Fitness Distance Correlation Strategy for Solving the RGV Dynamic Scheduling Problem

Rail﻿guide﻿vehicle﻿(RGV)﻿problems﻿have﻿the﻿characteristics﻿of﻿fast﻿running,﻿stable﻿performance,﻿and﻿ high﻿automation.﻿RGV﻿dynamic﻿scheduling﻿has﻿a﻿great﻿impact﻿on﻿the﻿working﻿efficiency﻿of﻿an﻿entire﻿ automated﻿warehouse.﻿However,﻿the﻿relative﻿intelligent﻿optimization﻿research﻿of﻿different﻿workshop﻿ components﻿for﻿RGV﻿dynamic﻿scheduling﻿problems﻿are﻿insufficient﻿scheduling﻿in﻿the﻿previous﻿works.﻿ They﻿appear﻿idle﻿when﻿waiting,﻿resulting﻿in﻿reduced﻿operating﻿efficiency﻿during﻿operation.﻿This﻿article﻿ proposes﻿a﻿new﻿distance﻿landscape﻿strategy﻿for﻿the﻿RGV﻿dynamic﻿scheduling﻿problems.﻿In﻿order﻿ to﻿solve﻿the﻿RGV﻿dynamic﻿scheduling﻿problem﻿more﻿effectively,﻿experiments﻿are﻿conducted﻿based﻿ on﻿the﻿type﻿of﻿computer﻿numerical﻿controller﻿(CNC)﻿with﻿two﻿different﻿procedures﻿programming﻿ model﻿in﻿solving﻿the﻿RGV﻿dynamic﻿scheduling﻿problems.﻿The﻿experiment﻿results﻿reveal﻿that﻿this﻿new﻿ distance﻿landscape﻿strategy﻿can﻿provide﻿promising﻿results﻿and﻿solves﻿the﻿considered﻿RGV﻿dynamic﻿scheduling﻿problem﻿effectively.


Fitness Distance Correlation
Thegenotypeofthefitnesslandscapeisreflectedfromonevarianttoanotherinthedistanceattribute. Animportantpartofsolvingthisdistanceattributeproblemistocollectpossibletopologicaland structuralfeaturesinthefitnesslandscapeandtoanalyzeandexpressthemintothepotentialand observablemechanismsinevolutionaryalgorithms.Thefitnessdistancecorrelation(FDC)analysis methodcanquantifythedistancerelationshipbetweenthefitnessofasetofpointsinthelocalfitness landscapeandtheglobalminimum(Müller&Sbalzarini2011).Thissectionwillusethefitness distancecorrelationanalysismethodtorepresentthetopologicalfeaturesofthefitnesslandscape. ThecustomizedtripleofthefitnesslandscapeFisdefinedas: x min is considered to be a priori known, andx min is usually approximated by x x j f x j

RGV Single-Procedure Scheduling Model
Intheintelligentprocessingsystem,oneRGVisdispatchedtocarryoutloadingandunloading andwashingof8CNCmachinesarrangedonbothsides.Theloadingandunloadingofthe nth round only have two status of going forward and stopping, we introduce 0 and 1 variables expressionasfollows: f move the n round of loading and unloading goes to BeforetheRGVstartsworking,allCNCsareidle,whichisdifferentfromthestatusafterworking, sothesedifferentstatuseswillbeanalyzedseparately.Inordertodistinguishbetweenthefirst8 Thematerialneedstobecleanedaftereachunloading,using t wash i n _ , denotethecleaning timeofthe nth round.

DISTANCe LANDSCAPe STRATeGy FoR THe RGV DyNAMIC SCHeDULING PRoBLeM
Thedistancelandscapestrategywillbediscussedinthissection.Accordingtothefitnessdistance correlation method, the local fitness landscape features will be represented and analyzed for the dynamic scheduling problem.The distance landscape strategy relationship of the RGV dynamic scheduleproblemwillbedesignedandperformed.

Distance Landscape Strategy
For a population P X X X = 1 2 , , , ω { } , where each X x x is a solution on R n .
Step 3: ,where ε istheerrorvalue.Thisvalueisaddedtomaintainthediversity of the population and avoided the local optimum or global convergence in the late stage of optimization.

Process of Solving the RGV Scheduling Model
Itisdifficulttoobtaintheglobaloptimalsolutiondirectlythroughthemodel,wedesignaheuristic algorithm to obtain the approximate optimal solution by the distance landscape strategy for the shortestloadingandunloadingofeachround.Thebasicstepsandprinciplesofthealgorithmare describedinAlgorithm1.
of loading and unloading doesn t go

Figure 1 .
Figure 1.A sample of the RGV dynamic scheduling system

Figure
Figure 2. The RGV scheduling route under one procedure

Table 1 . The symbol description of RGV dynamic scheduling model
The nth roundtooktimefrom s n toe n .

Table 11 . The execution efficiency of the RGV scheduling model under two procedures
Inthispaper,adistancelandscapestrategybasedonthefitnesslandscapeisusedtosolvethedynamic scheduling problem of RGV.With the development of our science and technology, the modern intelligentlogisticssystemhasbeenconstantlyimproved.Inordertomakeupfortheproblemsof lowefficiencyandhighmaintenancecostexposedbygeneralautomationsystemandwarehouse,the RGVdynamicschedulingmodelcanbeeasilyconnectedwithotherlogisticssystemstoautomatically transport,cleanandprocessmaterials.Inaddition,itneedsnohumanoperationandrunsfast.Thus, theworkloadofwarehousemanagersissignificantlyreduced,andlaborproductivityisimproved.Meanwhile, the application of shuttle vehicles can make the logistics system very simple and convenient.AreasonableRGVschedulingstrategyalsoprovidesmorespaceforitsdevelopmentin industrialproduction,terminalsequencing,cloudschedulingsystemandotherfields.