Efficient Task Allocation in Services Delivery Organizations
Service delivery organizations are constantly under pressure to meet service level agreements (SLAs) operate under tight costs, utilize their resources well and to improve the operational performance. A well planned task allocation plays an important role in meeting these objectives. This work considers the problem of efficient task allocation to employees in such organizations with the aim of meeting the SLAs by minimizing the number of tasks missing their deadlines and the magnitude of these deadline misses taking into account employees skills, productivity, utilization and fairness. For this problem, it proposes an integer linear programming (ILP) based solution which is SLA resource skill productivity utilization and fairness aware. It also proves an approximation guarantee for the problem using the primal dual technique. Further, in empirical evaluations using real world transaction processing data from a large services delivery organization, the proposed ILP based solution outperforms the currently practiced manual allocation in the organization. It is able to reduce the number of deadline violations by 90% and the magnitude of violations by 95% and increases the productivity of resources by 23% 38% Further, the ILP based solution never took more than 2 mins to allocate transactions in our evaluations making it a promising solution to deploy in real time.
2016 IEEE International Conference on Services Computing (SCC)