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B. Liang
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3 records found
1
Journal article
(2023)
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Zeinab Elmi, Bokang Li, Benbu Liang, Yui yip Lau, Marta Borowska-Stefańska, Szymon Wiśniewski, Maxim A. Dulebenets
Time management is crucial for liner shipping services. A variety of unexpected events can disrupt liner shipping schedules. A real-time port capacity analysis and rescheduling the original ship operations would be necessary to counteract the negative effects of such disruptions. Different ship schedule recovery options can be adopted in response to disruptive events (e.g., ship sailing speed adjustment, skipping of disrupted ports). However, shipping lines face conflicting decisions when selecting ship schedule recovery options. As an example, the commonly-used ship speeding-up option could effectively reduce delays during the voyage but would increase the fuel cost. Similarly, the skipping of disrupted ports may substantially decrease the associated delays but would incur additional costs associated with supply chain disruptions and misconnected cargo. Nevertheless, there is a lack of analytical methods that enable the evaluation of competing objectives in ship schedule recovery and effective multi-objective solution approaches. Therefore, this study proposes a novel multi-objective model for ship schedule recovery that aims not only to minimize the total late ship arrivals at ports but also to minimize the total profit loss due to disruptive events that may occur at sea and/or at ports. An epsilon-constraint-based exact optimization algorithm is adopted to obtain optimal Pareto Fronts. The computational experiments conducted for a real-life transit route demonstrate that the adopted exact optimization algorithm is able to generate Pareto Fronts in a timely manner. Moreover, the conducted sensitivity analyses provide interesting insights regarding the effects of different disruption types and unit fuel costs on ship schedule recovery.
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Time management is crucial for liner shipping services. A variety of unexpected events can disrupt liner shipping schedules. A real-time port capacity analysis and rescheduling the original ship operations would be necessary to counteract the negative effects of such disruptions. Different ship schedule recovery options can be adopted in response to disruptive events (e.g., ship sailing speed adjustment, skipping of disrupted ports). However, shipping lines face conflicting decisions when selecting ship schedule recovery options. As an example, the commonly-used ship speeding-up option could effectively reduce delays during the voyage but would increase the fuel cost. Similarly, the skipping of disrupted ports may substantially decrease the associated delays but would incur additional costs associated with supply chain disruptions and misconnected cargo. Nevertheless, there is a lack of analytical methods that enable the evaluation of competing objectives in ship schedule recovery and effective multi-objective solution approaches. Therefore, this study proposes a novel multi-objective model for ship schedule recovery that aims not only to minimize the total late ship arrivals at ports but also to minimize the total profit loss due to disruptive events that may occur at sea and/or at ports. An epsilon-constraint-based exact optimization algorithm is adopted to obtain optimal Pareto Fronts. The computational experiments conducted for a real-life transit route demonstrate that the adopted exact optimization algorithm is able to generate Pareto Fronts in a timely manner. Moreover, the conducted sensitivity analyses provide interesting insights regarding the effects of different disruption types and unit fuel costs on ship schedule recovery.
Mapping the knowledge domain of soft computing applications for emergency evacuation studies
A scientometric analysis and critical review
Review
(2022)
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Benbu Liang, C. Natalie van der Wal, Kefan Xie, Yun Chen, Frances M.T. Brazier, Maxim A. Dulebenets, Zimei Liu
Emergency evacuation is viewed as a common strategy adopted during the disaster preparedness stage of evacuation to ensure the safety of potentially affected populations. In emergency evacuation studies, soft computing approaches and methodologies have been widely used to support effective decision-making, providing robust and low-cost solutions. To understand the current status and trends of research on soft computing applications for emergency evacuation studies, 778 related studies published in the core database of Web of Science from 2000 to 2020 were considered in this study. A scientometric analysis and a comprehensive review were performed using a scientific mapping of the knowledge domain. This paper presents a set of analyses with the following primary objectives: (1) to explore and visualize the bibliometric characteristics and contents of the academic field concerned with the soft computing approaches for emergency evacuation; and (2) to review and analyze the knowledge, hotspots, and future outlooks related to soft computing approaches for emergency evacuation. The results provide some important insights regarding the existing soft computing methods that have been used in the emergency evacuation field over the past 20 years. Based on the conducted review, this paper proposes that future studies should concentrate on exploring the potential of innovative soft computing approaches for crowd modelling and enabling more accurate evacuation simulation and optimization.
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Emergency evacuation is viewed as a common strategy adopted during the disaster preparedness stage of evacuation to ensure the safety of potentially affected populations. In emergency evacuation studies, soft computing approaches and methodologies have been widely used to support effective decision-making, providing robust and low-cost solutions. To understand the current status and trends of research on soft computing applications for emergency evacuation studies, 778 related studies published in the core database of Web of Science from 2000 to 2020 were considered in this study. A scientometric analysis and a comprehensive review were performed using a scientific mapping of the knowledge domain. This paper presents a set of analyses with the following primary objectives: (1) to explore and visualize the bibliometric characteristics and contents of the academic field concerned with the soft computing approaches for emergency evacuation; and (2) to review and analyze the knowledge, hotspots, and future outlooks related to soft computing approaches for emergency evacuation. The results provide some important insights regarding the existing soft computing methods that have been used in the emergency evacuation field over the past 20 years. Based on the conducted review, this paper proposes that future studies should concentrate on exploring the potential of innovative soft computing approaches for crowd modelling and enabling more accurate evacuation simulation and optimization.
In order to explore impacts of crowd intervention strategies on indoor respiratory exposure risks during major pandemics, a variety of crowd motion scenarios were established in general indoor conditions based on improved pedestrian dynamics model and respiratory infection probability model. Then, multi-agent simulation technology was utilized to simulate impacts of strategies, including protection optimization, pedestrian flow optimization and route optimization, on the exposure risks. The results show that indoor respiratory exposure risks are mainly determined by total pedestrian flow, individuals’ stay length, movement route planning and duration of stay in contaminated areas. The carryover effect will be formed due to pedestrians’ obedience behavior of social distancing, which will further increase exposure time to contaminated areas. The lower pathogen permeability of masks, and the greater space ventilation are, the lower infection probability the crowd will face.
...
In order to explore impacts of crowd intervention strategies on indoor respiratory exposure risks during major pandemics, a variety of crowd motion scenarios were established in general indoor conditions based on improved pedestrian dynamics model and respiratory infection probability model. Then, multi-agent simulation technology was utilized to simulate impacts of strategies, including protection optimization, pedestrian flow optimization and route optimization, on the exposure risks. The results show that indoor respiratory exposure risks are mainly determined by total pedestrian flow, individuals’ stay length, movement route planning and duration of stay in contaminated areas. The carryover effect will be formed due to pedestrians’ obedience behavior of social distancing, which will further increase exposure time to contaminated areas. The lower pathogen permeability of masks, and the greater space ventilation are, the lower infection probability the crowd will face.