This study analyzes snow maintenance data of Borlänge municipality of Sweden based on the data of 2017 to 2018. The goal of this study is to reduce operational overhead of snow maintenance, for example, fuel and time consumption of the snow maintenance vehicles, work hour of dedicated personnel, etc. Borlänge Energy equipped the snow maintenance vehicles with GPS devices which stored the record of the snow maintenance activities. The initial part of this study obtained insights out of the GPS data by using spatiotemporal data analysis. Derivation of the different snow maintenance treatments (plowing, sanding and salting) as well as the efficiency of the sub-contractors (companies which are responsible for snow maintenance) and inspectors (personnel who are liable to call the subcontractors if they think it is time for snow maintenance) are performed in the beginning of this study. The efficiency of the subcontractors and inspectors are measured to compare their performance with each other. The latter part of this study discusses a simulated annealing-based heuristics technique to find out optimal location for dispatching snow maintenance vehicles. In the existing system of snow maintenance, drivers of the maintenance vehicles decide to start location of maintenance work based on their experience and intuition, which might vary from one driver to another driver. The vehicle dispatch locations are calculated based on the availability of the vehicles. For example, if a subcontractor has three vehicles to perform snow maintenance on a specific road map, the proposed solution would suggest three locations to dispatch those vehicles. The purpose of finding the optimal dispatch location is to reduce the total travel distance of the maintenance vehicles, which yield less fuel and time consumption. The study result shows the average travel distance for 1, 3, and 5 vehicles on 15 road networks. The proposed solution would yield 18% less travel than the existing system of snow maintenance.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:du-30548 |
| Date | January 2019 |
| Creators | Raihana, Nishat |
| Publisher | Högskolan Dalarna, Mikrodataanalys |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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