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Unplanned urban mobility causes negative effects on the population and the environment. This study aims to understand how higher education institutions (HEIs) are managing the transportation issues related to their activities and how they are implementing actions towards more sustainable practices in this regard.

A systematic review and bibliometric analysis were performed using the Proknow-C method, and the bibliographic portfolio was evaluated to answer guiding questions about distribution of articles over the years and around the world, the most relevant and frequent topics, the travel behavior of university life and the main methodologies used. The meta-analysis was modeled using a programming language in R to execute Bibliometrix package.

Sharing systems, active transport, public transport, urban planning, car parking management and travel behavior are the most relevant topics related to sustainable mobility in HEIs. Different strategies to reduce car use are adopted, and the geographic location of the university and the availability of housing and shopping services in close proximity directly influence travel patterns. As a result, seven guidelines and strategic actions associated were proposed to promote the engagement of institutions in the development of sustainable transport and guide future studies about new solutions to promote sustainable university commutes.

This paper presents a new perspective by performing a critical literature review based on the experiences reported by several isolated studies on the subject. Initiatives of sustainable transport guidelines can be used by academics, urban planners, higher education administrators and other stakeholders to make universities more environmentally friendly, inclusive and accessible.

This paper aims to define the efficiency achieved by urban transport companies in Spain concerning the resources they use, considering the type of management used for implementation, public-private, and size.

This study consisted of an analysis of the efficiency of 229 public-private urban transport operators during the period 2012–2021 using Data Envelopment Analysis, the Malmquist Index and inference estimators to determine productivity, efficiency change into Pure Technical Efficiency Change (PTECH), and scale efficiency change.

Based on the efficiency analysis, the authors concluded that of the 229 companies studied, more than 35 were inefficient in all analysed periods. Considering the sample used, direct management is considered significantly more efficient. It cannot be concluded that the size of these companies influences their efficiency, as the data show unequal development behaviours in the studied years.

This study provides arguments on whether there is a significant difference between the two types of management in the urban transport sector. It also includes firm size as a study variable, which has not been previously considered in other studies related to urban transport efficiency. Efficiency should be a crucial factor in determining funding allocation in this sector, as it encourages operators to optimize and improve their services.

Ores and minerals are extracted from the earth’s crust depending on the type of deposit. Iron ore mines come under massive deposit patterns and have their own mine development and life cycles. This study aims to depict the development and life cycle of large open-pit iron ore mines and the intertwined organizational design of the departments/sections operated within the industry.

Primary data were collected on the site by participant observation, in-depth interviews of the field staff and executives, and field notes. Secondary data were collected from the literature review to compare and cite similar or previous studies on each mining activity. Finally, interactions were conducted with academic experts and top field executives to validate the findings. An organizational ethnography methodology was employed to study and analyse four large-scale iron ore mines of India’s largest iron-producing state, Odisha, from January to April 2023.

Six stages were observed for development and life cycle, and the operations have been depicted in a schematic diagram for ease of understanding. The intertwined functioning of organizational set-up is also discovered.

The paper will benefit entrepreneurs, mining and geology students, new recruits, and professionals in allied services linked to large iron ore mines. It offers valuable insights for knowledge enhancement, operational manual preparation and further research endeavours.

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.

Prior research has emphasised the importance of the early phases of construction projects, as well as the difficulties of procuring engineering services – especially due to the uncertainties. Despite that, studies on the public procurement of engineering services are scarce. Although scholars have shown that uncertainty may affect the choice of control modes, the level of uncertainty that characterises services is not addressed by the two task characteristics: knowledge of the transformation process and output measurability. The purpose is to investigate organisational control in public procurement of engineering services.

The existing control model was adjusted in this study by conceptually adding uncertainty as a third aspect to the two task characteristics. A single case study of the Swedish Transport Administration was used. The empirical data, comprising 14 interviews with managers from the client and engineering consulting companies, were analysed using flexible pattern matching and visual mapping approaches and then illustrated using the model.

The public client did not base its choice of control modes on uncertainty, but rather on the other two task characteristics. Consequently, the service providers argued that the chosen control modes reduced their creativity, increased their financial risks and caused unclear responsibilities. This study therefore shows that uncertainty is an important factor to consider in the choice of control modes, both from a theoretical perspective and from the service providers' point of view. The developed model may therefore be useful for researchers as well as practitioners.

This study is the first attempt to add uncertainty as a task characteristic when choosing control modes. The results contribute to the scarce control literature regarding the procurement of engineering services for construction projects and the procurement of other services with high uncertainty.

Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

This study aims to develop an inclusive, multidisciplinary, flexible and organizationally adaptable safety risk management framework, including diversity management, that will be implemented to ensure safety is and remains at the desired level. If the number of incidents and potential incidents that could lead to accidents and their impact rates are to be reduced operationally and administratively, aviation safety risks and sources of risk must be better understood, sources of risk identified, and the safety risk management framework designed in an organization-specific and organization-wide sustainable way. At this point, it is necessary to draw the conceptual framework well and to define the boundaries of the concepts well. In this study, a framework model that can be adapted to the organization is proposed to optimize the management of risks and provide both efficient and effective resource allocation and organizational structure design in its operations and management functions.

The qualitative research method – triple techniques – was deemed appropriate for this study, which aims to identify, examine, interpret and develop the situations of safety management models. In this context, document analysis, business process modeling technique and Delphi techniques from qualitative research methods were used via integration as the methodology of this research.

To manage dynamic civil aviation management activities and business processes effectively and efficiently, the risk management process is the building block of the “Proposed Process Model” that supports the decision-making processes of aviation organizations and managers. This “Framework Conceptual Model” building block also helps build capacity and resilience by enabling continuous development, organizational learning, and flexible structuring.

This research is limited to air transportation and aviation safety management issues. This research is limited specifically to a safety-based risk management framework for the aviation industry. This research may have social implications as source saving, optimum resource use and capacity building will make a contribution to society and add value besides operational and practical implementation.

This research may contribute to more safe operations and functions in the aviation industry.

Management and academia may gain considerable support from this research to manage their safety risks via a corporate-tailored risk management framework, both improving resilience and developing corporate capacity. With this model presented, decision-makers will have a guiding structure that can optimally manage the main risk types that may be encountered in the safety risk in the fields of suppliers, manufacturers, demand changes, logistics, information management, environmental, legal and regulatory. Existing studies in the literature are generally in the form of algorithms and cannot be used as a decision-making support tool. This model aims to fill the gap in the literature. In addition, added value may be created by applying this model to optimum management safety risks in the real aviation industry and its related sectors.

To address the absence of Lean in transportation logistics in the digital era, this study aims to investigate the application of Lean transportation (LT) tools to reduce waste and facilitate the digital transformation of dedicated road transportation in the offshore industry.

The study adopts action research with a multimethod approach, including a scoping review, focus groups (FG) and participant observation. The research is conducted within the offshore supply chain of a major oil and gas company.

Implementing LT’s continuous improvement tools, particularly value stream mapping (VSM), reduces offshore transportation waste and provides empirical evidence about the intersection of Lean and digital technologies. Applying techniques drawn from organisational learning theory (OLT), stakeholders involved in VSM mapping and FGs engage in problem-solving and develop action plans, driving digital transformation. Waste reduction in loading and unloading stages leads to control actions, automation and process improvements, significantly reducing downtime. This results in an annual monetary gain of US$1.3m. The study also identifies waste related to human effort and underutilised digital resources.

This study contributes to theory and practice by using action research and LT techniques in a real intervention case. From the lens of OLT, it highlights the potential of LT tools for digital transformation and demonstrates the convergence of waste reduction through Lean and Industry 4.0 technologies in the offshore supply chain. Practical outputs, including a benchmarking questionnaire and a plan-do-check-act cycle, are provided for other companies in the same industry segment.

This study aims to explore the connection between demographic traits and the choice of attraction patterns among international repeat tourists.

The study employed a questionnaire survey to collect data from 1550 international repeat tourists who visited Tanzania between November 2022 and July 2023. Convenient sampling was employed as tourists were selected from the three international airports of Tanzania, namely Kilimanjaro International Airport, Julius Nyerere International Airport, and Abeid Aman Karume International Airport. A multinomial logistic regression model was used to examine the impact of socio-demographic characteristics on the selection of attraction patterns among international repeat tourists.

The study revealed that demographic factors, including age, marital status, income level, occupation, and education level, exhibit statistically significant correlations with preferences for distinct attraction patterns. This significance was established through a p-value of less than 0.05 for all the aforementioned variables.

This study is primarily focused on international repeat tourists, thereby limiting insights into the preferences of domestic tourists. To better inform strategies aimed at attracting a larger domestic tourist base, future research may prioritize the investigation of choice of attractions patterns among domestic tourists in relation to their demographic characteristics.

This study contributes to the nuanced understanding of international tourist behavior by unraveling the extent to which demographic traits impact tourists’ choices of attraction patterns, thereby providing insights crucial for effective marketing strategies, improved visitor experiences, and sustainable tourism development strategies.

This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.

Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.

Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.

In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.