A multi-objective fuzzy programming model for port tugboat scheduling based on the Stackelberg game.

To solve the optimization problem of tugboat scheduling for assisting ships in entering and exiting ports in uncertain environments, this study investigates the impact of the decisions of tugboat operators and port dispatchers on tugboat scheduling under the scenario of dynamic task arrival and fuzzy tugboat operation time. Considering the features of the shortest distance tugboat principle, the first available tugboat principle, and the principle of fairness in the task volume of each tugboat, the tugboat company aims to minimize the total daily fuel consumption of tugboat operations, maximize the total buffer time of dynamic tasks, and minimize the total completion time as the objective functions. Due to the limitations of port vessel berthing and departure, as well as the allocation standards for piloting or relocating tugboats, the present study proposes a Stackelberg game-based fuzzy model for port tugboat scheduling with the tugboat operator and port dispatcher acting as decision makers at the upper and lower levels, respectively. A seagull optimization algorithm based on priority encoding and genetic operators is designed as a solution approach. CPLEX, genetic algorithm, standard seagull optimization algorithm, and simulated annealing algorithm are used to compare and analyze the solution results for the 45 problem cases generated from the actual data obtained from the Guangzhou Port. The results verify the efficiency of the proposed seagull optimization algorithm based on priority encoding and genetic operators. Furthermore, additional experiments are conducted to evaluate the changes in fairness coefficient, uncertain parameter correlation coefficients, and objective function correlation coefficients to demonstrate the practicality of the fuzzy programming model. This analysis involves adjusting the confidence level incrementally from 0 to 100% with respect to the model's uncertain parameters.

An Improved Migratory Birds Optimization Algorithm for Closed- Loop Supply Chain Network Planning in a Fuzzy Environment.

Recycling of used products can provide substantial economic and environmental benefits for supply chain players. However, many factors associated with the design of closed-loop supply chain networks are uncertain in their nature, including demand, opening cost of facilities, capacity of opened facilities, transportation cost, and procurement cost. Therefore, this study proposes a novel fuzzy programming model for closed-loop supply chain network design, which directly relies on the fuzzy ranking method based on a credibility measure. The objective of the presented optimization model aims at minimizing the total cost of the network when selecting the facility locations and transportation routes between the nodes of the network. Based on the problem characteristics, a Migratory Birds Optimization Algorithm with a new product source encoding scheme is developed as a solution approach. The inspiration for the product source coding method originates from the label information of raw material supplier and manufacturing factories on product packaging, as well as the information of each logistics node on the delivery order. This novel encoding method aims to address the limitations of four traditional encoding methods: Prüfer number based encoding, spanning tree based encoding, forest data structure based encoding, and priority based encoding, thereby increasing the likelihood of heuristic algorithms finding the optimal solution. Thirty-five illustrative examples are developed to evaluate the proposed algorithm against the exact optimization method (LINGO) and a Genetic Algorithm, Ant Colony Optimization, Simulated Annealing, which are recognized as well-known metaheuristic algorithms. The results from extensive experiments show that the proposed algorithm is able to provide optimal and good-quality solutions within acceptable computational time even for large-scale numerical examples. The suitability of the model is confirmed through a meticulous sensitivity analysis. This analysis involves adjusting the confidence level incrementally from 50% to 100%, in 5% intervals, with respect to the model's uncertain parameters. Consequently, it yields valuable managerial insights. The outcomes of this research are expected to provide scientific support for related supply chain enterprises and stakeholders.

Impact of COVID-19 Pandemic on Daily Mobility of the Elderly Living in Small Cities in Lodz Province.

The article presents a study into the impact that the COVID-19 pandemic had on the daily mobility of those over 60 residing in small towns in the Lodz Province. The study determines the impact on the trip destination, trip frequency, preferred means of transport, distance and duration of trips, and length of the target activity. To achieve these objectives, a survey was conducted using the CATI technique (Computer Assisted Telephone Interviewing), which comprised 500 residents of small towns in the Lodz Province aged 60+, who were divided into three classes of small towns (by population size). In order to determine the impact of the COVID-19 pandemic on the daily mobility of those over 60, the tools the authors decided to use descriptive statistics and hypothesis testing. Overall, the pandemic was found to have had only a minor impact on the changes in transport behavior of those over 60 in small towns. Only 9% of respondents declared any effect on their daily mobility. The impact mainly involved a reduction in travel time and frequency, primarily among the oldest residents. Since a low level of daily mobility leads to low social activity, especially for the elderly-with a consequent sense of loneliness or even depression-towns should take measures to improve the already poor situation, one that has been further exacerbated by the pandemic.

From concept to practicality: Unmanned vessel research in China.

Maritime Autonomous Surface Ships (MASS) have received increasing attention from industrial practitioners, researchers, and policymakers because of industry 4.0 and the digitization of the maritime industry. Crucial questions related to security, safety of personnel and vessels, and socio-economic domains have been addressed to a certain extent. In recent years, China has arisen as one of the leading maritime players worldwide, and unmanned vessels could remarkably influence the Chinese maritime industry. However, there is still a lack of systematic studies aiming to develop a deep understanding of potential advantages and challenges associated with the deployment of unmanned vessels in China. Therefore, using a mixed-method research design, this study attempts to obtain valuable insights based on the viewpoints of the key Chinese stakeholders concerning unmanned vessels, including the benefits, the restrictions, the obstacles to large-scale implementation, the risks, and how to mitigate possible implementation barriers. The main advantage of deploying unmanned ships was found to be the reduction in the ship crew size or complete elimination of the ship crew, which would reduce the operating costs and eliminate human errors on board the ships. Nevertheless, along with important advantages, a number of challenges associated with the development and deployment of unmanned ships were identified, including technological challenges, regulatory challenges, safety and security challenges, and technology investment challenges. All these challenges have to be adequately addressed by the relevant stakeholders to ensure the successful deployment of unmanned ships around the globe in the following years.

A survey of multi-criteria decision-making techniques for green logistics and low-carbon transportation systems.

With the increasing severity of environmental problems, low-carbon development has become an inevitable choice. Nowadays, low-carbon green sustainable development is influenced by a variety of factors such as social, environmental, technological, and economic development levels, making its development complex, which in turn imposes challenges on decision-makers. In this context, the application of multi-criteria decision-making (MCDM) in different areas of sustainable development engineering has become a hot topic. Although many reviews of MCDM techniques already exist, there is a lack of holistic review efforts on MCDM in the field of low-carbon transport and green logistics. Considering these shortcomings in the state of the art, this paper systematically reviews more than 190 papers from 2010 to 2022, constructs a general structure of MCDM techniques for this research topic, provides a comprehensive review and analysis of it, and clarifies the current practices. Furthermore, future directions for the development of MCDM techniques for green logistics and low-carbon transportation systems are presented as well.

Supply chain disruption during the COVID-19 pandemic: Recognizing potential disruption management strategies.

The COVID-19 pandemic has made a significant impact on various supply chains (SCs). All around the world, the COVID-19 pandemic affects different dimensions of SCs, including but not limited to finance, lead time, demand changes, and production performance. There is an urgent need to respond to this grand challenge. The catastrophic impact of the COVID-19 pandemic prompted scholars to develop innovative SC disruption management strategies and disseminate them via numerous scientific articles. However, there is still a lack of systematic literature survey studies that aim to identify promising SC disruption management strategies through the bibliometric, network, and thematic analyses. In order to address this drawback, this study presents a set of up-to-date bibliometric, network, and thematic analyses to identify the influential contributors, main research streams, and disruption management strategies related to the SC performance under the COVID-19 settings. The conducted analyses reveal that resilience and sustainability are the primary SC topics. Furthermore, the major research themes are found to be food, health-related SCs, and technology-aided tools (e.g., artificial intelligence (AI), internet of things (IoT), and blockchains). Various disruption management strategies focusing on resilience and sustainability themes are extracted from the most influential studies that were identified as a part of this work. In addition, we draw some managerial insights to ensure a resilient and sustainable supply of critical products in the event of a pandemic, such as personal protective equipment (PPE) and vaccines.

Healthcare Supply Chain Management under COVID-19 Settings: The Existing Practices in Hong Kong and the United States.

COVID-19 is recognized as an infectious disease generated by serious acute respiratory syndrome coronavirus 2. COVID-19 has rapidly spread all over the world within a short time period. Due to the coronavirus pandemic transmitting quickly worldwide, the impact on global healthcare systems and healthcare supply chain management has been profound. The COVID-19 outbreak has seriously influenced the routine and daily operations of healthcare facilities and the entire healthcare supply chain management and has brough about a public health crisis. As making sure the availability of healthcare facilities during COVID-19 is crucial, the debate on how to take resilience actions for sustaining healthcare supply chain management has gained new momentum. Apart from the logistics of handling human remains in some countries, supplies within the communities are urgently needed for emergency response. This study focuses on a comprehensive evaluation of the current practices of healthcare supply chain management in Hong Kong and the United States under COVID-19 settings. A wide range of different aspects associated with healthcare supply chain operations are considered, including the best practices for using respirators, transport of life-saving medical supplies, contingency healthcare strategies, blood distribution, and best practices for using disinfectants, as well as human remains handling and logistics. The outcomes of the conducted research identify the existing healthcare supply chain trends in two major Eastern and Western regions of the world, Hong Kong and the United States, and determine the key challenges and propose some strategies that can improve the effectiveness of healthcare supply chain management under COVID-19 settings. The study highlights how to build resilient healthcare supply chain management preparedness for future emergencies.

A Review of Historical Changes of Tropical and Extra-Tropical Cyclones: A Comparative Analysis of the United States, Europe, and Asia.

Tropical cyclones are highly destructive weather systems, especially in coastal areas. Tropical cyclones with maximum sustained winds exceeding 74 mph (≈119 kph) are classified as typhoons in the Northwest Pacific, whilst the term 'hurricanes' applies to other regions. This study aims to investigate the general characteristics of the most devastating and catastrophic tropical cyclones in the USA Europe, and Asia. To achieve the study objectives, the three most devastating typical tropical cyclones in each region were selected. The tropical cyclones were examined based on various features, such as the number of deaths, minimum pressure, highest wind speed, total financial losses, and frequency per year. In contrast to Europe and Asia, the USA has recorded the highest number of catastrophic tropical cyclones. The damage induced by hurricanes Katrina, Harvey, and Maria in the USA totalled approximately USD USD 380 billion. In addition, the present research highlights the demand to improve the public attitude and behaviour toward the impact of climate change along with the enhancement of climate change alleviation strategies. The number of intense tropical cyclones is expected to rise, and the tropical cyclone-related precipitation rate is expected to increase in warmer-climate areas. Stakeholders and industrial practitioners may use the research findings to design resilience and adaptation plans in the face of tropical cyclones, allowing them to assess the effects of climate change on tropical cyclone incidents from an academic humanitarian logistics viewpoint in the forthcoming years.

Implementing Public Health Strategies-The Need for Educational Initiatives: A Systematic Review.

In the absence of a specific treatment or vaccines, public health strategies are the main measures to use in the initial stages of a pandemic to allow surveillance of infectious diseases. During the ongoing global pandemic of coronavirus disease 2019 (COVID-19), several countries initiated various public health strategies, such as contact tracing and quarantine. The present study aims to conduct a systematic literature review to identify the presence of educational initiatives that promote the implementation of public health strategies before public health emergencies, with a special focus on contact tracing applications. Using Science Direct, PubMed, Scopus, and Gothenburg University search engines, all published scientific articles were included, while conference, reports, and non-scientific papers were excluded. The outcomes of the reviewed studies indicate that the effective implementation of public health strategies depends on the peoples' willingness to participate and collaborate with local authorities. Several factors may influence such willingness, of which ethical, psychological, and practical factors seem to be the most important and frequently discussed. Moreover, individual willingness and readiness of a community may also vary based on the acquired level of knowledge about the incident and its cause and available management options. Educational initiatives, proper communication, and timely information at the community level were found to be the necessary steps to counteract misinformation and to promote a successful implementation of public health strategies and attenuate the effects of a pandemic. The systematic review conducted as a part of this study would benefit the relevant stakeholders and policy makers and assist with effective designing and implementation.

Preventive maintenance for the flexible flowshop scheduling under uncertainty: a waste-to-energy system.

Nowadays, an efficient and robust plan for maintenance activities can reduce the total cost significantly in the equipment-driven industry. Maintenance activities are directly associated with the impact on the plant output, production quality, production cost, safety, and the environmental performance. To address this challenge more broadly, this paper presents an optimization model for the problem of flexible flowshop scheduling in a series-parallel waste-to-energy (WTE) system. To this end, a preventive maintenance (PM) policy is proposed to find an optimal sequence for processing tasks and minimize the delays. To deal with the uncertainty of the flexible flowshop scheduling of waste-to-energy in practice, the work processing time is modeled to be uncertain in this study. Therefore, a robust optimization model is applied to address the proposed problem. Due to the computational complexity of this model, a novel scenario-based genetic algorithm is proposed to solve it. The applicability of this research is shown by a real-life case study for a WTE system in Iran. The proposed algorithm is compared against an exact optimization method and a canonical genetic algorithm. The findings confirm a competitive performance of the proposed method in terms of time savings that will ultimately save the cost of the proposed PM policy.

The Impact of Risk Perception on Social Distancing during the COVID-19 Pandemic in China.

Social distancing is one of the most recommended policies worldwide to reduce diffusion risk during the COVID-19 pandemic. Based on a risk management perspective, this study explores the mechanism of the risk perception effect on social distancing in order to improve individual physical distancing behavior. The data for this study were collected from 317 Chinese residents in May 2020 using an internet-based survey. A structural equation model (SEM) and hierarchical linear regression (HLR) analyses were conducted to examine all the considered research hypotheses. The results show that risk perception significantly affects perceived understanding and social distancing behaviors in a positive way. Perceived understanding has a significant positive correlation with social distancing behaviors and plays a mediating role in the relationship between risk perception and social distancing behaviors. Furthermore, safety climate positively predicts social distancing behaviors but lessens the positive correlation between risk perception and social distancing. Hence, these findings suggest effective management guidelines for successful implementation of the social distancing policies during the COVID-19 pandemic by emphasizing the critical role of risk perception, perceived understanding, and safety climate.

Multivariate random parameter Tobit modeling of crashes involving aging drivers, passengers, bicyclists, and pedestrians: Spatiotemporal variations.

The increase in 65 years and older population in the United States compels the investigation of the crashes involving all aging (65+) roadway users (drivers, passengers, bicyclists, and pedestrians) in order to ensure their safety. As such, the objective of this research is to provide a spatiotemporal comparative investigation of the crashes involving these aging roadway users in Florida via concurrently using the same set of predictors in order to obtain comparable findings among them. First, a new metric, namely Crash Rate Difference (CRD) approach is developed, which enables one to capture potential spatial and temporal (e.g., weekend and weekday) variations in crash rates of aging user-involved crashes. Second, a multivariate random parameter Tobit model is utilized to determine the factors that drive both the crash occurrence probability and the crash rate of 65+ roadway users, accounting for the unobserved heterogeneity. Findings show that there are statistically significant heterogeneous effects of predictors on the crash rates of different roadway users, which evidences the unobserved heterogeneity across observations. Results also indicate that the presence of facilities such as hospitals, religious facilities, or supermarkets is very influential on crash rates of 65+ roadway users, advocating that roadways around these facilities should be particularly scrutinized by road safety stakeholders. Interestingly, the effect of these facilities on crashes also differs significantly between weekdays and weekends. Moreover, the roadway segments with high crash rates vary temporally depending on whether it is a weekday or a weekend. These findings regarding the spatiotemporal variations clearly indicate the need to develop and design better traffic safety measures and plans addressing these specific roadway segments, which can be tailored to alleviate traffic safety problems for 65+ roadway users.