Xi Chen

TL;DR: A model of technician routing that explicitly models individualized, experience-based learning is introduced and demonstrates that explicit modeling and the resulting ability to capture changes in productivity over time due to learning lead to significantly better and different solutions than those found when learning and workforce heterogeneity is ignored.

TL;DR: The multi-period technician scheduling problem with experience-based service times and stochastic customers is introduced and an approximate dynamic programming-based solution approach is introduced that is adapted to handle cases of worker attrition and new task types.

TL;DR: An Approximate Dynamic Programming-based approach is presented that incorporates into daily assignment decisions estimates of the long-term benefits associated with experience accumulation and it is shown that the ADP approach outperforms this one-step lookahead approach.

TL;DR: The demand for power keeps rising with rapid economic development and growth of industrialization, and the frequent mismatch created between demand and supply can be mitigated by the use of energy conservation techniques.

TL;DR: A model of technician routing is introduced that explicitly models individualized, experience-based learning and the resulting ability to capture changes in productivity over time due to learning lead to significantly better and different solutions than those found when learning and workforce heterogeneity is ignored.