Search Results (32)

Reducing carbon dioxide emissions in freight transportation is considered a key objective in contemporary sustainable supply chain management. While several tools and standards have been developed to estimate transport-related emissions, most rely on static assumptions, generic emission factors and are limited to single-scenario evaluation. Therefore, their operational applicability remains restricted especially in dynamic and complex environments where fast responsiveness is essential. Moreover, these tools are often disconnected from real-world constraints and rarely incorporate expert’s input. To address this gap, this study introduces a hybrid decision-support CO₂ assessment framework combining theoretical models with field-based inputs. The proposed approach combines structured interviews conducted with 300 supply chain consultants and is operationalized through a dynamic digital tool that enables users to simulate multiple scenarios simultaneously. The tool accounts for critical variables including transport mode, routing distance, vehicle configuration, and shipment characteristics, thereby enabling a contextualized and flexible analysis of carbon emissions. A validation case study was conducted to confirm the applicability of the tool to industrial settings. Computational results show significant variation in emissions across different routing strategies and modal configurations, highlighting the tool’s capacity to support environmentally informed decisions. This research offers both a replicable methodology and a practical contribution: a user-centered, multi-scenario tool that improves the accuracy, adaptability, and strategic value of CO₂ emission calculations in freight transport planning. Full article

Transportation planning is a complex process that aims to achieve the maximum level of effectiveness in terms of costs, usage of transport resources, reliability of deliveries, and minimizing the negative impact on the environment. Most traditional models focus on cost minimization at the expense of risk, road dynamics, and emissions constraints. In contrast, the current paper presents a mixed-integer linear programming (MILP) model for scheduling fleets, selecting transportation modes in multiple modes of transportation, and meeting emissions regulation requirements according to dynamic transportation requirements. Risk-aware routing and taking the factor of congestion and CO₂ emission limits proposed by the government into consideration, this model can offer a more efficient and flexible optimization strategy. From the case study, we observe the significant result that the proposed model achieves, a 23% reduction in transport costs, a 25% improvement in fleet use, a 33.3% decrease in the delivery delay, and a 24.6% decrease in CO₂ emissions. The model dynamically delivers shipments utilizing both road and rail transportation and improves mode choice by minimizing idle vehicle time. This is confirmed through sensitivity analysis which addresses factors such as traffic congestion, changing fuel prices, and changing environmental standards. Full article

The sea–rail intermodal container terminal serves as a key transportation hub for green logistics, where efficient resource coordination directly enhances multimodal connectivity and operational synergy. To address limited storage capacity and trans-shipment inefficiencies, this study innovatively proposes a resource-sharing strategy between the seaport and the railway container terminal, focusing on the joint allocation of yard space and internal trucks. For indirect trans-shipment operations between ships, the port, the railway container terminal, and trains, a mixed-integer programming model is formulated with the objective of minimizing the container trans-shipment cost and the weighted turnaround time of ships and trains. This model simultaneously determines yard allocation, container transfers, and truck allocation. A two-layer hybrid heuristic algorithm incorporating adaptive Particle Swarm Optimization and Greedy Rules is designed. Numerical experiments verify the model and algorithm performance, revealing that the proposed method achieves an optimality gap of only 1.82% compared to CPLEX in small-scale instances while outperforming benchmark algorithms in solution quality. And the shared yard strategy enhances ship and train turnaround efficiency by an average of 33.45% over traditional storage form. Sensitivity analysis considering multiple realistic factors further confirms the robustness and generalizability. This study provides a theoretical foundation for sustainable port–railway collaboration development. Full article

As an important pillar of the national economy, the green transformation of SMEs is the key to promoting sustainable economic development. However, SMEs generally face issues such as information opacity and high operational risks, which make it difficult for them to obtain traditional financing support, thereby hindering green development. Green Supply Chain Finance has opened up new financing channels for SMEs, but the accuracy of credit risk evaluation remains a bottleneck that limits its widespread application. This paper constructs a credit risk evaluation index system that integrates multiple sources of information, covering factors such as the situations of SMEs themselves, stakeholder feedback, and expert ratings. It compares and analyzes the performance of the genetic algorithm-optimized random forest model (GA-RF), the BP neural network, the support vector machine, and the logistic regression model in credit risk evaluation. The empirical results indicate that the GA-RF model is significantly better than the other models in terms of accuracy, precision, and F1 score, and has the highest AUC value, making it more effective in identifying credit risk. In addition, the GA-RF model reveals that the asset–liability ratio, the time of establishment, the growth rate of operating revenue, the time of collection of accounts receivable, the return on net assets, and daily shipments are the key indicators affecting the credit risk assessment. Full article

This study develops a Green Vehicle Routing Problem (GVRP) model to address key logistics challenges, including time windows, simultaneous pickup and delivery, heterogeneous vehicle fleets, and multiple trip allocations. The model incorporates emissions-related costs, such as carbon taxes, to encourage sustainable supply chain operations. Emissions are calculated based on the total shipment weight and the travel distance of each vehicle. The objective is to minimize operational costs while balancing economic efficiency and environmental sustainability. A Genetic Algorithm (GA) is applied to optimize vehicle routing and allocation, enhancing efficiency and reducing costs. A Liquid Petroleum Gas (LPG) distribution case study in Yogyakarta, Indonesia, validates the model’s effectiveness. The results show significant cost savings compared to current route planning methods, alongside a slight increase in carbon. A sensitivity analysis was conducted by testing the model with varying numbers of stations, revealing its robustness and the impact of the station density on the solution quality. By integrating carbon taxes and detailed emission calculations into its objective function, the GVRP model offers a practical solution for real-world logistics challenges. This study provides valuable insights for achieving cost-effective operations while advancing green supply chain practices. Full article

The complexities arising from disparate data sources, conflicting contracts, residency requirements, and the demand for multiple AI models in trade finance supply chains have hindered small and medium-sized enterprises (SMEs) with limited resources from harnessing the benefits of artificial intelligence (AI) capabilities, which could otherwise enhance their business efficiency and predictability. This paper introduces a decentralized AI orchestration framework that prioritizes transparency and explainability, offering valuable insights to funders, such as banks, and aiding them in overcoming the challenges associated with assessing SMEs’ financial credibility. By utilizing an orchestration technique involving symbolic reasoners, language models, and data-driven predictive tools, the framework empowers funders to make more informed decisions regarding cash flow prediction, finance rate optimization, and ecosystem risk assessment, ultimately facilitating improved access to pre-shipment trade finance for SMEs and enhancing overall supply chain operations. Full article

Small islands are supplied with water from underground sources, simple seawater desalination facilities, or water supply shipment. However, this water supply can be interrupted because of the sudden depletion of groundwater, as groundwater level prediction is inaccurate. Additionally, seawater desalination facilities are difficult to maintain, resulting in frequent breakdowns. When the water tank capacity is below a certain level, island residents contact the water supply shipment manager to request a shipment from land. In Korea, a seawater desalination plant project using ships was newly attempted to solve the water supply problem for island regions. Through this project, an attempt was made to supply water to many island areas suffering water supply disruptions due to drought. The purpose of this study is to compare water supply routes to multiple island regions using existing water supply shipment with desalination plants on ships through network analysis based on a geographic information system. To optimize sailing route, length (m), road connection type, and name of each road section, actual operation data, distance, etc., were set up on a network dataset and analyzed. In addition, the operational model predicted the stability of water supply using the GoldSim simulator. As a result, when sailing on the optimal route based on network analysis, the existing water supply routes could be reduced (2153 km -> 968 km) by more than 55%, and operational costs can be verified to be reduced. Additionally, the validity of the network analysis results was confirmed through actual travel of the representative route. Full article

The dispatching of automated guided vehicles (AGVs) is essential for efficient horizontal transportation at automated container terminals. Effective planning of AGV transportation can reduce equipment energy consumption and shorten task completion time. Multiple AGVs transport containers between storage blocks and vessels, which can be regarded as the supply sides and demand points of containers. To meet the requirements of shipment in terms of timely and high-efficient delivery, multiple AGVs should be dispatched to deliver containers, which includes assigning tasks and selecting paths. A contract net protocol (CNP) is employed for task assignment in a multiagent system, while machine learning provides a logical alternative, such as Q-learning (QL), for complex path planning. In this study, mathematical models for multi-AGV dispatching are established, and a QL-CNP algorithm is proposed to tackle the multi-AGV dispatching problem (MADP). The distribution of traffic load is balanced for multiple AGVs performing tasks in the road network. The proposed model is validated using a Gurobi solver with a small experiment. Then, QL-CNP is used to conduct experiments with different sizes. The other algorithms, including Dijkstra, GA, and PSO, are also compared with the QL-CNP algorithm. The experimental results demonstrate the superiority of the proposed QL-CNP when addressing the MADP. Full article

Brazil has emerged as the world’s largest soybean producer and exporter in recent years. In the Brazilian Amazon Biome, the state of Pará has become a new agricultural frontier over the last two decades due to a significant increase in soybean cultivation throughout its territory. However, it is essential to understand the associated effects on the environment at every point in the supply chain. This research aims to measure the effects on the environment of the soybean supply chain of two production poles utilising openLCA software and the life cycle assessment (LCA) methodology in the northeast (Paragominas) and south (Redenção) of the state of Pará in Brazil. In addition, we determine which is the most efficient route between the shipment port and the ultimate destination. The Recipe Midpoint (H) and Intergovernmental Panel on Climate Change (IPCC) methods of environmental impact categories were used in accordance with the cradle-to-grave scope. The BRLUC regionalised model (v1.3) was used to quantify land use change (LUC). According to the observed results, LUC was primarily responsible (between 3.8 and 32.69 tCO₂ Eq·ha⁻¹·year⁻¹) for the global warming potential (GWP) of the soybean supply chain when rainforest-occupied land was converted into cropland. The soybean harvest in the Redenção pole is better loaded through the port of Itaqui (TEGRAM), which is in São Luis (state of Maranhão), due to the use of multiple modes of transport (lorry + train), allowing for better logistical performance and less impact on the environment, despite the longest distance (road + railway = 1306 km). Due to the short road distance (approximately 350 km) and consequently lower environmental impact, soybean harvested in the Paragominas pole is better loaded through the ports around Barcarena in the state of Pará. Full article

Traditional approaches for process modeling and process analysis tend to focus on one type of object (also referred to as cases or instances), and each event refers to precisely one such object. This simplifies modeling and analysis, e.g., a process model merely describes the lifecycle of one object (e.g., a production order or an insurance claim) in terms of its activities (i.e., event types). However, in reality, there are often multiple objects of different types involved in an event. Think about filling out an electronic form referring to one order, one customer, ten items, three shipments, and one invoice. Object-centric process mining (OCPM) takes a more holistic and more comprehensive approach to process analysis and improvement by considering multiple object types and events that involve any number of objects. This paper introduces object-centric event data (OCED) and shows how these can be used to discover, analyze, and improve the fabric of real-life, highly intertwined processes. This tutorial-style paper presents the basic concepts, object-centric process-mining techniques, examples, and formalizes OCED. Fully embracing object centricity provides organizations with a “three-dimensional” view of their processes, showing how they interact with each other, and where the root causes of performance and compliance problems lie. Full article

Background: Finding trends in freight transportation activities enables businesses and policy makers to build an understanding of freight transportation patterns and their impact on logistics planning when making investments in a region’s transportation infrastructure and intermodal freight transport system. To the best of our knowledge, there is limited literature and data-driven analysis about trends in transportation mode choices and the influencing factors in Atlantic Canada. Methods: In this study, a data-driven method has been used to analyze the Canadian Freight dataset to identify trends in transportation activities within Maritime, Canada. Freight transportation mode, product categories, distance, number/weight of shipments, and revenue were examined. Results: The results revealed that the top five product categories exported from Atlantic provinces to the rest of Canada, the US, and Mexico are miscellaneous items, food products, forest products, minerals, and other manufactured goods, where Truck for Hire is the most deployed mode of transportation. A multiple linear regression analysis indicated that the weight, distance, and number of shipments are positively and rather strongly correlated with revenue generation. Conclusions: This study provides a unique overview of Canadian Freight Analysis Framework (CFAF) data with a focus on maritime activities. Full article

This paper introduces Split Delivery Clustered Vehicle Routing Problem with Soft cluster conflicts and Customer-related costs (SDCVRPSC) arising in automotive parts of milk-run logistics with supplier cluster distribution in China. In SDCVRPSC, customers are divided into different clusters that can be visited by multiple vehicles, but each vehicle can only visit each cluster once. Penalty costs are incurred when traveling between clusters. The transportation cost of a route is calculated as the maximum direct shipment cost between customers on the route plus the total drop costs. The SDCVRPSC aims to minimize the sum of transportation costs and penalty costs by determining the assignment of customers to vehicles and the visiting order of clusters. We propose an integer linear programming model and a two-level variable neighborhood descent algorithm (TLVND) that includes two-stage construction, intensification at cluster and customer levels, and a perturbation mechanism. Experimental results on designed SDCVRPSC benchmark instances demonstrate that TLVND outperforms the Gurobi solver and two adapted algorithms at the business operation level. Moreover, a real case study indicates that TLVND can bring significant economic savings compared to expert experience decisions. TLVND has been integrated into the decision support system of the case company for daily operations. Full article

The new breakthrough cystic fibrosis (CF) drug combination of ivacaftor (IVA), tezacaftor (TEZ), and elexacaftor (ELX), namely “caftor” drugs, directly modulates the activity and trafficking of the defective CF transmembrane conductance regulator protein (CFTR) underlying the CF disease. The role of therapeutic drug monitoring (TDM) of caftor drugs in clinical settings has recently been established. The availability of reliable and robust analytical methods for the quantification of IVA, TEZ, and ELX is essential to support dose–concentration–effect studies. We have developed and validated a new liquid chromatography–tandem mass spectrometry (LC–MS/MS) for the rapid and simultaneous quantification of IVA, TEZ, and ELX from the plasma of CF patients. The method was based on a rapid extraction protocol from 50 μL human plasma and separation on a reversed-phase C-18 HPLC column after the addition of deuterated internal standards. Accurate analyte quantification using multiple reaction monitoring (MRM) detection was then obtained using a Thermofisher Quantiva triple-quadrupole MS coupled to an Ultimate 3000 UHPLC. The method has been validated following international (EMA) guidelines for bioanalytical method validation and has been tested on plasma samples from 62 CF patients treated with the three-drug combination IVA/TEZ/ELX, marketed as Kaftrio^® or Trikafta^®, in steady-state condition. The assay was linear over wide concentration ranges (0.008–12 mg/L) in plasma for IVA, TEZ, and ELX, suitable for a broad range of plasma concentrations, and accurate and reproducible in the absence of matrix effects. The stability of analytes for at least 30 days at room temperature could allow for cost-effective shipment and storage. On the same day of sample collection, a sweat test was evaluated for 26 associated patients’ samples, FEV1 (%) for 58, and BMI was calculated for 62. However, Spearman correlation showed no correlation between C_through plasma concentrations of analytes (IVA, TEZ, ELX) and sweat test, FEV1 (%), or BMI. Our method proved to be suitable for TDM and could be helpful in assessing dose–concentration–response correlations in larger studies. Full article

Intermodal transportation is the use of multiple modes of transportation, which can lead to greater sustainability by reducing environmental impact and traffic congestion and increasing the efficiency of supply chains. One of the preconditions for efficient intermodal transport is the efficient intermodal terminal (IT). ITs allow for the smooth and efficient handling of cargo, thus reducing the time, cost, and environmental impact of transportation. Adequate selection of subsystem technologies can significantly improve the efficiency and productivity of an IT, ultimately leading to cost savings for businesses and a more efficient and sustainable transportation system. Accordingly, this paper aims to establish a framework for the evaluation and selection of appropriate technologies for IT subsystems. To solve the defined problem, an innovative hybrid multi-criteria decision making (MCDM) model, which combines the fuzzy factor relationship (FFARE) and the fuzzy combinative distance-based assessment (FCODAS) methods, is developed in this paper. The FFARE method is used for obtaining criteria weights, while the FCODAS method is used for evaluation and a final ranking of the alternatives. The established framework and the model are tested on a real-life case study, evaluating and selecting the handling technology for a planned IT. The study defines 12 potential variants of handling equipment based on their techno-operational characteristics and evaluates them using 16 criteria. The results indicate that the best handling technology variant is the one that uses a rail-mounted gantry crane for trans-shipment and a reach stacker for horizontal transport and storage. The results also point to the conclusion that instead of choosing equipment for each process separately, it is important to think about the combination of different handling technologies that can work together to complete a series of handling cycle processes. The main contributions of this paper are the development of a new hybrid model and the establishment of a framework for the selection of appropriate IT subsystem technologies along with a set of unique criteria for their evaluation and selection. Full article

Ambrosia beetles are the largest group of beetles living mutualistically with ambrosia fungi. Increased global shipments of forest and agricultural products have expanded the distribution of some species of ambrosia beetle. We investigated the partitioning diversity of the ambrosia beetle community on teak plantations in Indonesia’s Java, Sumbawa, and Sulawesi Islands. The ambrosia beetles were collected on the twelve sites of teak plantations with different managements (un-thinned and thinned) in Java, Sulawesi, and Sumbawa Islands. Ambrosia beetles were collected ten times at 7-day intervals. The diversity of ambrosia beetles recorded in teak plantations across twelve sites in Java, Sumbawa, and Sulawesi Islands were 17 species and 6154 individuals. Xyleborus affinis (47.17%), Xylosandrus crassiusculus (27.64%), and Hypothenemus sp. (12.33%) were the three dominant species. The highest and lowest species richness were found in the teak plantations in Java and Sumbawa Islands, respectively. The highest and lowest populations of ambrosia beetles were in Sulawesi and Sumbawa islands, respectively. Three factors contribute to the species richness of ambrosia beetles, i.e., temperature, rainfall, and altitude. Stand age, temperature, rainfall, altitude, and teak management contribute to ambrosia beetle abundance. Ambrosia beetle communities among islands show differences between each group, as confirmed by analysis of variance based on homogeneity of multivariate dispersion (sig. 0.001) and permutation test for homogeneity of multivariate dispersions (sign. 0.001). For the group of teak managements, there are differences between both teak managements, as confirmed by analysis of variance based on homogeneity of multivariate dispersion (sig. 0.001) and permutation test for homogeneity of multivariate dispersions (sign. 0.01). Based on the eigenvalues for PCoA axes by the Bray–Curtis method, Sulawesi Island is separate from both Java, and Sumbawa islands. However, Java and Sumbawa islands overlap each other. For groups of teak managements (thinning and non-thinning), there are overlap with each other based on the eigenvalues for PCoA axes by the Bray–Curtis method. The β_-1 (Within bottle trap/local scale) contributes the highest to γ-diversity (42.46%). The relative contribution of species replacement (β_-sim) in multiple sites across Java, Sumbawa, and Sulawesi Islands (regional scale) provides a high contribution (85%) to overall beta diversity, and the relative contribution of β_-nes to the β_-sor among sites is 14.03%. Full article