Emergency Material Allocation Problem Considering Post-Disaster Impact

Cui, Shibo; Liu, Sheng; Tang, Xiaoya; Zhu, Tianlu

doi:10.1109/icitm.2019.8710729

Cited by 8 publications

(9 citation statements)

References 10 publications

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“…First, we set Q 111 = 2 + 3 = 5, Q 211 = 4 + 9 = 13, and Q 111 = 3 + 2 = 5. Due to the current number of sandstone in b 1 exceeds its demands, it does not meet the constraints (1) and (2). Then, we randomly select the storage warehouse and return the remaining value to the rescue point b 3 .…”

Section: Variable Neighborhood Local Search Operationmentioning

confidence: 99%

“…Otherwise, it does not change the amount of flood control materials in the food source. After finishing the cross operation, it is necessary to verify whether the newly generated food sources satisfy the constraints ( 1) and (2). If it does not, the overallocated materials will be cut to obtain the new food sources which meet the constraints.…”

Section: Variable Neighborhood Local Search Operationmentioning

confidence: 99%

“…MSA_PABC enters the onlooker bee phase. Calculating the crowdedness among food sources by formula (11) to determine the selected probability of each food source, it implements the following operation for m2 times and obtains m2 * 2 child food sources: it chooses a food source based on the rules of roulette and selects the neighbor food source with nearest European distance; then, it performs across the operation to generate two food sources and corrects the new two food sources to obtain two-child food sources satisfying the constraints ( 1) and (2). The m2 * 2 child food sources and the food sources in the current population combine into a food source set, and MSA_PABC performs the Pareto dominance strategy on the food source set to obtain a nondominated solution set.…”

Section: Algorithm Implementationmentioning

confidence: 99%

“…For instance, in 2019, 130 million people are suffered from disasters such as typhoons. The direct economic losses were 327.09 billion RMB [2]. Zhejiang Province locates on the southeastern coast of China.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

AI‐Driven Multiobjective Scheduling Algorithm of Flood Control Materials Based on Pareto Artificial Bee Colony

Liu

Chen

et al. 2021

Wireless Communications and Mobile Computing

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Considering the competition between rescue points, we use artificial intelligence (AI) driven Internet of Thing (IoT) and regional material storage data to propose a multiobjective scheduling algorithm of flood control materials based on Pareto artificial bee colony (MSA_PABC). To address the scheduling of flood control materials, the multiple types of flood control materials, the multiple disaster sites, and entertain both emergency and fairness of rescue need to be considered comprehensively. The MSA_PABC has the constraints such as storage quantity constraint of warehouse materials, material demand constraint, and maximum transportation distance of flood control materials. We establish the scheduling optimization model of flood control materials for each disaster rescue point and the total scheduling optimization model for all flood control materials. Then, MSA_PABC uses the modified Pareto artificial bee colony algorithm to solve the multiobjective models. Three types of initialization strategies are proposed to calculate the fitness of each rescue point and the overall evaluation value of the food source. We propose the employ bee operations such as niche technology and local search of the variable neighborhood, the onlooker bee operations such as Pareto nondominated sorting and crossover operation, the scout bee operations such as maximum evolutionary threshold, and end elimination mechanism. Finally, our proposed solution obtains the nondominated solution set and its optimal solution. The experimental results show that no matter how the number of rescue points changes, MSA_PABC can find the nondominated solution set and optimal solution quickly. It improves the convergence rate of MSA_PABC and material satisfaction rate. Our solution also reduces the average maximum transportation distance, the standard deviation of maximum transportation distance, and the standard deviation of material satisfaction rate. The evaluation also demonstrates MSA_PABC outperforms the state-of-arts such as ABC (artificial bee colony), NSGA2 (nondominated sorting genetic algorithm 2), and MOPSO (multiobjective particle swarm optimization).

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Section: Variable Neighborhood Local Search Operationmentioning

confidence: 99%

Section: Variable Neighborhood Local Search Operationmentioning

confidence: 99%

Section: Algorithm Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

AI‐Driven Multiobjective Scheduling Algorithm of Flood Control Materials Based on Pareto Artificial Bee Colony

Liu

Chen

et al. 2021

Wireless Communications and Mobile Computing

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show abstract

“…ey further established the nonlinear programming model where objective function was the maximization of time-satisfaction degree. Cui et al [8] considered supply chain disruption and path risk to achieve the minimum supply transportation time and supply transportation cost. e postdisaster supply transportation model under uncertainty conditions was established in their research as well.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters

Chen

Yang

et al. 2020

Complexity

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It is common that many roads in disaster areas are damaged and obstructed after sudden-onset disasters. The phenomenon often comes with escalated traffic deterioration that raises the time and cost of emergency supply scheduling. Fortunately, repairing road network will shorten the time of in-transit distribution. In this paper, according to the characteristics of emergency supplies distribution, an emergency supply scheduling model based on multiple warehouses and stricken locations is constructed to deal with the failure of part of road networks in the early postdisaster phase. The detailed process is as follows. When part of the road networks fail, we firstly determine whether to repair the damaged road networks, and then a model of reliable emergency supply scheduling based on bi-level programming is proposed. Subsequently, an improved artificial bee colony algorithm is presented to solve the problem mentioned above. Finally, through a case study, the effectiveness and efficiency of the proposed model and algorithm are verified.

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Quantum competitive decision algorithm for the emergency siting problem under given deadline conditions

Zhao,

Gao,

Gao

et al. 2024

Cluster Comput

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Emergency Material Allocation Problem Considering Post-Disaster Impact

Cited by 8 publications

References 10 publications

AI‐Driven Multiobjective Scheduling Algorithm of Flood Control Materials Based on Pareto Artificial Bee Colony

AI‐Driven Multiobjective Scheduling Algorithm of Flood Control Materials Based on Pareto Artificial Bee Colony

Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters

Quantum competitive decision algorithm for the emergency siting problem under given deadline conditions

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