Search results

Although extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for transportation cost optimization. Traditional irregular component loading methods are based on past performance, which frequently wastes vehicle space. Additionally, real-time road conditions, precast component assembly times, and delivery vehicle waiting times due to equipment constraints at the construction site affect transportation time and overall transportation costs. Therefore, this paper aims to provide an optimization model for Just-In-Time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time.

In order to propose a JIT (just-in-time) delivery optimization model, the effects of the sizes of irregular precast components, the assembly time, and the loading methods are considered in the 3D loading constraint model. In addition, for JIT delivery, incorporating real-time road conditions in the transportation process is essential to mitigate delays in the delivery of precast components. The 3D precast component loading problem is solved by using a hybrid genetic algorithm which mixes the genetic algorithm and the simulated annealing algorithm.

A real case study was used to validate the JIT delivery optimization model. The results indicated this study contributes to the optimization of strategies for loading irregular precast components and the reduction of transportation costs by 5.38%.

This study establishes a JIT delivery optimization model with the aim of reducing transportation costs by considering 3D loading constraints, real-time road conditions and assembly time. The irregular precast component is simplified into 3D bounding box and loaded with three-space division heuristic packing algorithm. In addition, the hybrid algorithm mixing the genetic algorithm and the simulated annealing algorithm is to solve the 3D container loading problem, which provides both global search capability and the ability to perform local searching. The JIT delivery optimization model can provide decision-makers with a more comprehensive and economical strategy for loading and transporting irregular precast components.

Road transport is an important sector of the economy, however, its negative impacts on the environment, human health and sustainability of potential economic growth are significant. Transport externalities tend to be neglected within the market process. The damage generated through significant externalities of road transport in Czechia was identified, based on unique data processing, to reach almost 3.5 billion € annually. This chapter presents an overview of internalization taxes and fees, their current rates and generated receipts. If excise duty is disregarded as fundamentally unsuitable for the internalization of externalities, then the receipts from other applied taxes and fees, particularly the road tax, seem to be insufficient. Although economic growth is encouraged, its form is not sustainable in view of the rising phase of the Kuznets curve and the related irreversible environmental impacts.

The optimal payment in the whole operation and maintenance period of water environment treatment PPP projects has become the main approach to realize sustainable development of projects. This study is aimed at constructing an effective payment model for the whole life period of projects to achieve win-win among all stakeholders, so as to provide a theoretical reference and managerial implications for the public sector in the whole operation and maintenance period.

In the whole operation and maintenance period of water environment treatment PPP projects, this article investigates how the public sector optimizes the payment in the whole operation and maintenance period of projects. Firstly, the projects' whole operation and maintenance period is divided into several stages according to the performance appraisal period. And then, the multi-stage dynamic programming model is constructed to design the payment construct model for the public sector in each performance appraisal stage. The payment from the public sector is the decision variable, and the deduction from the private sector is a random variable.

The optimal payment model showed that the relatively less objective weight of public sector leaded to its relatively more total payment and vice versa. Therefore, the sustainable development of the projects can only be ensured when the objective weights both of them should be balanced. Additionally, the deduction from the performance appraisal of private sector plays an important role in the model construction. The larger deduction the private sector undertakes, the smaller profits private sector has. Since the deduction at each stage is a random variable, the deduction varies with the different probability distributions obeyed by the practical deduction in each stage.

The findings from this study have provided theoretical and application references, and some managerial implications are also given. First, the improvement of the pricing system of public sector should be accelerated. Second, the reasonable profit of the private sector must be guaranteed. While pursuing the maximization of social benefits, the public sector should make full use of the price sharing mechanism in the market and supervise the real income situation of the private sector. Third is increasing the public to participate in pricing. Additionally, it is a limitation that the deduction is assumed to conform to a uniform distribution in this study. Other probability distributions on deduction can be essentially further sought, so as to be more line with the actual situation of the projects.

The optimal payment in whole operation and maintenance period of the projects has become an important issue, which is a key to project success. This study constructs a multi-stage dynamic programming model to optimize payment in the whole period of projects. Additionally, this study adds its value through deeply developing the new theories of optimal payment to more suitable for the practical problems, so that to optimize the design of payment mechanism. Meanwhile, a valuable reference for public and private sectors is provided to ensure the sustainable development of the projects.

The internet of vehicles (IoV) communication has recently become a popular research topic in the automotive industry. The growth in the automotive sector has resulted in significant standards and guidelines that have engaged various researchers and companies. In IoV, routing protocols play a significant role in enhancing communication safety for the transportation system. The high mobility of nodes in IoV and inconsistent network coverage in different areas make routing challenging. This paper aims to provide a lane-based advanced forwarding protocol for internet of vehicles (LAFP-IoV) for efficient data distribution in IoV. The proposed protocol’s main feature is that it can identify the destination zone by using position coordinates and broadcasting the packets toward the direction of destination. The novel suppression technique is used in the broadcast method to reduce the network routing overhead.

The proposed protocol considers the interferences between different road segments, and a novel lane-based forwarding model is presented. The greedy forwarding notion, the broadcasting mechanism, and the suppression approach are used in this protocol to reduce the overhead generated by standard beacon forwarding procedures. The SUMO tool and NS-2 simulator are used for the vehicle's movement pattern and to simulate LAFP-IoV.

The simulation results show that the proposed LAFP-IoV protocol performs better than its peer protocols. It uses a greedy method for forwarding data packets and a carry-and-forward strategy to recover from the local maximum stage. This protocol's low latency and good PDR make it ideal for congested networks.

The proposed paper provides a unique lane-based forwarding for IoV. The proposed work achieves a higher delivery ratio than its peer protocols. The proposed protocol considers the lanes while forwarding the data packets applicable to the highly dense scenarios.

The Internet of Things (IoT), as one of the new digital technologies, has created wide applications in various industries, and one of the most influential industries of this technology is the transportation industry. By integrating the IoT with the transportation industry, there will be dramatic changes in the industry, and it will provide many entrepreneurial opportunities for entrepreneurs to develop new businesses. Opportunity identification is at the heart of the entrepreneurial process, and entrepreneurs identify innovative goods or services to enter a new market by identifying, evaluating, and exploiting opportunities. Despite the desire of transportation managers to invest in the IoT and the increase in research in this area, limited research has focused on IoT-based entrepreneurial opportunities in the transportation industry. Therefore, the present study aims to identify IoT-based entrepreneurial opportunities in the transportation industry and examine their importance.

To achieve the research objective, the authors applied a mixed approach. First, adapting the lens of the industry value chain theory, a comprehensive literature review, besides a qualitative approach including semi-structured interviews with experts and thematic analysis, was conducted to identify the entrepreneurial opportunities. The identified opportunities were confirmed in the second stage using a quantitative survey method, including the Student t-test and factor analysis. Finally, the identified opportunities were weighted and ranked using the best worst method (BWM).

Entrepreneurial opportunities are classified into five main categories, including “smart vehicles”, “business partners/smart transportation supply side”, “supporting services”, “infrastructures”, and “smart transport management and control”. The infrastructures group of opportunities ranked the highest amongst the identified groups.

This study adds to the digital entrepreneurship opportunity recognition literature by addressing opportunities in a smart industry propelled by digital technologies, including developing new products or new applications of the available technologies. Additionally, inspired by the industry value chain theory, this article develops a framework including various digital entrepreneurial opportunity networks which are necessary to add value to any industry and, thus, could be applied by entrepreneurs to recognize opportunities for new intermediaries to enter other digital-based industries. Finally, the present study identifies the IoT-based entrepreneurial opportunities in the smart transportation industry and prioritizes them, providing practical insights regarding the creation of entrepreneurial ecosystems in the field of smart transportation for entrepreneurs and policymakers.

The purpose of this study is to extensively explore the vehicular network paradigm, challenges faced by them and provide a reasonable solution for securing these vulnerable networks. Vehicle-to-everything (V2X) communication has brought the long-anticipated goal of safe, convenient and sustainable transportation closer to reality. The connected vehicle (CV) paradigm is critical to the intelligent transportation systems vision. It imagines a society free of a troublesome transportation system burdened by gridlock, fatal accidents and a polluted environment. The authors cannot overstate the importance of CVs in solving long-standing mobility issues and making travel safer and more convenient. It is high time to explore vehicular networks in detail to suggest solutions to the challenges encountered by these highly dynamic networks.

This paper compiles research on various V2X topics, from a comprehensive overview of V2X networks to their unique characteristics and challenges. In doing so, the authors identify multiple issues encountered by V2X communication networks due to their open communication nature and high mobility, especially from a security perspective. Thus, this paper proposes a trust-based model to secure vehicular networks. The proposed approach uses the communicating nodes’ behavior to establish trustworthy relationships. The proposed model only allows trusted nodes to communicate among themselves while isolating malicious nodes to achieve secure communication.

Despite the benefits offered by V2X networks, they have associated challenges. As the number of CVs on the roads increase, so does the attack surface. Connected cars provide numerous safety-critical applications that, if compromised, can result in fatal consequences. While cryptographic mechanisms effectively prevent external attacks, various studies propose trust-based models to complement cryptographic solutions for dealing with internal attacks. While numerous trust-based models have been proposed, there is room for improvement in malicious node detection and complexity. Optimizing the number of nodes considered in trust calculation can reduce the complexity of state-of-the-art solutions. The theoretical analysis of the proposed model exhibits an improvement in trust calculation, better malicious node detection and fewer computations.

The proposed model is the first to add another dimension to trust calculation by incorporating opinions about recommender nodes. The added dimension improves the trust calculation resulting in better performance in thwarting attacks and enhancing security while also reducing the trust calculation complexity.

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 paper aims to uncover the key enablers of an agile supply chain in the manufacturing sector amidst disruptions such as pandemics, trade wars and cross-border challenges. The study aims to assess the applicability of existing literature to manufacturing and identify additional industry-specific enablers contributing to the field of supply chain management.

The research methodology is comprehensively described, detailing the utilization of extent literature and semistructured interviews with mid- and top-level executives in a supply chain. The authors ensure the robustness of the data collection process and results interpretation.

The study identifies six essential dimensions of an agile supply chain: information availability, design robustness, external resource planning, quickness and speed, public policy influencing skills and cash flow management. The study provides valuable insights for industry professionals to develop agile supply chains capable of responding to disruptions in a rapidly changing world.

This study is limited by its focus on the manufacturing sector, and future research may explore the applicability of these findings to other industries. By focusing on these essential dimensions identified in the study, managers can develop strategies to improve the agility and responsiveness of their supply chains. In addition, further research may investigate how these enablers may vary in different regions or contexts.

The COVID-19 pandemic has forced executives to reconsider their sourcing strategies and reduce dependence on suppliers from specific geographies. To ensure business continuity, companies should assess the risk associated with their suppliers and develop a business continuity plan that includes multisourcing their strategic materials. Digital transformation will revolutionize the supply chain industry, allowing for end-to-end visibility, real time insights and seamless integration of business and processes. Companies should also focus on creating a collaborative workforce ecosystem that prioritizes worker health and well-being. Maintaining trust with stakeholders is crucial, and firms must revisit their relationship management strategies. Finally, to maintain business leadership and competitiveness during volatile periods, the product portfolio needs to be diversified and marketing and sales teams must work in tandem with product teams to position new products accordingly.

This work contributes substantially to the literature on supply chain agility (SCA) by adding several new factors. The findings result in a more efficient and cost-effective supply chain during a stable situation and high service levels in a volatile situation. A less complex methodology for understanding SCA provides factors with a more straightforward method for identifying well-springs of related drivers. First, the study contributes to reestablish the factors such as quickness, responsiveness, competency, flexibility, proactiveness, collaboration and partnership, customer focus, velocity and speed, visibility, robustness, cost-effectiveness, alertness accessibility to information and decisiveness as applicable factors for SCA. Second, the study suggests a few more factors, such as liquidity management, Vendors’ economic assessment and economic diversity, that are the study’s unique contributions in extending the enablers of SCA. Finally, public policy influencing skills, local administration connects and maintaining capable vendors are the areas that were never considered essential for SCA. These factors have emerged as a vital operational factor during the lockdown, and academicians may consider these factors in the future to assess their applicability.

This study provides new insights for decision-makers looking to enhance the resilience and agility of their supply chains. The identification of unique enablers specific to the manufacturing industry contributes to the existing body of literature on agile supply chains in the face of disruptions.

The purpose of this paper is to present an improved model based on center potential instead of surface potential which is physically more relevant and accurate. Also, additional analytic insights have been provided to make the model independent and robust so that it can be extended to a full range compact model.

The design methodology used is center potential based analytical modeling using Psuedo-2D Poisson equation, with ingeniously developed boundary conditions, which help achieve reasonably accurate results. Also, the depletion width calculation has been suitably remodeled, to account for proper physical insights and accuracy.

The proposed model has considerable accuracy and is able to correctly predict most of the physical phenomena occurring inside the broken gate Tunnel FET structure. Also, a good match has been observed between the modeled data and the simulation results. I_on/I_ambipolar ratio of 10^(−8) has been achieved which is quintessential for low power SOCs.

The modeling approach used is different from the previously used techniques and uses indigenous boundary conditions. Also, the current model developed has been significantly altered, using very simple but intuitive technique instead of complex mathematical approach.

The global resolution of embracing dynamic and intertwined production systems has made it necessary to adopt viable systems like circular economy (CE) to ensure excellency in the business. However, in emerging countries, it is challenging to implement the CE practices due to the existing problems in the supply chain network, as well as due to the vulnerable financial condition of the business after the deadly hit of COVID-19. The main aim of this research is to determine the barriers to implementing CE considering the recent pandemic and suggest strategies to organizations to ensure CE for a cleaner environment and greener economy.

After an extensive literature review and validation from experts, 24 sub-barriers under the class of 6 main barriers are finalized by Pareto analysis, which is further analyzed via the best-worst method to determine the weight and rank of the barriers Further, fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to rank the proposed startegies to overcome the analysed barriers.

The results identified “unavailability of initial funding capital”, “need long time investment”, “lack of integrating production system using advance technology” and “lack of strategic planning” as the most acute sub-barriers to CE implementation. Further, fuzzy TOPSIS method is used to suggest the best strategy to mitigate the ranked barriers. The results indicated “integrated design facility to CE”, “ensuring large scale funding for CE facility” as the best strategy.

This study will motivate managers to implement CE practices to enjoy proper utilization of the resources, sustainable benefits in business, and gain competitive advantage.

Periodically, a lot of work is done on CE practices but none of them highlighted the issues in the domain of the leather products industry (LPI) and COVID-19 toward achieving sustainability in production and consumption. Thus, some significant barriers and strategies to implement CE for achieving sustainability in LPI are highlighted in this study, which is a unique contribution to the literature.