Subventions et des contributions :

Titre :
A bi-level optimization model of the technician routing and scheduling problem for access_x000D_ communications
Numéro de l’entente :
EGP
Valeur d'entente :
25 000,00 $
Date d'entente :
22 mars 2018 -
Organisation :
Conseil de recherches en sciences naturelles et en génie du Canada
Location :
Saskatchewan, Autre, CA
Numéro de référence :
GC-2017-Q4-00044
Type d'entente :
subvention
Type de rapport :
Subventions et des contributions
Informations supplémentaires :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

Nom légal du bénéficiaire :
Almehdawe, Eman (University of Regina)
Programme :
Subventions d'engagement partenarial pour les universités
But du programme :

Access Communications is a cable television and telecommunications provider serving over 240 communitiesx000D
in Saskatchewan. These communities are widely spread across the province, spanning a large geographic area.x000D
One of the main functions for technical operations at Access Communications is to respond to customerx000D
requests, service issues and to provide preventive maintenance measures. Currently, Access Communicationsx000D
employs more than 50 full-time technicians in addition to a pool of 3rd-party contractors with a variety ofx000D
different skill sets. Access Communications is interested in investigating the process of assigning technicians tox000D
jobs/communities and where to locate technicians based on current demand zones and technician skill sets.x000D
They are also interested in finding an optimal routing schedule that will minimize travel time for technicians.x000D
The ultimate goal is to reduce drive time, reduce the number of contractors needed, and to enhance customerx000D
service.x000D
The problem of technician scheduling and routing is a hard optimization problem. Although there are a numberx000D
of research papers that discuss this problem, the setting and limitations faced by Access Communications arex000D
unique. For that reason, we plan to develop a bi-level optimization model to best represent the companyx000D
problem and limitations. Solving this kind of optimization problem is harder when the size of the problem isx000D
large, so we plan to develop a heuristic algorithm that can generate reasonable solutions within a realistic timex000D
frame.