DistributionoftheEmergencySuppliesintheCOVID-19 Pandemic ...

Research ArticleDistribution of the Emergency Supplies in the COVID-19Pandemic A Cloud Computing Based Approach

Jixiao Wu and Yinghui Wang

Department of Economics and Management Shijiazhuang Tiedao University Shijiazhuang 050043 China

Correspondence should be addressed to Yinghui Wang wang112111163com

Received 16 August 2021 Revised 24 September 2021 Accepted 6 October 2021 Published 21 October 2021

Academic Editor Dohyeong Kim

Copyright copy 2021 JixiaoWu and YinghuiWangis is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

e containment of the COVID-19 pandemic was significantly affected by the unbalanced distribution of emergency supplies lowcoordinated transport efficiency high costs and the inability of nonprofit organizations to handle the emergency suppliesefficiently Based on the COVID-19 experience in this paper we build a cloud platform for emergency supplies distribution toreduce the asymmetry of emergency logistics information reduce the costs and improve the efficiency of emergency suppliesdistribution Our proposed method uses a genetic algorithm with the monarch scheme to optimize the urban emergency suppliesdistribution e numerical results and sensitivity analysis for a sample network indicate that using the proposed platform theintegrated cost in different cities are reduced by 2901 2867 and 2273 the required time in different cities are reduced by2298 2659 and 3665 e results suggest that the proposed method reduces the integrated cost and transportation timeand finds the optimal distribution path

1 Introduction

eoutbreak of COVID-19 at the beginning of 2020 resultedin a significant increase in demand for emergency suppliesworldwide is unprecedented situation severely affectedthe coordination of demand and supply due to inaccuratedecision-making and inefficient distribution of emergencysupplies With the exponential increase in the number ofconfirmed COVID-19 patients the existing emergencysystem became quickly ineffective is resulted in theshortage of vital emergency supplies in some affected re-gions caused significant pressure on the emergency workersand further aggravated the difficulty of containing the spreadof the COVID-19 epidemic

e emergency services in the epidemic are defined bythe National Disaster Medical System (NDMS) as the set ofurgent healthcare or medical services required in response tosignificantcatastrophic incidents such as natural disasterssignificant outbreaks of infectious diseases and bioterroristattacks [1] Although epidemics inevitably happen theirspread can be effectively contained by building an effectiveemergency logistics system is paper proposes a cloud

computing-based platform to incorporate Big Data infor-matization and intelligence to address the emergencysupplies distribution problem in the affected regions Ourobjective is to optimize the distribution of emergencysupplies to accelerate epidemic relief Our approach is tointegrate the supply and demand information of theemergency supplies on the cloud platform is reduces theopacity of this information in the emergency logistics ser-vices the asymmetry of supply and demand cat egories andthe uncertainty of distribution Using the cloud platform theBig Data including the emergency supplies demand pointsdistribution vehicles and supply and demand informationof different emergency supplies are processed e cloudplatform then uses these data to optimize the connection ofeach node in the emergency logistics network while effec-tively reducing the cost of emergency logistics is ap-proach enables efficient and intelligent emergency suppliesdistribution

Emergency logistics is referred to the logistics servicethat responds to disasters (eg H1N1 influenza SARS virusEbola virus earthquake and terrorist attack) [1] To opti-mize the distribution of the supplies within the required

HindawiMathematical Problems in EngineeringVolume 2021 Article ID 5972747 18 pageshttpsdoiorg10115520215972747

time-frame in the emergency logistics various technologiesare used for example system dynamics [2] heuristic al-gorithm [3ndash7] and Internet of ings technology [8] In theexisting works the optimization of emergency logistics ismainly focused on time [9ndash11] and cost [6 12ndash15] Howeverthe distribution of supplies for COVID-19 is different fromthe traditional emergency logistics and has unique features[16] We incorporate the unique features of the disasterdemand points in COVID-19 [16] and adopt a genetic al-gorithm to optimize the emergency supplies distributionsubject to time constraints

With the development of information technology in-formation technology has been widely used to improve thelogistics process realizing the logistics informatization fromthe network to the client For instance logistics alliances[17] hyper-network theory [18] and other methods areadopted to combine emergency supply chains to optimizelogistics services Such techniques are based on constructinga comprehensive emergency supplies distribution platformto solve the multipath problem in logistics networks [18ndash21]Big Data plays an essential role in the auxiliary resourcescheduling and prevention decision-making of the epidemicFurthermore the application of intelligent logistics systemseffectively improves the resilience of cities in responding toepidemics [22ndash24] erefore establishing a unified trans-porting and coordination mechanism for the emergencysupply chain eliminates the imbalance in the supply ofsupplies and satisfies the demand for medical treatments[25] New technologies such as blockchain are also used indesigning efficient emergency supply chains For instanceblockchain technology is used to build an informationmanagement model of supply for targeted donation andepidemic prevention supplies which shows that usingblockchain technology improves the performance ofemergency logistics in terms of information transparencycredibility efficiency and fairness Furthermore it enablesefficient coordination of emergency supplies supply [26]e aforementioned research provides significant support-ing evidence that the combination of emergency logisticswith advanced information technologies such as blockchainand cloud computing platforms plays a pivotal role in theefficient design of such systems Nevertheless the currentliterature has not thoroughly investigated the model buildand function design of logistics informatizatione build oflogistics information platforms in emergency disasters(especially the COVID-19 epidemic) is still rare So we havecombined the supplies demand features of COVID-19 tobuild an emergency logistics cloud platform

Furthermore in some existing works a combination ofcloud computing and logistics is used to an intelligent lo-gistics system based on logistics informatization [27] Fur-thermore in Govindan et alrsquos study [23] the benefits ofintegrating Big Data and cloud technology into the logisticssupply chain are investigated e build of the emergencylogistics cloud platform can rely on government depart-ments [28] e risks associated with network security useracceptance information technology updates and datasharing in the logistics information platforms are managedby devising a ladder risk management mechanism and

optimizes the logistics cloud platformrsquos data sharing andinformation security capabilities [29] Also in Lijiarsquos study[30] considering the automatic matching based on the cloudtechnology functions formulated the optimal dynamic co-ordination among the users as a multiobjective optimizationproblem It is also shown by Fu et al [31] that cloudtechnology can optimize the distribution efficiency of theemergency logistics network Nevertheless the existingworks seldom combine the cloud platform with emergencylogistics and rarely realize the efficiency improvement ofemergency supplies distribution through the cloud platformTo strengthen the range of the cloud platform and improvethe distribution efficiency of emergency supplies we com-bine the cloud platform with emergency logistics design anemergency logistics cloud platform in COVID-19 andimprove the functions of the cloud platform

Recently some technologies are used in a wide range ofintelligent management applications for sustainability butthese methods are rarely used in human space networks forpublic health Based on the previous research the objectiveof this paper is to optimize the urban emergency suppliesdistribution plan by the cloud platform is paperrsquos con-tributions are as follows (1) We build a cloud platform tosolve the inefficiency of the supply distribution low dis-tribution efficiency and high transaction costs caused byinformation asymmetry in emergency logistics (2) eproposed cloud platform optimizes the urban emergencysupplies distribution routes ensures the timeliness ofemergency supplies and reduces the cost of emergencysupplies

2 Emergency Supplies Distribution DesignBased on a Cloud Platform

According to the ldquoTrust Report Cloud Computing in Chinardquojointly released by Alibaba Cloud IEEE China and AlibabaResearch Institute in 2018 more than 70 of companieshave transferred more than half of their businesses to a cloudcomputing platform e report also points out that about43 of companies transferred all their businesses to a cloudcomputing platform is report concludes that using cloudcomputing platforms has become a norm that the Chinesebusiness community has accepted Based on the existingresearch on Big Data and informatization in emergencylogistics [32ndash34] we built a cloud computing platform foremergency supplies distribution in the epidemic Comparedwith the supply distribution model built in the existingliterature this research adopts the cloud computing plat-form to handle multiple supply and demand informationWe then formulate the optimal supplies distribution planaccording to the demand for supplies in different epidemicareas Big Data and cloud computing have been applied togeneral logistics in intelligent logistics solving optimizationproblems supply chain management transportation pathplanning and supplies distribution [35] e supply dis-tribution model based on the cloud platform relies on cloudcomputing Big Data technology communication facilitiesand various servers and network facilities e cloud plat-forms also have unified technical standards system settings

2 Mathematical Problems in Engineering

and protocol specifications [30] With the Internet as thekey the cloud platform integrates subsystems to form in-telligent and complex decision-making systems with variousfunctions and modules [30] e proposed emergencysupplies distribution cloud platform (see Figure 1) com-prises an emergency logistics system a vehicle distributionsystem an emergency supplies system and a core module Inthe following we explain the modulersquos functions andelaborate on the data relationship and transmission routesbetween them

e emergency logistics system connects the supplyrequirements of each node to the cloud platform andpenetrates each link of the transporte vehicle distributionsystem also optimizes the cloud platform supplies distri-bution model in terms of the information and service flowse system also integrates the logistics capital and infor-mation flow involved in the entire supply chain into theemergency supplies distribution cloud platform Internet ofings is also used to monitor logistics and transport in real-time e emergency supplies system combines the emer-gency supplies provided by various suppliers with the cloudplatform to strengthen the supervision of emergency sup-plies by nonprofit organizations e core module is com-posed of 4 parts communication layer isolation layer cloudcomputing and Big Datais module quickly integrates thesupply-demand information of the emergency logisticscloud platform and feedback to each node in time and ef-fectively [36] is results in inefficient operation of thelogistics network reasonable distribution of the suppliesand optimizing the logistics costs

e core module of the cloud platform consists of fourparts communication layer isolation layer cloud com-puting and Big Data e communication layer uses thewired communication based on optical cable metal wireand wireless communication to transmit the data infor-mation of the vehicles supplies demand and supplies in-ventory provided by each node e isolation layer is toensure data securitye isolation methodmainly comprisesnetwork isolation with firewalls between internal and ex-ternal source networks and data isolation with encryptiontechnology [37] Cloud computing is then used to store amassive amount of data which are also classified and an-alyzed e processing of big data includes performingsecondary mining on the data to filter out the critical in-formation processing the filtered critical informationthrough intelligent algorithms and combining the actualdemand information to analyze the optimal supply distri-bution method e decision information is then trans-mitted to the nodes through the communication layer andfed back to the actual logistics transport process

Based on the aforementioned framework and combinedwith the epidemic prevention requirements of COVID-19this paper builds an emergency supplies distribution aux-iliary system in COVID-19 based on the existing Guang-Dong Platform for CommonGeoSptial Information Services(httpguangdongtianditugovcn) e functional struc-ture of the system is shown in Figure 2 It includes a publicservice system epidemic prevention and control systemmobile service system postepidemic decision auxiliary

system departmental coordination system emergencysupplies management system shared exchange system andinterface services with other systems [38] e system can bequickly built based on the services provided by theGuangDong Platform for Common GeoSptial InformationServices

e collaboration model between the emergency sup-plies distribution system and the auxiliary system based onthe cloud platform in the COVID-19 is shown in Figure 3

e system design and key technologies of emergencysupplies distribution on the cloud platform are as follows

21UnifiedDataManagement andSharing Service Based onthe unified data management provided by the cloud plat-form and hierarchical sharing services for emergency de-mand the hierarchical management of epidemic preventionand control can be quickly realized It can complete hier-archical prevention and control management at the citydistrict and town levels

22 Place Name and Address Matching Service Based on theplace name and address matching service provided by thecloud platform the rapid mapping of various types of ep-idemic data is realized and the epidemic prevention andcontrol map is formed promptly

23 Intelligent Algorithm Service rough intelligent algo-rithms combined with urban population data the distri-bution of floating populations in high-risk areas can also beanalyzed is facilitates targeted epidemic prevention andcontrol e intelligent algorithm service of the cloudplatform is mainly reflected in the following aspects

(i) Build a distributed spatiotemporal big data storageengine which uses NoSQL and SQL combinationand is compatible with HBase Hadoop andPostgreSQL to realize the organization and man-agement of large-scale heterogeneous spatiotem-poral data

(ii) Establish a distributed spatiotemporal Big Dataindex support multiple indexes such as R-Tree andGeoHash which used distributed GIS algorithmand combined with Spark cluster to provide large-scale distributed memory computing

(iii) Integrate GPUCPU large-scale parallel computingto provide high-performance Web-based GIScomputing services

24 Public Service Platform e public service platform canhelp the construction of an emergency logistics system Forexample Guangdong Province has met the demand ofdifferent departments and people for data by building theGuangDong Platform for Common GeoSptial InformationServices realized various demands from ldquozero-coderdquo con-struction to complex secondary development

Mathematical Problems in Engineering 3

25 Meet Multilevel Demands e construction of theemergency logistics system should consider the use of CDChospitals medical institutions and other government de-partments and consider the use of the public

e special distribution centers should handle thedistribution of emergency supplies e transport reservesystem is different from the general supplies for the supplyof emergency supplies in terms of financing reserve andtransport [39] To clarify the modelling boundaries thisresearch obtains emergency supplies supply informationand classifies the demand information through the cloudplatform focusing on emergency supplies distributione emergency supplies distribution steps of the cloudplatform can be briefly summarized as follows publishsupplies demand data at various demand points ⟶collect the emergency supplies from suppliers and dis-tribution centers in various places⟶ supplies reserveand distribution plan ⟶ intelligent cloud decision-making for supplies sorting vehicle management andpath selection⟶ supplies distribution at each demandpoint e detailed flowchart of the proposed emergencysupplies distribution is presented in Figure 4 e cloudplatform is mainly responsible for the information flowpart and the logistics part is handed over to the relevantemergency personnel

3 Methods

e emergency supplies are demanded during the incidentand after a major epidemic the emergency supplies reservedin the city become low Hence the reserves are not sufficientfor the long-term needs of the cityrsquos emergency Note thatthe demand of each demand point in the epidemic and theirurgency might be different erefore it is essential toreasonably plan the supply distribution to fluctuationemergency suppliesrsquo demands e existing supply chain ofemergency supplies is generally a 3-level distribution net-work of ldquosupplies supplier-distribution centers and de-mand pointsrdquo as shown in Figure 5 However such patternsrequire more data and excessively make unrealistic as-sumptions on time spent in the parts [40]

e traditional distributionmodel of emergency supplieshas complicated processes and insufficient informationcommunication between nodes is is also prone to a lot ofadditional costs e distribution model of emergencysupplies based on the cloud platform (as shown in Figure 6)can optimize the ldquoilowast jrdquo routes between the suppliers i andthe demand points j to ldquoi+ jrdquo routes by the cloud platformwhich greatly simplifies the distribution process Sufficientsharing of information resources enabled by the cloudplatform enables the supplier to fully understand the

Input information

Input information

BigData

DataMining

DataProcessing

DataDecision

Output Information

Output Information

Emergency Logistics System

CloudComputing

IsolationLayer

VehicleDistribution

SystemFirewallNetworkIsolation

DataStorage

DataClassification

DataAnalysis

CommunicationLayer

ShortDistance

MiddleDistance

LongDistance

Data Encryptionand

Authentication

VulnerabilityScanning Intrusion

Detection

CoreModule of

CloudPlatform

Input information Output Information

Emergency Supplies System

Figure 1 e framework of the emergency supplies distribution of the proposed cloud platform


supplies demand of the demand point which makes itunnecessary for the vehicles to transit through the distri-bution center as in the traditional model hence can directlycomplete the supplies distribution from the supplier to thedemand point To optimize the distribution model the timeand the cost of the overall emergency logistics network it isnecessary to reasonably plan the distribution route and theposition of each node to minimize the sum of the costs andtime of all routes Furthermore it is necessary to considerroute optimization and establish a cloud platform emer-gency supplies distribution model to achieve optimal sup-plies distribution

Concerning the actual scenario of emergency suppliesdistribution in COVID-19 the following assumptions aremade in combination with the characteristics of the cloudplatform

(1) In the process of emergency supplies distribution avehicle can only transport one kind of supplies at atime

(2) e transaction cost does not incur in the distri-bution of emergency supplies based on the cloudplatform

(3) e cloud platform in this paper is led by the stateHence the node does not need to consider theconstruction cost of the cloud platform

31 Parameter Setting e relevant parameters and decisionvariables of the emergency supplies distribution model arepresented in Table 1

32 SystemModel Based on the aforementioned model thecost of the emergency supplies distribution model in theepidemic is

MinC H + 1113944iisinV

1113944jisinV

1113944nisinN

1113944kisinK

Clxijkn

+ 1113944iisinV

1113944jisinV

1113944nisinN

1113944kisinK

cijkxijkn d2in

+ d3jn

1113874 1113875

(1)

MinCprime 1113944iisinV

1113944jisinV

1113944nisinN

1113944kisinK

Clap

ijk + 1113944iisinV

1113944jisinV

1113944kisinK

cijkap

ijkd1ij (2)

Public Service System

Epidemic Prevention andControl System

Mobile Service System

Emergency Supplies DistributionSystem Based on the Cloud Platform

Post-epidemic DecisionAuxiliary System

Department CoordinationSystem

Emergency SuppliesManagement System

Shared Exchange System

Interface Services withOther Systems

Epidemic Releases

Domestic Services

Public Facility

Epidemic Map

Real-time Monitoring

Source Tracking

Emergency Command Decision

WeChat Official Accounts

Mobile App

Wechat Mini Programs

Industrial Evaluation

Enterprise Evaluation

Tax Evaluation

Consumption Evaluation

Comprehensive Decision

Task Assignment

Staff Scheduling

Coordinate and Communicate

Transportation Management

Supplies Management

Supplies Distribution

Service Management

User Management

Assignment Management

Operation Management

Related Hospital System

Disease Control and Prevention System

Other Service Systems

Figure 2 e functions of the emergency supplies distribution system based on the cloud platform


MinT 1113944iisinV

1113944jisinV

1113944nisinN

1113944kisinK

Tlxijkn + 1113944iisinV

1113944jisinV

1113944nisinN

1113944kisinK

xijkn

d2in

+ d3jn

v

(3)

MinTprime 1113944iisinV

1113944jisinV

1113944nisinN

1113944kisinK

Tlap

ijk + 1113944iisinV

1113944jisinV

1113944kisinK

ap

ijk

d1ij

v

(4)

e objective function in equation (1) represents theminimum integrated cost of the traditional emergencysupplies distribution including the transaction cost in thedistribution of emergency supplies the loading andunloading cost and the transportation cost Equation (2)also represents the minimum integrated cost of emergencysupplies distribution based on the cloud platform includingthe loading and unloading costs and transportation costsEquations (3) and (4) are the minimum time in the tradi-tional and cloud platformmodels respectively including theloading and unloading and transportation time We alsohave the following constraints

1113944iisinNcup I

1113944nisinN

1113944kisinK

xijkn apijk forallj isin J forallp isin P (5)

1113944iisinNcup J

1113944nisinN

1113944kisinK

xijkn ap

ijkforallj isin Jforallp isin P (6)

1113944iisinN

1113944nisinN

1113944kisinK

xijkn 1forallj isin Jforallp isin P (7)

1113944jisinN

1113944nisinN

1113944kisinK

xijkn 1foralli isin Iforallp isin P (8)

1113944iisinN

1113944kisinK

ap

ijk 1forallj isin J forallp isin P (9)

1113944jisinN

1113944kisinK

ap

ijk 1forallj isin J forallp isin P

(10)

where equations (5) and (6) ensure that if the distributioncenter n is selected to serve the point pair p there must be avehicle to transport emergency supplies otherwise nodistribution center is selected Equations (7) and (8) indicatethat the emergency supplies from supplier i to demand pointj must be solely transported through the distribution centern Equations (9) and (10) also ensure that only one path canbe selected for transportation at a time in the traditionalmodel We further need to make sure the following

1113944iisinI

Di 1113944jisinJ

Sj (11)

1113944pisinP

ap

ijkdp le qijknxijknforalli isin Vforallj isin Vforallk isin Kforalln isin N

(12)



Vehicle Distribution System


Big Data

Cloud Computing

Isolation Layer

Communication Layer








Figure 3 e collaboration model between emergency systems


Emergency Supplies Demandin A Cloud Platform

Mobile Service SystemInterface Services with other SystemsEmergency Supplies

Public Service SystemShared Exchange System


DepartmentCoordination System


Shared Exchange SystemVehicle Distribution System


Suppliers

Storage

Demand Points

Logistics

Information Flow

SuppliesSorting

Distribution Centers

Transport byCommunication Layer

Warehousing distribution loadingand unloading by Big Data

Figure 4 e emergency supplies distribution process is based on the proposed cloud platform

Supplier 1

DistributionCenter 1

DemandPoint 1


DistributionCenter n

Supplier 2

Supplier 3

Supplier i

DemandPoint 2

DemandPoint j

Figure 5 e traditional distribution model of emergency supplies in the epidemic


1113944pisinP

apijk qijkn leWny foralln isin N (13)

qijkn ge 0foralli isin Vforallj isin Vforallk isin K (14)

1113944jisinJ

qijkn leDiforalli isin I (15)

1113944iisinI

qijkn le Sjforallj isin J (16)

ap

ijkisin 0 1 foralli isin Vforallj isin V forallk isin Kforallp isin P (17)

xijkn isin 0 1 foralli isin Vforallj isin V foralln isin Vforallk isin K (18)

DistributionCenters

Suppliers

Transport supervision

real-time communicationTransport supervisionreal-time communication

DemandPoints

Supply

CloudPlatform

Routes

Logistics

Information Flow

Vehicles Demand

Figure 6 e distribution model of emergency supplies in the epidemic based on the cloud platform

Table 1 Parameters sets and decision variables

Symbol Descriptiond1

ij e distance from the supplier i to the demand point j (km)d2

in e distance from the supplier i to the distribution center n (km)d3

jn e distance from the demand point j to the distribution center n (km)

qijkne unit transportation capacity of the vehicle k from the supplier i to the demand point j through the distribution center n

(tons)cijk e cost of per vehicle per unit distance (yuankm)v Vehicle speed (kmh)I e set including suppliers indexed by i I 0 1 2 iJ e set including demand points indexed by j J 0 1 2 jP e set including supply and demand point pairs from supplier i to the demand point jN e set including distribution centers indexed by n N 0 1 2 nK e set including the transport vehicles indexed by k K 12 kGn Maximum load of the distribution center n (tons)Tl e loading and unloading time (h)Cl e loading and unloading cost (yuan)Di e set including emergency supplies provided by the supplier iQk Maximum load of transport vehicles (tons)Sj e set including emergency supplies received by demand point jC e comprehensive cost of emergency supplies distribution of the cloud platform (yuan)dP e number of emergency supplies between the point pair p in the period pPH e transaction cost between nodes without cloud platform service (yuan) V IJNxijkn 01 If the vehicle k from the supplier i to the demand point j through the distribution center n then xijkn 1 otherwise xijkn 0y 01 If the cloud platform service is not opened then y 1 otherwise y 0a

p

ijk 01 If the supplier i to the demand point j is transported by the vehicle k and serves the point pair p then apijk 1 otherwise apijk 0


In the aforementioned equations equation (11) ensuresthat the number of emergency supplies provided by allsuppliers is the same as all demand points received itEquation (12) enforces the transportation capacity con-straint which requires that the distribution center n se-lected for the transport path between the point pair p mustbe active Equation (13) also enforces the transportationcapacity of the distribution center n Equations (14) to (16)also apply the transportation capacity constraints betweenthe points where equations (17) to (18) are the decisionvariables related to the emergency supplies distributionmodel

33 Algorithm Design e distribution of emergency sup-plies in the epidemic based on cloud platforms extends thetraditional logistics transport problem Furthermore theemergency supplies distributionmodel in this paper involvesmultiple distribution centers and multiple demand pointserefore it is necessary to adopt an intelligent optimizationalgorithm to solve the problem Here we propose using thegenetic algorithm e steps of the genetic algorithm in thisresearch are as follows

Step 1 Population Initialization Number each demandpoint randomly arrange and generate chromosomesand reserve them in a matrix to form the initialpopulationStep 2 Calculate Fitness Calculate the transport dis-tance of each chromosome in the initial populationen add the supply requirements of each demandpoint and the upper limit of the vehicle load into thefitness calculation to find the shortest average time androute Set the number of iterations g 1Step 3 Selecting the Chromosome Select the chromo-some with the lowest fitness in this generation as thefatherrsquos chromosome and leave the ascending chro-mosomes as the motherrsquos chromosomeStep 4 Cross-Variation Using the monarch schemethe chromosome with the highest fitness value is se-lected as the monarch chromosome It is then placed inthe odd-numbered position of the entire populationand forms a pair with the even-numbered position ofthe latter one e number of crossover points is thendetermined according to the crossover probability(Pc 08) and the population is randomly selectedaccording to the variation probability Pm (Pm 03)after the cross-operation [41] e next generation ofchromosomes is then generated

e algorithm calculates the fitness of the populationindividuals after cross-variationeminimum fitness of thegeneration g and the corresponding individual value arerecorded en it is checked whether the number of itera-tions exceeds the maximum allowed iterations G If so thecycle is stopped otherwise g g+ 1 and it returns to Step 4to re-execute the monarch plan [41ndash44]e flowchart of thealgorithm is shown in Figure 7

4 Experimental and Data Analysis

To optimize the distribution of emergency supplies we useMATLAB-generated data using the actual coordinate dis-tribution on the map We also modify some of the pa-rameters of the sample city for sensitivity analysis and thenanalyze the stability of the supplies distribution path Inthese models a distribution center n undertakes thetransportation task from multiple suppliers i to demandpoints j When the vehicle is transported in the traditionalmodel the supplies are loaded once at the supplier thenloaded and unloaded once at the distribution center andunloaded once at the demand point e vehicle only needsto load the supplies once at the supplier and unload thesupplies at the demand point in the cloud platform modelFor brevity this paper does not consider the fixed openingcost of each point in the emergency logistics network epoints and data in this experiment are based on the Baidumap (httpsmapbaiducom) and the relevant literature[42ndash45] optimizing the results through MATLAB

41 Emergency LogisticsOptimization in the SeriouslyAffectedCity Based on the Cloud Platform e main stations andlogistics parks in the seriously affected city are selected as thesuppliers e Red Cross Society and the relevant govern-ment departments in the city are selected as the distributioncenters e eight main hospitals in the city are selected asthe demand points e relevant data for each point in theemergency logistics model is presented in Table 2

Table 3 shows the distance between suppliers distri-bution centers and demand points in the seriously affectedcity

is paper assumes that during the opening of eachpoint the emergency supplies between suppliers and de-mand points in the seriously affected city are as presented inTable 4

After the optimization in MATLAB the integrated costof the emergency logistics model in the seriously affected cityis obtained and shown in Figure 8 e emergency logisticsplan is also presented in Table 5 e integrated cost of theemergency logistics in the traditional model is 21030 yuansand the integrated time is 1754 the integrated cost ofemergency logistics based on the cloud platform is 14930yuans and the integrated time is 1351 It means a reductionof 2901 of the cost and 2298 of the time using theproposed cloud platform e distribution path of emer-gency supplies from suppliers and demand points are alsoshown in Table 5 For instance for Demand Point 1 in thetraditional emergency logistics model the supplies it re-ceived are provided by Supplier 2 through the 3rd vehicle ofDistribution Center 3 Supplier 3 through the 1st vehicle ofDistribution Center 1 and also Supplier 4 through the 1stvehicle of distribution Center 3 In the emergency logisticsbased on the cloud platform Demand Point 1 can directlyreceive emergency supplies provided by Suppliers 2 3 and 4without transferring through the distribution center As it isseen using the proposed cloud platform emergency supplies


can be transported directly from the supplier to the demandpoint significantly reducing the transportation process

42 Emergency Logistics Optimization in the General AffectedCity Based on the Cloud Platform e main stations andlogistics parks in the general affected city are selected as thesuppliers e Red Cross Society and the relevant govern-ment departments in the city are selected as the distributioncenterse four main hospitals in the city are selected as thedemand points e relevant data for each point in theemergency logistics model is presented in Table 6

Table 7 shows the distance between suppliers distri-bution centers and demand points in the general affectedcity

is paper assumes that during the opening of eachpoint the emergency supplies between suppliers and de-mand points in the general affected city are as presented inTable 8

After the optimization in MATLAB the integrated costof the emergency logistics model in the general affected cityas shown in Figure 9 and the emergency logistics plan in thegeneral affected city as shown in Table 9 e integrated costof the emergency logistics in the traditional model is 11670yuans and the integrated time is 1020 the integrated cost ofemergency logistics based on the cloud platform is 9070yuans and the integrated time is 749 It means a reductionof 2867 of the cost and 2659 of the time using theproposed cloud platform e distribution path of emer-gency supplies from suppliers and demand points in thegeneral affected city is shown in Table 9

43 Emergency Logistics Optimization in the Minor AffectedCity Based on the Cloud Platform e main stations andlogistics parks in the minor affected city are selected as thesuppliers e Red Cross Society and relevant governmentdepartments in the city are selected as the distributioncenters e two main hospitals in the city are selected as thedemand points e relevant data for each point in theemergency logistics model is presented in Table 10

Table 11 shows the distance between suppliers distri-bution centers and demand points in the minor affectedcity

is paper assumes that during the opening of eachpoint the emergency supplies between suppliers and de-mand points in the minor affected city are as presented inTable 12

After the optimization in MATLAB the integratedcost of the emergency logistics model in the minor af-fected city as shown in Figure 10 and the emergencylogistics plan in the minor affected city as shown in Ta-ble 13 e integrated cost of the emergency logistics inthe traditional model is 4840 yuans and the integratedtime is 315 the integrated cost of emergency logisticsbased on the cloud platform is 3740 yuans and the in-tegrated time is 199 It means a reduction of 2273 of thecost and 3665 of the time using the proposed cloudplatform e distribution path of emergency suppliesfrom suppliers and demand points in the minor affectedcity is shown in Table 13

e aforementioned cases show that the cloud platformhas played a critical role in reducing the cost and time ofemergency supplies distribution With the cityrsquos emergency

Start

Population initialization

Calculate fitness

Cross-variation by using the monarch scheme

Calculate fitness and record the minimum

Meet theconditions to

terminate

Output the best result

Finish

Yes

No

Select chromosomes as fatherrsquoschromosome and motherrsquos chromosome

Figure 7 e flowchart of the genetic algorithm for optimization


levels the cloud platform has a bigger effect on costoptimization

44 SensitivityAnalysis Different levels of urban emergencylogistics have different integrated cost and distribution pathsdue to different factors such as supplies demand and

transportation capacity constraints With the level ofemergency increased the demand for emergency suppliesand the cost of transportation increased It is seen that theunit transportation cost cijkn and the loading and unloadingcost Cl have significant impacts on the supplies distributionTo further verify the impact of these factors on the emer-gency logistics based on the cloud platform this paper

Table 2 Relevant points and parameters in the seriously affected city

Parameter Symbol Value Parameter Symbol Value

Maximum load of transport vehicles Qk 100t Number of transport vehicles in the distributioncenter K 3

Vehicle speed v 60 Emergency supplies received by Demand Point 1 S1 120tMaximum load of Distribution Center 1 G1 200t Emergency supplies received by Demand Point 2 S2 160tMaximum load of Distribution Center 2 G2 220t Emergency supplies received by Demand Point 3 S3 155tMaximum load of Distribution Center 3 G3 170t Emergency supplies received by Demand Point 4 S4 135tMaximum load of Distribution Center 4 G4 150t Emergency supplies received by Demand Point 5 S5 110tMaximum load of Distribution Center 5 G5 180t Emergency supplies received by Demand Point 6 S6 95tEmergency supplies provided bySupplier 1 D1 180t Emergency supplies received by Demand Point 7 S7 140t

Emergency supplies provided bySupplier 2 D2 220t Emergency supplies received by Demand Point 8 S8 120t

Emergency supplies provided bySupplier 3 D3 140t Emergency supplies provided by Supplier 5 D5 150t

Emergency supplies provided bySupplier 4 D4 160t e loading and unloading time Tl 05

e loading and unloading cost Cl 100 yuan e transaction cost in traditional model H 300yuan

e cost of per vehicle per unit distance cijkn 7 yuantlowast km

Table 3 Distance of each point in the seriously affected city

Distribution Center 1 Distribution Center 2 Distribution Center 3 Distribution Center 4 Distribution Center 5Supplier 1 123 171 135 162 151Supplier 2 65 153 47 142 68Supplier 3 165 77 175 64 154Supplier 4 366 539 636 514 402Supplier 5 253 371 447 393 302Demand Point 1 343 332 328 285 322Demand Point 2 187 293 188 298 196Demand Point 3 155 58 167 45 152Demand Point 4 139 71 123 55 113Demand Point 5 63 152 43 139 78Demand Point 6 39 144 33 133 63Demand Point 7 56 184 5 156 57Demand Point 8 44 169 39 155 52

Table 4 Emergency supplies between suppliers and demand points in the seriously affected city

Supplier 1 Supplier 2 Supplier 3 Supplier 4 Supplier 5Demand Point 1 0 80 100 70 0Demand Point 2 80 50 0 0 30Demand Point 3 70 0 70 0 40Demand Point 4 0 40 60 30 0Demand Point 5 60 50 0 40 20Demand Point 6 0 30 50 30 30Demand Point 7 90 0 60 20 0Demand Point 8 60 60 0 70 0


24times104

23

22

21

2

19

18

17

16

15

140 20 40 60 80 100

Number of Iterations

Integrated Cost in the Seriously Affected CityIn

tegr

ated

Cos

t (Yu

an)

120 140 160 180 200

Without the Cloud PlatformWith the Cloud Platform

Figure 8 Integrated costs in the seriously affected city

Table 5 e distribution path of emergency supplies in the seriously affected city

Without the cloud platform With the cloud platformSupplier Demand Point Distribution path Supplier Demand Point Distribution path2 1 2⟶3⟶1 (1) 2 1 2⟶13 1 3⟶1⟶1 (3) 3 1 3⟶14 1 4⟶3⟶1 (1) 4 1 4⟶11 2 1⟶4⟶2 (3) 1 2 1⟶22 2 2⟶3⟶2 (1) 2 2 2⟶25 2 5⟶4⟶2 (1) 5 2 5⟶21 3 1⟶4⟶3 (1) 1 3 1⟶33 3 3⟶4⟶3 (2) 3 3 3⟶35 3 5⟶1⟶3 (2) 5 3 5⟶32 4 2⟶1⟶4 (3) 2 4 2⟶43 4 3⟶4⟶4 (3) 3 4 3⟶44 4 4⟶5⟶4 (2) 4 4 4⟶41 5 1⟶4⟶5 (3) 1 5 1⟶52 5 2⟶5⟶5 (2) 2 5 2⟶54 5 4⟶3⟶5 (2) 4 5 4⟶55 5 5⟶5⟶5 (2) 5 5 5⟶52 6 2⟶3⟶6 (3) 2 6 2⟶63 6 3⟶3⟶6 (2) 3 6 3⟶64 6 4⟶1⟶6 (2) 4 6 4⟶65 6 5⟶1⟶6 (3) 5 6 5⟶61 7 1⟶1⟶7 (3) 1 7 1⟶73 7 3⟶3⟶7 (3) 3 7 3⟶74 7 4⟶3⟶7 (2) 4 7 4⟶71 8 1⟶3⟶8 (1) 1 8 1⟶82 8 2⟶2⟶8 (3) 2 8 2⟶84 8 4⟶5⟶8 (2) 4 8 4⟶8


changes the value of cijkn and Cl keeping the other pa-rameters unchanged We then explore the impact of theseparameters on the integrated cost to investigate the stabilityof emergency logistics e final results are presented inTable 14

e sensitivity analysis of different parameters in Table 6suggests the following

(1) Given a limited amount of time with the intensifi-cation of emergency levels (or increasing the numberof demand points) the impact of the unit trans-portation cost of the emergency logistics is

decreased In contrast the impact of the loading andunloading cost of emergency logistics is increased

(2) e cloud platform can play a good role in opti-mizing the integrated cost of emergency logistics theeffect increases with the increasing emergency levels

(3) Although the integrated cost of the emergency lo-gistics fluctuates by the changes in parameters thedistribution path and the integrated time have notchangedis confirms that the distribution model isstable and can optimize the distribution path of theemergency supplies

Table 6 Relevant points and parameters in the general affected city



Maximum load of Distribution Center 1 G1 170t Vehicle speed v 60Maximum load of Distribution Center 2 G2 150t Emergency supplies received by Demand Point 1 S1 120tMaximum load of Distribution Center 3 G3 180t Emergency supplies received by Demand Point 2 S2 110tEmergency supplies provided bySupplier 1 D1 110t Emergency supplies received by Demand Point 3 S3 100t


Emergency supplies provided bySupplier 3 D3 90t e loading and unloading cost Cl

100yuan


e cost of per vehicle per unit distance cijkn7 yuantlowast km e transaction cost in traditional model H 300

yuan

Table 7 Distance of each point in the general affected city

Distribution Center 1 Distribution Center 2 Distribution Center 3Supplier 1 448 152 147Supplier 2 355 51 55Supplier 3 903 652 503Supplier 4 389 222 199Demand Point 1 486 84 95Demand Point 2 36 91 88Demand Point 3 333 96 77Demand Point 4 444 82 73

Table 8 Emergency supplies between suppliers and demand points in the general affected city

Supplier 1 Supplier 2 Supplier 3 Supplier 4Demand Point 1 0 100 0 80Demand Point 2 90 0 120 130Demand Point 3 130 100 80 0Demand Point 4 0 0 110 70


125times104

115

11

105

1

095

090 20 40 60 80 100


Integrated Cost in the General Affected CityIn

tegr

ated

Cos

t (Yu

an)

120 140 160 180 200

12


Figure 9 Integrated cost in the general affected city

Table 9 e distribution path of emergency supplies in the general affected city

Without the cloud platform With the cloud platformSupplier Demand point Distribution path Supplier Demand point Distribution path2 1 2⟶1⟶1 (3) 2 1 2⟶14 1 4⟶3⟶1 (3) 4 1 4⟶11 2 1⟶2⟶2 (1) 1 2 1⟶23 2 3⟶2⟶2 (2) 3 2 3⟶24 2 4⟶3⟶2 (3) 4 2 4⟶21 3 1⟶3⟶3 (3) 1 3 1⟶32 3 2⟶3⟶3 (1) 2 3 2⟶33 3 3⟶2⟶3 (3) 3 3 3⟶33 4 3⟶3⟶4 (2) 3 4 4⟶44 4 4⟶1⟶4 (1) 4 4 4⟶4

Table 10 Relevant points and parameters in the minor affected city



Maximum load of Distribution Center 1 G1 100t Vehicle speed v 60Maximum load of Distribution Center 2 G2 100t Emergency supplies received by Demand Point 1 S1 90tEmergency supplies provided bySupplier 1 D1 70t Emergency supplies received by Demand Point 2 S2 110t


e loading and unloading cost Cl 100 yuan e loading and unloading time Tl 05

e cost of per vehicle per unit distance cijkn 7 yuantlowast km e transaction cost in traditional model H 300yuan


Table 11 Distance of each point in the minor affected city

Distribution Center 1 Distribution Center 2Supplier 1 111 82Supplier 2 232 271Supplier 3 93 123Demand Point 1 28 28Demand Point 2 19 37

Table 12 Emergency supplies between suppliers and demand points in the minor affected city

Supplier 1 Supplier 2 Supplier 3Demand Point 1 100 0 120Demand Point 2 90 110 0

6000

5500

5000

4500

4000

35000 20 40 60 80 100


Integrated Cost in the Minor Affected City

Inte

grat

ed C

ost (

Yuan

)

120 140 160 180 200


Figure 10 Integrated cost in the minor affected city

Table 13 e distribution path of emergency supplies in the minor affected city

Without the cloud platform With the cloud platformSupplier Demand point Distribution path Supplier Demand point Distribution path1 1 1⟶1⟶1 (4) 1 1 1⟶13 1 3⟶1⟶1 (1) 2 1 2⟶11 2 1⟶1⟶2 (2) 2 2 2⟶22 2 2⟶2⟶2 (2) 3 2 3⟶2


5 Conclusion and Future Works

e logistics industry plays a vital role in the distribution ofemergency supplies In this paper we devised a cloud platformfor emergency supplies distribution By establishing an urbanemergency supplies distribution path optimization model ourproposed platform optimizes the supplies distribution routes inthe epidemic and reduces emergency logistics costs and time

Although this research has optimized the distributionmode and costs of emergency supplies based on cloudplatforms in the epidemic the results of this research can beextended in the following directions

(1) Further investigation of multiobjective optimizationto achieve the optimal cost and the shortest time forthe emergency supplies distribution based on thecloud platform

(2) Considering the situation of reverse logistics inemergency logistics and optimizing the emergencysupplies distribution model from a general perspective

(3) Considering cross-regionalcountries distribution ofemergency supplies based on a cloud platform

(4) At present there are not many academic studies onthe accuracy of cloud platforms supplies distribution[46ndash48] In the future new experimental results willbe combined to prove the accuracy and effectivenessof the cloud platform

Data Availability

e data that support the findings of this study are availablefrom the correspondence author (wang112111163com)upon reasonable request

Conflicts of Interest

e authors declare that there are no conflicts of interest

Acknowledgments

e authors would like to express their gratitude to Edit-Springs (httpswwweditspringscom) for the expert

linguistic services A high tribute shall be paid to Ms ZhangPei and Ms Zhang Qinghong whose profound knowledgetriggers their effort for revising this paper is research wasfunded by Hebei Provincial Department of Education (grantno SR 201913) and supported by the Development Fund ofInnovative Research Team in Shijiazhuang Tiedao Univer-sity (grant no 20210993)

References

[1] Y He and N Liu ldquoMethodology of emergency medical lo-gistics for public health emergenciesrdquo Transportation Re-search Part E Logistics and Transportation Review vol 79pp 178ndash200 2015

[2] L Zhu ldquoSystem dynamics analysis of cross-regional coor-dinative emergency suppliess allocation under sever epi-demicsrdquo Systems Engineering vol 35 no 6 pp 105ndash112 2017

[3] C Liu and G Kou ldquoLocation-routing problem of emergencylogistics system in post-earthquakerdquo Systems Engineeringvol 33 no 9 pp 63ndash67 2015

[4] Z Hu ldquoResearch on urban flood emergency supplies dispatchand transportation optimizationrdquo Postgraduate thesis She-nyang University of Technology Shenyang China 2018

[5] M-F Yang Y Lin L H Ho and W F Kao ldquoAn integratedmultiechelon logistics model with uncertain delivery lead timeand quality unreliabilityrdquo Mathematical Problems in Engi-neering vol 2016 Article ID 8494268 13 pages 2016

[6] J Jiang J Chen and C Wang ldquoMulti-granularity hybridparallel network simplex algorithm for minimum-cost flowproblemsrdquo =e Journal of Supercomputing vol 76 no 12pp 9800ndash9826 2020

[7] J Wang M K Lim Y Zhan and X Wang ldquoAn intelligentlogistics service system for enhancing dispatching operationsin an IoT environmentrdquo Transportation Research Part ELogistics and Transportation Review vol 135 p 101886 2020

[8] Z Gao DWang and H Zhou ldquoIntelligent circulation systemmodeling using bilateral matching theory under internet ofthings technologyrdquo =e Journal of Supercomputing 2021

[9] H Wang and X Ma ldquoResearch on multiobjective location ofurban emergency logistics under major emergenciesrdquoMathematical Problems in Engineering vol 202112 pages2021

[10] W Liu H Xu X Sun Y Yang and Y Mo ldquoOrder allocationresearch of logistics service supply chain with mass

Table 14 Sensitivity analysis of emergency logistics supplies distribution

Symbol ValueSeriously affected city General affected city Minor affected cityCost(yuan)

Degree ofchange

Cost(yuan)

Degree ofchange

Cost(yuan)

Degree ofchange

Without the cloud platform

cijkn35 16165 minus2313 8135 minus3029 3370 minus30377 21030 mdash 11670 mdash 4840 mdash14 30760 +4627 18740 +6058 7780 +6074

Cl

50 15830 minus2473 9670 minus1714 4040 minus1653100 21030 mdash 11670 mdash 4840 mdash200 31430 +4945 15670 +3428 6440 +3306

With the cloud platform


Cl



customization logistics servicerdquo Mathematical Problems inEngineering vol 2013 Article ID 957260 13 pages 2013

[11] Y Sun Y Ren and X Cai ldquoBiobjective emergency logisticsscheduling model based on uncertain traffic conditionsrdquoMathematical Problems in Engineering vol 202015 pages2020

[12] Z-H Hu and J-B Sheu ldquoPost-disaster debris reverse logisticsmanagement under psychological cost minimizationrdquoTransportation Research Part B Methodological vol 55pp 118ndash141 2013

[13] R A Garrido P Lamas and F J Pino ldquoA stochastic pro-gramming approach for floods emergency logisticsrdquo Trans-portation Research Part E Logistics and TransportationReview vol 75 pp 18ndash31 2015

[14] N Wang Z-P Fan and X Wang ldquoChannel coordination inlogistics service supply chain considering fairnessrdquo Mathe-matical Problems in Engineering vol 2016 Article ID9621794 15 pages 2016

[15] Y Liu and B Guo ldquoA lexicographic approach to postdisasterrelief logistics planning considering fill rates and costs underuncertaintyrdquo Mathematical Problems in Engineeringvol 201417 pages 2014

[16] M Sang Y Ding M Bao Y Fang and B Lu ldquoPropagationdynamics model considering the characteristics of 2019-nCOV and prevention-control measurementsrdquo Systems En-gineering-=eory amp Practice vol 41 no 1 pp 124ndash133 2021

[17] H-D Lin and G-J Gao ldquoResearch on the developmentstrategy of reverse logistics of abandoned medicine in Chinardquoin Proceedings of the 2017 3rd International Forum on EnergyEnvironment Science and Suppliess (IFEESM 2017) SuzhouChina October 2018

[18] C F Zhu Z K Zhang and C X Ma ldquoResearch on emergencylogistics dynamic network based on super networkrdquo LatinAmerican Applied Research vol 47 no 1-2 pp 11ndash16 2017

[19] L Xu Y Tu and Y Zhang ldquoA grasshopper optimization-based approach for task assignment in cloud logisticsrdquoMathematical Problems in Engineering vol 2020 Article ID3298460 10 pages 2020

[20] J Lv Y Zhang and Y Zhuang ldquoResearch on optimization ofemergency logistics capability based on smart logistics underthe public health crisisrdquo China Soft Science no S1 pp 16ndash222020

[21] X Li Y Wang and F Wang ldquoResearch on improvement ofemergency logistics based on application of blockchain duringpublic health emergenciesrdquo Contemporary Economic Man-agement vol 42 no 4 pp 57ndash63 2020

[22] D Jin and L Ying ldquoPlanning improves cityrsquos immunity awritten conversation on COVID-19 breakoutrdquo City PlanningReview vol 44 no 2 pp 115ndash136 2020

[23] K Govindan T C E Cheng N Mishra and N Shukla ldquoBigdata analytics and application for logistics and supply chainmanagementrdquo Transportation Research Part E Logistics andTransportation Review vol 114 pp 343ndash349 2018

[24] S Shafqat S Kishwer R U Rasool J Qadir T Amjad andH F Ahmad ldquoBig data analytics enhanced healthcare sys-tems a reviewrdquo=e Journal of Supercomputing vol 76 no 3pp 1754ndash1799 2020

[25] L Ning ldquoCollaborative management of epidemic emergencysupply chain dealing with COVID-19rdquo Health EconomicsResearch vol 37 no 4 pp 7ndash9 2020

[26] Q Hu J He and Q Dong ldquoResearch on emergency suppliesssupply information management of medical epidemic pre-vention under blockchain architecturemdashtargeted donation of

COVID-19 prevention suppliess as an examplerdquo HealthEconomics Research vol 37 no 4 pp 10ndash14 2020

[27] B R Chang N T Nguyen B Vo and H-H Hsu ldquoAdvancedcloud computing and novel applicationsrdquo MathematicalProblems in Engineering vol 2015 Article ID 630923 2 pages2015

[28] P Wang A study of information service paradigm of gov-ernment procurement cloud based on value chain collabora-tion PhD thesis Tianjin University Tianjin China 2017

[29] J Wang H Zhang and Y Wang ldquoResearch on the tiered riskmanagement mechanism of the global logistics cloud plat-formrdquo Modernization of Management vol 39 no 3pp 105ndash108 2019

[30] Lijia ldquoA research on intelligent logistics mode reconstructionbased on big data cloud computingrdquo China Business andMarket vol 33 no 2 pp 20ndash29 2019

[31] D Fu S Hu L Zhang S He and J Qiu ldquoAn intelligent cloudcomputing of trunk logistics alliance based on blockchain andbig datardquo =e Journal of Supercomputing 2021

[32] Y He and L Ren ldquoSupply chain management model based on4PL reverse logistics integrationrdquo Journal of Southeast Uni-versity (Philosophy and Social Science) vol 15 no 4 pp 41ndash452013

[33] Y Jia ldquoStochastic petri net modeling analysis of emergencyresources allocation process considering reverse logisticsrdquoJournal of Safety Science and Technology vol 15 no 4pp 12ndash18 2019

[34] Z Chu B Feng and F Lai ldquoLogistics service innovation bythird party logistics providers in China aligning guanxi andorganizational structurerdquo Transportation Research Part ELogistics and Transportation Review vol 118 pp 291ndash3072018

[35] B Lu Y Wu and C Huang ldquoResearch on collaborationplatform for urban reverse emergency logistics union in in-ternet of things environmentrdquo Science and TechnologyManagement Research vol 33 no 17 pp 220ndash226 2013

[36] M Arulprakash and R Jebakumar ldquoPeople-centric collectiveintelligence decentralized and enhanced privacy mobilecrowd sensing based on blockchainrdquo =e Journal of Super-computing 2021

[37] C-Y Chen and J-F Tu ldquoA novel cloud computing algorithmof security and privacyrdquo Mathematical Problems in Engi-neering vol 2013 Article ID 871430 6 pages 2013

[38] M Wang H Lin H He Z Qing and R Wu ldquoDesign andimplementation of COVID-19 epidemic prevention assistantsystem based on spatiotemporal big data cloud platformtaking Guangzhou as an examplerdquo Bulletin of Surveying andMapping no S1 pp 198ndash204 2020

[39] Y Wang and B Sun ldquoDynamic multi-stage allocation modelof emergency suppliess for multiple disaster sitesrdquo ChineseJournal of Management Science vol 27 no 10 pp 138ndash1472019

[40] X Hu ldquoResearch on optimal matching of urban emergencymedical supplies under major public health eventsrdquo ChinaJournal of Highway and Transport vol 33 no 11 pp 55ndash642020

[41] B Balcik B M Beamon and K Smilowitz ldquoLast mile dis-tribution in humanitarian reliefrdquo Journal of IntelligentTransportation Systems vol 12 no 2 pp 51ndash63 2008

[42] Y Wang ldquoDistribution routing optimization on fresh pro-duce-a case in Xianmatourdquo Postgraduate thesis ChangshaUniversity of Science amp Technology Changsha China 2014


[43] Z Yuan ldquoOptimization of cold chain logistics path based onimproved genetic algorithmrdquo Journal of Shandong IndustrialTechnology vol 10 p 216 2019

[44] C Li ldquoResearch on the distribution path of fresh products inCity under O2O operationmoderdquo Postgraduate thesis DalianMaritime University Dalian China 2017

[45] X Q Xue X PWang T Han and J H Ruan ldquoStudy on jointdispatch optimization of emergency suppliess consideringtraffic constraints and capacity limitsrdquo Chinese Journal ofManagement Science vol 28 no 3 pp 21ndash30 2020

[46] M Zakarya and L Gillam ldquoModelling resource heteroge-neities in cloud simulations and quantifying their accuracyrdquoSimulation Modelling Practice and =eory vol 94 pp 43ndash652019

[47] J Liu S Chen Z Zhou and T Wu ldquoAn anomaly detectionalgorithm of cloud platform based on self-organizing mapsrdquoMathematical Problems in Engineering vol 2016 Article ID3570305 9 pages 2016

[48] X Zhong G Yang L Li and L Zhong ldquoClustering andcorrelation based collaborative filtering algorithm for cloudplatformrdquo IAENG International Journal of Computer Sciencevol 43 no 1 2016


time-frame in the emergency logistics various technologiesare used for example system dynamics [2] heuristic al-gorithm [3ndash7] and Internet of ings technology [8] In theexisting works the optimization of emergency logistics ismainly focused on time [9ndash11] and cost [6 12ndash15] Howeverthe distribution of supplies for COVID-19 is different fromthe traditional emergency logistics and has unique features[16] We incorporate the unique features of the disasterdemand points in COVID-19 [16] and adopt a genetic al-gorithm to optimize the emergency supplies distributionsubject to time constraints

With the development of information technology in-formation technology has been widely used to improve thelogistics process realizing the logistics informatization fromthe network to the client For instance logistics alliances[17] hyper-network theory [18] and other methods areadopted to combine emergency supply chains to optimizelogistics services Such techniques are based on constructinga comprehensive emergency supplies distribution platformto solve the multipath problem in logistics networks [18ndash21]Big Data plays an essential role in the auxiliary resourcescheduling and prevention decision-making of the epidemicFurthermore the application of intelligent logistics systemseffectively improves the resilience of cities in responding toepidemics [22ndash24] erefore establishing a unified trans-porting and coordination mechanism for the emergencysupply chain eliminates the imbalance in the supply ofsupplies and satisfies the demand for medical treatments[25] New technologies such as blockchain are also used indesigning efficient emergency supply chains For instanceblockchain technology is used to build an informationmanagement model of supply for targeted donation andepidemic prevention supplies which shows that usingblockchain technology improves the performance ofemergency logistics in terms of information transparencycredibility efficiency and fairness Furthermore it enablesefficient coordination of emergency supplies supply [26]e aforementioned research provides significant support-ing evidence that the combination of emergency logisticswith advanced information technologies such as blockchainand cloud computing platforms plays a pivotal role in theefficient design of such systems Nevertheless the currentliterature has not thoroughly investigated the model buildand function design of logistics informatizatione build oflogistics information platforms in emergency disasters(especially the COVID-19 epidemic) is still rare So we havecombined the supplies demand features of COVID-19 tobuild an emergency logistics cloud platform

Furthermore in some existing works a combination ofcloud computing and logistics is used to an intelligent lo-gistics system based on logistics informatization [27] Fur-thermore in Govindan et alrsquos study [23] the benefits ofintegrating Big Data and cloud technology into the logisticssupply chain are investigated e build of the emergencylogistics cloud platform can rely on government depart-ments [28] e risks associated with network security useracceptance information technology updates and datasharing in the logistics information platforms are managedby devising a ladder risk management mechanism and

optimizes the logistics cloud platformrsquos data sharing andinformation security capabilities [29] Also in Lijiarsquos study[30] considering the automatic matching based on the cloudtechnology functions formulated the optimal dynamic co-ordination among the users as a multiobjective optimizationproblem It is also shown by Fu et al [31] that cloudtechnology can optimize the distribution efficiency of theemergency logistics network Nevertheless the existingworks seldom combine the cloud platform with emergencylogistics and rarely realize the efficiency improvement ofemergency supplies distribution through the cloud platformTo strengthen the range of the cloud platform and improvethe distribution efficiency of emergency supplies we com-bine the cloud platform with emergency logistics design anemergency logistics cloud platform in COVID-19 andimprove the functions of the cloud platform

Recently some technologies are used in a wide range ofintelligent management applications for sustainability butthese methods are rarely used in human space networks forpublic health Based on the previous research the objectiveof this paper is to optimize the urban emergency suppliesdistribution plan by the cloud platform is paperrsquos con-tributions are as follows (1) We build a cloud platform tosolve the inefficiency of the supply distribution low dis-tribution efficiency and high transaction costs caused byinformation asymmetry in emergency logistics (2) eproposed cloud platform optimizes the urban emergencysupplies distribution routes ensures the timeliness ofemergency supplies and reduces the cost of emergencysupplies

2 Emergency Supplies Distribution DesignBased on a Cloud Platform

According to the ldquoTrust Report Cloud Computing in Chinardquojointly released by Alibaba Cloud IEEE China and AlibabaResearch Institute in 2018 more than 70 of companieshave transferred more than half of their businesses to a cloudcomputing platform e report also points out that about43 of companies transferred all their businesses to a cloudcomputing platform is report concludes that using cloudcomputing platforms has become a norm that the Chinesebusiness community has accepted Based on the existingresearch on Big Data and informatization in emergencylogistics [32ndash34] we built a cloud computing platform foremergency supplies distribution in the epidemic Comparedwith the supply distribution model built in the existingliterature this research adopts the cloud computing plat-form to handle multiple supply and demand informationWe then formulate the optimal supplies distribution planaccording to the demand for supplies in different epidemicareas Big Data and cloud computing have been applied togeneral logistics in intelligent logistics solving optimizationproblems supply chain management transportation pathplanning and supplies distribution [35] e supply dis-tribution model based on the cloud platform relies on cloudcomputing Big Data technology communication facilitiesand various servers and network facilities e cloud plat-forms also have unified technical standards system settings



















3 Methods



Input information

Input information

BigData

DataMining

DataProcessing

DataDecision

Output Information

Output Information


CloudComputing

IsolationLayer

VehicleDistribution


DataStorage

DataClassification

DataAnalysis

CommunicationLayer

ShortDistance

MiddleDistance

LongDistance

Data Encryptionand

Authentication


Detection

CoreModule of

CloudPlatform













1113944jisinV

1113944nisinN

1113944kisinK

Clxijkn

+ 1113944iisinV

1113944jisinV

1113944nisinN

1113944kisinK

cijkxijkn d2in

+ d3jn

1113874 1113875

(1)


1113944jisinV

1113944nisinN

1113944kisinK

Clap

ijk + 1113944iisinV

1113944jisinV

1113944kisinK

cijkap

ijkd1ij (2)










Epidemic Releases

Domestic Services

Public Facility

Epidemic Map


Source Tracking



Mobile App




Tax Evaluation



Task Assignment

Staff Scheduling



Supplies Management


Service Management

User Management








MinT 1113944iisinV

1113944jisinV

1113944nisinN

1113944kisinK


1113944jisinV

1113944nisinN

1113944kisinK

xijkn

d2in

+ d3jn

v

(3)


1113944jisinV

1113944nisinN

1113944kisinK

Tlap

ijk + 1113944iisinV

1113944jisinV

1113944kisinK

ap

ijk

d1ij

v

(4)


1113944iisinNcup I

1113944nisinN

1113944kisinK


1113944iisinNcup J

1113944nisinN

1113944kisinK

xijkn ap


1113944iisinN

1113944nisinN

1113944kisinK


1113944jisinN

1113944nisinN

1113944kisinK


1113944iisinN

1113944kisinK

ap


1113944jisinN

1113944kisinK

ap


(10)


1113944iisinI

Di 1113944jisinJ

Sj (11)

1113944pisinP

ap


(12)





Big Data

Cloud Computing

Isolation Layer

Communication Layer


















Suppliers

Storage

Demand Points

Logistics

Information Flow

SuppliesSorting





Supplier 1


DemandPoint 1



Supplier 2

Supplier 3

Supplier i

DemandPoint 2

DemandPoint j



1113944pisinP



1113944jisinJ


1113944iisinI


ap



DistributionCenters

Suppliers



DemandPoints

Supply

CloudPlatform

Routes

Logistics

Information Flow

Vehicles Demand









p
























Start


Calculate fitness




terminate


Finish

Yes

No





















24times104

23

22

21

2

19

18

17

16

15

140 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200


















100yuan



yuan






125times104

115

11

105

1

095

090 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200

12

















6000

5500

5000

4500

4000

35000 20 40 60 80 100



Inte

grat

ed C

ost (

Yuan

)

120 140 160 180 200













Data Availability




Acknowledgments



References













Degree ofchange

Cost(yuan)

Degree ofchange

Cost(yuan)

Degree ofchange



Cl




Cl






























































3 Methods



Input information

Input information

BigData

DataMining

DataProcessing

DataDecision

Output Information

Output Information


CloudComputing

IsolationLayer

VehicleDistribution


DataStorage

DataClassification

DataAnalysis

CommunicationLayer

ShortDistance

MiddleDistance

LongDistance

Data Encryptionand

Authentication


Detection

CoreModule of

CloudPlatform













1113944jisinV

1113944nisinN

1113944kisinK

Clxijkn

+ 1113944iisinV

1113944jisinV

1113944nisinN

1113944kisinK

cijkxijkn d2in

+ d3jn

1113874 1113875

(1)


1113944jisinV

1113944nisinN

1113944kisinK

Clap

ijk + 1113944iisinV

1113944jisinV

1113944kisinK

cijkap

ijkd1ij (2)










Epidemic Releases

Domestic Services

Public Facility

Epidemic Map


Source Tracking



Mobile App




Tax Evaluation



Task Assignment

Staff Scheduling



Supplies Management


Service Management

User Management








MinT 1113944iisinV

1113944jisinV

1113944nisinN

1113944kisinK


1113944jisinV

1113944nisinN

1113944kisinK

xijkn

d2in

+ d3jn

v

(3)


1113944jisinV

1113944nisinN

1113944kisinK

Tlap

ijk + 1113944iisinV

1113944jisinV

1113944kisinK

ap

ijk

d1ij

v

(4)


1113944iisinNcup I

1113944nisinN

1113944kisinK


1113944iisinNcup J

1113944nisinN

1113944kisinK

xijkn ap


1113944iisinN

1113944nisinN

1113944kisinK


1113944jisinN

1113944nisinN

1113944kisinK


1113944iisinN

1113944kisinK

ap


1113944jisinN

1113944kisinK

ap


(10)


1113944iisinI

Di 1113944jisinJ

Sj (11)

1113944pisinP

ap


(12)





Big Data

Cloud Computing

Isolation Layer

Communication Layer


















Suppliers

Storage

Demand Points

Logistics

Information Flow

SuppliesSorting





Supplier 1


DemandPoint 1



Supplier 2

Supplier 3

Supplier i

DemandPoint 2

DemandPoint j



1113944pisinP



1113944jisinJ


1113944iisinI


ap



DistributionCenters

Suppliers



DemandPoints

Supply

CloudPlatform

Routes

Logistics

Information Flow

Vehicles Demand









p
























Start


Calculate fitness




terminate


Finish

Yes

No





















24times104

23

22

21

2

19

18

17

16

15

140 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200


















100yuan



yuan






125times104

115

11

105

1

095

090 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200

12

















6000

5500

5000

4500

4000

35000 20 40 60 80 100



Inte

grat

ed C

ost (

Yuan

)

120 140 160 180 200













Data Availability




Acknowledgments



References













Degree ofchange

Cost(yuan)

Degree ofchange

Cost(yuan)

Degree ofchange



Cl




Cl





















































1113944jisinV

1113944nisinN

1113944kisinK

Clxijkn

+ 1113944iisinV

1113944jisinV

1113944nisinN

1113944kisinK

cijkxijkn d2in

+ d3jn

1113874 1113875

(1)


1113944jisinV

1113944nisinN

1113944kisinK

Clap

ijk + 1113944iisinV

1113944jisinV

1113944kisinK

cijkap

ijkd1ij (2)










Epidemic Releases

Domestic Services

Public Facility

Epidemic Map


Source Tracking



Mobile App




Tax Evaluation



Task Assignment

Staff Scheduling



Supplies Management


Service Management

User Management








MinT 1113944iisinV

1113944jisinV

1113944nisinN

1113944kisinK


1113944jisinV

1113944nisinN

1113944kisinK

xijkn

d2in

+ d3jn

v

(3)


1113944jisinV

1113944nisinN

1113944kisinK

Tlap

ijk + 1113944iisinV

1113944jisinV

1113944kisinK

ap

ijk

d1ij

v

(4)


1113944iisinNcup I

1113944nisinN

1113944kisinK


1113944iisinNcup J

1113944nisinN

1113944kisinK

xijkn ap


1113944iisinN

1113944nisinN

1113944kisinK


1113944jisinN

1113944nisinN

1113944kisinK


1113944iisinN

1113944kisinK

ap


1113944jisinN

1113944kisinK

ap


(10)


1113944iisinI

Di 1113944jisinJ

Sj (11)

1113944pisinP

ap


(12)





Big Data

Cloud Computing

Isolation Layer

Communication Layer


















Suppliers

Storage

Demand Points

Logistics

Information Flow

SuppliesSorting





Supplier 1


DemandPoint 1



Supplier 2

Supplier 3

Supplier i

DemandPoint 2

DemandPoint j



1113944pisinP



1113944jisinJ


1113944iisinI


ap



DistributionCenters

Suppliers



DemandPoints

Supply

CloudPlatform

Routes

Logistics

Information Flow

Vehicles Demand









p
























Start


Calculate fitness




terminate


Finish

Yes

No





















24times104

23

22

21

2

19

18

17

16

15

140 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200


















100yuan



yuan






125times104

115

11

105

1

095

090 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200

12

















6000

5500

5000

4500

4000

35000 20 40 60 80 100



Inte

grat

ed C

ost (

Yuan

)

120 140 160 180 200













Data Availability




Acknowledgments



References













Degree ofchange

Cost(yuan)

Degree ofchange

Cost(yuan)

Degree ofchange



Cl




Cl













































MinT 1113944iisinV

1113944jisinV

1113944nisinN

1113944kisinK


1113944jisinV

1113944nisinN

1113944kisinK

xijkn

d2in

+ d3jn

v

(3)


1113944jisinV

1113944nisinN

1113944kisinK

Tlap

ijk + 1113944iisinV

1113944jisinV

1113944kisinK

ap

ijk

d1ij

v

(4)


1113944iisinNcup I

1113944nisinN

1113944kisinK


1113944iisinNcup J

1113944nisinN

1113944kisinK

xijkn ap


1113944iisinN

1113944nisinN

1113944kisinK


1113944jisinN

1113944nisinN

1113944kisinK


1113944iisinN

1113944kisinK

ap


1113944jisinN

1113944kisinK

ap


(10)


1113944iisinI

Di 1113944jisinJ

Sj (11)

1113944pisinP

ap


(12)





Big Data

Cloud Computing

Isolation Layer

Communication Layer


















Suppliers

Storage

Demand Points

Logistics

Information Flow

SuppliesSorting





Supplier 1


DemandPoint 1



Supplier 2

Supplier 3

Supplier i

DemandPoint 2

DemandPoint j



1113944pisinP



1113944jisinJ


1113944iisinI


ap



DistributionCenters

Suppliers



DemandPoints

Supply

CloudPlatform

Routes

Logistics

Information Flow

Vehicles Demand









p
























Start


Calculate fitness




terminate


Finish

Yes

No





















24times104

23

22

21

2

19

18

17

16

15

140 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200


















100yuan



yuan






125times104

115

11

105

1

095

090 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200

12

















6000

5500

5000

4500

4000

35000 20 40 60 80 100



Inte

grat

ed C

ost (

Yuan

)

120 140 160 180 200













Data Availability




Acknowledgments



References













Degree ofchange

Cost(yuan)

Degree ofchange

Cost(yuan)

Degree ofchange



Cl




Cl





















































Suppliers

Storage

Demand Points

Logistics

Information Flow

SuppliesSorting





Supplier 1


DemandPoint 1



Supplier 2

Supplier 3

Supplier i

DemandPoint 2

DemandPoint j



1113944pisinP



1113944jisinJ


1113944iisinI


ap



DistributionCenters

Suppliers



DemandPoints

Supply

CloudPlatform

Routes

Logistics

Information Flow

Vehicles Demand









p
























Start


Calculate fitness




terminate


Finish

Yes

No





















24times104

23

22

21

2

19

18

17

16

15

140 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200


















100yuan



yuan






125times104

115

11

105

1

095

090 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200

12

















6000

5500

5000

4500

4000

35000 20 40 60 80 100



Inte

grat

ed C

ost (

Yuan

)

120 140 160 180 200













Data Availability




Acknowledgments



References













Degree ofchange

Cost(yuan)

Degree ofchange

Cost(yuan)

Degree ofchange



Cl




Cl













































1113944pisinP



1113944jisinJ


1113944iisinI


ap



DistributionCenters

Suppliers



DemandPoints

Supply

CloudPlatform

Routes

Logistics

Information Flow

Vehicles Demand









p
























Start


Calculate fitness




terminate


Finish

Yes

No





















24times104

23

22

21

2

19

18

17

16

15

140 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200


















100yuan



yuan






125times104

115

11

105

1

095

090 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200

12

















6000

5500

5000

4500

4000

35000 20 40 60 80 100



Inte

grat

ed C

ost (

Yuan

)

120 140 160 180 200













Data Availability




Acknowledgments



References













Degree ofchange

Cost(yuan)

Degree ofchange

Cost(yuan)

Degree ofchange



Cl




Cl


































































Start


Calculate fitness




terminate


Finish

Yes

No





















24times104

23

22

21

2

19

18

17

16

15

140 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200


















100yuan



yuan






125times104

115

11

105

1

095

090 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200

12

















6000

5500

5000

4500

4000

35000 20 40 60 80 100



Inte

grat

ed C

ost (

Yuan

)

120 140 160 180 200













Data Availability




Acknowledgments



References













Degree ofchange

Cost(yuan)

Degree ofchange

Cost(yuan)

Degree ofchange



Cl




Cl






























































24times104

23

22

21

2

19

18

17

16

15

140 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200


















100yuan



yuan






125times104

115

11

105

1

095

090 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200

12

















6000

5500

5000

4500

4000

35000 20 40 60 80 100



Inte

grat

ed C

ost (

Yuan

)

120 140 160 180 200













Data Availability




Acknowledgments



References













Degree ofchange

Cost(yuan)

Degree ofchange

Cost(yuan)

Degree ofchange



Cl




Cl

























































100yuan



yuan






125times104

115

11

105

1

095

090 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200

12

















6000

5500

5000

4500

4000

35000 20 40 60 80 100



Inte

grat

ed C

ost (

Yuan

)

120 140 160 180 200













Data Availability




Acknowledgments



References













Degree ofchange

Cost(yuan)

Degree ofchange

Cost(yuan)

Degree ofchange



Cl




Cl













































125times104

115

11

105

1

095

090 20 40 60 80 100



tegr

ated

Cos

t (Yu

an)

120 140 160 180 200

12

















6000

5500

5000

4500

4000

35000 20 40 60 80 100



Inte

grat

ed C

ost (

Yuan

)

120 140 160 180 200













Data Availability




Acknowledgments



References













Degree ofchange

Cost(yuan)

Degree ofchange

Cost(yuan)

Degree ofchange



Cl




Cl

















































6000

5500

5000

4500

4000

35000 20 40 60 80 100



Inte

grat

ed C

ost (

Yuan

)

120 140 160 180 200













Data Availability




Acknowledgments



References













Degree ofchange

Cost(yuan)

Degree ofchange

Cost(yuan)

Degree ofchange



Cl




Cl




















































Data Availability




Acknowledgments



References













Degree ofchange

Cost(yuan)

Degree ofchange

Cost(yuan)

Degree ofchange



Cl




Cl













































DistributionoftheEmergencySuppliesintheCOVID-19 Pandemic ...

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