of the dumbbell molecules, and subsequently obtain the tracer flow lines and the dumbbell distribution of the flow front. The results are used to calculate the stress field, analyze the rheological evolution law, study the effect of temperature and shear rate and other parameters on the model and analyze the effectiveness of the FENE dumbbell molecular model for the fountain effect. The study shows that due to the fountain effect the dumbbell molecules stretch more with increasing shear rate, the polymer stress at the flow front will show a complex change of stress overshoot and then stabilize, and the dumbbell molecules are oriented along the fountain flow line.

A multi-objective optimization method for enclosed-space lighting design based on MOPSO

With the rapid development of underground spaces and manned cabins, the design of enclose-space has received attention. Humans working in enclosed-space for a long period of time are prone to fatigue, which in serious cases can lead to human errors and major accidents. Therefore, the optimized design of enclosed-space is of great research significance to enhance the efficiency and comfort of workers. The lighting environment is one of the important objectives of the optimized design of enclosed-space, and the current research on lighting design often focuses on single-objective reduction of energy consumption or improvement of visual effects, and lacks attention to non-visual effects. We propose multi-objective optimal control strategy for enclosed-space lighting systems, taking the visual effects, non-visual effects, and energy efficiency of lighting as the optimization objectives and constraints, combined with multi-objective particle swarm optimization algorithm. Firstly, we obtain a set of optimal Pareto front solution for the dimming level by improving the algorithm. Secondly, to select appropriate lighting strategies according to different working scenarios, we introduce the fuzzy integrated evaluation method of experts to find the best compromise. Finally, we used a manned simulated enclosed cabin based on DIALux evo for a case study to compare the optimization results with baseline solutions. The results show that when we employed the improved dimming strategy, the energy efficiency and visual comfort were significantly improved compared with the baseline lighting design. The proposed method can satisfy human non-visual effects requirements, providing a research reference for future enclosed-space lighting optimization.

A hybrid heuristic approach for the multi-objective multi depot vehicle routing problem

Efficiency in logistics is often affected by the fair distribution of the customers along the routes and the available depots for goods delivery. From this perspective, in this study, the Multi-depot Vehicle Routing Problem (MDVRP), by considering two objectives, is addressed. The two objectives in conflict for MDVRP are the distance traveled by vehicles and the standard deviation of the routes’ length. A significant standard deviation value provides a small distance traveled by vehicles, translated into unbalanced routes. We have used a weighted average objective function involving the two objectives. A Variable Neighborhood Search algorithm within a Chu-Beasley Genetic Algorithm has been proposed to solve the problem. For decision-making purposes, several values are chosen for the weight factors multiplying the terms at the objective function to build up a non-dominated front of solutions. The methodology is tested in large-size instances for the MDVRP, reporting noticeable results for managerial insights.

Stability of large-amplitude traveling fronts for a traffic flow model with moving barriers

In this paper, we study the existence and stability of traveling front solutions to a nonlinear hyperbolic system of balance law, which models the dynamics of a heterogeneous traffic flow with moving barriers (e.g. slow vehicles). We first prove via shooting method that, the balance law admits a family of monotone traveling fronts, if the barriers have small strength regardless of their shapes. Then we show that, under some assumptions on the nonlinearities and by applying detailed spectral analysis and $ C_0 $ semigroup theories, the traveling fronts are spectrally stable, linearly and nonlinearly exponentially stable in some exponentially weighted spaces, where the wave strengths can be large.

Traveling wave fronts in a single species model with cannibalism and strongly nonlocal effect

<p>In this paper we studied traveling front solutions of a single species model with cannibalism and nonlocal effect. For a particular class of kernels, the existence of traveling front solutions connecting the extinction state with the positive equilibrium was established for the strongly nonlocal effect case. Our approach was to reformulate it as a singular perturbed problem, and then tackle this problem by using dynamical systems techniques, in particular, geometric singular perturbation theory and Fenichel's invariant manifold theory.</p>

Front instabilities in the presence of convection due to thermal and compositional gradients.

Reaction fronts separate fluids of different densities due to thermal and compositional gradients that may lead to convection. The stability of convectionless flat fronts propagating in the vertical direction depends not only on fluid properties but also in the dynamics of a front evolution equation. In this work, we analyze fronts described by the Kuramoto-Sivashinsky (KS) equation coupled to hydrodynamics. Without density gradients, the KS equation has a flat front solution that is unstable to perturbations of long wavelengths. Buoyancy enhances this instability if a fluid of lower density is underneath a denser fluid. In the reverse situation, with the denser fluid underneath, the front can be stabilized with appropriate thermal and compositional gradients. However, in this situation, a different instability develops for large enough thermal gradients. We also solve numerically the nonlinear KS equation coupled to the Navier-Stokes equations to analyze the front propagation in two-dimensional rectangular domains. As convection takes place, the reaction front curves, increasing its velocity.

Dual-Objective Mechanobiological Growth Optimization for Heterogenous Lattice Structures

Abstract Computational design is growing in necessity for advancing biomedical technologies, particularly for complex systems with numerous trade-offs. For instance, in tissue scaffolds constructed from repeating unit cells, the structure’s porosity and topology affect biological tissue and vasculature growth. Here, we adapt curvature-based tissue growth and agent-based vasculature models for predicting scaffold mechanobiological growth. A non-dominated sorting genetic algorithm (NSGA-II) is used for dual-objective optimization of scaffold tissue and blood vessel growth with heterogeneous unit cell placement. Design inputs consist of unit cells of two different topologies, void unit cells, and beam diameters from 64 to 313 µm. Findings demonstrate a design heuristic for optimizing scaffolds by placing two selected unit cells, one that favors high tissue growth density and one that favors blood vessel growth, throughout the scaffold. The pareto front of solutions demonstrates that scaffolds with large porous areas termed channel voids or small voids improve vasculature growth while lattices with no larger void areas result in higher tissue growth. Results demonstrate the merit in computational investigations for characterizing tissue scaffold design trade-offs and provide a foundation for future design multi-objective optimization for complex biomedical systems.

A decision support system for autonomous ship trajectory planning

This study presents an approach to enhance human–machine collaboration in autonomous ship trajectory planning. A decision support system is developed, considering crucial environmental factors such as ocean currents, wind, and tidal information, alongside the integration of narrow channel geometry, squat effect, and Under-keel-clearance (UKC). The Dynamic Consequence Analysis (DCA) risk assessment method is utilized to establish dynamic safety domains, incorporating ship maneuvering characteristics and potential failure scenarios. The Multi-objective Particle Swarm Optimization (MOPSO) algorithm is then employed to generate alternative trajectories, optimizing five objective functions: minimizing safety domain violation, consecutive speed changes, path length, deviation from the initial plan, and deviation from the initial estimated time of arrival. Furthermore, two Multi-criteria Decision Making (MCDM) methods, Multi-Objective Optimization by Ratio Analysis (MOORA) with user preferences, and the Entropy Weight Method (EWM) with automatic weight allocation, are employed to rank alternative solutions from the Pareto front. Finally, a clustering method is employed on the Pareto front solutions. The outcomes that serve as representatives for these clusters are then merged with the highest-rated alternative solutions from MCDM methods. This combined set forms the basis for the final decision-making process carried out by the operator. While dynamic obstacles are not considered in this study, evaluation across three scenarios demonstrates the effectiveness of the proposed decision support system for trajectory planning.

Collaborative scheduling of energy-saving spare parts manufacturing and equipment operation strategy using a self-adaptive two-stage memetic algorithm

Previous studies on production scheduling predominantly focus on developing optimal production plans in the context of deterministic customer requirements, achieving certain optimization goals related to the manufacturer. However, little research has been conducted on scheduling customized spare parts manufacturing on the supply side and equipment operation strategy on the demand side simultaneously. In real-world engineering projects, an efficient collaborative scheduling solution not only helps the manufacturer develop optimal production plans for critical spare parts, but also guides equipment users to achieve maximum output. Therefore, this paper studies a collaborative scheduling problem that both considers energy-saving spare parts manufacturing in a flexible job shop and equipment operation strategy of distributed users. To solve this problem effectively, a self-adaptive two-stage memetic algorithm (STMA) is proposed to minizine total energy consumption of the manufacturer and maximize total operation utility of equipment simultaneously. In detail, four heuristic rules are designed to generate a high-quality initial population. In the first stage, the crossover and mutation are utilized for global exploration, and a self-adaptive local search operator is presented to enhance the local development ability of the STMA. In the second stage, several energy-saving and utility-improving strategies are proposed to further optimize the Pareto front solutions generated in the previous period. Extensive experiments are conducted, and the numerical results indicate that STMA is the most promising compared to other well-known algorithms.

A bi-criteria approach to the truck-multidrone routing problem

In the rapidly expanding field of e-commerce logistics, the optimisation of last-mile delivery solutions is paramount. This paper introduces a novel methodology that addresses this challenge by generating approximate Pareto fronts in a hybrid truck-drone delivery system. Specifically, we examine a generalized single truck multi-drone problem that allows multi-visit flight missions and rendezvous points distinct from launch locations. Our goal is providing decision-makers with a portfolio of optimal routing solutions that balance service time and environmental impact, criteria that are increasingly shaping decision-making in this domain. To achieve this, we introduce a bivector coding scheme inspired by flow-shop scheduling problems and implement a Simulated Annealing algorithm. This algorithm features an advanced stopping mechanism, negating the need for manual adjustments by utilizing a distinctive blend of a domination rate and a Kalman filter. Importantly, our framework employs an iterated greedy search algorithm to evolve from initial solutions towards identifying non-dominated solutions sets, which are then ranked using a hypervolume coefficient. To validate our methodology, we conduct a sensitivity analysis on two different size instances using a full factorial design of experiments. Our analysis reveals crucial insights into the impact of the number of drones, their autonomy, and their flight speed settings. From it, we conclude that it is a robust and adaptable framework for its practical application for obtaining Pareto fronts solutions among which picking the ultimate routing to be implemented.