In this research, we are studying the possibility of contribution in solving the Vehicle
Routing Problem with Time Windows(VRPTW),that is one of the optimization problems
of the NP-hard type.
Moreover, Hybrid algorithm (HA) provided that integrate
s between Tabu Search
Algorithm and Guided Local Search algorithm And existence 2- Opt Local Search, based
on the savings algorithm in terms of continued of a particular objective to provide a lot of
savings. As we will compare the presented approach with standard tests to demonstrate
the efficiency, and their impact on the quality of the solution in terms of speed of
convergence and the ability to find better solutions.
Query rewrite (QR) is an emerging component in conversational AI systems, reducing user defect. User defect is caused by various reasons, such as errors in the spoken dialogue system, users' slips of the tongue or their abridged language. Many of the
user defects stem from personalized factors, such as user's speech pattern, dialect, or preferences. In this work, we propose a personalized search-based QR framework, which focuses on automatic reduction of user defect. We build a personalized index for each user, which encompasses diverse affinity layers to reflect personal preferences for each user in the conversational AI. Our personalized QR system contains retrieval and ranking layers. Supported by user feedback based learning, training our models does not require hand-annotated data. Experiments on personalized test set showed that our personalized QR system is able to correct systematic and user errors by utilizing phonetic and semantic inputs.