A robust bi-objective location-routing model for providing emergency medical services

Adarang, Hesam; Bozorgi-Amiri, Ali; Khalili‐Damghani, Kaveh; Tavakkoli–Moghaddam, Reza

doi:10.1108/jhlscm-11-2018-0072

Cited by 26 publications

(22 citation statements)

References 57 publications

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“…Parameters with uncertainty in this model are listed as follows: Damaged facilities, casualties because of the intensity of an incidence, and the travel time of the relief supplier vehicles. In a recent study, Adarang et al [16] proposed a temporary robust biobjective location-routing model for providing emergency medical services. is model aimed to minimize the relief supplying time and total cost.…”

Section: Literature Reviewmentioning

confidence: 99%

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Vehicle Routing Problem in Relief Supply under a Crisis Condition considering Blood Types

Kallaj

Abolghasemian

Pirbalouti

et al. 2021

Mathematical Problems in Engineering

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Despite the advances achieved in Medical Sciences, no substitute has been found for blood as a vital factor. Therefore, preparing sufficient and healthy blood in crisis conditions is a challenge that health systems encounter. Along with examining the conducted investigations in this field, the main contribution of current research is to develop a biobjective Mixed-Integer Linear Programming (MILP) model for relief supply under crisis condition. For this purpose, this paper proposes a model for routing of bus blood receiver under crisis conditions considering different blood groups. Besides, hours of unnecessary travel by bloodmobiles (buses) between each blood station (BS) and the crisis-stricken city for dispatching the collected blood is prevented thanks to considering a helicopter. The mentioned model has two objectives: maximizing the amount of blood collected by bloodmobiles and minimizing the arrival time of the blood receiver buses and a helicopter to a crisis-stricken city after the collected blood is used up. The model is coded by CPLEX software, and the results obtained from solving the model indicate that, without considering a helicopter, the demand is not supplied within the critical period after crisis. Given that blood cannot be artificially produced, its primary resource is blood donors. Concerning the importance of this issue under crisis conditions, this research investigates the relief vehicles’ routing problem, including bus and helicopter, in a crisis considering supply and transfer of different blood groups to a crisis-stricken city for maximum relief supply and blood transfer within the shortest period.

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Section: Literature Reviewmentioning

confidence: 99%

“…Similar to constraint (5), constraint (16) guarantees that the helicopter cannot travel from city i to i (loop is not allowed).…”

Section: Model Notation and Formulationmentioning

confidence: 99%

Vehicle Routing Problem in Relief Supply under a Crisis Condition considering Blood Types

Kallaj

Abolghasemian

Pirbalouti

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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“…Of the articles found for the classification, only 14.10% did so in the pre-disaster stage. Adarang et al [69] consider a problem of location and routing to provide medical services in order to plan and manage transportation under uncertainty, using the shuffled frog jump algorithm and the NSGA-II. In the work of Akdogan et al [70], the location of emergency vehicles is studied through a queueing model, a mathematical model is used to minimize response time and a Genetic Algorithm is also used.…”

Section: Model Type and Phasesmentioning

confidence: 99%

Metaheuristics in the Humanitarian Supply Chain

et al. 2021

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Everyday there are more disasters that require Humanitarian Supply Chain (HSC) attention; generally these problems are difficult to solve in reasonable computational time and metaheuristics (MHs) are the indicated solution algorithms. To our knowledge, there has not been a review article on MHs applied to HSC. In this work, 78 articles were extracted from 2016 publications using systematic literature review methodology and were analyzed to answer two research questions: (1) How are the HSC problems that have been solved from Metaheuristics classified? (2) What is the gap found to accomplish future research in Metaheuristics in HSC? After classifying them into deterministic (52.56%) and non-deterministic (47.44%) problems; post-disaster (51.28%), pre-disaster (14.10%) and integrated (34.62%); facility location (41.03%), distribution (71.79%), inventory (11.54%) and mass evacuation (10.26%); single (46.15%) and multiple objective functions (53.85%), single (76.92%) and multiple (23.07%) period; and the type of Metaheuristic: Metaphor (71.79%) with genetic algorithms and particle swarm optimization as the most used; and non-metaphor based (28.20%), in which search algorithms are mostly used; it is concluded that, to consider the uncertainty of the real context, future research should be done in non-deterministic and multi-period problems that integrate pre- and post-disaster stages, that increasingly include problems such as inventory and mass evacuation and in which new multi-objective MHs are tested.

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“…Bertsimas and Sim [10] introduced an efficient linear interval-based methodology to control the conservatism level of the solutions under uncertain conditions, which has been then investigated by many researchers in different fields of optimization [38][39][40][41]. Adarang et al [42], formulated a location-routing problem using a robust optimization approach. In this research, the number of patients was uncertain and the robust counterpart of the problem was developed by the robust method of Bertsimas and Sim [10].…”

Section: Robust Counterpart Modelmentioning

confidence: 99%

Multi-objective robust mathematical modeling for emergency relief in disaster under uncertainty

et al. 2020

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This paper presents a robust location-allocation planning problem for emergency relief in a disaster situation, which is formulated as a robust optimization model. It is a multi-objective, multi-commodity, multi-vehicle and multi-level logistics model considering injury variety through service prioritizing for more injuries and considering unmet demand of particular item type in various damaged areas, public donation of different relief goods, using capacitated medical centers and emergency centers regarding damage type and capacitated relief distribution centers and disaster management centers. This a non-linear mixed-integer programming model that simultaneously optimizes three objectives; i.e., maximizing service fairness to damaged areas, maximizing fair commodity disaster management, and minimizing the total logistics cost. To solve such a hard problem, a non-dominated sorting genetic algorithm (NSGA-II) is developed and the Taguchi method is applied to adjust its parameters. The ε-constraint method is used for the evaluation of the proposed algorithm performance. For more accurate validation, three comparison metrics, including diversification, spacing and mean ideal distance, are used. The results verify the algorithm's effectiveness in a reasonable computational time. Eventually, to examine the applicability of the presented model and the proposed algorithm, a case study is analyzed in the area located in the north of Iran, known with historical earthquake records and aggregated active faults.

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A robust bi-objective location-routing model for providing emergency medical services

Cited by 26 publications

References 57 publications

Vehicle Routing Problem in Relief Supply under a Crisis Condition considering Blood Types

Vehicle Routing Problem in Relief Supply under a Crisis Condition considering Blood Types

Metaheuristics in the Humanitarian Supply Chain

Multi-objective robust mathematical modeling for emergency relief in disaster under uncertainty

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