Demand uncertainty is an important issue that influences the strategic, tactical, and operational-level decision making in the transportation/logistics/supply chain planning. In this study, we explore the effect of demand uncertainty on the operational-level freight routing problem in the capacitated multimodal transportation network that consists of schedule-based rail transportation and time-flexible road transportation. Considering the imprecise characteristic of the demand, we adopt fuzzy set theory to model its uncertainty and use trapezoidal fuzzy numbers to represent the fuzzy demands. We set multiple transportation orders as the optimization object and employ soft time windows to reflect the customer requirement on on-time transportation. Under the above situation, we establish a fuzzy mixed integer nonlinear programming (FMINLP) model to formulate the capacitated road–rail multimodal routing problem with demand uncertainty and time windows. We first use the fuzzy expected value model and credibility measure based fuzzy chance-constrained programming to realize the defuzziness of the model and then adopt linearization technique to reformulate the crisp model to finally generate an equivalent mixed integer linear programming (MILP) model that can be solved by standard mathematical programming software. Finally, a numerical case is presented to demonstrate the feasibility of the proposed method. Sensitivity analysis and fuzzy simulation are combined to quantify the effect of demand uncertainty on the routing problem and also reveal some helpful insights and managerial implications.

Resource allocation in patient care relies heavily on individual judgements of healthcare professionals. Such professionals perform coordinating functions by managing the timing and execution of a multitude of care processes for multiple patients. Based on advances in simulation, new technologies that could be used for establishing realistic representations have been developed. These simulations can be used to facilitate understanding of various situations, coordination training and education in logistics, decision-making processes, and design aspects of the healthcare system. However, no study in the literature has synthesized the types of simulations models available for non-technical skills training and coordination of care. A systematic literature review, following the PRISMA guidelines, was performed to identify simulation models that could be used for training individuals in operative logistical coordination that occurs on a daily basis. This article reviewed papers of simulation in healthcare logistics presented in the Web of Science Core Collections, ACM digital library, and JSTOR databases. We conducted a screening process to gather relevant papers as the knowledge foundation of our literature study. The screening process involved a query-based identification of papers and an assessment of relevance and quality. Two hundred ninety-four papers met the inclusion criteria. The review showed that different types of simulation models can be used for constructing scenarios for addressing different types of problems, primarily for training and education sessions. The papers identified were classified according to their utilized paradigm and focus areas. (1) Discrete-event simulation in single-category and single-unit scenarios formed the most dominant approach to developing healthcare simulations and dominated all other categories by a large margin. (2) As we approached a systems perspective (cross-departmental and cross-institutional), discrete-event simulation became less popular and is complemented by system dynamics or hybrid modeling. (3) Agent-based simulations and participatory simulations have increased in absolute terms, but the share of these modeling techniques among all simulations in this field remains low. An extensive study analyzing the literature on simulation in healthcare logistics indicates a growth in the number of examples demonstrating how simulation can be used in healthcare settings. Results show that the majority of studies create situations in which non-technical skills of managers, coordinators, and decision makers can be trained. However, more system-level and complex system-based approaches are limited and use methods other than discrete-event simulation.

Hazardous materials transportation involves extensive risk and cannot be avoided in practice. An advanced routing, however, can help to reduce the risk by planning the best transportation routes for hazardous materials that can make effective tradeoffs between the risk objective and the economic objective. In this study, we explore the hazardous materials routing problem in the road-rail multimodal transportation network with a hub-and-spoke structure, in which the risk is measured by the multiplication of population exposure and the associated volume of hazardous materials, and minimizing the total risk of all the transportation orders of hazardous materials is set as the risk objective. It is difficult to estimate the population exposure exactly during the routing decision-making process, which results in its uncertainty. In this study, we formulate the uncertain population exposure from a fuzzy programming perspective by using triangular fuzzy numbers. Moreover, the carbon dioxide emission constraint is formulated to realize the sustainable transportation of hazardous materials. To optimize the problem under the above framework, we first establish a bi-objective fuzzy mixed integer nonlinear programming model, and then develop a three-stage exact solution strategy that the combines fuzzy credibilistic chance constraint, linearization technique, and the normalized weighting method. Finally, a computational experiment is carried out to verify the feasibility of the proposed method in dealing with the problem. The experimental results indicate that tradeoffs between the two conflicting objectives can be effectively made by using the Pareto frontier to the hazardous materials routing problem. Furthermore, the credibility level and carbon dioxide emission cap significantly influence the hazardous materials routing optimization. Their effects on the optimization result are quantified by using sensitivity analysis, which can draw some useful insights to help decision makers to better organize the hazardous materials road-rail multimodal transportation under uncertainty and sustainability.

Medical simulations have led to extensive developments in emergency medicine. Apart from the growing number of applications and research efforts in patient safety, few studies have focused on modalities, research methods, and professions via a synthesis of simulation studies with a focus on non-technical skills training. Intersections between medical simulation, non-technical skills training, and emergency medicine merit a synthesis of progress over the first two decades of the 21st century. Drawing on research from the Web of Science Core Collection’s Science Citation Index Expanded and Social Science Citation Index editions, results showed that medical simulations were found to be effective, practical, and highly motivating. More importantly, simulation-based education should be a teaching approach, and many simulations are utilised to substitute high-risk, rare, and complex circumstances in technical or situational simulations. (1) Publications were grouped by specific categories of non-technical skills, teamwork, communication, diagnosis, resuscitation, airway management, anaesthesia, simulation, and medical education. (2) Although mixed-method and quantitative approaches were prominent during the time period, further exploration of qualitative data would greatly contribute to the interpretation of experience. (3) High-fidelity dummy was the most suitable instrument, but the tendency of simulators without explicitly stating the vendor selection calls for a standardised training process. The literature study concludes with a ring model as the integrated framework of presently known best practices and a broad range of underexplored research areas to be investigated in detail.

Mobile short-form video entertainment enables mainstream public sites to expedite the dissemination of essential information, increase transparency, and maintain trust among decision-making bodies. In view of these benefits, it is necessary to understand the mechanism of citizen engagement with short-form videos using social media almetrics. The purpose of this study is to delineate the driving factors and moderating effects of large-scale interactive participation in TikTok’s public accounts committed to sustainable poverty alleviation, a grand challenge that spreads quickly on the official agenda. Specifically, this study estimates negative binomial models based on the complete set of videos, released between 2018 August (the earliest publication date available) and September 2021, in poverty-striken provinces. The results show that appropriate combinations of common themes, music genres, and sentiment can strengthen the involvement and awareness of short-form video entertainment. It serves as convincing proof that 1) content related to human resource development and public policies stimulated the highest level of citizen engagement, and it exceeded all other categories by a large margin; 2) sentiment’s moderating roles could be partially confirmed across the common themes and music genres; and 3) while being moderated, popular and epic music could either arise or weaken citizen engagement under the topical series, but the benefits of containing these genres were not limited to performance.

Simulation-based analysis has been used for planning, control, and decision-making support of physical internet enabled logistics networks. However, multiagent modelling and simulation based on micro-level interactions have been rarely developed for the pre-studies of digital transformation of urban rail transit systems. This hinders a wider industrial deployment of agent technology in the physical internet enabled transport infrastructure. To fill in this knowledge gap, this work presents an agent-based simulation that explicitly models the micro-level protocols of mobile recourse units and their interaction with the physical infrastructure in a rail-road intermodal transport network. Parameterisation of the simulation model is changeable to examine the influences of different efficiency factors. This allows understanding of which structural functions and resource configuration would make an impact system-wide. Through a practical application, a multiagent system is developed for modelling and analysis of sustainable logistics with individually operated mobile resource units. An agent-based simulation assessment is performed to quantify the improvement options. The results reveal that the physical internet can prevent trucks from empty driving, which has a positive effect on the sustainable logistics operations. The proposed model can be used to support the deployment and planning of digital transformation that could be implemented in urban rail transit systems serving urban distribution and passenger transport.