Hartmut Zadek

A key challenge in port–road logistics is the need to align operational priorities with efficient and sustainable freight transportation decisions. This study develops an Analytic Hierarchy Process (AHP)-based decision-support system for the Multi-Objective Electric Vehicle Routing Problem (MOEVRP) in port–road logistics. Initially, the AHP method is used to determine the relative importance of cost-efficient route planning, vehicle and port management, environmental impact management, energy efficiency, and operational efficiency and well-being based on expert judgment. Next, the resulting priority weights are then used to inform the decision-making framework for the MOEVRP, which determines routing decisions by minimizing total cost, carbon emissions, and maximum vehicle working time while accounting for electric vehicle constraints and charging behavior. This issue is critical in rapidly developing industrial corridors such as Thailand’s Eastern Economic Corridor, where growing freight demand, energy constraints, and environmental pressures must be managed simultaneously. By linking port–road operational priorities with the routing model, the proposed framework provides a structured approach for evaluating trade-offs between economic, environmental, and operational considerations during the transition toward low-carbon freight transportation. A case study in Chonburi–Rayong provinces demonstrates the applicability of the integrated system in a real-world maritime–land logistics corridor. The findings contribute to the design of more sustainable supply chain systems that support renewable and decarbonized logistics.

Hydrogen supply chains require coordinated planning from upstream production to downstream distribution and end-user delivery; however, significant logistical challenges remain under emerging hydrogen infrastructure constraints. In particular, the transportation sector faces difficulties in achieving efficient distribution while accounting for limited hydrogen refueling availability and vehicle range restrictions. This study evaluates key network design decisions involving distribution center location and fuel cell electric vehicle (FCEV) routing while incorporating hydrogen refueling stations within the transportation system. An integrated framework is proposed by combining K-means clustering for DC location planning with a hydrogen-powered FCEV routing model. Hydrogen refueling stations are incorporated as routing constraints to ensure feasible distribution operations. Next, a case study in Thailand is conducted to validate the proposed model under realistic logistical conditions. The results illustrate how clustering-based allocation improves network coordination, while the integrated FCEV routing approach ensures feasible and efficient delivery under refueling constraints. Comparative analysis further highlights improvements in system performance and provides practical insights for designing coordinated hydrogen logistics systems across integrated supply chain networks.

Effective management of the hydrogen supply chain (HSC), starting with supplier selection, is crucial for advancing the hydrogen industry and economy. Supplier selection, a complex Multi-Criteria Decision Analysis (MCDA) problem in an inherently uncertain environment, requires careful consideration. This study proposes a novel hybrid MCDA framework that integrates the Bayesian Best–Worst Method (BWM), Fuzzy Analytic Hierarchy Process (AHP), and Entropy Weight Method (EWM) for robust criteria weighting, which is aggregated using a game theory-based model to resolve inconsistencies and capture the complementary strengths of each technique. Next, the globally weighted criteria, emphasizing sustainability performance and techno-risk considerations, are used in the TODIM method grounded in prospect theory to rank hydrogen suppliers. Numerical experiments demonstrate the approach’s ability to enhance decision robustness compared to standalone MCDA methods. The comparative evaluation and sensitivity analysis confirm the stability and reliability of the proposed method, offering valuable insights for strategic supplier selection in the evolving hydrogen landscape in the HSC.

Automated buses are a component of future mobility concepts. However, these vehicles will only realize their full potential when they are highly automated, without a driver on board, and operate on public roads. In addition to the further development of vehicle technology, the planning of automated bus deployment represents an important field of research. The selection of suitable routes is influenced by various criteria related to infrastructure, road users, and the environment. There is limited literature on the criteria for the route evaluation of automated vehicles, and specifically, no studies exist concerning automated buses. To address this research gap and determine the weighting of these criteria, expert interviews and a case study were conducted. The expert interviews were utilized to identify the criteria that influence the operation of automated buses. Furthermore, the weighting of these criteria was determined, facilitating their prioritization. The case study examined the extent to which these criteria could be derived from publicly available data. With the criteria catalog developed in this work, transport authorities and transport companies will be able to assess entire operational areas regarding the deployment of automated buses and prioritize suitable routes at an early planning stage.

Green hydrogen can be generated through electrolysis using electricity from renewable sources, such as wind and solar, to split water into hydrogen. This study evaluates the green hydrogen supply chain (GHSC) upstream process using the two-phase integrated multi-criteria decision analysis (MCDA) framework. In the first phase, the data envelopment analysis (DEA) technique is applied to measure the relative efficiency of provincial alternatives with multiple criteria. The input criteria include provincial area, population density, gross domestic product value, and land cost data. In contrast, the sustainability-based governmental criteria concerning people, prosperity, planet, peace, and partnership indices are used as output criteria. Then, the technique for order of preference by similarity to ideal Solution (TOPSIS) is further applied to evaluate regional districts for Ubonratchathani province, one of the twelve relatively efficient provinces, to obtain the ranking list of potential renewable energy sites in the GHSC. Criteria related to geographic and climate data relevant to the efficiency of solar and wind are, thus, collected and analyzed. Our results show that the top three district areas are Kut Khaopun District, Pho Sai District, and Na Tan District, respectively. Finally, the obtained results are verified to evaluate the robustness of the assessment. Our results offer a strategic and practical analysis for policymakers involved in the energy site appraisal process.

In this study, the upstream process of the Green Hydrogen Supply Network (GHSC) is investigated, in which electricity from solar renewable energy is used in the electrolysis technique for the hydrogen production process. Initially, multiple criteria relevant to the solar plant location based on geographic, climate, and sun-earth interaction data are examined. In particular, seven input criteria are obtained from the area, the average air temperature, the average rainfall amount, the average precipitation days, the average relative humidity, the wind force, and the average yearly sunshine hours. Additionally, three output criteria are the population density, the direct normal irradiation, and the specific photovoltaic power output, respectively. Then, the case study of the state of Saxony-Anhalt in Germany inclusive of eleven districts and three independent cities is used as Decision-Making Units (DMUs), in which the Data Envelopment Analysis (DEA) technique is applied to explore each DMU’s relative efficiency. The obtained results show that about 57 percent of all the DMUs are found to be relatively efficient. The outcome of the study can be used to analyze strategic policy for GHSC involving the upstream and other operations in future studies.

In today’s world, crises like the COVID-19 pandemic and ongoing global changes pose significant challenges for manufacturing companies. Resilience, the ability to withstand and recover from disruptions, is essential for survival. To make resilience actionable, the discussion introduces a four-step Circular Resilience Assessment Tool. To assess their resilience score, companies undergo a risk and vulnerability assessment, a qualitative resilience factor assessment, a suitable strategies identification phase, and a quantitative performance assessment. This tool guides companies in evaluating their resilience before, during, and after hypothetical or occurred crises. The balance among qualitative and quantitative aspects, encompassing technical, social, and organizational considerations, ensures that an omni comprehensive point of view is adopted in evaluating the overall resilience score of a company. This innovative approach empowers companies to not only survive crises but also to gain a competitive advantage and expand their market share in the long term. The work provides a thorough description of each of the four steps, accompanied by examples. The Circular Resilience Assessment Tool is designed to be as specific as necessary and as general as possible, thus making it a valuable resource for a variety of enterprises.