Vehicle Manufacturers Research Articles

Augmented ɛ‐constraint‐based matheuristic methodology for Bi‐objective production scheduling problems

AbstractMatheuristic is an optimisation methodology that integrates mathematical approaches and heuristics to address intractable combinatorial optimisation problems, where a common framework is to insert mixed integer linear programming (MILP) models as local search functions for evolutionary algorithms. However, since a mathematical programming formulation only tries to find the solution with the best objective value, matheuristics are rarely adopted to multi‐objective scenarios asking for a set of Pareto optimal solutions, for example, vehicle routing problems and production scheduling problems. In this situation, the ɛ‐constraint, which transforms multi‐objective problems into single‐objective formulations by considering selected objectives as constraints, seems to be a promising approach. First, an augmented ɛ‐constraint‐based matheuristic methodology (ɛ‐MH) is proposed to apply the idea of ɛ‐constraint to embedded MILP models, so that Pareto fronts obtained by meta‐heuristics can be further improved by solving a set of MILP models. Afterwards, four speed‐up strategies are developed to alleviate the computational burden resulting from repeatedly solving mathematical formulations, which also imply preferable scenarios for taking advantages of the ɛ‐MH. Finally, several real‐world bi‐objective scheduling problems are discussed to present potential applications for the proposed methodology.

THE MODERN AND PROSPECTIVE STRUCTURAL MATERIALS` FOR WHEELED ARMORED VEHICLES STRENGTH CHARACTERISTICS ANALYSIS

The article presents the results of research in those the modern structural materials` strength characteristics for wheeled armored vehicles (WAV) production, as well as the technical requirements for them, were analyzed. It has been established that ordinary structural steels are used for armored car frames, and special armored steels with high strength and hardness characteristics are used for armored plating. It is expedient to use new promising materials - martensitic steels, which are characterized by extremely high strength, new ceramic materials and glass, and the latest nanomaterials - for the WAV armoring. A military vehicles protection promising direction is modern modular armor protection with the use of various materials.

Generation Y and the Shift to Electric Vehicles in Indonesia: Exploring Key Drivers and Barriers

Purpose: Compared With other Asian countries, the usage rate of electric vehicles in Indonesia remains slow owing to a lack of public interest. This paper explores some significant factors that affect the interest of electric vehicles for Gen Y EV products, including environmental concerns and individual consequences. Research Methodology: We extend the previous study by adding other variables, including individual and environmental consequences. Data from 446 respondents were collected through a judgmental sampling procedure and analyzed using partial least squares structural equation Modeling. Results: The results showed that attitudes towards the five dimensions of the UTAUT-2 model significantly influenced intention to purchase. However, attitude did not mediate the relationship between habit and purchase intention. On the other hand, attitude mediation vastly influences not only the association between individual consequences and purchase intention, but also that between environmental concerns and purchase intention. On the other hand, subjective norms also have a positive influence on purchase intention, while perceived risk is not considered to affect the purchasing decisions of Generation Y. Recommendation: This study recommends that both electric vehicle manufacturers and the Indonesian government improve infrastructure and raise public awareness to increase interest in electric vehicles.

Enhancing the recyclability of ELV plastic bumpers: Characterization of molecular, morphological, rheological, mechanical properties and ageing degradation

While the automotive industry has traditionally prioritized metal recycling, the increasing use of plastics in vehicles underlines the need for sustainable management of these materials. Recycling rates of plastics from end-of-life vehicles (ELVs) in Europe are low, but recent EU legislation requires 25 % of the plastics in new vehicles to be recycled, forcing innovative designs and strategies to enhance the recovery and quality of recycled resins. This study focuses on post-consumer bumpers, a significant recoverable component of ELVs, by assessing their molecular, morphological, rheological, and mechanical properties to investigate their homogeneity and quality from various sources to assess their suitability for recycling. Since the aging of these materials, caused by thermo-oxidative and thermo-mechanical degradation processes, can compromise the quality of recycled bumpers, we propose a blend of recycled PP from different bumper cars with virgin resin, representing a realistic scenario where the post-consumer ELV bumpers are collected. Interestingly, this blend, which can replace up to 50 % of the virgin resin without additional compatibilizers or additives, mitigates the degradation effects. Furthermore, the study evaluates the degradation resistance of these blends through multiple extrusion cycles and accelerated weathering tests in a temperature-controlled UV chamber to ascertain the number of cycles the material can tolerate without significant quality degradation, and to determine its suitability for long-term applications. Our results not only support the feasibility of using recycled PP for automotive components but also contribute to meeting the EU's recycling targets. This research highlights the potential for significant advances in the circularity of automotive plastics, providing a sustainable pathway for integrating recycled materials into new vehicle production.

India's Shift to Electric Mobility

The Automotive sector is a pivotal contributor to India’s economic growth and contributes 49% to India’s manufacturing GDP. In FY24, the country produced approximately 49 lakh passenger vehicles, 9.9 lakh three-wheelers, 214.7 lakh two-wheelers, and 10.7 lakh commercial vehicles. The world’s primary modes of transportation are facing two major problems: rising oil costs and increasing carbon emissions. As a result, electric vehicles (EVs) are gaining popularity as they are independent of oil and do not produce greenhouse gases. However, despite their benefits, several operational issues still need to be addressed for EV adoption to become widespread. This research delves into the evolution of EVs over time and highlights their benefits, including reducing carbon emissions and air pollution. India's transition from traditional internal combustion engines (ICE) to electric vehicles (EVs) is driven by a combination of environmental, economic, and policy factors. This shift is aimed at reducing the country's dependency on fossil fuels, lowering carbon emissions, and promoting cleaner air, particularly in urban areas plagued by pollution. Government initiatives such as the Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME) scheme.

Self-building or sharing? The strategy analysis of building charging stations

Charging station sharing, as a new business model, can effectively reduce the building of unnecessary public charging stations and promote sustainable urban development. This study discusses whether the manufacturers introduce a charging station sharing strategy and identify the introduction conditions. We develop a strategic competition model where the two manufacturers engage in electric vehicles (EVs) and charging stations. The results show that charging stations have a facilitating effect on the EV market. In a sharing strategy, the low-end vehicle manufacturer can profit more when the contract cost is smaller. Comparing the strategies of self-building and sharing, the manufacturers should choose a self-building strategy when the cost coefficient of building the station, the brand advantage, and the contract cost are all larger. However, when the brand advantage and the contract cost are smaller, or both in the medium, the manufacturers should choose a sharing strategy. In addition, we also find that the substitutability of EVs and the cross-sensitivity of EVs and charging stations can lead to lower market demand for EVs. In a monopoly production scenario, encroachment by both the platform and the service provider leads to lower profits for the manufacturer. Therefore, it is more favorable for the manufacturer to self-build charging stations in the no-invader condition. Our analysis provides insights into when charging station sharing may be beneficial and when it may be detrimental to vehicle manufacturers.

Fatigue limit prediction of 7050 aluminium alloy based on experimental and shallow + deep hybrid neural network

7050 aluminium alloy, as an important lightweight high-strength structural material because of its excellent characteristics, has increasing applications in aviation, aerospace, and launch vehicle manufacturing. Fatigue failure accounts for approximately 80 % of the structural failures in the engineering field, which leads to numerous losses and is highly uncertain and sudden. Therefore, fatigue life prediction of 7050 aluminium alloy has become a prominent research field. In this study, considering the two variables of stress ratio and stress concentration coefficient, fatigue tests of 7050 aluminium alloy under five different working conditions were conducted to evaluate the effect of these factors on the durability of 7050 aluminium alloy. A novel hybrid neural network model was proposed to solve the time-consuming problem of obtaining a large amount of S-N curve data through traditional fatigue tests. The model uses relatively low cycle fatigue test data for training, realizes data derivation, accurately predicts the fatigue limit of materials, and realizes the purpose of quickly evaluating the fatigue properties of 7050 aluminium alloy materials under different working conditions.

Statistical Approaches Used in Studies Evaluating the Reliability of Autonomous Vehicles Based on Disengagements and Reaction Times

The rapid development of autonomous vehicle (AV) technology highlights the critical importance of enhancing the reliability of these vehicles. Due to the need to test the reliability of AVs, since 2014, the California Department of Motor Vehicles has permitted autonomous vehicle manufacturers to establish an AV Testing program, enabling them to test automated systems on the transportation network. With this, studies on the reliability of AVs have increased rapidly. The most emphasized issues regarding the reliability of AVs have been disengagements, accidents, and reaction times. In this study, disengagements and reaction times are categorized and explained in detail according to the data type, company, period, and statistical method. The data used in the studies cover the years 2014-2020. When examining studies on the reliability of AVs, until 2018, inferences were generally made using real data and descriptive statistics, particularly with methods such as correlation analysis and calculation of disengagements per mile, which investigates the relationship between distance traveled and disengagements. However, since 2018, machine learning has gained importance in evaluating AV reliability. It has been observed that regression, classification, and decision trees were frequently used during this period. Techniques such as deep transfer learning, text mining, and natural language processing also stand out. Furthermore, Software Reliability Growth Models are used to measure software reliability, playing an essential role in evaluating, analyzing, and improving the performance and reliability of AVs. This study aims to reveal the development and diversity of the statistical methods used to determine AV reliability. Additionally, this study aims to guide and provide insights to researchers in the field about the statistical approaches they can utilize.

Evaluating Sustainable Materials for Automotive Applications Through Comprehensive MCDM Analysis Using CODAS, COPRAS, VIKOR, and ENTROPY

The automotive industry faces increasing pressure to incorporate sustainable materials in vehicle manufacturing to meet environmental, economic, and performance requirements. This research develops a comprehensive Multi-Criteria Decision-Making (MCDM) framework to evaluate and select sustainable materials for automotive body and instrument panels. The study integrates CODAS, COPRAS, VIKOR, and ENTROPY methods to systematically assess various material alternatives. By considering multiple criteria, including cost, environmental impact, and mechanical properties, the proposed framework provides a holistic evaluation to identify the most suitable materials. The findings contribute to advancing sustainable practices in the automotive industry, offering manufacturers a robust tool for informed decision-making. This approach supports the industry's transition towards environmentally responsible and efficient vehicle production, ensuring that the selected materials meet the stringent demands of modern automotive design.

Research on the Impact of Supply Chain Integration on Supply Chain Resilience in NEV Manufacturing Enterprises

This paper explores the direct impact of different types of supply chain integration on supply chain resilience in new energy vehicle manufacturing enterprises. It also elucidates the mediating role of supply chain risk management and the moderating role of regulatory uncertainty, proposing nine research hypotheses. Finally, it employs SPSS 26.0 software to analyze the research hypotheses using collected 309 sample data. The research results indicate the following: (1) Internal integration, supplier integration, and customer integration all positively influence supply chain resilience, with supplier integration having the most significant impact. (2) Supply chain risk management mediates the relationship between internal integration, supplier integration, customer integration, and supply chain resilience. (3) Regulatory uncertainty significantly negatively moderates the impact of internal integration and customer integration on supply chain resilience, but it does not significantly negatively moderate the impact of supplier integration on supply chain resilience.

Failure analysis-based mass reduction of an aluminium alloy engine mounting bracket using Design of Experiments approach

In this study, mass reduction of an engine mounting bracket has been performed without changing the failure mode. Firstly, failure modes were determined experimentally using failure loads provided by the vehicle manufacturer. Then critical stress concentration areas and suitable mass reduction regions were determined by using Finite Element Analysis (FEA) for these load cases. A structural optimisation process was carried out using Design of Experiment-Response Surface Methodology (DOE-RSM) to minimise the mass while keeping the failure load as constant as possible. When the stress values obtained from the preliminary structure are compared with the values obtained from the optimised part, it was seen that this target has been mostly achieved. A total mass reduction of 4.31 % was achieved in the new part through failure mode analysis. Furthermore, it was found that a financial saving of 129,000 Euro could also be achieved.

Lower and upper bounds for scheduling a real-life assembly problem with precedences and resource constraints

In this work, we consider a real-life scheduling problem involving the production of off-road vehicles using a three-level assembly process, subject to precedence, machines, and resource constraints. This problem shares multiple characteristics with other scheduling problems such as the Parallel Machine Scheduling and Flexible Flow Shop Problems. However, it also comprises less investigated aspects such as a specific job precedence structure resulting in a directed rooted in-tree and a global resource constraint limiting the number of simultaneously active machines. We present a straightforward adaptation of a time-indexed mathematical formulation to the problem and introduce a new lower-bounding procedure. To solve the problem, we propose constructive heuristics based on classical priority rules and adaptations of job sequencing heuristics. We also propose CORE, a specialized approach tailored to leverage the problem structure. All the algorithms are extensively evaluated on two benchmark sets, various shop floor configurations, and eight real-life scenarios. Our results reveal the general effectiveness of job sequencing approaches and demonstrate the overall superiority of CORE.

Multi-depot and product vehicle routing problem with precedence constraint and selective backhauling in animal feed manufacturing industry

ABSTRACT Multi-depot vehicle routing with precedence constraints and selective backhauling, along with practical challenges such as outsourcing, demand splitting, and flexible pickup and delivery formats, is a common issue in the animal feed industry. However, few studies address demand splitting and flexible pickup constraints. The industry struggles with inadequate management, leading to difficulties in efficiently utilizing limited vehicles for both raw material pickup and product delivery. This inefficiency results in higher transportation costs, including subcontractor vehicle expenses. This study proposes the Extended Adaptive Grey Wolf Optimizer to minimize total transportation costs by incorporating essential factors such as multi-depots, outsourcing services, optimization of split delivery and pickup operations, precedence constraints, and selective backhauling. The results show a remarkable 97.6% improvement toward optimality, representing a significant enhancement of 38.1% improvement over current practice. The findings emphasize the benefits to the industry of adopting proper route sequencing and utilizing subcontractor vehicles.

Проблемы и перспективы производства грузового транспорта в России

The article discusses the problems and prospects for the production of freight transport as a branch of the automotive industry in the coming years. The goal is to identify problems and highlight prospects for the production of freight transport in Russia. Objectives: to conduct a brief overview of the freight transport segment of the Russian automotive industry, analyze the dynamics of production in the freight segment in recent years, identify the main problems in the production of freight vehicles, highlight the main prospects for the development of the studied segment of the Russian automotive industry in the coming years. Conclusions: Conclusions were drawn about the positive dynamics of production and sales of freight road transport in Russia in 2023, despite the crisis years for the industry in 2020-2022; leadership of the KAMAZ company in the Russian market in the segment of domestic freight transport; serious import dependence and high competition from Chinese companies as key problems for Russian truck manufacturers; positive forecasts for the development of freight transport production in Russia, taking into account existing production capacities at some Russian enterprises, intensified efforts of the state and automakers to reduce dependence on imports, the state’s understanding of the need to increase financing of the industry, expanding the need for freight vehicles with an increase in the share of cargo transportation across Russia along highways.

Analysis of a Biopolymer Implementation in the Automotive Instrument Panel

Objective: The purpose of this research is to investigate the effectiveness of different materials for vehicle dashboards in mitigating knee injuries during accidents, with a focus on environmentally friendly options. Theoretical Framework: The research is based on the premise that modern vehicle design should not only prioritize safety standards but also integrate sustainable practices. This framework underscores the importance of utilizing materials that not only meet safety requirements but also minimize environmental impact throughout their lifecycle. Method: The study compares the performance of two materials, polypropylene and poly-lactic acid (PLA), in vehicle dashboards. Data is gathered through experimentation to assess each material's ability to absorb energy and reduce knee impact injuries, as well as their environmental sustainability. Results and Discussion: The findings indicate that PLA demonstrates superior performance compared to Polypropylene in mitigating knee injuries during collisions, while also being more environmentally friendly due to its biodegradability and renewable sourcing. Research Implications: This research contributes to the development of safer vehicle designs by identifying materials that better protect occupants from knee injuries, while also considering the environmental impact of vehicle production and disposal. Implementing materials like PLA could lead to reduced injury rates and improved vehicle safety standards, benefiting both manufacturers and consumers, and promoting sustainability in the automotive industry. Originality/Value: This study offers original insights into the potential of PLA as a superior material for vehicle dashboards in terms of both safety and ecological impact. By exploring alternative materials, such as biopolymers, the research adds value to the ongoing efforts to enhance passive safety measures in automobiles while considering environmental sustainability as a critical factor in material selection.

Optimizing Quality Control on Electric Vehicle Production Lines with AI and Machine Learning

The study explores the use of AI and ML technologies to optimize quality control in EV production lines. By applying neural networks and machine learning algorithms, the research achieved significant improvements: a 74.4% reduction in torque deviation, a 75.9% enhancement in speed consistency, and a 70.8% decrease in defect rates. These gains also resulted in a 16.0% reduction in production cycle time and a 50.0% decrease in downtime, leading to an 8.4% increase in Overall Equipment Effectiveness (OEE). The methods employed included AI-driven predictive maintenance, real-time monitoring, and statistical process control (SPC). Despite the clear benefits, challenges such as integrating these technologies with existing systems and ensuring robust data infrastructure remain. Future research should focus on refining these approaches and extending their application across the automotive industry.

Predicting costs of local public bus transport services through machine learning methods

The present study developed several machine learning-based cost models to predict an efficient total economic cost per vehicle revenue-mile of urban public bus transport. The models were trained on a built-in dataset from 269 transit agencies providing urban services in the United States from 2015 to 2019. A feature selection strategy was implemented, finding that, for each proposed model, a subset of features determined a large impact on unit cost. These “core” features included commercial speed, average salary expenses per employee, vehicle productivity, and fleet ownership cost per vehicle. Machine learning techniques outperformed the linear regression method in terms of predictive power and robustness (understood as the dispersion of predictive power measures over the training sets). Based on SHAP values, the sensitivity analyses showed that the proposed models could be used to predict the impact of changes in some critical features on corresponding unit costs. The results may be useful for: (i) introducing regulatory constraints to the allocation of national public resources to local public bus transport services, aimed at minimizing the resources needed to provide a given level of service; (ii) defining the maximum economic compensation required by firms involved in competitive tendering for the allotment of service concessions, or firms with monopoly rights (by political choice and/or local public ownership); and (iii) improving service contract management and design by identifying key cost drivers of transit services.

Investigation of cabin heating in electric vehicles with integrating solar cells and heat storage systems

In recent years, the production and usage of electric vehicles have been encouraged due to zero emissions, efficiency, and economic factors. Efficient cabin heating and thermal management in electric vehicles are crucial for enhancing passenger comfort, extending battery life, and optimizing overall energy usage, thus contributing to the sustainability and practicality of electric transportation. Heating the cabin of electric vehicles in winter has a negative effect on range. Therefore, methods are being researched to use energy efficiently without sacrificing passenger comfort and minimizing the effect on the vehicle's range. This study presents an innovative radiator design specifically crafted for Electric Vehicles (EVs), leveraging solar panels to heat water for the radiator. This system enables the vehicle to harness solar energy for heating a water tank while stationary, effectively serving as an energy storage reservoir. Upon vehicle movement, the radiator system activates to disperse the stored thermal energy throughout the cabin, ensuring superior thermal comfort. Consequently, optimizing battery range is achieved as the heating load typically leads to significant range reductions. From the obtained results in experiment 2, the results indicated that the cabin temperature rose from 3 °C to around 15 °C within a few minutes, reflecting an increase of approximately 12 °C. According to the results, this indicates that there will be a reduction in energy consumption of between 1.9 % and 3 % for a one-hour travel range in this electric vehicle. The findings of this investigation demonstrate that utilizing warm water energy storage effectively enhances cabin thermal management.

Volatile organic compounds in typical coal chemical industrial park in China and their environmental and health impacts

The coal chemical industry produces a large amount of volatile organic compounds (VOCs), and the emission characteristics and associated impact on the environment and health of the residents are still unclear. This study determined the VOC concentrations and compositions in the Jinjie Coal Chemical Industry Park which is located in northern China. The average concentrations of total measured VOCs (TVOCs) in the industrial areas in summer and winter were 231.5 and 103.2 μg/m3, which were higher than those in the residential areas (123.7 and 70.3 μg/m3), respectively. Aromatics, Oxygenated volatile organic compounds (OVOCs), and alkanes were the dominant VOC classes in the industrial areas, while halocarbons, OVOCs, and alkenes had higher compositions in the residential areas where were not only affected by industrial emissions and also other anthropogenic sources. OVOCs contributed over 43% of ozone formation potential (OFP), while aromatics contributed over 61% of secondary organic aerosol (SOA) formation in the Park in both seasons. Using the source apportionment method, biogenic emission and anthropogenic source (gasoline production, coking emission, fuel combustion, solvent coating, and vehicle exhaust) were major contributors to VOCs in residential areas. The industrial-related emissions were the main components of anthropogenic source, accounting for 53.5%–58.7% of the overall VOCs. With reliable estimations of the health aspects, exposures to acrolein (HQ: 7.4–126.6) and formaldehyde (ILCR: 5.5 × 10−3-5.7 × 10−2) posed the highest non-carcinogenic and carcinogenic risks, accounting for 94.3%–98.6% and 55.8%–93.8% of the total HQ and ILCR, respectively. The results demonstrated that substantial environmental and health co-benefits to the residents could be achieved by reducing the industrial emissions from gasoline production, coking process, and diesel-fueled vehicles in the Jinjie Coal Chemical Industry Park. Prioritizing the establishment of efficient air pollution measures and tightening industrial emission standards, especially for hazardous VOCs, are recommended according to the findings of the valuable work.

Developments in world motor vehicle production and the future of hydrogen use in the automotive industry

This article provides an overview of World motor vehicle production and hydrogen utilization technologies for current and future use in the Automotive Industry. Hydrogen is the simplest and most abundant element in the Universe; however it is always combined with other elements. Still, the costs of hydrogen production from its combined elements remain high compared to other energy sources; it is extremely difficult to store onboard, the use of natural gas as a feedstock emits CO2, and there are many technical challenges to overcome with these systems. Many years of research and development, as well as changes to the energy infrastructure, may be required before hydrogen can have a significant impact on Earth energy use. In the very long term, electricity and hydrogen are likely to become the preferred complementary energy vectors. Hydrogen and the fuel cell are complementary and each enables the other. Nowadays, due to their low production costs, hydrogen engines are becoming more and more advantageous over fuel cells and are be-coming more widespread. Ammonia has the potential to be used as a hydrogen carrier, mainly due to its high hydrogen capacity. A significant effort would be needed to develop onboard ammonia cracker systems or direct ammonia fuel cells for vehicles.