Limits...
Multi-Objective Algorithm for Blood Supply via Unmanned Aerial Vehicles to the Wounded in an Emergency Situation.

Wen T, Zhang Z, Wong KK - PLoS ONE (2016)

Bottom Line: This is a complex problem that includes maintenance of the supply blood's temperature model during transportation, the UAVs' scheduling and routes' planning in case of multiple sites requesting blood, and limited carrying capacity.Then, by introducing the idea of transportation appendage into the traditional Capacitated Vehicle Routing Problem (CVRP), this new problem is proposed according to the factors of distance and weight.By comparing our technique with the traditional ones, our algorithm can obtain better optimization results and time performance.

View Article: PubMed Central - PubMed

Affiliation: Software School, Xiamen University, Xiamen, Fujian, China.

ABSTRACT
Unmanned aerial vehicle (UAV) has been widely used in many industries. In the medical environment, especially in some emergency situations, UAVs play an important role such as the supply of medicines and blood with speed and efficiency. In this paper, we study the problem of multi-objective blood supply by UAVs in such emergency situations. This is a complex problem that includes maintenance of the supply blood's temperature model during transportation, the UAVs' scheduling and routes' planning in case of multiple sites requesting blood, and limited carrying capacity. Most importantly, we need to study the blood's temperature change due to the external environment, the heating agent (or refrigerant) and time factor during transportation, and propose an optimal method for calculating the mixing proportion of blood and appendage in different circumstances and delivery conditions. Then, by introducing the idea of transportation appendage into the traditional Capacitated Vehicle Routing Problem (CVRP), this new problem is proposed according to the factors of distance and weight. Algorithmically, we use the combination of decomposition-based multi-objective evolutionary algorithm and local search method to perform a series of experiments on the CVRP public dataset. By comparing our technique with the traditional ones, our algorithm can obtain better optimization results and time performance.

No MeSH data available.


Related in: MedlinePlus

An example of UAV flight plan for multiple disaster areas.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4862655&req=5

pone.0155176.g001: An example of UAV flight plan for multiple disaster areas.

Mentions: A group of UAVs U = {u1,…,um} transport the blood bags to each blood needing place from the warehouse. The warehouse (starting point) and several blood needing places forms a bi-directional graph G = (V,E); V = {v0,v1,…,vn} that consists of the n + 1 nodes in the graph and E = {(i,j)/i,j ∈ V,i ≠ j} is the set of edges; v0 stands for the starting point while v1,…, vn are n blood needing places, and eij is the flight distance between vi and vj. Each UAV has a limited carrying capacity w, the amount of blood each blood needing place needs is di, each blood needing place can be supplied at most once, and the UAV will return to the starting point after finishing its task (Fig 1).


Multi-Objective Algorithm for Blood Supply via Unmanned Aerial Vehicles to the Wounded in an Emergency Situation.

Wen T, Zhang Z, Wong KK - PLoS ONE (2016)

An example of UAV flight plan for multiple disaster areas.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4862655&req=5

pone.0155176.g001: An example of UAV flight plan for multiple disaster areas.
Mentions: A group of UAVs U = {u1,…,um} transport the blood bags to each blood needing place from the warehouse. The warehouse (starting point) and several blood needing places forms a bi-directional graph G = (V,E); V = {v0,v1,…,vn} that consists of the n + 1 nodes in the graph and E = {(i,j)/i,j ∈ V,i ≠ j} is the set of edges; v0 stands for the starting point while v1,…, vn are n blood needing places, and eij is the flight distance between vi and vj. Each UAV has a limited carrying capacity w, the amount of blood each blood needing place needs is di, each blood needing place can be supplied at most once, and the UAV will return to the starting point after finishing its task (Fig 1).

Bottom Line: This is a complex problem that includes maintenance of the supply blood's temperature model during transportation, the UAVs' scheduling and routes' planning in case of multiple sites requesting blood, and limited carrying capacity.Then, by introducing the idea of transportation appendage into the traditional Capacitated Vehicle Routing Problem (CVRP), this new problem is proposed according to the factors of distance and weight.By comparing our technique with the traditional ones, our algorithm can obtain better optimization results and time performance.

View Article: PubMed Central - PubMed

Affiliation: Software School, Xiamen University, Xiamen, Fujian, China.

ABSTRACT
Unmanned aerial vehicle (UAV) has been widely used in many industries. In the medical environment, especially in some emergency situations, UAVs play an important role such as the supply of medicines and blood with speed and efficiency. In this paper, we study the problem of multi-objective blood supply by UAVs in such emergency situations. This is a complex problem that includes maintenance of the supply blood's temperature model during transportation, the UAVs' scheduling and routes' planning in case of multiple sites requesting blood, and limited carrying capacity. Most importantly, we need to study the blood's temperature change due to the external environment, the heating agent (or refrigerant) and time factor during transportation, and propose an optimal method for calculating the mixing proportion of blood and appendage in different circumstances and delivery conditions. Then, by introducing the idea of transportation appendage into the traditional Capacitated Vehicle Routing Problem (CVRP), this new problem is proposed according to the factors of distance and weight. Algorithmically, we use the combination of decomposition-based multi-objective evolutionary algorithm and local search method to perform a series of experiments on the CVRP public dataset. By comparing our technique with the traditional ones, our algorithm can obtain better optimization results and time performance.

No MeSH data available.


Related in: MedlinePlus