An improved harmony search algorithm for emergency inspection scheduling

Kallioras, Nikos Ath.; Lagaros, Nikos D.; Karlaftis, Matthew G.

doi:10.1080/0305215x.2013.854351

Cited by 17 publications

(12 citation statements)

References 33 publications

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“…A cost function is assigned to paths between two nodes (i and j), represented by the distance between the two nodes di,j (ij). A solution of the TSP is a permutation p= [p (1), …, p(N)] T of the node indices [1, …, N], as every node must not appear more than once in a solution. The solution that minimizes the total length L(p) given by:…”

Section: Optimal Inspection Problem Formulationmentioning

confidence: 99%

“…In this work, two algorithms are used for designing the emergency inspection plan of urban areas. The first algorithm, Improved Harmony Search (IHS), was presented by Kallioras et al [1] in 2014 while the original version of the algorithm was introduced by Geem et al [2] in 2001. The second algorithm used in this work is Ant Colony Optimization (ACO) which was presented by Dorigo and Stützle [3] in 2004.…”

Section: Introductionmentioning

confidence: 99%

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A Real-Time Emergency Inspection Scheduling Tool Following a Seismic Event

Kallioras

Lagaros

2016

Computational Methods in Applied Sciences

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Emergency infrastructure inspections are of the essence after a seismic event as a carefully planned inspection in the first and most critical hours can reduce the effects of such an event. Metaheuristics and more specifically nature inspired algorithms have been used in many hard combinatorial engineering problems with significant success. The success of such algorithms has attracted the interest of many researchers leading to an increased interest regarding metaheuristics. In the present literature many new and sophisticated algorithms have been proposed with interesting performance characteristics. On the other hand, up to date developments in the field of computer hardware have also had a significant influence on algorithm design. The increased computational abilities that are available to researchers through parallel programming have opened new horizons in architecture of algorithms. In this work, a methodology for real-time planning of emergency inspections of urban areas is presented. This methodology is based on two nature inspired algorithms, Harmony Search Algorithm (HS) and Ant Colony Optimization (ACO). HS is used for dividing the area into smaller blocks while ACO is used for defining optimal routes inside each created block. The proposed approach is evaluated in an actual city in Greece, Thessaloniki.

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Section: Optimal Inspection Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Real-Time Emergency Inspection Scheduling Tool Following a Seismic Event

Kallioras

Lagaros

2016

Computational Methods in Applied Sciences

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“…Some scholars [15][16][17][18] focus on establishing multiple target optimization models, such as minimizing the transportation time of materials and maximizing reliability. Then, they use single or multiple hybrid algorithms, such as quick sorting genetic algorithm, harmony algorithm, artificial bee col-ony algorithm, Monte Carlo algorithm, and stepwise method, to obtain the optimal scheduling schemes.…”

Section: Introductionmentioning

confidence: 99%

“…Considering that the emergency supplies provided to rescue points may be insufficient or excessive, Chen et al [20] take the minimization of the loss caused by insufficient material distribution and oversupply and the minimization of vehicle scheduling costs as optimization objectives and propose a vehicle scheduling optimization model of disaster emergency logistics based on discrete bee colony. In [8][9][10][11][12][13][14][15][16][17][18][19][20], they use intelligent optimization algorithms to solve emergency rescue scheduling problems but do not take account of the urgency and fairness of multiobjective rescue scheduling at the same time, only meet the rescue service requirements of individual rescue points, which makes the overall rescue service quality poor, and only focus on resource allocation, without considering the unexpected factors that affect the service quality such as road damage in the rescue process.…”

Section: Introductionmentioning

confidence: 99%

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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“…As ant colony optimization utilizes positive feedback to guide the behavior of agents and supports distributed computing, it has more advantages than other approaches while solving such NP-hard problems (Kallioras et al , 2014). It is often more robust compared with other methods in solving vehicle routing and scheduling problems (Fernández-Vargas et al , 2013).…”

Section: Introductionmentioning

confidence: 99%

Emergency materials transportation model in disasters based on dynamic programming and ant colony optimization

Liu

Xie

2017

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Purpose While scheduling and transporting emergency materials in disasters, the emergency materials and delivery vehicles are arriving at the distributing center constantly. Meanwhile, the information of the disaster reported to the government is updating continuously. Therefore, this paper aims to propose an approach to help the government make a transportation plan of vehicles in response to the disasters addressing the problem of material demand and vehicle amount continual alteration. Design/methodology/approach After elaborating the features and process of the emergency materials transportation, this paper proposes an emergency materials scheduling model in the case of material demand and vehicle amount continual alteration. To solve this model, the paper provides the vehicle transportation route allocation algorithm based on dynamic programming and the disaster area supply sequence self-learning algorithm based on ant colony optimization. Afterwards, the paper uses the model and the solution approach to computing the optimal transportation scheme of the food supply in Lushan earthquake in China. Findings The case study shows that the model and the solution approach proposed by this paper are valuable to make the emergency materials transportation scheme precise and efficient. The problem of material demand and vehicle amount changing continually during the process of the emergency materials transportation is solved promptly. Originality/value The model proposed by this paper improves the existing similar models in the following aspects: the model and the solution approach can not only solve the emergency materials transportation problem in the condition of varying demand and vehicle amount but also save much computing time; and the assumptions of this model are consistent with the actual situation of the emergency relief in disasters so that the model has a broad scope of application.

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An improved harmony search algorithm for emergency inspection scheduling

Cited by 17 publications

References 33 publications

A Real-Time Emergency Inspection Scheduling Tool Following a Seismic Event

A Real-Time Emergency Inspection Scheduling Tool Following a Seismic Event

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

Emergency materials transportation model in disasters based on dynamic programming and ant colony optimization

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