Road haulage solutions are incredibly adaptable, having the capacity to link domestically and internationally. Road transportation offers a greener, more efficient, and safer future through sophisticated technology. Symmetry and asymmetry exist widely in industrial applications, and logistics and supply chains are no exception. The multi-criteria decision-making (MCDM) model is considered as a complexity tool to balance the symmetry between goals and conflicting criteria. This study can assist stakeholders in understanding the current state of transportation networks and planning future sustainability measures through the MCDM approach. The main purpose of this paper is to evaluate and compare the sustainable development of existing road transportation systems to determine whether any of them can be effectively developed in the Organization for Economic Cooperation and Development (OECD) countries. The integrated entropy–CoCoSo approach for evaluating the sustainability of road transportation systems is introduced, and the framework process is proposed. The entropy method defines the weight of the decision criteria based on the real data. The advantage of the entropy method is that it reduces the subjective impact of decision-makers and increases objectivity. The CoCoSo method is applied for ranking the road transportation sustainability performance of OECD countries. Our findings revealed the top three countries’ sustainability performance: Japan, Germany, and France. These are countries with developed infrastructure and transportation services. Iceland, the United States, and Latvia were in the last rank among countries. This approach helps governments, decision-makers, or policyholders review current operation, benchmark the performance of other countries and devise new strategies for road transportation development to achieves better results.

This paper develops a mathematical model for intermodal freight transportation. It focuses on determining the flow of goods, the number of vehicles, and the transferred volume of goods transported from origin points to destination points. The model of this article is to minimize the total cost, which consists of fixed costs, transportation costs, intermodal transfer costs, and CO₂ emission costs. It presents a mixed integer linear programming (MILP) model that minimizes total costs, and a fuzzy mixed integer linear programming (FMILP) model that minimizes imprecise total costs under conditions of uncertain data. In the models, node capacity, detour, and vehicle utilization are incorporated to estimate the performance impact. Additionally, a computational experiment is carried out to evaluate the impact of each constraint and to analyze the characteristics of the models under different scenarios. Developed models are tested using real data from a case study in Southern Vietnam in order to demonstrate their effectiveness. The results indicate that, although the objective function (total cost) increased by 20%, the problem became more realistic to address when the model was utilized to solve the constraints of node capacity, detour, and vehicle utilization. In addition, on the basis of the FMILP model, fuzziness is considered in order to investigate the impact of uncertainty in important model parameters. The optimal robust solution shows that the total cost of the FMILP model is enhanced by 4% compared with the total cost of the deterministic model. Another key measurement related to the achievement of global sustainable development goals is considered, reducing the additional intermodal transfer cost and the cost of CO₂ emissions in the objective function.

The efficiency of land transportation contributes significantly to determining a country’s economic and environmental sustainability. The examination of land transportation efficiency encompasses performance and environmental efficiency to improve system performance and citizen satisfaction. Evaluating the efficiency of land transportation is a vital process to improve operation efficiency, decrease investment costs, save energy, reduce greenhouse gas emissions, and enhance environmental protection. There are many methods for measuring transportation efficiency, but few papers have used the input and output data to evaluate the ecological efficiency of land transportation. This research focuses on evaluating the environmental efficiency for land transportation by using the data envelopment analysis (DEA) method with undesirable output to handle unwanted data. By using this, the paper aims to measure the performance of land transportation in 25 Organization for Economic Co-operation and Development (OECD) countries in the period of 2015–2019, considered as 25 decision-making units (DMUs) in the model. For identifying the ranking of DMUs, four inputs (infrastructure investment and maintenance, length of transport routes, labor force, and energy consumption) are considered. At the same time, the outputs consist of freight transport and passenger transport as desirable outputs and carbon dioxide emission (CO₂) as an undesirable output. The proposed model effectively determines the environment-efficient DMUs in a very time-efficient manner. Managerial implications of the study provide further insight into the investigated measures and offer recommendations for improving the environmental efficiency of land transportation in OECD countries.

Vietnam has enormous potential for solar power thanks to its favorable geographical location. In recent years, the Vietnamese government has strived to develop renewable energy. The objective of this paper is to measure the operational efficiency of solar photovoltaic (PV) power plants using data envelopment analysis (DEA) with the epsilon-based measure (EBM) model. The utilization of the EBM model is due to its advantage to integrate radial and non-radial measurements to get a more exact estimate of relative efficiency. Using the merits of the EBM model, a case study of 18 decision-making units (DMUs) in Vietnam is presented. In the proposed model, three input variables are selected, which are capital cost, installed capacity, solar irradiation, while energy production and consumer density are considered as output variables. Following that, the technique for order of preference by similarity to ideal solution (TOPSIS) method is used to assess the validity and applicability of results. The results illustrate that two methods reach common rankings, in which the priority rankings of the best performing DMUs are very similar. This shows that the applied models are robust in nature. This paper offers significant materials that serve as practical and timelier solutions for decision-makers in the operating and management strategies of the solar energy industry, also a critical guideline in many related decision-making problems for any other industries around the world.