abstract · web viewword count: 6033 + 250 × 5 tables = 7283 submission date: 01/08/2019 abstract...

DESIGN OF HOSPITAL DELIVERY NETWORKS USING UNMANNED AERIAL VEHICLES

Alejandra Otero ArenzanaMEng in Civil and Environmental Engineering Department of Civil and Environmental EngineeringImperial College LondonSW7 2BU, [email protected]

Jose Javier Escribano MaciasPhD Candidate in Sustainable Civil EngineeringDepartment of Civil and Environmental EngineeringImperial College LondonSW7 2BU, [email protected]

Dr. Panagiotis AngeloudisSenior Lecturer in Transport Systems & LogisticsDepartment of Civil and Environmental EngineeringImperial College LondonSW7 2BU, [email protected]

Word count: 6033 + 250 × 5 tables = 7283

Submission Date: 01/08/2019

12

345678

91011121314

151617181920

21

22

Otero-Arenzana, Escribano-Macias, Angeloudis 2

ABSTRACTUnmanned Aerial Vehicles (UAVs) are being increasingly implemented in a range of applications. Their low payload capacity and ability to overcome congested road networks enables them to provide fast delivery services for urgent high-value low-volume cargo. This work investigates the economic viability of integrating UAVs into urban hospital supply chains. In doing so, a strategic model that determines the optimal configuration of supporting infrastructure for urgent UAV delivery between hospitals is proposed. The model incorporates a tailored facility location algorithm that selects an optimal number of hubs given a set of candidates and determines the number of UAVs required to fulfil total demand. The objective is to minimize the total cost of implementation, computed as the sum of generalised, battery, vehicle and hub establishment costs. The model is applied to a case study based on the establishment of a UAV delivery network for deliveries between NHS hospitals in London. A baseline scenario is also developed using current NHS vehicles for delivery. Our results demonstrate that UAV-based delivery provide significant reductions in operational costs compared to the baseline. Furthermore, our analysis indicates the location of hubs is more significant to the solution optimality than any increase in range or payload.

Keywords: UAV, Healthcare, Delivery, Optimisation

123456789

101112131415161718


INTRODUCTIONHospitals are progressively being exposed to the pressures of an ageing population, an increment in morbidity and increases in treatment costs. Public Health England (2019) estimates that the total burden of morbidity, defined as the number of years lived experiencing a chronic disease, rose by almost 17% between 1990 and 2013 in England. The main contributor is the ageing population as a result of healthcare advancements, as morbidity rate increases with age. Over the 1997-2017 period, the 65 and over age group grew by 2,445,000 people, which represents an increase of 2.3% (Office for National Statistics, 2019).

As these trends prevail, the average spending per individual can be expected to increase (Kim, 2017). Most notably, UK National Health Service (NHS) drug procurement expenditure grew on average by 12% from 2011 to 2017 every year (Ewbank et al., 2018), while NHS overall expenditure doubled from £74.5 to £144.3 billion between 2000 and 2017 (Harker, 2019).

The healthcare supply chain is characterised by high transportation costs and recurrent difficulties to fulfil demand (Wright et al., 2018). Every year, the NHS needs to collect, process and transport 1.6 million blood units to treat over 5 million patients (NHS Blood and Transplant, 2019). Due to its high perishability, yearly wastage levels range 3-5%. (Stanger et al., 2012; BSMS 2012).

Blood supply regulation is outlined by the Blood Safety and Quality Regulation (UK Government (2005). Thus, the supply chain is generally centralised as blood must be processed and tested prior to distribution. The delivery from these blood banks to hospitals rely on a combination of owned vehicle fleets, volunteers, and specialist outsourced services. Standard deliveries of blood supplies are carried out periodically, with supplementary deliveries deployed on an on-demand basis (National Audit Office, 2000).

The Blood Stocks Management Scheme (BSMS), which monitors the blood supply chain in the UK, highlights that reductions in wastage are to be achieved by facilitating movement of blood components between NHS facilities, and providing an agile response to changes in demand (BSMS, 2015). As a result, UAVs are increasingly being contemplated as an alternative to traditional road vehicles (Nesta, 2018).

The integration of UAVs can help reduce operational costs as they are less labour-intensive (Thiels et al., 2015). Moreover, they require significantly less take-off and landing space than helicopters, which are occasionally used for urgent delivery (Haidari et al., 2016). Battery powered UAVs also present a reduction in carbon emissions when compared to petrol or diesel-based road transport (Goodchild and Toy, 2016; Figliozzi, 2017). Finally, UAVs bypass road networks, which are vulnerable to infrastructure damage and traffic congestion.

Nevertheless, UAV delivery systems currently face important barriers precluding implementation. Most applications entail flying UAVs Beyond Visual Line of Sight (BVLOS) making use of its autonomous features. Doing so at scale presents a major regulatory and operational challenge. Additionally, flight performance is susceptible to weather constraints (SESAR, 2016). Finally, negative public perception remains a major concern (Nesta, 2018).

While the integration of UAVs for hospital deliveries of urgent medicines and blood has been studied in previous literature, a strategic framework to quantify the efficiency of hospital operations has yet to be proposed. To address this gap, we present an optimisation model that can be used to design a UAV delivery network for hospital deliveries. In doing so, the model minimises drone travelling time, battery consumption levels, vehicle investment, and

123456789

101112131415161718192021222324252627282930313233343536373839404142434445


infrastructure costs. The trajectories designed between hospitals conform to the latest Air Traffic Management regulations.

The following section presents a review of published research related to the model proposed. The Methodology Section describes the optimisation problem, which is tested using a problem instance based on the NHS facilities in London in the Case Study Section. An analysis of the relevance and limitations of the study is presented in the Discussion Section, with final thoughts and considerations captured in the Conclusions.

LITERATURE REVIEWRecent improvements in cost efficiency, reliability and speed of service of UAV systems has motivated the development of many optimization models that consider its use for deliveries. Models that combine trucks and drones have been the focus of commercial delivery and suburban medical deliveries (Murray and Chu, 2015; Scott and Scott, 2017; Hong, Kuby and Murray, 2018).

This review focuses on drone-only systems as these are most suitable to provide on-demand fast and reliable inter-urban hospital delivery due to the short distances between hospitals and urgency in delivery (Nesta, 2018).

Within urban deliveries, Shavarani et al. (2018) presented a hierarchical facility location considering recharge, while Kim et al. (2017) proposed a similar problem for health care delivery for chronic disease patients in rural areas. However, the studies considered hubs with infinite capacity, which is not applicable to urban hospitals due to the limited availability and high cost of land. Furthermore, the operational costs estimations are overly simplified, as they ignore variations in occupancy levels.

In disaster relief, Fereiduni & Shahanaghi (2016) studied the design of a dynamic UAV blood supply network for emergencies. Wen et al. (2016) proposed a vehicle routing model that considers blood temperature variation based on the quantity of blood transported and length of each trip. Escribano Macias et al. (2020) incorporated energy management and recharge to a location-routing algorithm for drone-based relief distribution.

Other more qualitative studies have focused specifically on the feasibility of implementing large scale UAV delivery networks in the healthcare supply chain (Nesta, 2018; Wright et al., 2018). However, their discussion presented simplistic cost estimations.

To the extent of this literature review, no research paper presents a model that determines the optimal configuration and minimizes the total cost of establishing a permanent UAV delivery system for medical deliveries between hospitals. This report covers the existing gap in the literature by presenting a strategic model that considers travel, battery, hub establishment and vehicle costs.

METHODOLOGYAs highlighted in our Introduction, hospitals require persistent and on-demand blood deliveries from blood banks. This paper proposes the selection of hospitals to be used to store and deliver blood on an on-demand basis using UAVs. Given a set of candidate hubs I , the algorithm selects the optimal hub locations z i and determines the number of vehicles required at each centre to deliver demand D j to each hospital j∈ J .

Blood stock is transported periodically from blood banks to the selected hubs. These are equipped with UAVs, batteries, take-off and landing pads, and charging facilities depending on the demand requirements.

123456789

10111213141516171819202122232425262728293031323334353637383940414243444546


After the completion of a delivery, UAVs returned to their original hub for re-charging and maintenance until the next delivery is requested (refer to Assumption 4). The development of communication systems between hospitals to request orders is out of the scope of this paper.

Figure 1 Model Overview. Processes shown in blue, inputs in orange, and outputs in green.The model contains three stages as presented in Figure 1. The trajectory design stage

calculates flight times and energy requirements of UAV travel between all hospitals. These trajectories are used to select hub candidates along with other hospital properties to ensure that all hospitals lie within the UAV’s operational range.

Finally, the hub selection stage contains a facility location optimisation problem. The objective of the model is to minimize the total costs computed as the sum of generalised, energy, investment, and emission costs subject to hub capacity and vehicle range constraints. The following assumptions are adopted:

Assumption 1: It is assumed that the corridor altitude is established so that no obstacles are encountered. Furthermore, UAVs will operate in BVLOS conditions, as it is expected that regulations will evolve to become more supportive of this type of operations.

Assumption 2: Vehicles are assumed to be fully powered by electric batteries as over 95% of the market comprises UAVs which are battery powered (Drone Industry Insights, 2017). Additionally, the implementation of electric vehicles aligns with the targets being imposed by most countries to drive consumption away from combustion engines (Inagaki, Sanderson and Clover, 2018). Cruise velocity is set to be constant and payload variations are assumed to be the only factor which affects battery consumption. The impact of weather conditions and wind speed on flight performance is also neglected. Required take-off and landing times are not considered in this model as on average, take-off and landing combined constitute a negligible proportion of the total flight time (Dorling et al., 2017; Nesta, 2018).

Assumption 3: A uniform commodity problem is considered. Only the delivery of a standardised delivery package is considered in the problem.

Assumption 4: UAVs return to their base after a delivery is completed. This assumption is based on two factors: (a) the short lead times in blood demand and high uncertainty suggests that hospitals will likely want to maintain a known fleet size of UAVs that would be able to provide a guaranteed level of service in case of urgent delivery requirements, and (b) UAV balancing will require hospitals to be fitted with excess capacity to store and maintain drones, thus the cost requirements to maintain the load-balanced fleet may outweigh the operational costs of the hubs returning to their base.

123

456789

10111213141516171819202122232425262728293031323334


Indices and Setsj∈ J Set of hospitalsi∈ I Set of hub candidatesk⊂ i∈ I Subset of hub candidates which are hospitalsz⊂ i∈ I Subset of hub candidates which are blood banks

Decision Variables x ij Integer: determines the number of UAV deliveries from hub i to hospital jz i Boolean: defines whether a hub candidate i is selected or not

Hospital Parameters:d j Concurrent demand at each hospital j[blood units]

Hub Parameters:hi Capacity of each hub i[kg]f i Set up cost of hub i[£]γ Maximum number of hubs that can be established [-]

UAV Parameters:τ Maximum vehicle range [km]υ Cruise velocity [km/ hour]mv UAV empty mass [kg]mp Payload capacity mass [kg]ρ Payload capacity in blood units [blood units]cv UAV cost [£]

Battery Parameters: cb Battery cost [£/ kWh]l Battery expected life [cycles]ce Electricity cost [£/ kWh]η Power transfer efficiency for the motor and propeller [-]κ Lift-to-drag ratio [-]p Power consumed by the electronic components [kW]ϵ Charging efficiency [-]

Emissions Parameters:σ v Electricity emissions [kgCO2e/ kWh]σ f Battery production emissions [kgCO2e]λ Carbon price [£/tCO2e]

Link Parameters:e ij Energy cost of travelling from hub i to hospital j[£]e ij Energy cost of travelling from hub i to hospital when empty j[£]rij Energy requirements of travelling from hub i to hospital j[£]rij Energy requirements of travelling from hub i to hospital when empty j[£]t ij Generalised travel cost from each hub i to each hospital j[£]sij Distance from each hub i to each hospital j [km ]

12345

678

910

11121314

15161718192021

2223242526272829

30313233

34353637383940


α Generalised cost of time [£/ hour]

Cost Parameters:TC Total Costs [£]GC Generalised Costs [£]BC Battery Costs [£]VC Vehicle Costs [£]HC Hub Establishment Costs [£]C Cd Carbon Emission Costs [£]

The demand parameter d j specifies the number of blood units required for the operation time horizon. This section describes a strategic problem formulation, such that it evaluates a typical business operation which, depending on the supply chain structure, may constitute a day, week, or month. The formulation presented below is generic to ensure its applicability to other supply chains, with greater detail presented in the Case Study section.

The model minimises the aggregate cost function shown in (1.1). Travel times are monetised using a generalised value of time α that quantifies potential risks related to the delay in transporting blood.

TC=GC+BC+VC+HC+C Cd (1.1)GC= ∑

i, j∈ I ,J2x ij t ij (1.2)

t ij=αij sij

v∀ i , j∈ I , J (1.3)

BC= ∑i , j∈ I ,J

xij(e ij+ eij ) (1.4)

e ij=( cb

l+

ce

ϵ )rij ∀ i , j∈ I , J (1.5)

e ij=( cb

l+

ce

ϵ ) rij ∀ i , j∈ I , J (1.6)

r ij=( mv+mp

370 ηκ+ p

υ )sij ∀ i , j∈ I , J (1.7)

r ij=( m v

370 ηκ+ p

υ ) sij ∀ i , j∈ I , J (1.8)

VC=cv ∑i , j∈ I ,J

xij (1.9)

HC=∑i∈ I

f i z i (1.10)

C Cd=λ( σ v

1000 ∑i , j∈I ,J

x ij ( rij+ rij)+σ f

1000 ∑i , j∈I ,J

x ij) (1.11)

The battery costs, e ij and e ij, have been computed using the approximation provided by D’Andrea (2014). It is important to note that these matrices are not necessarily symmetrical due to variable weather conditions and asymmetrical trajectory design. Given the strategic nature of this study, these aspects have been ignored as per Assumption 2.

The complete problem formulation is presented:

1

23456789

1011121314151617

1819202122


MinimiseTC (2)subject ¿:hi z i≥ ρ∑

j∈Jx ij ∀ i∈ I (2.1)

ρ∑i∈ I

x ij=d j ∀ j∈ J (2.2)

∑i∈I

zi ≤ γ (2.3)2 xij s ij ≤ xij τ ∀ i , j∈ I , J (2.4)zi∈ {0 , 1} ∀ i∈ I (2.5)x ij∈N ∀ i , j∈ I , J (2.6)

Constraint (2.1) limits cargo delivered from each hub to its maximum capacity. It also guarantees no UAV deliveries are assigned to non-selected hubs. Constraint (2.2) ensures demand is fulfilled at every hospital. Constraint (2.3) establishes a maximum number of hubs selected. Constraint (2.4) ensures UAV operational range is not exceeded. Constraints (2.5) and (2.6) define the decision variables as Boolean and positive integer values respectively.

CASE STUDYThe implementation of a UAV delivery network between hospitals in London has been considered as an alternative to road transport for two reasons. First is the need to improve efficiency and reduce costs of NHS facilities. Secondly, the government has recently stressed the importance of reducing congestion and pollution (Mayor of London, 2018).

This case study is based on the work published by Nesta (2018) which studies the potential use of UAVs for the delivery of laboratory samples between Guy’s and St Thomas hospitals in London. We have extended the problem instance to the NHS hospital network in London. In total, the study aims to optimise delivery of urgent medicines and blood between 24 NHS hospitals with an Accident and Emergency (A&E) department in Greater London (refer to Figure 2).

Five hospitals are selected as hub candidates based on their high demand and strategic location. The selection process is performed under the assumption that the pertinent locations have sufficient space to accommodate the required number of UAVs and blood storage facilities. Designating hospital hub candidates according to their location ensures that all demand points are accessible. An indicative example is selecting Queens Hospital, so Kings George Hospital is not unreachable by drone. Further hub candidates include the two NHS Blood and Transplant Centres (B&T), which are the main blood distributors to hospitals in Greater London.Hub ParametersThe demand at each hospital is estimated by considering daily A&E attendees obtained from the NHS database and complementary reports, shown in (NHS Digital and NHS England, 2018; Care Quality Commission, 2019). These values are converted into demand levels by considering the NHS target of attending patients within four-hours of arrival, such that a new group of incoming patients is treated every four hours (NHS England, 2019).

According to Figure 3c, peak demand occurs between daylight hours (8am-8pm) and remains constant for each four-hour time-window within this interval. We assume this level of demand during the implementation of our model, as the provision of peak daily demand ensures

123456789

10111213141516171819202122232425262728293031323334


the feasibility of the solution throughout the year given that weekly and monthly demand levels are uniformly distributed (see Figure 3a and b).

Figure 2 Locations considered in the case study.

Of these incoming patients, only 1% require urgent blood deliveries on average (NHS Digital and NHS England, 2018). Finally, a conservative 5 units per patient – equivalent to approximately 450 ml per unit (Hudson 2016) – is assumed (Thiels et al. 2015).

The incurred storage costs to accommodate hospital candidate hubs are estimated using the price and dimensions of the HXC-1308 refrigerator model offered by Wolflabs UK (Wolflabs, 2019). This company has been selected because it is the preferred supplier of laboratory equipment among most NHS trusts (Laboratory Talk 2019).

The resulting hub establishment costs are estimated using the following expression:f i=Γ i+Ωi

∀ i∈ I (3)

where Γ i is the cost of installing storage facilities and Ωi denotes the cost of building a landing platform and enabling the space necessary for the UAVs to operate. The B&T candidates are assigned a storage facility cost Γ z=0, as blood storage facilities already exist. Moreover, it is assumed that B&T locations have very large capacities M (Transplant, 2017). Finally, the landing platform cost Ωi is that provided by Nesta (2018) which considers the fixed infrastructure cost resulting from integrating landing spots in existing buildings.

12

3456789

10111213

1415161718192021


Apr May June July Aug Sept Oct Nov Dec Jan Feb MarMonth

0

5

10

15

20Pr

opor

tion

of A

&E

atte

ndee

s (%

)

Mon Tue Wed Thu Fri Sat Sun

Day

0

5

10

15

20

Prop

ortio

n of

A&

E at

tend

ees

(%)

0 5 10 15 20Hour

0

0.05

0.1

0.15

0.2

0.25

0.3

Prop

ortio

n of

A&

E at

tend

ees

(%)

Figure 3 A&E average attendance between April 2016 and March 2018 (NHS Digital and NHS England, 2018).

Drone ParametersThree UAV models are considered in this case study (see Table 1): the Matternet M2 (Matternet, 2017), and two versions of the Wingcopter 178 model (Wingcopter, 2019b, 2019c). The latter has been selected because it is being used for urgent deliveries and its product specification is also public (Wingcopter, 2019a). Note that the cruise velocity of the Wingcopter models is set as the lower bound to provide maximum range.

While the M2 is a multi-rotor, the Wingcopters are hybrid UAVs that operate like a conventional plane. A key feature of both UAV types is their vertical take-off and landing capability, which is essential in urban environments.

The cost of the UAVs cv has been estimated from Nesta et al. (2018) and coincides with Thiels (2015) and Doole et al. (2018). This parameter includes the initial investment, maintenance, liability insurance, and other costs that relate to any additional modification necessary to operate in urban areas for hospital delivery purposes when accounting for innovation in UAV technology and the time elapsed until implementation. Note that the cost of hybrid UAVs has been assumed to be 50% given their technological immaturity compared to multi-rotors (Chapman, 2016). Furthermore, the cost of the Wingcopter 178 Heavy Lift is assumed higher given the increased vehicle payload capacity.

The vehicle capacity in blood units ρ, has been estimated using Zipline’s (2019) model as a reference due to the similar payload capacity. The Wingcopter 178 HL ρ value is assumed to increase in relation to their m p compared to the other UAV models.

(c) Average daily attendance

(a) Average monthly attendance (b) Average weekly attendance

1

3456789

10111213141516171819202122232425262728


The battery cost, cb and expected life, l are adopted from D’Andrea (2014) as the approximate values of a high-end lithium battery. As discussed by Gottlieb (1997), η and p vary throughout the different flight stages and is also dependant on design characteristics. Additionally, battery performance can be altered for many reasons such as variations in cell chemistry (Linden, 1984). However, an aggregate value η has been used as the derivation of complex battery performance relationships is beyond the scope of this work. This value has been considered as appropriate as it has been previously used by Goodchild & Toy (2016) and Escribano Macias et al. (2020). The lift-to-drag ratio r and charging efficiency ϵ are provided by D’Andrea (2014). The electricity cost ce has been approximated as the average of the six largest energy providers in the UK at the time of writing (Vasili, 2019).

The predicted emissions caused during battery production σ f have been adopted from Hall & Lutsey (2018) and Stolaroff et al. (2018), while the estimated emissions of electricity production σ v in the UK have been obtained from Penistone (2019). A carbon price λ is used to monetise final carbon-equivalent emissions (Bowen, 2018).

Given the complexity, current level of congestion, and limited capacity of London’s airspace, missions which are beneficial to the public are likely to be prioritised. Therefore, in accordance with Assumption 1, civic applications are likely to take precedence over commercial applications, meaning that specific corridors for medical deliveries may be established to ensure non-conflict (Nesta, 2018).

The generalised cost of time has been approximated to α=42 £ /h and considers typical logistics costs (Holguin-Veras et al., 2013). While this α value has been previously applied for disaster relief, it has been considered appropriate given the urgency of the deliveries of this case study. The key component of α is that it must remain greater than the potential loss of life.

Table 1 Model parameters. UAV parameters are obtained from (Matternet, 2017), (Wingcopter, 2019b) and (Wingcopter, 2019c).

Parameter SymbolValue

M2 Matternet

Wingcopter 178

Wingcopter 178 HL

Vehicle ParametersMaximum vehicle range [km] τ 20* 100 45Vehicle cruise velocity [km/ hour] υ 36 40 40Vehicle mass [kg] mv 9.5 9.1 14Vehicle payload capacity [kg] mp 2 2 6Vehicle capacity [blood units] ρ 3 3 6Vehicle cost [£] cv 10,000 15,000 20,000Required storage dimensions [cm3] - 26×128×128 60×84.5×108 60×84.5×108

Battery ParametersBattery cost [ £/ kWh] cb 240Battery expected life [cycles] l 300Power transfer efficiency [-] η 0.5Lift-to-drag ratio [-] r 3Power consumed by electronics [kWh] p 0.1Charging efficiency [-] ϵ 0.8

123456789

101112131415161718192021222324252627


Electricity cost [ £/ kWh] ce 0.15Electricity emissions [kgCO2e/ kWh] σ v 0.31Battery production emissions [kgCO2e] σ f 4.7Carbon price [£/tCO2e] λ 18.1

Hub ParametersStorage facility cost of hospital candidate hub k[£]

Γ k 12,000

Landing platform cost [£] Ω 5,000Set up cost of hospital candidate hub k[£] f k 17,000Set up cost of blood bank candidate hub z[£] f z 5,000Hospital candidate hub k capacity [blood units]

hk 32

Blood bank candidate hub z capacity [blood units]

hz M (very big number)

Maximum number of established hubs [-] γ 4*The range of Matternet’s M2 UAV is extended to 25 km to ensure its viability in the presented case study.

ResultsThe model is solved using CPLEX and is run in an Intel Xeon E5-1650 with 64GB machine. A summary of the results is shown in Table 2. With γ=4, Matternet’s M2 UAV model yields the most cost-effective solution with a total cost of approximately £455,000. However, the requirement for this implementation is to extend vehicle range from 20 to 25 km for viability.

Wingcopter 178 HL provides the second-best solution even though its vehicle cost is the highest out of the three models. Our results indicate that increased payload capacity is significantly more beneficial to operational cost than vehicle range, provided a minimum range is satified. As observed in Figure 4a, the provision of increased operational range provides a negligible improvement over 45km range when the maximum number of hubs γ=4, and has no effect for γ=∞.

Table 2 Cplex model initial implementation results.UAV Model M2 Matternet Wingcopter 178 Wingcopter 178 HL

Hubs EstablishedK 2 3 3Z 2 1 1

UAVs required - 41 36 21

Generalised Cost£ 998 716 465% 0.22 0.12 0.09

Battery Costs£ 12 9 10% <0.01 0.01 <0.01

Vehicle Cost£ 410,000 540,000 420,000% 90.11 90.49 88.15

Hub Setup Costs£ 44,000 56,000 56,000% 9.67 9.38 11.76

Carbon Costs£ 0.486 0.496 0.318% <0.01 <0.01 <0.01

Total Cost £ 455,010 596,725 476,475

123456789

1011121314


Figure 4 CPLEX model Wingcopter 178HL results for varying parametersFigure 4b shows that increasing payload capacity ρ initially drives the cost down significantly until ρ=6. Surprisingly, further increments of ρ increases total cost due exceeded the maximum concurrent demand value at any hospital. Consequently, the total number of UAVs required remains constant, and the additional cargo capacity available remains unused. Note that greater ρ values would likely further increase vehicle costs cv. Such relationship has not been factored in this sensitivity analysis.

Table 3 CPLEX model results when the maximum hub number constraint in equation (2.3) is relaxed.

UAV Model

Optimal Number of Hubs

[-]

Number of

Vehicles [-]

Increase in Hub Costs[£]

Reduction in Vehicle

Cost[£]

Net Reduction in Cost [£

]Total Cost

[£]

Total Cost Reduction [

£]K ZM2 Matternet 6 0 30 58,000 110,000 52,000 402,600 51,730Wingcopter 178 6 0 27 46,000 90,000 44,302 552,423 89,725Wingcopter 6 0 18 46,000 60,000 14,000 462,300 14,200

30 40 50 60 70 80 90 100Drone range [km]

460000

470000

480000

490000

500000

510000

520000

530000

540000

550000To

tal c

ost [

£]

= 4 =

4 6 8 10 12 14 16 18Vehicle capacity [blood units]

450000

500000

550000

600000

650000

700000

750000

Tota

l cos

t [£]

20000 30000 40000 50000 60000 70000 80000 90000 100000Hospital hub establishment cost, fk

460000

480000

500000

520000

540000

560000

580000

600000

620000

Tota

l cos

t [£]

(a) Total cost variation with drone range. (b) Total cost variation with vehicle capacity.

(c) Total cost variation with hub establishment cost.123456789

1011


178 HLA further run is carried out for γ=∞, with results summarized in Table 3. For all UAV

models, the increased cost of setting additional hubs is compensated by a reduction in the number of UAVs required. As a result, all instances select the hospitals as hubs, driving down total cost. Changes in the generalised and battery costs are negligible.

Table 4 CPLEX model sensitivity analysis.Hospital Hub Cost f k Hospital Hub Capacity hk Drone Cost cv

f k [£] Z[£] Hubs[-] hk [£] Z[£] Hubs[-] cv [£] Z[£]

17,000 462,293 CW, KC, NM, RF, RL, QN 10 547,555 CW, KC, NM, RF,

RL, QN, TT 20,000 476,500

20,000 480,293 CW, KC, NM, RF, RL, QN 20 467,293 CW, KC, NM, RF,

RL, QN, TT 18,000 426,293

30,000 515,475 CW, KC, RF, TT 30 462,293 CW, KC, NM, RF,

RL, QN 16,000 390,293

40,000 545,475 CW, KC, RF, TT 40 462,255 CW, KC, NM, RF,

RL, QN 14,000 350,475

50,000 545,675 KC, TT 50 462,251 CW, KC, NM, RF, RL, QN 12,000 308,475






The solutions suggest that establishing smaller distribution hubs is more beneficial to the overall system performance. In all scenarios, with the exemption of the M2 model when γ=4, hospital candidates are selected by the algorithm over existent blood distribution systems despite the additional setup costs. The same behaviour is observed when hub capacity hk>20 in Table 4. This suggests that the benefits of selecting optimal hub locations outweigh initial economical disadvantages.

We further investigate the effects of varying initial hospital setup costs f k in Table 4 and Figure 4c. Logically, any increment in f k results in higher total system costs, despite reducing the number for hospital hub candidates designated. For f k>40,000, only Kings College Hospital (KC) and Tooting B&T (TT) are selected. KC provides a centralised hub location to ensure network viability.

Our analysis also indicates operational costs are significantly lower than the initial cost of purchasing the vehicles and establishing the supportive infrastructure. As shown in Table 4, the vehicle cost cv has a large impact total system cost. Therefore, as UAV cost decreases the viability of the proposed system would increase dramatically. Baseline Scenario ComparisonA baseline scenario problem is developed where ground vehicles deliver the urgent blood samples that imitate the current healthcare logistics structure. In doing so, a modified version of

123456

789

101112131415161718192021222324


the proposed formulation shown in equations set (4) is derived based on the following assumptions:

The two B&T serve as the only network hubs. Therefore, x ij is the only decision variable. The fleet is homogeneous, and we test two different vehicle types: a Yamaha FJR 1300 or

a Ford Focus (UK Emergency Vehicles, 2019). The corresponding emission costs are monetized in the new objective function to reflect the benefits of using battery powered UAVs. Furthermore, the fleet is owned by the NHS and hence no vehicle costs are incurred.

The generalized travel cost is approximated using the average commuting times between each origin destination pair based on traffic congestion patterns and traffic restrictions.

Link Parameters:q ij Commuting time of travelling from each hub i to each hospital j[hour]

The rest of the parameters are carried forward from the previous formation.Minimise :TC=GC+FC+C Cr (4)

GC=2α ∑i , j∈ I ,J

x ij qij (4.1)

FC=2 ζϑ ∑i , j∈I , J

x ij sij (4.2)

C C r=2 μ

1000λ ∑

i , j∈ I ,Jxij s ij (4.3)

Where FC represents the fuel cost, and C C r denotes the carbon emissions monetary cost for road vehicles. The number of constraints is reduced to (2.2, 2.4-2.6). Constraints (2.1) and (2.3) are no longer applicable because the number of hubs is now fixed, and these are assumed to have a very large capacity in comparison to concurrent demand levels. The baseline scenario road distance and commuting times sij are been obtained using the Here routing API (Here, 2019). The rest of the parameters estimated are included in Table 5.Table 5 Baseline scenario modified parameters

Parameter Symbol Yamaha FJR 1300 (YM)

Ford Focus (FF) Reference

Maximum vehicle range [km] τ 370 700 (Motorcycle Market, 2019;

Sherwood Ford, 2019)Vehicle capacity in blood units [blood units] ρ 6 10 (Wright et al., 2018)

Fuel consumption [L/km] ϑ 0.068 0.067 (Motorcycle Market, 2019; Sherwood Ford, 2019)

Fuel cost [£/L] ζ 1.2 (Bailey, Qurashi and Yuan, 2019)

Emissions [kgCO2e/km] μ 0.153 0.120 (AECC, 2008; Europe Energy Portal, 2019)

We compare the Wincopter UAV systems and the baselines. The results are presented in Figure5. Note that Matternet’s M2 model is not considered as a reference because its operational range is not viable.

123456789

101112131415

16171819202122

23242526


WC WC-HL YM FFVehicle

0

200

400

600

800

1000

1200

Cos

ts [£

]

Generalised CostBattery/Fuel CostsEmissions Cost

WC/YM WC-HL/YM WC/FF WC-HL/FF

Vehicle Comparison

0

50

100

150

200

250

Year

ly C

ost S

avin

gs [£

000s

]

Dry Weather (20th Percentile)Average WeatherRainy Weather (80th Percentile)

Figure 5 Vehicle mode cost and savings comparison.

Both Wingcopter models provide significant reductions in operational costs compared to the baselines. The greatest improvement is found between the Yamaha and the Wingcopter 178HL, with the operational cost of the Yamaha FJR300 being approximately three times higher. An important distinction is the large energy/fuel costs savings of ~90% estimated using UAVs. Furthermore, the travel generalized costs of using UAVs is approximately 40% lower than road transport.

It is important to note that the presented results are based on concurrent demand levels. If the annual difference between the compared models is considered, the monetary difference becomes even more substantial. Figure 5b) presents the estimated yearly savings under different annual weather previsions based on the recorded number of days of rainfall in London (Kew Gardens weather station) between 1981 and 2010. It is assumed that hospitals would revert to existing delivery methods should weather impede drone flight.DiscussionThe results obtained in this study highlight the economic viability of inter-urban UAVs-based hospital deliveries. Against the baseline scenario, both UAVs outperform road-based transport modes in terms of energy and travelling costs. Note that the estimate is conservative, as the inclusion of vehicle maintenance and labour costs should further increase cost savings as UAV networks require fewer personnel for operation (Wright et al., 2018). Moreover, if a carbon emission charge is imposed by the UK government, additional financial savings would emerge from using battery-powered UAVs.

(b) Operational Cost Savings Comparison under different weather conditions.

(a) Operational Cost Comparison.

WC – Wingcopter 178; WC-HL – Wingcopter 178HL; YM – Yamaha FJR 1300; FF – Ford Focus1

23456789

1011121314151617181920212223


Hub location and payload capacity emerge as the most significant factors conditioning overall system cost between UAV platforms, provided the UAV range is sufficient to travel to each hub and hospital. Payload capacity is highly dependent on expected demand, as a minimum cost solution was found at ρ=6. Lower ρ values increased cost significantly, while larger values resulted in less pronounced cost increments.

Another interesting result is the hub selection variation with hub capacity hi. For hi ≥ 30, all hubs selected were hospitals – no blood bank was selected despite their lower hub establishment cost. These results align with the recommendations outlined by BSMS (2015) to reduce wastages, as they suggest that hospitals should hold larger stocks. The proposed on-demand system also supports the BSMS strategy by providing an agile response to changes in demand and facilitating the movement of blood components between NHS facilities.

While the model presented has highlighted the many advantages to develop a UAV-based inter-hospital blood delivery network, several limitations preclude its application today. Firstly, despite reasonable assumptions regarding airspace corridor design based on urgency and public use, the feasibility of such approach must be investigated further given the high airspace congestion levels and associated risks estimated.

While exemptions to aviation regulations have been provided for testing purposes (Nesta, 2018), the application envisioned in this paper would require on-demand clearance. Platforms such as OpenSky and Unifly aim to provide Unmanned Traffic Management capabilities in collaboration with Civil Aviation Authorities, but have yet to be applied at scale (Unifly, 2019; Wing, 2019). Wing, the commercial cargo delivery by Alphabet, was given air carrier certification in 2019, permitting them to deliver goods commercially (Chappell, 2019).

The initial investment required to develop the network, and requirement to train current staff prove important barriers to overcome. As highlighted by Stander (2012), current strategies to reduce wastages involve improving inventory procedures, staff training, and increase inter-hospital collaboration, which incur lower risk than developing a UAV delivery network.

Moreover, despite blood delivery systems already in operation (Bright, 2019), the negative public perception of UAVs delay their implementation, with most concerns related to safety, security and privacy (Kwon, Kim and Park, 2017; Government Computing, 2019).

Persistent UAV flight presents safety risks to the population that must be considered before deployment. While avoidable in rural areas, drones will fly over populated areas, posing a risk of damage and noise nuisance. EASA is currently developing regulation to minimise the risks after a collision and loss of control, which include the development safe recovery systems that rely on external systems such as GNSS (EASA, 2017).

In the United States, NASA is developing the Unmanned Aerial System Traffic Management (UTM) to enable large scale small drone operations (Prevot et al., 2016). Within its capabilities, the UTM system will plan urban drone flights in real-time and provide contingency volumes to manage risk (Chakrabarty et al., 2019). Therefore, we expect that many concerns regarding drone operation safety will be addressed in due course by the relevant regulatory bodies.

The presented case study suggests that no congestion will be present at the hubs, as 15 UAVs will approximately take off and land in 4 hours. Assuming that flights are randomly distributed within the 4-hour horizon, there is 12% probability that at least 2 flights will take place within the same 5-minute window.

However, this analysis considers demand to be deterministic. Under stochastic demand levels, a safety blood stock and buffer drone capacity may be necessary to cope with unexpected

123456789

10111213141516171819202122232425262728293031323334353637383940414243444546


peaks in demand, leading to increased investment requirements and likelihood of congestion at hubs. Further modelling and simulation of the operational aspect of the deliveries is required to ensure this is indeed the case. We intend to explore this topic in our future work.

Further simplifications have been made related to weather, which affects battery consumption patterns and may alter UAV performance. Given the strategic nature of this study, calm weather conditions are assumed. The accurate estimation of weather effects on battery expenditure and cost necessitates extensive data collecting and testing, which may constitute future work.

The presented problem instance would be transformed to a stochastic problem where e ij and e ij have an associated uncertainty parameter, which would relate to performance variations due to weather. The magnitude of the variation would depend on the evaluated weather conditions: temperature affects battery performance and lift generation (Chen and Li, 2014), while wind and humidity disrupt UAV flight and control (Ranquist, Steiner and Argrow, 2016).CONCLUSIONSConsiderable effort is being invested to design the frameworks and technologies to encourage the widespread use of UAVs, including airspace management originations (US Federal Aviation Authority or European Aviation Safety Agency), companies (Zipline, Amazon, Google), and other public organisations (Nesta UK). Given the light payloads and reliability of UAVs, urgent delivery has been the focus of practitioners and researchers alike. In response, this paper presents a unique strategic model which serves to quantify the costs of developing a long-term UAV-based blood delivery system in urban environments.

Our case study based in London demonstrates that UAVs present numerous advantages in comparison with traditional road transport. With operational costs savings of up to 300%, depending on UAV model type and operational parameters, such as vehicle range and payload size, the UAV-based model increases service reliability (lower variability in travel time) and overcome initial investment. Furthermore, our study suggests that CO2 levels may be reduced, which aside from environmental benefits could result in further monetary savings if carbon emission taxes are implemented. However, it is important to note that the aim of the study is not to present a holistic emissions assessment framework for UAVs.

The encouraging results obtained regarding the potential savings of UAV systems sets the basis for pursuing a more detailed analysis of the secondary costs such as labour and vehicle maintenance costs as more data becomes available. The model proposed could also be extended to explore if any additional benefits arise from considering heterogeneous vehicle fleets. Additionally, when the current Air Traffic Management research efforts materialise, the model could also be adjusted to incorporate vehicle routing capabilities in which trajectories that fulfil new regulations.

ACKNOWLEDGMENTSThe research was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) as part of the Sustainable Civil Engineering Centre for Doctoral Training (Grant number EP/L016826/1).

AUTHOR CONTRIBUTION STATEMENTThe authors confirm contribution to the paper as follows: Otero, Escribano-Macias and Angeloudis carried out the study conception and design, Otero developed the models and analysed the results. Escribano-Macias and Otero prepared the manuscript. All authors have

123456789

10111213141516171819202122232425262728293031323334353637383940414243444546


reviewed the results and approved the final version of the manuscript. The author(s) do not have any conflicts of interest to declare.

12


REFERENCESAECC (2008) ‘AECC Motorcycle Test Programme’, pp. 1–9. Available at: https://circabc.europa.eu/webdav/CircaBC/GROW/automotive/Library/commission_expert/mveg_motorcycles/7th_meeting_2005/moto_95.pdf.Bailey, D., Qurashi, S. and Yuan, T. (2019) ‘Fuel price calculator: How much do you pay?’, BBC News, 24 June, pp. 1–5. Available at: https://www.bbc.co.uk/news/business-21238363 (Accessed: 2 June 2019).Bowen, A. (2018) ‘What is a carbon price and why do we need one?’, London School of Economics and Political Science, 17 May. Available at: http://www.lse.ac.uk/GranthamInstitute/faqs/what-is-a-carbon-price-and-why-do-we-need-one/ (Accessed: 2 June 2019).Bright, J. (2019) ‘Drone delivery startup Zipline launches UAV medical program in Ghana’, TechCrunch, 24 April, pp. 1–10. Available at: https://techcrunch.com/2019/04/24/drone-delivery-startup-zipline-launches-uav-medical-program-in-ghana/.BSMS (2015) The 2012-2014 Annual BSMS Report. Available at: http://www.bloodstocks.co.uk/pdf/annual_report_2012_14.pdf.Care Quality Commission (2019) Publications. Available at: https://www.cqc.org.uk/publications#cqc-solr-search-theme-form (Accessed: 28 April 2019).Chakrabarty, A. et al. (2019) ‘Real-time path planning for multi-copters flying in UTM-TCL4’, in AIAA Scitech 2019 Forum. San Diego, CA, pp. 1–16. doi: 10.2514/6.2019-0958.Chapman, A. (2016) Types of Drones: Multi-Rotor vs Fixed-Wing vs Single Rotor vs Hybrid VTOL. Available at: https://www.auav.com.au/articles/drone-types/ (Accessed: 29 March 2019).Chappell, B. (2019) ‘FAA Certifies Google’s Wing Drone Delivery Company To Operate As An Airline’, NPR, 23 April. Available at: https://text.npr.org/s.php?sId=716360818.Chen, K. and Li, X. (2014) ‘Accurate determination of battery discharge characteristics - A comparison between two battery temperature control methods’, Journal of Power Sources. Elsevier B.V, 247, pp. 961–966. doi: 10.1016/j.jpowsour.2013.09.060.D’Andrea, R. (2014) ‘Guest Editorial. Can Drones Deliver?’, IEEE Transactions on Automation Science and Engineering, 11(3), pp. 647–648. doi: 10.1109/TASE.2014.2326952.Doole, M., Ellerbroek, J. and Hoekstra, J. (2018) ‘Drone Delivery Urban airspace traffic density estimation’, in Eight SESAR Innovation Days. Salzburg, Austria, pp. 1–8.Dorling, K. et al. (2017) ‘Vehicle Routing Problems for Drone Delivery’, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(1), pp. 70–85. doi: 10.1109/TSMC.2016.2582745.Drone Industry Insights (2017) Drone Energy Sources – Pushing the Boundaries of Electric Flight, Manufacturer, Market, Reseach, Technology. Available at: https://www.droneii.com/drone-energy-sources (Accessed: 18 February 2018).EASA (2017) Notice of Proposed Amendment 2017-05 (A) - Introduction of a Regulatory Framework for the Operation of Drones. Available at: https://www.easa.europa.eu/sites/default/files/dfu/NPA 2017-05 %28A%29_0.pdf.Escribano Macias, J., Angeloudis, P. and Ochieng, W. (2020) ‘Optimal hub selection for rapid medical deliveries using unmanned aerial vehicles’, Transportation Research Part C: Emerging Technologies. Elsevier, 110(November 2019), pp. 56–80. doi: 10.1016/j.trc.2019.11.002.Europe Energy Portal (2019) Ford - 2009 CO 2 Emissions, Carbon Dioxide Emissions of Cars. Available at: https://www.energy.eu/car-co2-emissions/ford.php (Accessed: 10 June 2019).Ewbank, L. et al. (2018) ‘The rising cost of medicines to the NHS: what’s the story’, The King’s Fund, (April), pp. 1–345. doi: 10.1186/1475-2891-10-117.

123456789

10111213141516171819202122232425262728293031323334353637383940414243444546


Fereiduni, M. and Shahanaghi, K. (2016) ‘A robust optimization model for blood supply chain in emergency situations’, International Journal of Industrial Engineering Computations, 7(4), pp. 535–554. doi: 10.5267/j.ijiec.2016.5.002.Figliozzi, M. (2017) ‘Drones for Commercial Last-Mile Deliveries: A Discussion of Logistical, Environmental, and Economic Trade-Offs’, Civil and Environmental Engineering Faculty Publications and Presentations.Goodchild, A. and Toy, J. (2016) ‘Delivery by Drone : An Evaluation of Unmanned Aerial Vehicle Technology In Reducing CO 2 Emissions in the Delivery Service Industry’, in TRB 2016 Annual Meeting.Gottlieb, I. (1997) Practical Electric Motor Handbook. 1st edn. Oxford, United Kingdom: Reed Educational and Professional Publishing Ltd.Government Computing (2019) ‘PwC survey nds drone adoption in UK hit by negative public perception’, Government Computing, 4 June, pp. 1–9. Available at: https://www.governmentcomputing.com/social-media/news/pwc-survey-drones-uk.Haidari, L. A. et al. (2016) ‘The economic and operational value of using drones to transport vaccines’, Vaccine, 34(34), pp. 4062–4067. doi: 10.1016/j.vaccine.2016.06.022.Hall, D. and Lutsey, N. (2018) Effects of battery manufacturing on electric vehicle life-cycle greenhouse gas emissions, ICCT Briefing. Available at: https://www.theicct.org/sites/default/files/publications/EV-life-cycle-GHG_ICCT-Briefing_09022018_vF.pdf.Harker, R. (2019) NHS funding and expenditure. London, UK. Available at: www.parliament.uk/briefing-papers/sn00724.pdf.Here (2019) here Developer. Available at: https://developer.here.com (Accessed: 15 May 2019).Holguin-Veras, J. et al. (2013) ‘On the appropriate objective function for post-disaster humanitarian logistics models’, Journal of Operations Management, 31, pp. 262–280.Hong, I., Kuby, M. and Murray, A. T. (2018) ‘A range-restricted recharging station coverage model for drone delivery service planning’, Transportation Research Part C: Emerging Technologies. Elsevier, 90(February), pp. 198–212. doi: 10.1016/j.trc.2018.02.017.Hudson, P. (2016) ‘Blood transfusion’, NHS Foundation Trust.Inagaki, K., Sanderson, H. and Clover, C. (2018) ‘Global carmakers race to lock in lithium for electric vehicles’, Financial Times, 24 February. Available at: https://www.ft.com/content/e9b83834-155b-11e8-9376-4a6390addb44.Kim, S. J. et al. (2017) ‘Drone-Aided Healthcare Services for Patients with Chronic Diseases in Rural Areas’, Journal of Intelligent & Robotic Systems, pp. 163–180. doi: 10.1007/s10846-017-0548-z.Kwon, H., Kim, J. and Park, Y. (2017) ‘Applying LSA text mining technique in envisioning social impacts of emerging technologies: The case of drone technology’, Technovation. Elsevier Ltd, 60–61(February), pp. 15–28. doi: 10.1016/j.technovation.2017.01.001.Laboratory Talk (2019) Wolflabs is the top preferred supplier for laboratory equipment to the UK public sector A. Available at: http://laboratorytalk.com/article/2024086/wolflabs-is-the-top-preferred-supplier-for-laboratory-equipment-to-the-uk-public-sector-a (Accessed: 29 May 2019).Linden, D. (1984) Handbook of Batteries and Fuel Cells. New York, NY, United States: McGraw-Hill Book Co. doi: 10.1002/9780470974001.Matternet (2017) ‘In action for public health : Matternet M2’. Available at: https://www.post.ch/-/media/post/ueber-uns/medienmitteilungen/2017/drohnen/spezifikationen-matternet-m2.pdf?la=en.

123456789

10111213141516171819202122232425262728293031323334353637383940414243444546


Mayor of London (2018) ‘Mayor’s Transport Strategy for London’, (March), p. 163.Motorcycle Market (2019) Yamaha FJR 1300. Available at: http://www.motorcycle-market.com/review.php?motorcycle=Yamaha-FJR 1300 (Accessed: 2 June 2019).Murray, C. C. and Chu, A. G. (2015) ‘The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery’, Transportation Research Part C: Emerging Technologies. Elsevier Ltd, 54, pp. 86–109. doi: 10.1016/j.trc.2015.03.005.National Audit Office (2000) The National Blood Service. London, UK. Available at: https://www.nao.org.uk/wp-content/uploads/2000/12/00016.pdf.Nesta (2018) Flying High: Shaping the future of drones in UK cities.NHS Digital and NHS England (2018) Hospital Accident and Emergency Activity 2017-2018, Government Statistical Service. Richmond, UK. Available at: https://digital.nhs.uk/data-and-information/publications/statistical/hospital-accident--emergency-activity/2017-18.NHS England (2019) A&E Attendances and Emergency Admissions. Available at: https://www.england.nhs.uk/statistics/statistical-work-areas/ae-waiting-times-and-activity (Accessed: 23 May 2019).Office for National Statistics (2019) Population estimates- local authority based by five year age bank, Official Labour Market Statistics. Available at: https://www.nomisweb.co.uk/query/construct/summary.asp?mode=construct&version=0&dataset=31 (Accessed: 28 March 2019).Penistone, A. (2019) 2018 UK greenhouse gas emissions, provisional figures, National Statistics. London, UK. Available at: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/790626/2018-provisional-emissions-statistics-report.pdf.Prevot, T. et al. (2016) ‘UAS Traffic Management (UTM) Concept of Operations to Safely Enable Low Altitude Flight Operations’, in 16th AIAA Aviation Technology, Integration, and Operations Conference, pp. 1–16. doi: 10.2514/6.2016-3292.Public Health England (2019) Chapter 3: trends in morbidity and behavioural risk factors, Research and Analysis. Available at: https://www.gov.uk/government/publications/health-profile-for-england/chapter-3-trends-in-morbidity-and-behavioural-risk-factors (Accessed: 28 March 2019).Ranquist, E., Steiner, M. and Argrow, B. (2016) ‘Exploring the Range of Weather Impacts on UAS Operations’, in 18th Conference on Aviation, Range and Aerospace Meteorology. Seattle, WA: American Meteorological Society, p. 11.Scott, J. E. and Scott, C. H. (2017) ‘Delivery Models for Healthcare’, in Proceedings of the 50th Hawaii International Conference on System Sciences, pp. 3297–3304. doi: 10.4018/IJHISI.2018070102.SESAR (2016) European Drones Outlook Study. Brussels, Belgium. Available at: http://www.sesarju.eu/sites/default/files/documents/reports/European_Drones_Outlook_Study_2016.pdf (Accessed: 8 November 2018).Shavarani, S. M. et al. (2018) ‘Application of hierarchical facility location problem for optimization of a drone delivery system: a case study of Amazon prime air in the city of San Francisco’, International Journal of Advanced Manufacturing Technology. The International Journal of Advanced Manufacturing Technology, 95(9–12), pp. 3141–3153. doi: 10.1007/s00170-017-1363-1.Sherwood Ford (2019) How far can you take the 2018 Ford Focus on a full tank. Available at: https://www.sherwoodford.ca/blog/2018-ford-focus-fuel-economy-and-maximum-driving-range (Accessed: 2 June 2019).

123456789

10111213141516171819202122232425262728293031323334353637383940414243444546


Stolaroff, J. K. et al. (2018) ‘Energy use and life cycle greenhouse gas emissions of drones for commercial package delivery’, Nature Communications. Springer US, 9(1). doi: 10.1038/s41467-017-02411-5.Thiels, C. A. et al. (2015) ‘Use of unmanned aerial vehicles for medical product transport’, Air Medical Journal. Air Medical Journal Associates, 34(2), pp. 104–108. doi: 10.1016/j.amj.2014.10.011.Transplant, N. B. and (2017) NHS Blood and Transplant Annual Report and Accounts 2017/18. Available at: www.gov.uk/government/publications.UK Emergency Vehicles (2019) Blood Service. Available at: http://www.ukemergency.co.uk/category/others/blood-service (Accessed: 2 June 2019).UK Government (2005) The Blood Safety and Quality Regulations 2005. United Kingdom. Available at: http://www.legislation.gov.uk/uksi/2005/50/made.Unifly (2019) Unifly ’ s Unmanned Traffic Management platform. Available at: https://www.unifly.aero/en/unifly-utm (Accessed: 19 July 2019).Vasili, A. (2019) Electricity Cost per kWh: Should You Go Solar?, The Eco Experts. Available at: https://www.theecoexperts.co.uk/kwh-electricity-prices (Accessed: 30 May 2019).Wen, T., Zhang, Z. and Wong, K. K. L. (2016) ‘Multi-objective algorithm for blood supply via unmanned aerial vehicles to the wounded in an emergency situation’, PLoS ONE, 11(5), pp. 1–22. doi: 10.1371/journal.pone.0155176.Wing (2019) UAS / Unmanned Traffic Management, Airpsace Integration. Available at: https://wing.com/resource-hub/articles/uas-unmanned-traffic-management (Accessed: 19 July 2019).Wingcopter (2019a) Technology. Available at: https://wingcopter.com/technology/ (Accessed: 27 May 2019).Wingcopter (2019b) Wingcopter 178, Geo-matching. Available at: https://geo-matching.com/uas-for-mapping-and-3d-modelling/wingcopter-178 (Accessed: 28 May 2019).Wingcopter (2019c) Wingcopter 178 Heavy Lift, Geo-matching. Available at: https://geo-matching.com/uas-for-mapping-and-3d-modelling/wingcopter-178-heavy-lift (Accessed: 15 June 2019).Wolflabs (2019) Fridges - Blood Bank. Available at: https://www.wolflabs.co.uk/laboratory-products/fridges-blood-bank (Accessed: 29 May 2019).Wright, C. et al. (2018) What should you deliver by unmanned aerial systems?

123456789

10111213141516171819202122232425262728293031323334

abstract · web viewword count: 6033 + 250 × 5 tables = 7283 submission date: 01/08/2019 abstract...

Documents