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This research aims to present a novel cooperative control architecture designed specifically for roads with variations in height and curvature. The primary objective is to enhance the longitudinal and lateral tracking accuracy of the vehicle.

In addressing the challenges posed by time-varying road information and vehicle dynamics parameters, a combination of model predictive control (MPC) and active disturbance rejection control (ADRC) is employed in this study. A coupled controller based on the authors’ model was developed by utilizing the capabilities of MPC and ADRC. Emphasis is placed on the ramifications of road undulations and changes in curvature concerning control effectiveness. Recognizing these factors as disturbances, measures are taken to offset their influences within the system. Load transfer due to variations in road parameters has been considered and integrated into the design of the authors’ synergistic architecture.

The framework's efficacy is validated through hardware-in-the-loop simulation. Experimental results show that the integrated controller is more robust than conventional MPC and PID controllers. Consequently, the integrated controller improves the vehicle's driving stability and safety.

The proposed coupled control strategy notably enhances vehicle stability and reduces slip concerns. A tailored model is introduced integrating a control strategy based on MPC and ADRC which takes into account vertical and longitudinal force variations and allowing it to effectively cope with complex scenarios and multifaceted constraints problems.

This study aims to examine the relationships between functional value (quality and price), social value (extrinsic and intrinsic), emotional value and attitude toward a brand, as well as the direct relationship between attitude toward a brand and the purchase intention of tires. This research also explores the moderating effect of social influence on the relationship between attitude toward a brand and purchase intention.

A conceptual model based on literature is developed and tested using an online survey, with a sample of 760 active drivers gathered through purposive sample judgment. The data were analyzed using structural equation modeling with AMOS 28 and Hayes Process Macro 4.

The results demonstrate that extrinsic social value has a positive direct relationship with attitude toward brands. The findings also indicate that intrinsic social value has a positive influence on attitudes toward brands. Attitude toward a brand is found to have a positive direct relationship with purchase intention.

This research extends the existing literature on consumption values and offers insights into the specific values that influence attitudes toward tire brands as well as purchase intention. The findings provide insights to tire businesses in values that they could focus on when developing strategies to increase positive brand attitude and purchase intention.

The paper aims to identify a suitable generic brake force distribution ratio (β) corresponding to optimal brake design attributes in a diminutive driving range, where road conditions do not exhibit excessive variations. This will intend for an appropriate allocation of brake force distribution (BFD) to provide dynamic stability to the vehicle during braking.

Two techniques are presented (with and without wheel slip) to satisfy both brake stability and performance while accommodating variations in load sharing and road friction coefficient. Based on parametric optimization of the design variables of hydraulic brake using evolutionary algorithm, taking into account both the laden and unladen circumstances simultaneously, this research develops an improved model for computing and simulating the BFD applied to commercial and passenger vehicles.

The optimal parameter values defining the braking system have been identified, resulting in effective β = 0.695 which enhances the brake forces at respective axles. Nominal slip of 3.42% is achieved with maximum deceleration of 5.72 m/s² maintaining directional stability during braking. The results obtained from both the methodologies are juxtaposed and assessed governing the vehicle stability in straight line motion to prevent wheel lock.

Optimization results establish the practicality, efficacy and applicability of the proposed approaches. The findings provide valuable insights for the design and optimization of hydraulic drum brake systems in modern automobiles, which can lead to safer and more efficient braking systems.

Realistic composite vehicles with 2, 3, 5 and 9 axles, consisting of a truck with one or two trailers, are addressed in this paper by computational models for vehicle–bridge interaction analysis.

The vehicle–bridge interaction (VBI) models are formed by sets of 2-D rigid blocks interconnected by mass, damping and stiffness elements to simulate their suspension system. The passage of the vehicles is performed at different speeds. Several rolling surface profiles are admitted, considering the maintenance grade of the pavement. The spectral density functions are generated from an experimental database to form the longitudinal surface irregularity profiles. A computational code written in Phyton based on the finite element method was developed considering the Euler–Bernoulli beam model.

Several models of composite heavy vehicles are presented as manufactured and currently travel on major roads. Dynamic amplification factors are presented for each type of composite vehicle.

The VBI models for compound heavy vehicles are 2-D.

This work contributes to improving the safety and lifetime of the bridges, as well as the stability and comfort of the vehicles when passing over a bridge.

The structural response of the bridge is affected by the type and size of the compound vehicles, their speed and the conservative grade of the pavement. Moreover, one axle produces vibrations that can be superposed by the vibrations of the other axles. This effect can generate not usual dynamic responses.

Efficient management of earthmoving equipment is critical for decision-makers in construction engineering management. Thus, the purpose of this paper is to prudently identify, select, manage and optimize the associated decision variables (e.g. capacity, number and speed) for trucks and loaders equipment to minimize cost and time objectives.

This paper addresses an innovative multiobjective and multivariable mathematical optimization model to generate a Pareto-optimality set of solutions that offers insights of optimal tradeoffs between minimizing earthmoving activity’s cost and time. The proposed model has three major stages: first, define all related decision variables for trucks and loaders and detect all related constraints that affect the optimization model; second, derive the mathematical optimization model and apply the multiobjective genetic algorithms and classify all inputs and outputs related to the mathematical model; and third, model validation.

The efficiency of the proposed optimization model has been validated using a case study of earthmoving activities based on data collected from the real-world construction site. The outputs of the conducted optimization process promise the model’s originality and efficiency in generating optimal solutions for optimal time and cost objectives.

This model provides the decision-maker with an efficient tool to select the optimal design variables to minimize the activity's time and cost.

This study evaluates the potential of using the material extrusion (MEX) process for recycling waste tire rubber (WTR). By investigating the process parameters, mechanical behaviour and morphological characterisation of a thermoplastic polyurethane-waste tire rubber composite filament (TPU-WTR), this study aims to establish a framework for end-of-life tire (ELT) recycling using the MEX technology.

The research assesses the impact of various process parameters on the mechanical properties of the TPU-WTR filament. Hysteresis analysis and Poisson’s ratio estimation are conducted to investigate the material’s behaviour. In addition, the compressive performance of diverse TPU-WTR triply periodic minimal surface lattices is explored to test the filament suitability for printing intricate structures.

Results demonstrate the potential of the TPU-WTR filament in developing sustainable structures. The MEX process can, therefore, contribute to the recycling of WTR. Mechanical testing has provided insights into the influence of process parameters on the material behaviour, while investigating various lattice structures has challenged the material’s capabilities in printing complex topologies.

This research holds significant social implications addressing the growing environmental sustainability and waste management concerns. Developing 3D-printed sustainable structures using recycled materials reduces resource consumption and promotes responsible production practices for a more environmentally conscious society.

This study contributes to the field by showcasing the use of MEX technology for ELT recycling, particularly focusing on the TPU-WTR filament, presenting a novel approach to sustainable consumption and production aligned with the United Nations Sustainable Development Goal 12.

In this paper, improvements in reducing transmitted accelerations in a full vehicle are obtained by optimizing the gain parameters of an active control in a roughness road profile.

For a classically designed linear quadratic regulator (LQR) control, the vibration attenuation performance will depend on weighting matrices Q and R. A methodology is proposed in this work to determine the optimal elements of these matrices by using a genetic algorithm method to get enhanced controller performance. The active control is implemented in an eight degrees of freedom (8-DOF) vehicle suspension model, subjected to a standard ISO road profile. The control performance is compared against a controlled system with few Q and R parameters, an active system without optimized gain matrices, and an optimized passive system.

The control with 12 optimized parameters for Q and R provided the best vibration attenuation, reducing significantly the Root Mean Square (RMS) accelerations at the driver’s seat and car body.

The research has positive implications in a wide class of active control systems, especially those based on a LQR, which was verified by the multibody dynamic systems tested in the paper.

Better active control gains can be devised to improve performance in vibration attenuation.

The main contribution proposed in this work is the improvement of the Q and R parameters simultaneously, in a full 8-DOF vehicle model, which minimizes the driver’s seat acceleration and, at the same time, guarantees vehicle safety.

The study attempts to explore the effectiveness of green supply chain strategies (GSCS) and sustainable practices (SP) in achieving a circular supply chain (CSC) within a business-to-business (B2B) context. The study further investigates the moderating role of green innovation (GIN) on the relationship between GSCS and SP.

The conceptual model was developed by adopting constructs from the existing studies. A self-administered tool was created, and data were gathered from supply chain (SC) specialists in the food, energy, tire, textile and paper industries. The structural equation model was employed to test the hypothesis, analyzing 243 responses obtained.

The findings indicate an affirmative association between GSCS, SP and the achievement of CSC, with SP acting as a partial mediator between GSCS and CSC. Results show that GSCS and SP are crucial for transitioning toward a circular model in the SC, emphasizing resource regeneration and sustainability. The data from our sample suggest that GIN significantly moderates the relationship between GSCS and CSC. These insights underline the importance of green strategies and sustainable practices (SP) in fostering CSCs in a B2B setting. The study’s implications are significant for SC management, suggesting that firms must integrate green and SP to achieve circularity and long-term viability.

This article brings forward a distinctive perspective on sustainability within the field of SC management emphasizing the crucial need for implementing CSC and GSCS in a B2B context.

Industries worldwide have been striving to serve the increasing demand of consumers alongside providing importance to environmental issues. Yet, there are concern-raising changes on the planet, such as greenhouse gas (GHG) emissions resulting in a temperature rise. India remains a vital party of the United Nations Convention on Climate Change. Henceforth, the paper aims to study the increased emissions of GHG in Puducherry, an Indian Union Territory that faces tremendous pressure owing to its denser population.

The research is designed as a case study conducted in a tyre manufacturing unit in Puducherry. The industrial sector was chosen, as it is the largest contributor (78%) of the total GHG emissions. Case studies were chosen to analyse the GHG emissions and the effects of implementing the policies and imposing interventions over time. The identified areas of improvement, proposed changes and the implemented ones with the results over a three-year period have been discussed.

The present study’s GHG inventorisation for Puducherry paved the way for preparing mitigation and adaptation plans. A total of 21 and 48 changes were incorporated to conserve fuel and power, respectively. A significant 11% reduction in power consumption and 1,113,008/litres of furnace oil was achieved. This translates to 5,115 tCO₂ and 3,306 tCO₂, respectively.

This research will help to improve the importance of climate change management in the manufacturing sector, and it will pave the way for achieving effective sustainable practices.

Such case studies could cumulatively impact the policy directives/ interventions on GHG emissions. Though this seems a small leap, putting them into practice at firm levels would contribute significantly towards achieving Sustainable Development Goals.

This study investigates the extent to which a firm’s centrality and autonomy in its supply network are associated with the intensity and complexity of its competitive actions.

Utilizing social network analysis and dynamic panel data models, this study analyzes a comprehensive panel dataset with 10,802 firm-year observations across various industries between 2011 and 2018 to test the hypotheses.

Our findings show that a firm’s level of centrality in its supply network has an inverted U-shaped relationship with both competitive intensity and competitive complexity. In addition, the turning points of these two inverted U-shaped relationships differ in that firms with a lower level of centrality tend to compete aggressively by launching more actions within fewer categories, while firms with a higher level of centrality tend to compete aggressively by launching fewer actions that cover a larger range of categories. Finally, we find that a firm’s structural autonomy has a positive relationship with competitive complexity.

This study bridges the gap between the supply chain management literature and strategic management literature and investigates how supply networks shape competitive aggressiveness. In particular, this research investigates how a firm’s structural position in its supply network affects its competitive actions, an important intermediate mechanism for competitive advantage that has been overlooked in the supply chain management literature.