An orienteering-based approach to manage emergency situation

Baffo, Ilaria; Carotenuto, Pasquale; Rondine, Stefania

doi:10.1016/j.trpro.2017.03.036

Cited by 5 publications

(9 citation statements)

References 11 publications

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“…Such methods become highly time-consuming as the problem instances increase in size. Therefore, the main body of literature dealing with TOP is dominated by heuristic and metaheuristics methods [7] , [9] , [10] .…”

Section: Related Workmentioning

confidence: 99%

“…Baffo et al. [9] presented an orienteering-based approach to manage emergency situation, where the purpose is to collect the highest number of people from several origins and bring them into a unique destination, using a limited number of capacitated vehicles and while respecting a time limit. The problem was modeled as multi-origins capacitated TOP and solved by the ant colony metaheuristic.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, each customer can be served at most once. The TOP was used to model different routing problems in rescue and emergency situations [7] , [8] , [9] , [10] . Given the computational challenge of TOP (NP-hard), heuristic and metaheuristic algorithms are very suitable for finding near-optimal solutions for large sized instances that cannot be solved exactly in an acceptable computation time [5] .…”

Section: Introductionmentioning

confidence: 99%

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Towards an efficient collection and transport of COVID-19 diagnostic specimens using genetic-based algorithms

Tlili

Masri

Krichen

2022

Applied Soft Computing

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The speed by which the COVID-19 pandemic spread throughout the world makes the emergency services unprepared to answer all the patients’ requests. The Tunisian ministry of health established a protocol planning the sample collection from the patients at their location. A triage score is first assigned to each patient according to the symptoms he is showing, and his health conditions. Then, given the limited number of the available ambulances in each area, the location of the patients and the capacity of the nearby hospitals for receiving the testing samples, an ambulance scheduling and routing plan needs to be established so that specimens can be transferred to hospitals in short time. In this paper, we propose to model this problem as a Multi-Origin-Destination Team Orienteering Problem (MODTOP). The objective is to find the optimal one day tour plan for the available ambulances that maximizes the collected scores of visited patients while respecting duration and capacity constraints. To solve this NP-hard problem, two highly effective approaches are proposed which are Hybrid Genetic Algorithm (HGA) and Memetic Algorithm (MA). The HGA combines (i) a k-means construction method for initial population generation and (ii) a one point crossover operator for solution recombination. The MA is an improvement of HGA that integrates an effective local search based on three different neighborhood structures. Computational experiments, supported by a statistical analysis on benchmark data sets, illustrate the efficiency of the proposed approaches. HGA and MA reached the best known solutions in 54.7% and 73.5% of instances, respectively. Likewise, MA reached a relative error of 0.0675% and performed better than four existing approaches. Real-case instances derived from the city of Tunis were also solved and compared with the results of an exact solver Cplex to validate the effectiveness of our algorithm.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards an efficient collection and transport of COVID-19 diagnostic specimens using genetic-based algorithms

Tlili

Masri

Krichen

2022

Applied Soft Computing

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“…Argany et al, (2018), developsd a GIS-based context-aware for optimization sensor coverage in an urban area (Argany, 2018). In 2017, Ilaria Baffo et al, Improved the Emergency Routing using an Ant Colony Algorithm (Baffo et al 2017) and Vafaeinejad developed Spatio-temporal GIS for Dynamic Guidance of an Autonomous Vehicle (Vafaeinejad 2017). in 2016 Gyeongtaek Oh et al, has been studied to assign optimal particle swarm algorithm based tasks for scheduling collaborative activities.…”

Section: Literature Reviewmentioning

confidence: 99%

Designing a Context-Aware Recommender System in the Optimization of the Relief and Rescue

Bahrami

Argany

Samani

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The context-aware is the knowledge that leads to better cognition and recognition of the environment, objects and factors, and the way of communication and interactions between them. As a result, it can have a great impact in providing appropriate solutions to various problems. It is possible to integrate consciousness into relief and rescue discussions and to take steps to improve and make realistic solutions. In this study, this issue was addressed in the earthquake crisis, due to a large number of seismic faults in Iran, is one of the major crises in Iran and many parts of the world. Hence, the contexts of rescuers, teams, and environment as the main textures in the above-mentioned issue are investigated and their relationship with each other and the priorities of activities and locations by identifying specialties and the physical and situational conditions of the relief workers, and an algorithm was designed and optimized to optimize the allocation of the relief workers to the affected areas and the necessary activities. Finally, the improvement of the 2.4 fold results of the algorithm and the proposed structure of this research resulted in the ratio of non-use of this algorithm.

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“…Orienteering problem (OP) is a class of routing problems, where the ultimate goal is to create a route through a given points of interest (PoIs), which would satisfy given conditions and constraints. Depending on domain PoIs can be touristic attraction places [1], crowdsourcing tasks [2] or safety places in case of emergency situations [3]. Diversity of possible applications led to a great variety of OP tasks from classic OP [4] and its generalization [5] to exotic ones dedicated to politician movements [6] or band tour planning [7].…”

Section: Introductionmentioning

confidence: 99%

Orienteering Problem with Functional Profits for multi-source dynamic path construction

2019

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Orienteering problem (OP) is a routing problem, where the aim is to generate a path through set of nodes, which would maximize total score and would not exceed the budget. In this paper, we present an extension of classic OP—Orienteering Problem with Functional Profits (OPFP), where the score of a specific point depends on its characteristics, position in the route, and other points in the route. For solving OPFP, we developed an open-source framework for solving orienteering problems, which utilizes four core components of OP in its modular architecture. Fully-written in Go programming language our framework can be extended for solving different types of tasks with different algorithms; this was demonstrated by implementation of two popular algorithms for OP solving—Ant Colony Optimization and Recursive Greedy Algorithm. Computational efficiency of the framework was shown through solving four well-known OP types: classic Orienteering Problem (OP), Orienteering Problem with Compulsory Vertices (OPCV), Orienteering Problem with Time Windows (OPTW), and Time Dependent Orienteering Problem (TDOP) along with OPFP. Experiments were conducted on a large multi-source dataset for Saint Petersburg, Russia, containing data from Instagram, TripAdvisor, Foursquare and official touristic website. Our framework is able to construct touristic paths for different OP types within few seconds using dataset with thousands of points of interest.

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An orienteering-based approach to manage emergency situation

Cited by 5 publications

References 11 publications

Towards an efficient collection and transport of COVID-19 diagnostic specimens using genetic-based algorithms

Towards an efficient collection and transport of COVID-19 diagnostic specimens using genetic-based algorithms

Designing a Context-Aware Recommender System in the Optimization of the Relief and Rescue

Orienteering Problem with Functional Profits for multi-source dynamic path construction

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