A multi-objective optimization approach for simultaneously lightweighting and maximizing functional performance of vehicle body structure

Multi-objective optimization design of steel cross section of integrated supports and hangers based on NSGA-Ⅱ and MDOS

This paper studies a multi-objective mixture inventory problem for a pharmaceutical distributor. The work starts with a discussion of a mixture inventory model and three objectives, namely the minimization of: 1) ordering and holding costs, 2) number of units that stockout and 3) frequency of stockout occasions. Multi-objective particle swarm optimization (MOPSO) is used to determine the non-dominated solutions and generate Pareto curves for the inventory system. Two variants of MOPSO are proposed, based on the selection of inertia weight. The performance of the proposed MOPSO algorithms is evaluated in comparison with two robust algorithms like non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective cuckoo search (MOCS). The metrics that are used for the performance measurement of the algorithms are error ratio, spacing and maximum spread. Furthermore, the technique of order preference by similarity to ideal solution (TOPSIS) is used to rank the non-dominated solutions and determine the best compromise solution among them. A factorial analysis develops the linear regression expressions of optimal cost, service level measures, lot size and safety stock factor for practitioners. Lastly, the results of the regression equations are compared using a MOPSO–TOPSIS approach and the validity of the developed equations are checked.

Purpose The paper aims to develop a useful tool that can reliably and accurately find the critical paths of high-rise buildings and provide optimal solutions considering the uncertainty based on Monte Carlo simulation (MCS) to enhance project implementation performance by assisting site workers and project managers in high-rise building engineering. Design/methodology/approach This research proposes an approach integrating the improved nondominated sorting genetic algorithm II (NSGA-II) considering uncertainty and delay scenarios simulated by MCS with the technique for order preference by similarity to an ideal solution. Findings The results demonstrate that the proposed approach is capable of generating optimal solutions, which can improve the construction performance of high-rise buildings and guide the implementation management for shortening building engineering project schedule and cost under the delay conditions. Research limitations/implications In this study, only the construction data of the two floors was focused due to the project at the construction stage, and future work can analyze the whole construction stage of the high-rise building to examine the performance of the approach, and the multi-objective optimization (MOO) only considered two factors as objectives, where more objectives, such as schedule, cost and quality, can be expanded in future. Practical implications The approach proposed in this research can be successfully applied to the construction process of high-rise buildings, which can be a guidance basis for optimizing the performance of high-rise building construction. Originality/value The innovations and advantages derived from the proposed approach underline its capability to handle project construction scheduling optimization (CSO) problems with different performance objectives under uncertainty and delay conditions.

Machine configuration is a crucial strategic decision in designing a flow shop system (FSS) and directly affects its performance. This involves selecting device suppliers and determining the number of machines to be configured. This study addresses a bi-objective optimization problem for an FSS that considers repair actions and aims to determine the most suitable machine configuration that balances the production reliability and purchase cost. A nondominated sorting genetic algorithm II (NSGA-II) is used to determine all the Pareto solutions. The technique for order preference by similarity to an ideal solution is then used to identify a compromise alternative. It is necessary to assess the production reliability of any machine configuration identified by the NSGA-II. The FSS under the machine configuration is modeled as a multistate flow shop network, and Absorbing Markov Chain and Recursive Sum of Disjoint Products are integrated into the NSGA-II for reliability evaluation. The experimental results of solar cell manufacturing demonstrate the applicability of the proposed hybrid method and validate the efficiency of the NSGA-II compared with an improved strength Pareto evolutionary algorithm.

Orthogonal turning of AISI 310S austenitic stainless steel under hybrid nanofluid-assisted MQL and a sustainability optimization using NSGA-II and TOPSIS

The present experimental investigation deals with the electric discharge m achinin g of H-1 1 die steel using metal matrix composite (MMC) as tool electrode. The MMC was prepared using powder metallurgy (P/M) route at a sintering temperature of 800°C. A comparative study has been made using conventional Cu and powder metallurgy (P/M) processed Cu-Cr-Ni MMC as tool electrode. The effect of input parameters like peak current (I p ), pulse-on-time (T on ), duty cycle (DC) and gap voltage (V g ) on response variables like material removal rate (MRR), tool wear rate (TWR), surface roughness (SR) and diametral overcut (DOC) have been analyzed. Multi-objective optimization has been done using a hybrid method of principal component analysis (PCA) and technique for order preference by similarity to ideal solution (TOPSIS) to obtain the optimal set of parameters for copper and Cu-Cr-Ni tool. The optimum parameter setting for copper tool was found to be at I p =3A, T on =200 μs , DC=9, V g =50V and for Cu-Cr-Ni tool was found to be at I p =6A, T on =150μs, DC=9, Vg=30V.

As China’s “Dual Carbon” strategy is implemented and the new urbanization advances, balancing economic development, emission reduction, and carbon sequestration has become an important issue during the growth of emerging metropolitan areas, and it is also important for achieving high-quality urban development. Therefore, this study had three major objective functions: economic growth, carbon emission reduction, and increased carbon storage. The multi-objective land use quantity structure was solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the best solution in the solution set was introduced using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for evaluation. Finally, combined with the Future Land Use Simulation (FLUS) model, the low-carbon evolution of the metropolitan area was predicted on a spatial scale. The trade-off plan results show that by 2035, the economic benefits will reach CNY 7.65 trillion, carbon emissions will be kept under 99.24 million tons, and carbon storage will steadily increase by 15.2 million tons. Therefore, optimizing land use from the perspective of balancing carbon emissions, carbon sequestration, and economic development can provide valuable references for planning low-carbon development and the rational use of spatial resources in future metropolitan areas.

In this paper, a method based on a Multiobjective Multi-Verse Optimizer (MOMVO) is proposed and successfully implemented to solve the unmanned aerial vehicles’ path planning problem. The generation of each coordinate of the aircraft is reformulated as a multiobjective optimization problem under operational constraints. The shortest and smoothest path by avoiding all obstacles and threats is the solution of such a hard optimization problem. A set of competitive metaheuristics such as Multiobjective Salp Swarm Algorithm (MSSA), Grey Wolf Optimizer (MOGWO), Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are retained as comparison tools for the problem’s resolution. To assess the performance of the reported algorithms and conclude about their effectiveness, an empirical study is firstly performed for solving different multiobjective test functions from the literature. These algorithms are then used to obtain a set of optimal Pareto solutions for the multi-criteria path planning problem. An efficient Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) of Multi Criteria Decision-Making (MCDM) model is investigated to find the optimal solution from the non-dominant ones. Demonstrative results and statistical analysis are presented and compared in order to show the effectiveness of the proposed MOMVO-based path planning technique.

Sustainable design and multi-objective optimization of heat pump assisted extractive distillation process for separating a ternary mixture of methyl acetate, tetrahydrofuran and methanol

Ultra-high molecular weight polyethylene (UHMWPE) is widely used in biomedical applications due to its excellent wear resistance, low friction, and high impact strength than other polymers. However, its challenging machinability especially under dry machining stemming from poor thermal conductivity, viscoelasticity, and susceptibility to burr formation necessitates advanced process control to ensure high surface quality and productivity. This study proposes a data-driven, multi-objective optimization framework for UHMWPE high speed slot milling, aimed at minimizing surface roughness and burr height while maintaining high material removal rates (MRR). The methodology integrates principal component analysis (PCA) for constructing a unified surface roughness index (SRI) from twelve measured topographic parameters, gaussian process regression (GPR) for surrogate modeling of process responses, non-dominated sorting genetic algorithm II (NSGA-II) for pareto-based optimization, and the technique for order preference by similarity to ideal solution (TOPSIS) for decision support. Milling experiments were conducted under three cooling strategies including dry, super critical CO 2 (scCO₂), and scCO₂+ bio MQL, with four cutting speeds and four feed per tooth levels. Results demonstrate that the PCA-based dimensionality reduction captured over 87% of the surface texture variance, enabling efficient and interpretable optimization. Pareto front analysis revealed that scCO₂+MQL dominated the low-SRI and low-burr region. Under this condition, the process achieved superior surface quality with minimum SRI of − 2.57 and a burr height as low as 0.03 mm, while sustaining high MRR. TOPSIS identified the optimal condition (f z = 0.086 mm/tooth, v c = 970 m/min), which was experimentally validated with a prediction error below 20%.

Recently, forward-scatter radars (FSRs) utilizing the Global Navigation Satellite System (GNSS) as a radiation source have gained increasing attention. The radar system enables aerial target surveillance by deploying multiple receiving nodes on the ground. It offers a low-cost and easily deployable solution. Therefore, how to deploy the receiving nodes to achieve efficient utilization of node resources is an urgent problem to be addressed. In this paper, a deployment method was proposed for receiving nodes in a single-transmitter and multiple-receiver configuration. First, the problem was reformulated as an optimal equal-circle covering problem via geometric approximation. A multi-objective optimization model was subsequently established with the objective functions of minimizing node cost and maximizing spatial detection area. Second, a method based on the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm was introduced to obtain the sub-optimal solution of node cost, thereby reducing the computational complexity of the optimization process. Finally, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) was proposed to derive the deployment schemes. Then, these schemes were ranked using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on the Entropy Weight Method (EWM). The results indicate that the proposed method can obtain the optimal deployment scheme compared to the existing method and enhance the diversity of the solutions.

ABSTRACTUnlike conventional approaches where optimization is performed on a unique component of a specific product, optimum design of a set of components for employing in a product family can cause significant reduction in costs. Increasing commonality and performance of the product platform simultaneously is a multi-objective optimization problem (MOP). Several optimization methods are reported to solve these MOPs. However, what is less discussed is how to find the trade-off points among the obtained non-dominated optimum points. This article investigates the optimal design of a product family using non-dominated sorting genetic algorithm II (NSGA-II) and proposes the employment of technique for order of preference by similarity to ideal solution (TOPSIS) method to find the trade-off points among the obtained non-dominated results while compromising all objective functions together. A case study for a family of suspension systems is presented, considering performance and commonality. The results indicate the effectiveness of the proposed method to obtain the trade-off points with the best possible performance while maximizing the common parts.

Three-dimensional numerical investigation on flow and heat transfer characteristics and multi-objective optimization of interconnected microchannels

For a high-speed and high-acceleration planar parallel mechanism, a design for control (DFC) approach is proposed for the optimization of the mechanism structure. Several evaluation indexes of kinematics and dynamics performance are established for the object functions and constraints, and then a standard multi-objective optimization problem can be formed. The dimension synthesis is carried out by solving the multi-objective optimization with NSGA-II (non-dominated sorting genetic algorithm-II) algorithm. The final result shows that the mechanism with the optimized parameters can eliminate the systematic coupling and enhance the dynamic performance greatly, which provides a solid mechanism hardware platform for designing the high-speed control system.

This study deals with the multi-objective lightweight optimization of the front end structure of an automobile body, as the main assembly to withstand impact force and protect occupants from injuries in frontal collision, based on entropy-based grey relational analysis (EGRA). First, basic noise, vibration, and harshness (NVH) models of the automobile body and crashworthiness models of the vehicle are established and then validated by corresponding actual experiments; hence the lightweight controlling quotas are extracted. Next, the contribution analysis method determines the final parts for lightweight optimization, for which both continuous thickness variables and discrete material variables are simultaneously taken into account. Subsequently, design of experiment (DoE) using the optimal Latin hypercube sampling (OLHS) method is carried out, considering the total mass and the torsional stiffness of the automobile body, the maximum intrusion deformation on the firewall, the maximum impact acceleration at lower end of the B-pillar, and the total material cost of the selected optimization parts as five competing optimization objectives. After that, the optimal combination of thickness and material parameters of the optimization parts is determined using EGRA and confirmed by technique for order preference by similarity to ideal solution (TOPSIS). Finally, a comparison between the original design and the post-lightweight design, namely the optimized design, further confirms the effectiveness of the lightweight optimization. According to the outcomes, the automobile body is lightweight optimized with a mass decrease of 4.98 kg on the basis of well guaranteeing other relevant mechanical performance. Accordingly, the EGRA could be well employed to the multi-objective lightweight optimization of the automobile body.