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Recently, interest in sustainability has grown globally in the heavy vehicle and equipment industry (HVEI). However, this industry's complexity poses a challenge to the implementation of generic sustainable supply chain management (SSCM) practices. This study aims to identify SSCM's barriers, practices and performance (BPP) indicators in the HVEI context.

The results are derived from case studies of four multinational manufacturers. Within-case and cross-case analyses were conducted to categorise the SSCM BPP indicators that are unique to HVEI supply chains.

This study's analysis revealed that supply chain cost implications and a deficient information flow between focal firms and supply chain partners are the key barriers to SSCM in the HVEI. This analysis also revealed a set of policies, programmes and procedures that manufacturers have adopted to address SSCM barriers. The most common SSCM performance indicators included eco-portfolio sales to assess economic performance, health and safety indicators for social sustainability and carbon- and energy-related measures for environmental sustainability.

The insights can help HVEI firms understand and overcome the typical SSCM barriers in their industry and develop, deploy and optimise their SSCM strategies and practices. Managers can use this knowledge to identify appropriate mechanisms with which to accelerate their transition into a sustainable business and effectively measure performance outcomes.

The extant SSCM literature has focused on the light vehicle industry, and it has lacked a concrete examination of HVEI supply chains' sustainability BPP. This study develops a framework that simultaneously analyses SSCM BPP in the HVEI.

To assess the introduction and performance of light electric freight vehicles (LEFVs), more specifically cargo cycles in major 3PL organizations in at least two Nordic countries.

Case studies. Interviews. Company data on performance before as well as after the introduction. Study of differing business models as well as operational setups.

The results from the studied cases show that LEFVs can compete with conventional vans in last mile delivery operations of e-commerce parcels. We account for when this might be the case, during which circumstances and why.

Inherent limitations of the case study approach, specifically on generalization. Future research to include more public–private partnership and multi-actor approach for scalability.

Adding to knowledge on the public sector facilitation necessary to succeed with implementation and identifying cases in which LEFVs might offer efficiency gains over more traditional delivery vehicles.

One novelty is the access to detailed data from before the implementation of new vehicles and the data after the implementation. A fair comparison is made possible by the operational structure, area of delivery, number of customers, customer density, type of packages, and to some extent, the number of packages being quite similar. Additionally, we provide data showing how city hubs can allow cargo cycles to work synergistically with delivery vans. This is valuable information for organizations thinking of trying LEFVs in operations as well as municipalities/local authorities that are interested.

There is a lot of talk about the electric car today, but these vehicles are not new. Indeed, thebeginning of the 20th century saw electricity and the automobile take hold in North American society, so that by 1910, the electric car was everywhere. Until the turn of the 1920s, a new era dawned for transportation in the USA, but without the electric car. The purpose of this study is to question Why did it happen.

This paper develops such a comparison, not of the cars themselves, through a detailed engineering analysis, but rather of the marketing of electric vehicles in the USA in 1910 and 2010, as it appeared in the marketing strategies of the manufacturers.

There are many technical and economic reasons for this, but not only; there are also commercial strategy reasons. The position of manufacturers, especially through advertising and the press, can tell us about this golden age of the electric car, what precipitated its fall, and its reappearance a century later.

It is a comparison of images, of how electric vehicles had been and are proposed to the public, through the exploration of mainly promotional material and newspaper articles.

Vehicle allocation problems (VAPs), which are frequently confronted in many transportation activities, primarily including but not limited to full truckload freight transportation operations, induce a significant economic impact. Despite the increasing academic attention to the field, literature still fails to match the needs of and opportunities in the growing industrial practices. In particular, the literature can grow upon the ideas on sustainability, Industry 4.0 and collaboration, which shape future practices not only in logistics but also in many other industries. This review has the potential to enhance and accelerate the development of relevant literature that matches the challenges confronted in industrial problems. Furthermore, this review can help to explore the existing methods, algorithms and techniques employed to address this problem, reveal directions and generate inspiration for potential improvements.

This study provides a literature review on VAPs, focusing on quantitative models that incorporate any of the following emerging logistics trends: sustainability, Industry 4.0 and logistics collaboration.

In the literature, sustainability interactions have been limited to environmental externalities (mostly reducing operational-level emissions) and economic considerations; however, emissions generated throughout the supply chain, other environmental externalities such as waste and product deterioration, or the level of stakeholder engagement, etc., are to be monitored in order to achieve overall climate-neutral services to the society. Moreover, even though there are many types of collaboration (such as co-opetition and vertical collaboration) and Industry 4.0 opportunities (such as sharing information and comanaging distribution operations) that could improve vehicle allocation operations, these topics have not yet received sufficient attention from researchers.

The scientific contribution of this study is twofold: (1) This study analyses decision models of each reviewed article in terms of decision variable, constraint and assumption sets, objectives, modeling and solving approaches, the contribution of the article and the way that any of sustainability, Industry 4.0 and collaboration aspects are incorporated into the model. (2) The authors provide a discussion on the gaps in the related literature, particularly focusing on practical opportunities and serving climate-neutrality targets, carried out under four main streams: logistics collaboration possibilities, supply chain risks, smart solutions and various other potential practices. As a result, the review provides several gaps in the literature and/or potential research ideas that can improve the literature and may provide positive industrial impacts, particularly on how logistics collaboration may be further engaged, which supply chain risks are to be incorporated into decision models, and how smart solutions can be employed to cope with uncertainty and improve the effectiveness and efficiency of operations.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

This paper presents the Edge Load Management and Optimization through Pseudoflow Prediction (ELMOPP) algorithm, which aims to solve problems detailed in previous algorithms; through machine learning with nested long short-term memory (NLSTM) modules and graph theory, the algorithm attempts to predict the near future using past data and traffic patterns to inform its real-time decisions and better mitigate traffic by predicting future traffic flow based on past flow and using those predictions to both maximize present traffic flow and decrease future traffic congestion.

ELMOPP was tested against the ITLC and OAF traffic management algorithms using a simulation modeled after the one presented in the ITLC paper, a single-intersection simulation.

The collected data supports the conclusion that ELMOPP statistically significantly outperforms both algorithms in throughput rate, a measure of how many vehicles are able to exit inroads every second.

Furthermore, while ITLC and OAF require the use of GPS transponders and GPS, speed sensors and radio, respectively, ELMOPP only uses traffic light camera footage, something that is almost always readily available in contrast to GPS and speed sensors.

This study aims to examine the links between carbon emissions, electric vehicles, economic growth, energy use, and urbanisation in 15 countries from 2010 to 2020.

This study adopts seminal panel methods of moments quantile regression with fixed effects to trace the distributional aspect of the relationship. The reliability of methods is confirmed via fully modified ordinary least squares coefficients.

This study reveals that fossil fuel use, economic activity, and urbanisation negatively impact environmental quality, whereas renewable energy sources have a significant positive long-term effect on environmental quality in the selected panel of countries.

The main limitation of this study is the generalisability of the findings, as the study is confined to a limited number of countries, and focuses on non-renewable and renewable energy sources.

Finally, this study proposes several policy recommendations for decision-makers and policymakers in the 15 nations to address climate change, boost sales of electric vehicles, and increase the use of renewable energy sources.

This study calls for a comprehensive transition towards green energy in the transportation sector, enhancing economic growth, fostering employment opportunities, and improving environmental quality.

As voice search has progressively become a new way of information acquisition and human–computer interaction, this paper aims to explore the users' voice search behavior in human–vehicle interaction.

This study employed mixed research methods, including questionnaires and interviews. A total of 151 Amazon MTurk volunteers were recruited to complete a questionnaire based on their most recent and impressive voice search experience. After the questionnaire, this paper conducted an online interview with the participants.

This paper studied users' voice search behavior characteristics in the context of the human–vehicle interaction and analyzed the voice search content, search need, search motivation and user satisfaction. In addition, this paper studied the barriers and suggestions for voice search in human–vehicle interaction through a content analysis of the interviews.

This paper's analysis of users' barriers and suggestions has a specific reference value for optimizing the voice search interaction system and improving the service.

This study is exploratory research that seeks to identify users' voice search needs and tasks and investigate voice search satisfaction in human–vehicle interaction context.

This study aims to demonstrate the importance of organizational networks in organizational performance is relatively rich; less understood are processes in organizational networking that entrepreneurs and organizations use in making sense of rapidly changing contexts for organizational performance.

This study conducts an exploratory organizational-level narrative analysis into firms’ experiences in two major emerging markets (EMs), namely, Russia and India – to identify organizational networking processes in the midst of institutional upheavals. The study is based on in-depth case studies of firms in EMs sourced from interview data from senior management and consolidated with secondary data.

The authors find that initially firms rely on informal networks (including blat/svyazi and jaan-pehchaan/jan-pehchan) and later formal (in the form of bureaucratic followed by proprietary) networks to make sense of the changes and uncertainties in turbulent environments. The authors also demonstrate the cyclical nature of strategic sensemaking in the process of developing organizational networks for performance.

The study has a number of theoretical and practical contributions. First, it extends the well-established business networking construct to a more inclusive organizational networking construct. Second, it demonstrates that sensemaking is dependent on interorganizational networking from the outset and throughout the growth of an organization in turbulent markets – from informal to formal bureaucratic and proprietary networks. Finally, this study is unique in documenting the entire process of sensemaking from scanning to performance as well as successfully demonstrating the cyclical nature of sensemaking.

Under the repeated action of the construction load, opening deformation and disturbed deformation occurred at the precast box culvert joints of the shield tunnel. The objective of this paper is to investigate the effect of construction vehicle loading on the mechanical deformation characteristics of the internal structure of a large-diameter shield tunnel during the entire construction period.

The structural response of the prefabricated internal structure under heavy construction vehicle loads at four different construction stages (prefabricated box culvert installation, curved lining cast-in-place, lane slab installation and pavement structure casting) was analyzed through field tests and ABAQUS (finite element analysis software) numerical simulation.

Heavy construction vehicles can cause significant mechanical impacts on the internal structure, as the construction phase progresses, the integrity of the internal structure with the tunnel section increases. The vertical and horizontal deformation of the internal structure is significantly reduced, and the overall stress level of the internal structure is reduced. The bolts connecting the precast box culvert have the maximum stress at the initial stage of construction, as the construction proceeds the stress distribution among the bolts gradually becomes uniform.

This study can provide a reference for the design model, theoretical analysis and construction technology of the internal structure during the construction of large-diameter tunnel projects.