Probabilistic allocation and scheduling of multiple resources for emergency operations; a Victorian bushfire case study
Introduction
In emergency operations, relief is defined by the Victorian Bushfires Royal Commission (2010) as providing assistance to individuals or groups in danger, or easing their distress. In times of emergency, relief is needed first; the focus later shifts to recovery. The transition from relief to recovery is not always easily defined. A bushfire, more widely known as a wildfire, is one of the many kinds of disasters that affect communities all around the world. A bushfire is an uncontrolled, un-contained fire that burns freely, usually affecting rural and regional areas (Commission & Teague, 2010). While normally viewed as a natural hazard, bushfires may sometimes occur as a result of arson with malicious intent, drawing on emergency relief task forces and resources in a large-scale natural disaster scenario.
In recent years, the number and intensity of bushfires have increased substantially around the globe (Shahparvari, Abbasi, Chhetri, & Abareshi, 2017). Research has been able to draw correlations between this increase in bushfire activity and climate change, as highlighted in various case studies across Australia, USA, Canada, and Russia (Shahparvari et al., 2017, Shahparvari et al., 2017).
Bushfires in Australia have become a recurring seasonal event and the risk is only increasing, as is the cost of bushfire-related disasters. This presents substantial planning and organizational challenges for emergency services to respond effectively and efficiently to bushfire risks. Of the total number of deaths and injuries caused by natural disasters in Australia over the past few decades, Victoria, a state in the south-east of the country, has accounted for 57% of the injuries and 39% of the deaths (Commission & Teague, 2010).
On Saturday, February 7, 2009, also known as Black Saturday, over 400 bushfires started in Victoria, Australia. The day is remembered for the deadliest and most devastating bushfires in the nation's history, with 173 deaths occurring as a result of the uncontrollable fires. However, the Victorian emergency response teams' efforts were hampered by the fact that medical services were unavailable in local areas and some first aid services were uncoordinated in their initial response. Resources such as medical teams and medical supplies were poorly coordinated, especially in providing relief to smaller, remote communities and people who stayed on their properties (Commission & Teague, 2010).
A substantial amount of technology and research is typically available to commercial supply chains. However, the problems and challenges that arise when managing a humanitarian supply chain after a large-scale emergency are usually significantly different from those in commercial application (Campbell, Vandenbussche, & Hermann, 2008; Song, Chen, & Lei, 2018). For governments and humanitarian organizations to respond to such disasters effectively, they must consider the multiple and unique aspects of emergency operations, such as the scarceness of resources and the uncertain nature of disasters (Hu, Liu, & Hua, 2016; Sahebjamnia, Torabi, & Mansouri, 2017). Furthermore, reducing human casualties and fatalities in disaster-stricken locations depends substantially on the earliest possible arrival and rapid deployment of resources for emergency operations. Failure to assign adequate resources in a timely manner has been the root cause of adverse impacts in disaster situations (Arora, Raghu, & Vinze, 2010; Hasanzadeh & Bashiri, 2016; Lee, Fried, Albers, & Haight, 2013; Rolland, Patterson, Ward, & Dodin, 2010). Furthermore, contingencies must be considered in emergency operations because there are multiple uncertain and unpredictable factors in emergency situations. In the case of bushfires, the lack of historical data and the numerous possible patterns of propagation makes it difficult to set exact parameters for the problem. The variables that must be considered in order to achieve better response rates include: crucial demands, competing priorities, time imperatives, and the availability of the necessary resource allocations. However, potential transportation and situational constraints hinder the provision of emergency services (Sheu, 2007a, Sheu, 2007b).
Humanitarian coordinators and decision makers frequently make poor decisions in times of natural disaster by over-relying on their past experiences and being over-confident in their ability to make unaided decisions, as well as utilizing simple decision heuristics (Gonçalves, 2011; Wex, Schryen, Feuerriegel, & Neumann, 2014). The poor decisions that result from this are what inspires this paper to explore a stochastic optimization module that could be deployed by decision makers facing complex tasks and uncertain outcomes. More specifically, this paper puts forward an optimization model which can be used by the Australian agencies in charge of providing rapid responses to natural disasters so that they have a support system to work with when making their decisions.
To achieve better decisions, the proposed research aims to develop a decision support system for emergency relief operations while taking into consideration the management of different types of resources. One of the main contributions of our study is to highlight that the number of available non-expendable resources (i.e. rescue units and medical personnel) affects the processing time and therefore planning of emergency operations. For example, if a vehicle carries four doctors and three nurses as non-expendable resources, and if the next incident requires only two nurses and two doctors, since the remaining one nurse and two doctors can help to expedite the operations, the processing time of this incident is reduced. Therefore, this adjustment should be performed to obtain a more accurate schedule. In addition, the system utilizes a combination of geographical information systems (GIS) and optimization to calculate proximate emergency facilities, the grouping of the demand points as a common approach (e.g. Sheu, 2010), immediate relief centers, and the deployment of material and human resources.
GIS provides more accurate information for the Mixed Integer Programming (MIP) model, increases the level of reliability of the assigned resources and facilities, locates the distribution centers using proximity analysis, and identifies the vulnerability per prone-area, while highlighting fast and safe travel routes. The objective function of the proposed model aims to minimize the total completion time of all demand points weighted by the disaster severity levels. The main contribution of this study is to develop a stochastic multiple resource scheduling (MRSU) framework, which will enable the efficient allocation and scheduling of resources for emergency operations at all demand points during a disaster relief scenario, while also taking into account the uncertain fire severity and disaster relief needs. There are many unpredictable factors to consider when we schedule emergency resources, which should be explicitly considered when modeling disaster relief. This study suggests various relief strategies that have the potential to achieve a nimble, more efficient operation time than the deterministic approach. This research, furthermore, identifies the greatest sources of uncertainty as the resource requirements, emergency operation time and severity level of each demand point. Considering the inherent uncertainty of an emergency operations scenario, 1000 plausible scenarios have been considered for scheduling of multiple resources from the 2009 Black Saturday Bushfire; each scenario is analyzed and solved by the proposed deterministic programming model solved for multiple stochastic scenarios (Shahparvari, Abbasi, Chhetri, & Abareshi, 2017).
Section snippets
Literature review
Emergency resources can be categorized as being either: (1) expendable resources that entail medical supplies and fuel or (2) non-expendable resources, which include rescue units and medical personnel. In this sense, expendable and non-expendable resources facilitate effective resource management when dealing with emergency relief operations, as they ensure the welfare of the disaster victims (Rodríguez-Espíndola, Albores, & Brewster, 2018).
Recent reviews such as Caunhye, Nie, and Pokharel
Problem statement and model conceptualization
The proposed decision support system integrates the GIS and optimization techniques. The GIS is utilized as a means to analyze potential bushfire scenarios and provide information about the intensity of the bushfire in the impacted region. Although numerous studies use GIS for data pre-processing, network analysis and data display, there are several untapped ways to implement GIS in multiple disaster relief phases (Özdamar & Ertem, 2015). In contrast to earthquakes, wherein the damage depends
The multi resource scheduling model under uncertainty (MRSU)
The formulation of the MIP mathematical model aims to identify the optimal allocation and scheduling solution of resources for the emergency operations at an entire set of demand points in a disaster relief scenario. The main goals of the MIP model have been set as: (a) determining the sequence of demand points visited by chosen vehicle(s) to deliver requisite resources, and (b) minimizing the completion times of relief operations at individual demand points. It is important to note that the
Solution approach
The non-linear term in Constraint (8) could be linearized by adding αQktv where α is very small constant value, to force the Qktv to take the largest possible values to the objective function and replace Constraint (8) with the following Constraint:
The non-linear term in Constraint (11) could be linearized by Proposition 5.1, as stated below. Proposition 5.1 describes a conventional McCormick envelope linearization (McCormick, 1976). The non-linear terms of this
Case study: the Kilmore East Black Saturday bushfire
On Saturday, February 7, 2009, over 400 bushfires set ablaze in Victoria, Australia, resulting in the deaths of 173 people. This day in history has since been referred to as Black Saturday.
Of all the raging fires on Black Saturday, the Kilmore East fire was the most severe, impacting more than 20 localities (Fig. 1). The fire spread through the Shires of Mitchell Nillumbik and Yarra Ranges and the City of Whittlesea, 85 km from Melbourne. During the first 12 h, the fire impacted more than 5000
Baseline: the deterministic operation plan
The results indicate that it is possible to cover the impacted townships by utilizing 16 available rescue fleets. Fig. 3 presents the scheduled routing pattern of assigned vehicles for when there is a baseline of 16 vehicles available. The route of each emergency vehicle is indicated via different colors. Additionally, it outlines the routing schedule and designated emergency vehicles for the emergency operations on the network. As shown, due to the long distance but high severity level of
Summary and implications
This study investigates the emergency response efforts to the Black Saturday bushfire and proposes an innovative system to optimize scheduling and sequencing during disaster management. Earlier models and systems in the literature have failed to take into consideration three crucial aspects of emergency relief responses. Most significantly, majority of models have failed to integrate the sequencing and scheduling of both expendable and non-expendable resources in emergency situations. The
References (59)
- et al.
An exact solution approach for multi-objective locationtransportation problem for disaster response
Computers & Operations Research
(2014) - et al.
Modeling integrated supply chain logistics in real-time large-scale disaster relief operations
Socio-Economic Planning Sciences
(2012) Disaster do-gooders can actually hinder help
- et al.
Stochastic network models for logistics planning in disaster relief
European Journal of Operational Research
(2016) - et al.
Multi-depot multi-compartment vehicle routing problem, solved by a hybrid adaptive large neighborhood search
Omega
(2018) - et al.
Resource allocation for demand surge mitigation during disaster response
Decision Support Systems
(2010) - et al.
Wildfire initial response planning using probabilistically constrained stochastic integer programming
International Journal of Wildland Fire
(2014) - et al.
Last mile distribution in humanitarian relief
Journal of Intelligent Transportation Systems
(2008) - et al.
A two-stage stochastic programming framework for transportation planning in disaster response
Journal of the Operational Research Society
(2004) - et al.
Persistence in discrete optimization under data uncertainty
Mathematical Programming
(2006)
An optimization model for scheduling emergency operations with multiple teams
Bi-objective multi-resource scheduling problem for emergency relief operations
Production Planning & Control
Routing for relief efforts
Transportation Science
A location-routing model for prepositioning and distributing emergency supplies
Transportation Research Part E: Logistics and Transportation Review
Optimization models in emergency logistics: A literature review
Socio-Economic Planning Sciences
A scenario planning approach for the flood emergency logistics preparation problem under uncertainty
Transportation Research Part E: Logistics and Transportation Review
2009 victorian bushfires royal commission
A two-echelon stochastic facility location model for humanitarian relief logistics
Optimization Letters
Using discrete event simulation cellular automata models to determine multi-mode travel times and routes of terrestrial suppression resources to wildland fires
European Journal of Operational Research
Logistics service network design for humanitarian response in East Africa
Omega
An optimization model for volunteer assignments in humanitarian organizations
Socio-Economic Planning Sciences
Review of recent developments in or/ms research in disaster operations management
European Journal of Operational Research
Balancing provision of relief and recovery with capacity building in humanitarian operations
Operations Management Research
Formulation and solution of a multi-commodity, multi-modal network flow model for disaster relief operations
Transportation Research Part A: Policy and Practice
An efficient network for disaster management: Model and solution
Applied Mathematical Modelling
A bi-objective robust model for emergency resource allocation under uncertainty
International Journal of Production Research
Modeling multiple humanitarian objectives in emergency response to large-scale disasters
Transportation Research Part E: Logistics and Transportation Review
Models for relief routing: Equity, efficiency and efficacy
Transportation Research Part E: Logistics and Transportation Review
Decision support system for forest fire protection in the euro-mediterranean region
European Journal of Forest Research
Cited by (17)
The wildfire suppression problem with multiple types of resources
2024, European Journal of Operational ResearchA flexible multi-objective task allocation method for major marine emergencies
2024, Ocean EngineeringMulti-period dynamic multi-objective emergency material distribution model under uncertain demand
2023, Engineering Applications of Artificial IntelligenceCitation Excerpt :Su et al. (2016) used a parallel method to distribute a variety of emergency relief materials for concurrent events after the disaster. Bodaghi et al. (2020b, a) mentioned resource scheduling in a variety of random scenarios. They divided emergency resources into expendable resources and non-expendable resources.
A decomposition approach for the stochastic asset protection problem
2022, Computers and Operations ResearchCitation Excerpt :Shahparvari et al. (2019) studied a bushfire evacuation routing problem under a short-notice condition with uncertainty due to road network and shelter disruption. Bodaghi et al. (2020) addressed emergency relief operations with both expandable and non-expandable resources under 1000 plausible scenarios in the severity level and needs at each bushfire affected demand point. Stochastic APP, the focus of this study, considers uncertain timing of a wind change during wildfires that requires consideration of multiple scenarios.
Multi-resource scheduling and routing for emergency recovery operations
2020, International Journal of Disaster Risk Reduction