A decomposition-based heuristic for stochastic emergency routing problems

Fontem, Belleh A.; Melouk, Sharif H.; Keskin, Burcu B.; Bajwa, Naeem

doi:10.1016/j.eswa.2016.04.002

Cited by 13 publications

(4 citation statements)

References 27 publications

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“…After the disaster, the location of the temporary shelter areas was determined by the linear programming method (Kilci et al, 2015), the conversion of the facility plan into a multi-facility with a stochastic model (Drezner et al, 2016), the negative infrastructure problem that prevents access to disaster areas was determined by an asymmetric model determined according to the desired alternative route (Salman and Yucel, 2015) and the examination of emergency transportation routes (Buldurur and Kurucu, 2015) and the multi-purpose linear programming method (Sheu and Pan, 2014). Proper transportation of aid materials to the distribution points after the disaster has been Grey p-median LP model to prioritize EAPs realized through the analytical hierarchy process (Peker et al, 2016) and rescue teams' transfer of medication resources to emergency areas by a cognitive method (Fontem et al, 2016). Focusing on where and how individuals access resources, information and emotional support in the short-term recovery following a disaster event.…”

Section: Methodology Scientific Literaturementioning

confidence: 99%

Prioritization of emergency assembly points in a campus using grey p-median linear programming model

Yalçıner Çal,

Aydemir

2024

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PurposeThe purpose of this paper is to propose a scenario-based grey methodology using clustering and optimizing with imprecise and uncertain body size data in an emergency assembly point area to assign the people on a campus to reach the emergency assembly points under uncertain disaster times.Design/methodology/approachGrey clustering and a new grey p-median linear programming model are developed to determine which units to assign to the pre-determined assembly points for a main campus in case of a disaster. The models have two scenarios: 70 and 100% occurrence capacities of administrative and academic personnel and students.FindingsIn this study, the academic and administrative units have been assigned to determine five different emergency assembly points on the main campus by using the numbers of the academic and administrative personnel and student and distances of the units to the assembly point areas of each other. The alternative solutions are obtained effectively by evaluating capacity utilization rates in the scenarios.Practical implicationsIt is often unclear when disasters can occur and therefore, a preliminary preparation time must be required to minimize the risk. In the case of natural, man-made (unnatural) or technological disasters, the people are required to defend themselves and move away from the disaster area as soon as possible in a proper direction. The proposed assignment model yields a final solution that effectively eliminates uncertainty regarding the selection of emergency assembly points for administrative and academic staff as well as students, in the event of disasters.Originality/valueGrey clustering suggests an assignment plan and concurrently, an investigation is underway utilizing the grey p-median linear programming model. This investigation aims to optimize various scenarios and body sizes concerning emergency assembly areas. All campus users who are present at the disaster in units of the campus are getting uncertainty about which emergency assembly point to use, and with this study, the vital risks aim to be ultimately reduced with reasonable plans.

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Section: Methodology Scientific Literaturementioning

confidence: 99%

Prioritization of emergency assembly points in a campus using grey p-median linear programming model

Yalçıner Çal,

Aydemir

2024

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“…Simulation results show that the efficiency of the proposed algorithm is 63.57% and 46.15% higher than that of the standard genetic algorithm and the greedy algorithm (based on 1000 iterations), respectively [20]. Fontem et al [21] proposed a factory-based heuristic to decompose the multi-depot routing problem into multiple single-depot routing problem in order to realize the transportation of relief materials from relief depots to disaster-affected population centres [21]. Zhang & Xiong [22] designed a hybrid algorithm combining artificial immune algorithm and ant colony optimization algorithm to effectively solve the VRP for emergency grain scheduling [22].…”

Section: Introductionmentioning

confidence: 96%

Multi-depot vehicle routing problem with drones in emergency logistics

Weng,

She,

Fan

et al. 2024

Preprint

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In order to expand the application of truck-drone cooperative delivery in emergency logistics, this paper investigates the multi-depot vehicle routing problem with drones for emergency logistics scheduling. A mixed-integer programming model with the objective of minimizing the maximum expected rescue time from urban distribution centres to temporary distribution stations is proposed. To address this problem, a new two-stage hybrid heuristic algorithm is proposed. In the first stage, we employ an improved K-means clustering to cluster the temporary distribution stations. In the second stage, a combination of Tabu search (TS), enhanced genetic algorithm (GA), and simulated annealing (SA) is applied to optimize the routes of both trucks and drones. The numerical study involves the validation of the model using real-world cases. Through comparison experiments with the Gurobi solver, we demonstrate the significant advantages of the proposed heuristic algorithm in terms of solution quality and efficiency.

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“…However, research on travel time/cost uncertainty is relatively insufficient [8]. Although the study in [7] proposed a decision support framework for addressing emergency routing problems, taking into account both travel time and deadline uncertainty, the travel time uncertainty was assigned with different levels according to the author's experience.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The complex nature of emergencies, as well as the lack of knowledge of data (e.g., demand, supply, or cost) in such situations, imply uncertainties in rescue vehicle route optimization. Although some models have been developed for emergency logistics with demand or supply uncertainty [6], for instance, one study [7] proposed a decision support framework for addressing emergency routing problems by taking into account both travel time and deadline uncertainty, the research on travel time/cost uncertainty is relatively insufficient [8]. The lack of research in this direction may be attributed to the lack of real disaster-related travel time data.…”

Section: Introductionmentioning

confidence: 99%

Reliable Rescue Routing Optimization for Urban Emergency Logistics under Travel Time Uncertainty

2018

IJGI

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Abstract:The reliability of rescue routes is critical for urban emergency logistics during disasters. However, studies on reliable rescue routing under stochastic networks are still rare. This paper proposes a multiobjective rescue routing model for urban emergency logistics under travel time reliability. A hybrid metaheuristic integrating ant colony optimization (ACO) and tabu search (TS) was designed to solve the model. An experiment optimizing rescue routing plans under a real urban storm event, was carried out to validate the proposed model. The experimental results showed how our approach can improve rescue efficiency with high travel time reliability.

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A decomposition-based heuristic for stochastic emergency routing problems

Cited by 13 publications

References 27 publications

Prioritization of emergency assembly points in a campus using grey p-median linear programming model

Prioritization of emergency assembly points in a campus using grey p-median linear programming model

Multi-depot vehicle routing problem with drones in emergency logistics

Reliable Rescue Routing Optimization for Urban Emergency Logistics under Travel Time Uncertainty

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