Online optimization of casualty processing in major incident response: An experimental analysis

Wilson, Duncan T.; Hawe, Glenn I.; Coates, Graham; Crouch, Roger S.

doi:10.1016/j.ejor.2016.01.021

Cited by 16 publications

(11 citation statements)

References 31 publications

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“…The interaction between scheduling, real-world implementation, and uncertain transportation time confirmation is modeled by a simulative experimental environment proposed in this paper. The mechanics of the information update are the major difference between the experiments in this paper and other existing studies [4], [14], [24], [25].…”

Section: Simulative Experimental Environmentsmentioning

confidence: 86%

“…"Online (or real-time) optimization" is a more appropriate term that describe the methodology employed in this paper. Wilson et al [24] investigated a real-time scheduling problem for mass casualty instance response operations and considered the communication between optimization and the environment. Two types of dynamics affecting the solution space and the value of the objective function were discussed.…”

Section: Related Workmentioning

confidence: 99%

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Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

Zhang¹,

Liu²

2021

IEEE Access

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In the immediate aftermath of large-scale disasters, emergency logistics services play important roles in saving lives and reducing losses. Efficient relief logistics scheduling depends on the accurate transport time information for available routes. However, this information cannot be obtained precisely until a vehicle uses the road. Considering the correlation between information acquisition and logistics operations, this paper focuses on a multiperiod online decision-making problem to simulate the information acquiring process. This problem can be referenced for emergency resource scheduling scenarios in which previous decisions impact knowledge for future logistics plans. A multi-trip cumulative capacitated vehicle routing problem with uncertain transportation time is investigated as the basic model. The tradeoff between transportation efficiency and the unknown transport time discovery rate is considered in a multiobjective evolutionary algorithm (MOEA). A memetic algorithm (MA) and a robust optimization (RO) -MA for single-period postdisaster emergency logistics are also proposed to solve the problem for comparison. In these algorithms, evolutionary operators that benefit solution fixing and variation are proposed. In the experiments, a realworld instance is employed. A simulative experimental environment is established. Dynamic information gained within the process of logistics scheduling is highlighted via multi-period online optimization. Different scenarios corresponding to estimates in emergency situations are provided to validate the performance of the algorithms. The experimental results show that the hybrid strategy, MOEA+MA, can obtain the best result in more than half of the considered cases which demonstrates the necessary balance between obtaining information and transportation efficiency.INDEX TERMS Emergency service, uncertain environment, humanitarian logistics, Pareto optimization, robustness, multiphase scheduling.

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Section: Simulative Experimental Environmentsmentioning

confidence: 86%

Section: Related Workmentioning

confidence: 99%

Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

Zhang¹,

Liu²

2021

IEEE Access

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“…In the literature, various multi-objective disaster relief planning models have been proposed and solved using special multi-objective solution techniques such as the weighted sum method [61][62][63], lexicographic or hierarchical order [64,65], ε-constraint [66,67], and goal programming [68]. The purpose of the bi-objective model is to obtain a set of efficient solutions instead of a single value.…”

Section: Phase 2: Bi-objective Programming Modelmentioning

confidence: 99%

A Hybrid Approach of VIKOR and Bi-Objective Decision Model for Emergency Shelter Location–Allocation to Respond to Earthquakes

2021

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Earthquakes have catastrophic effects on the affected population, especially in undeveloped countries or regions. Minimizing the impact and consequences of earthquakes involves many decisions and disaster relief operations that should be optimized. A critical disaster management problem is to construct shelters with reasonable capacity in the right locations, allocate evacuees, and provide relief materials to them within a reasonable period. This study proposes a bi-objective hierarchical model with two stages, namely, the temporary shelter stage and the short-term shelter stage. The proposed objectives at different stages are to minimize the evacuation time, maximize the suitability based on qualitative factors, and minimize the number of sites while considering the demand, capacity, utilization, and budget constraints. The performance evaluation of the emergency shelter was carried out by fuzzy-VIKOR, and the most ideal location of the shelter was determined through multiple standards. Emergency management organizations can benefit from the collective expertise of multiple decision-makers because the proposed method uses their knowledge to automate the location and allocation process of shelters. In the case of Chengdu, Sichuan Province, China, the results of using this hybrid approach provide the government with a range of options. This method can realize the trade-off between efficiency and cost in the emergency shelter location and material distribution, and realize reliable solutions in disaster emergencies.

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“…When designing an optimization model for use in mass casualty incident response, the dynamic and uncertain nature of the problem environment poses a significant challenge. Wilson et al (2016) developed a multi-objective combinatorial optimization model for mass casualty incident response to improve performance in dynamic and uncertain environments. Also, Zhang and Kang (2015) proposed a novel probabilistic model with chance constraints for locating and sizing emergency medical service stations.…”

Section: Literature Reviewmentioning

confidence: 99%

A multi-objective location-allocation model in mass casualty events response

Pouraliakbari-Mamaghani

Mohammadi

Mirzazadeh

2018

JM2

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Purpose When designing an optimization model for use in a mass casualty event response, it is common to encounter the heavy and considerable demand of injured patients and inadequate resources and personnel to provide patients with care. The purpose of this study is to create a model that is more practical in the real world. So the concept of “predicting the resource and personnel shortages” has been used in this research. Their model helps to predict the resource and personnel shortages during a mass casualty event. In this paper, to deal with the shortages, some temporary emergency operation centers near the hospitals have been created, and extra patients have been allocated to the operation center nearest to the hospitals with the purpose of improving the performance of the hospitals, reducing congestion in the hospitals and considering the welfare of the applicants. Design/methodology/approach The authors research will focus on where to locate health-care facilities and how to allocate the patients to multiple hospitals to take into view that in some cases of emergency situations, the patients may exceed the resource and personnel capacity of hospitals to provide conventional standards of care. Findings In view of the fact that the problem is high degree of complexity, two multi-objective meta-heuristic algorithms, including non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA), were proposed to solve the model where their performances were compared in terms of four multi-objective metrics including maximum spread index (MSI), spacing (S), number of Pareto solution (NPS) and CPU run-time values. For comparison purpose, paired t-test was used. The results of 15 numerical examples showed that there is no significant difference based on MSI, S and NPS metrics, and NRGA significantly works better than NSGA-II in terms of CPU time, and the technique for the order of preference by similarity to ideal solution results showed that NRGA is a better procedure than NSGA-II. Research limitations/implications The planning horizon and time variable have not been considered in the model, for example, the length of patients’ hospitalization at hospitals. Practical implications Presenting an effective strategy to respond to a mass casualty event (natural and man-made) is the main goal of the authors’ research. Social implications This paper strategy is used in all of the health-care centers, such as hospitals, clinics and emergency centers when dealing with disasters and encountering with the heavy and considerable demands of injured patients and inadequate resources and personnel to provide patients with care. Originality/value This paper attempts to shed light onto the formulation and the solution of a three-objective optimization model. The first part of the objective function attempts to maximize the covered population of injured patients, the second objective minimizes the distance between hospitals and temporary emergency operation centers and the third objective minimizes the distance between the warehouses and temporary centers.

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Online optimization of casualty processing in major incident response: An experimental analysis

Cited by 16 publications

References 31 publications

Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

A Hybrid Approach of VIKOR and Bi-Objective Decision Model for Emergency Shelter Location–Allocation to Respond to Earthquakes

A multi-objective location-allocation model in mass casualty events response

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