Scheduling with the assistance of Machine learning

Detta arbete har utvärderat om maskininlärning kan tillföra nytta vid schemaplanering.Utvärderingen baserades på tester där prototyper använde arbetskalendrar föratt träna och mäta sin prediktiva förmåga. Kalendrarna tillhandahölls från två service-och installationsbolag i Stockholmsområdet. Genom att testa vilka utförandetiderprototyperna krävde utvärderades om tillämpningen skulle vara praktiskt användbarpå arbetsverktyg som exempelvis smartphones.Totalt utvecklades tre prototyper som gjordes prediktiva med hjälp av algoritmernaDensity-based Spatial Clustering of Applications with Noise (DBSCAN), LogisticRegression och Weighted K-Nearest Neighbors (wKNN). Resultatet visade attDBSCAN var den algoritm som sammantaget presterade bäst. Dock kunde inte enslutsats dras om maskininlärning skulle vara användbart. Andelen lyckade prediktioneröverskred inte andelen tillgängliga tider på de berörda dagarna som testernautfördes, vilket antogs vara ett otillfredsställande resultat. Datahanteringen krävdeen betydande mängd resurser, vilket skulle kunna vara ett problem vid praktisk tilllämpning.This study has been analyzing if machine learning could be useful to work-relatedscheduling. The analysis was based on predictions generated by prototypes usingbusiness calendars. The business calendars were collected from two service and installationcompanies in the Stockholm region. An analysis was conducted regardingif the application could be practically applied to devices such as a smartphone. Theanalysis was based on tests regarding the prototypes required time to perform theirtasks.Three prototypes were developed with algorithms that made them predictive. Density-based Spatial Clustering of Applications with Noise (DBSCAN), Logistic Regressionand Weighted K-Nearest Neighbors (wKNN) were the implemented algorithms.DBSCAN was the best-performing algorithm according to the tests. However, a conclusioncould not be found concerning whether machine learning could be useful.The number of successful predictions did not exceed the number of available timeson concerned days, which was assumed as unsatisfying results. In addition, the prototypesneeded a significant amount of resources which could be a problem in practicaluse