Suppliers can use different strategies to distribute their products, Among the most common complex optimization problems related to the transportation of products is the traveling salesman problem. In the traveling-salesman problem, a route is chosen that visits each node exactly once, taking into account the shortest travel time, and finally returns to the original node. In this problem, all nodes must be visited. If we consider the application of this problem in logistics, we can study the necessity of this problem in transportation means such as trucks or drones. The upcoming paper is thoroughly studied and researched considering the related articles published in the last three decades, and bibliometric analysis is used for the details of this problem. This paper aims to statistically evaluate the influence and importance of the traveling salesman on logistics without considering financial risk by presenting an analysis of the works published between 1983 and 2023. As part of our comprehensive literature review table with analysis of export, we will conduct a comprehensive review of the most relevant articles in the field from 2020 to 2023 to better understand the trend in the subject in the last few years. Data were obtained from the Web of Science and focused on metrics such as the total number of publications, citations, average citations per publication, and trending countries. Graphical and statistical analysis was performed using Excel and R-Studio. China, the USA, and Germany are the countries with the most publications. Laporte is the most prolific author with 8 publications. Much research has been done on this topic, especially in the Journal of transportation research part E-logistic with 43 articles, and the main application areas are logistics, vehicles, and drones. These data may prove useful to researchers seeking an overview of the traveling salesman problem to determine future research directions.

Hospital hazards and human errors pose a significant and complex problem, with rising incidents and irreversible consequences. Managing laboratory errors and risks is vital due to the presence of chemicals, electrical equipment, and the involvement of students, professors, and staff. The high value of laboratory equipment further underscores the need for robust risk management strategies. To address these challenges, researchers have explored the Failure Mode and Effects Analysis (FMEA) method for risk identification and assessment in healthcare settings. However, recognizing its limitations, this study aims to prioritize and evaluate laboratory errors using an integrated approach that combines the Best-Worst Method (BWM) and Complex Proportional Assessment with a Fuzzy Spherical Environment (CoCoSo-FSE). By applying the BWM, criteria such as severity, detectability, and occurrence probability are weighted to account for the nature of laboratory errors. The CoCoSo-FSE is then employed to evaluate and prioritize 18 identified laboratory errors, reducing uncertainty and enhancing decision-making. The fuzzy spherical set is used to address uncertainties by providing a flexible framework for decision-makers to define membership functions in specific spherical regions, enhancing the representation of knowledge and decision-making information. The proposed approach is compared with other decision-making methods, namely MOORA and COPRAS, demonstrating reliable ranking results. Sensitivity analysis confirms the stability of the approach's ranking when adjusting the flexibility parameter. This integrated approach offers a reliable and robust decision-making technique for managing laboratory errors, providing valuable insights to enhance laboratory safety and risk management for stakeholders, managers, and policymakers.

Nowadays, tardiness has become a significant risk in the logistics industry. To address this problem, we introduce the tardiness risk index to quantify both the magnitude of the tardiness risk and the maximum probability of tardiness occurring. In this paper, we investigate the contract design problem with the tardiness risk index to mitigate the tardiness risk when a fourth-party logistics company (4PL) delegates the delivery task of a client to a third-party logistics company (3PL). Specifically, the contracts are designed in a decentralized system with information symmetry and information asymmetry when 3PL is risk neutral and risk averse. Furthermore, the incentive problems demonstrated that the 3PL is encouraged to make the optimal effort for delivery and the 4PL determines the optimal fixed payment and penalty coefficient. Through analyzing the experimental simulation results, we can find that the contract can effectively mitigate the tardiness risk and the maximum probability of risk occurrence.

The rapid development of technology has enabled companies to integrate internal and external partners working together in the supply chain network. Supply chain integration allows fast information to facilitate real-time and reliable decision-making. This study investigates the role of supply chain integration on firm performance through adopting lean manufacturing, green supply chain management, and risk management. The study surveyed manufacturing companies implementing ISO 14000 to represent green supply chain management and integrated information technology as a form of integration. The questionnaires were distributed using a Google form, and 93 valid responses were obtained. Data analysis employed a partial least square approach with SmartPLS software 4.1 version. The data processing results found that supply chain integration increased lean manufacturing by 0.684, green supply chain management by 0.451, and supply chain risk management by 0.333. Lean manufacturing companies using a continuous process control system and process improvements significantly improve green supply chain management by a path coefficient of 0.477, supply chain risk management by 0.206, and firm performance by 0.370. Green supply chain management significantly impacts supply chain risk management by a coefficient of 0.416 and firm performance by 0.189. Supply chain risk management with a system for detecting operational process risks and emergency procedures in overcoming changes in customer orders affects the increase in firm performance by 0.354. The practical contribution of research provides insight for practitioners to invest in information technology and adopt ISO 14000 implementation. Theoretical contributions in developing resources-based view theory in adopting green supply chain management and lean manufacturing.

In this descriptive analytical study conducted within the Jordanian Customs Department, the influence of Customs Intelligence and Risk Management on Sustainable Supply Chain practices and their subsequent effects on Customs Department Logistics were investigated. Data collected through a structured questionnaire distributed to department employees were analyzed using Smart PLS-4. The findings revealed that Customs Intelligence significantly shapes Sustainable Supply Chain practices, emphasizing the importance of data driven decision-making in achieving sustainability goals. Effective Risk Management strategies were found to positively contribute to Sustainable Supply Chain initiatives, highlighting the symbiotic relationship between risk mitigation and sustainability. Sustainable Supply Chain practices, in turn, were demonstrated to enhance the efficiency of Customs Department Logistics. Furthermore, the study unveiled that Sustainable Supply Chain acts as a mediator, enhancing the impact of both Risk Management and Customs Intelligence on Logistics outcomes. These findings collectively underscore the intricate dynamics of these factors in the context of the Jordanian Customs Department, providing valuable insights for optimizing logistics operations, ensuring compliance, and fostering sustainability. This paper contributes valuable insights and empirical evidence to the fields of customs-related logistics, risk management, sustainable supply chain management, and logistics operations within the Jordanian Customs Department. The paper provides recommendations that have the potential to inform and improve logistics practices, benefiting stakeholders in both the public and private sectors and advancing the understanding of these critical dynamics in the broader logistics and supply chain management discipline.

As supply networks become more complex and international, the task of controlling associated risks becomes more difficult. This article investigates the usefulness of risk alert technologies in supply chain management, with a focus on Big Data Analytics (BDA) and Artificial Intelligence (AI). The study investigates the impact of BDA capabilities, solid IT infrastructure, managerial views, and AI-apprehensions on the effectiveness of risk alert tools using a questionnaire-based survey of 420 managerial personnel and Structural Equation Modeling (SEM) via SMART PLS. The work proposes the concept of AI-Apprehensions as a moderating variable, which is a relatively unexplored field. According to the findings, while BDA capabilities and IT infrastructure considerably improve the effectiveness of risk alert tools, AI-apprehensions can negate these advantages. The study provides useful insights for policymakers and practitioners, emphasizing the importance of balancing technical and human components for effective risk management.

This paper uses the DCC-GARCH and Value at Risk (VaR) model to analyze the fluctuation, linkage, and price volatility risk among coffee price series in the period of 2004 - 2020. In terms of the fluctuation, the study points out, the volatility of Vietnamese coffee price and the price of Robusta coffee in two markets were affected by two ARCH terms and GARCH terms at 1 percent level. Meanwhile, the coffee price of Brazil and Colombia is only impacted by the ARCH term. The linkage between Brazil and Colombia is the biggest. The average coefficient linkage among Vietnam with two main competitors is relatively small. In terms of price volatility risk, the price volatility risk of Vietnamese coffee is the smallest and the biggest risk is belonging to Brazilian coffee price. The results obtained would be a valuable reference for stakeholders, policymakers, coffee processing and exporting enterprises, and coffee farmers to clearly understand the fluctuation and linkage among coffee export price series, and thereby have appropriate and effective solutions and strategies in price volatility risk management to sustainable development.

The purpose of this paper is to investigate potential links between Blockchain technology (BCT) and big data analytics (BDA) with supply chain risk management (SCRM) and supply chain performance (SCP) in the Jordanian Chemical and Cosmetic Industries Sector. Additionally, the paper tests a conceptual model that links SCRM to indirect effects. To test our proposition, data were collected from 364 employees working in Jordanian Chemical and Cosmetic Industries Sector. The data were analyzed using structural equation modeling with aid of the Lavaan R package. The results show that the influences of blockchain technology and big data analytics on supply chain performance do occur directly, and indirectly through the cascading of a supply chain risk management.

Despite the importance of risk management in the banking sector, little research has been devoted to the Ara-bian settings. This paper investigates the effect of risk management practices (i.e., understanding risk and risk management, risk analysis and assessment, risk identification, risk monitoring, and credit risk analysis) on Jordanian commercial banks’ performance. The study utilizes a quantitative approach by obtaining survey data from risk managers and employees in risk management departments (n=23) of commercial banks. A partial least squares structural equation modeling (PLS-SEM) was applied on the data and the results showed that the components of risk management practices had positive and significant impacts on the performance of banks. In sum, the findings corroborate existing work in the Western settings and underscore the importance of risk management in the Arabian banking context.

Project Risk Management contains processes ranging from planning to control. It is applied to identify risks, analyze them, and design responses to change the occurrence rate and/or the effect of project risks. It is important for project managers to analyze the effects of the risks in projects and also consider project risks in their decisions. If project risks are not addressed during the risk management process, issues such as schedule delays, cost overruns, and even project failure may occur. This paper aims to introduce a Markov method to integrate project risk analysis and risk response planning. This method is applied to forecast the following status of the project when limited information about the project is available. Moreover, earned value management (EVM) methods were used to include various types of project risks through the project lifecycle. The model also offers the capability to choose the most effective risk response for managing project risks through the application of the Markov decision process (MDP). Eventually, we introduce a case study to demonstrate functionality and effectiveness of the presented approach. Solving the model allows for identifying the best set of risk response strategies tailored to each specific project state. The computational results illustrate that the current state of the project has a significant impact in the process of risk response planning. Since uncertainty is the inherent characteristic of projects, the use of the project’s current state is more reliable than the previous status of projects, and the Markov method is applied in this research because it uses the current state for its modelling. Using this method, managers can predict the future state of projects and find the best response in each status of projects.