A Study on Emergency Supply Chain and Risk Based on Urgent Relief Service in Disasters

Ji, Guojun; Zhu, Caihong

doi:10.1016/j.sepro.2012.04.049

Cited by 31 publications

(26 citation statements)

References 15 publications

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“…A fire scenario is characterized in terms of multiple interrelated variables, such as the ignition site, the nature of the fire, the fuel configuration, the number and locations of the occupants, the characteristics of the structure, and the ventilation, among others (Matellini et al 2012). Several other studies have analyzed the emergency levels in affected areas by evaluating the degrees of severity associated with the affected areas Ji and Zhu 2012).…”

Section: Disaster Dynamic Predictionmentioning

confidence: 99%

Emergency logistics for wildfire suppression based on forecasted disaster evolution

2017

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This paper aims to develop a two-layer emergency logistics system with a single depot and multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire propagation model is first built using both the flame-igniting attributes of wildfires and the factors affecting wildfire propagation and patterns. Second, based on the forecasted propagation behavior, the emergency levels of fire sites in terms of demand on suppression resources are evaluated and prioritized. For the second layer, considering the prioritized fire sites, the corresponding resource allocation problem and vehicle routing problem (VRP) are investigated and addressed. The former is approached using a model that can minimize the total forest loss (from multiple sites) and suppression costs incurred accordingly. This model is constructed and solved using principles of calculus. To address the latter, a multi-objective VRP model is developed to minimize both the travel time and cost of the resource delivery vehicles. A heuristic algorithm is designed to provide the associated solutions of the VRP model. As a result, this paper provides useful insights into effective wildfire suppression by rationalizing resources regarding different fire propagation rates. The supporting models can also be generalized and tailored to tackle logistics resource optimization issues in dynamic operational environments, particularly those sharing the same feature of single supply and multiple demands in logistics planning and operations (e.g., allocation of ambulances and police forces).

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Section: Disaster Dynamic Predictionmentioning

confidence: 99%

Emergency logistics for wildfire suppression based on forecasted disaster evolution

2017

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“…SC resilience is important for the effective emergency logistics so that the disaster affected areas receive relief services on time and as fast as possible in the post-disaster phase (Jia and Zhu, 2012). SC resilience can be enhanced through integrated logistics capabilities contributing to the unifi cation and increased connectivity among the SC members, and inter-related SC capabilities (Jüttner and Maklan, 2011) including (Mandal, et al, 2016): the SC's visibility (the degree to which SC partners have access to both operational and managerial information related to the SC) (Barratt and Oke, 2007;Wei and Wang, 2010); SC fl exibility (readiness of a SC with regard to alternate courses of actions through fl exibility in resource planning); and SC velocity (speed to react to market changes) (Richey, 2009).…”

Section: Post-disaster Phase Evacuation and Rescue Phasementioning

confidence: 99%

“…Lill and Perera (2017) emphasized that between 2005-2015, disasters adversely affected approximately 1.5 billion people and caused approximately $1.3 trillion loss (The United Nations Offi ce for Disaster Risk Reduction UNISDR, 2015). For example, as a result of the Sichuan earthquake in 2008, "69,185 people died, 18,403 people went missing and a large number of houses were destroyed, and a large number of people were homeless unable to secure livelihoods" (Lefei and Shuming, 2008;Jia and Zhu, 2012).…”

Section: Introductionmentioning

confidence: 99%

Building Information Modeling as a Tool for Enhancing Disaster Resilience of the Construction Industry

Sertyeşilışık¹

2017

TRANSACTIONS of the VŠB – Technical University of Ostrava, Safety Engineering Series

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As frequencies of the disasters are increasing, new technologies can be used to enhance disaster resilience performance of the construction industry. This paper investigates the usage of BIM (Building Information Modeling) in enhancing disaster resilience of the construction industry and in the establishment of the resilient built environment. In-depth literature review findings reveal BIM’s contribution to the disaster resilience in the pre-disaster and post-disaster phases especially through influencing the performance of the supply chain, construction process, and rescue operations. This paper emphasises the need for BIM’s integration to the education and training curriculums of the built environment professionals. Policy makers, construction professionals, professional bodies, academics can benefit from this research.

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“…Yi and Ozdamar (2007) proposed a mixed integer multi-commodity network flow model that treats vehicles flows for coordinating logistics support in disaster response activities. Likewise Ji and Zhu (2012) and Afshar and Haghani (2012) that developed optimization model describing the integrated logistics operations in response to disasters.…”

Section: The Logistics Featurementioning

confidence: 99%

A Review of Relief Supply Chain Optimization

Manopiniwes¹,

Irohara²

2014

Industrial Engineering and Management Systems

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With a steep increase of the global disaster relief efforts around the world, the relief supply chain and humanitarian logistics play an important role to address this issue. A broad overview of operations research ranges from a principle or conceptual framework to analytical methodology and case study applied in this field. In this paper, we provide an overview of this challenging research area with emphasis on the corresponding optimization problems. The scope of this study begins with classification by the stage of the disaster lifecycle system. The characteristics of each optimization problem for the disaster supply chain are considered in detail as well as the logistics features. We found that the papers related to disaster relief can be grouped in three aspects in terms of logistics attributes: facility location, distribution model, and inventory model. Furthermore, the literature also analyzes objectives and solution algorithms proposed in each optimization model in order to discover insights, research gaps and findings. Finally, we offer future research directions based on our findings from the investigation of literature review.

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A Study on Emergency Supply Chain and Risk Based on Urgent Relief Service in Disasters

Cited by 31 publications

References 15 publications

Emergency logistics for wildfire suppression based on forecasted disaster evolution

Emergency logistics for wildfire suppression based on forecasted disaster evolution

Building Information Modeling as a Tool for Enhancing Disaster Resilience of the Construction Industry

A Review of Relief Supply Chain Optimization

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