Abstract We study a variant of the offline Dial-a-Ride problem, where each request has a source and destination and the goal is to maximize the number of requests served within a specified time limit. We investigate this problem for the uniform metric space and show that the problem is NP-hard. We then present a 2/3 approximation algorithm called $$\textsc {twochain}$$ T W O C H A I N , which simply looks for pairs of requests that are “chained” together and serves those before serving requests that are not connected to any others. We also show that a natural generalization of this algorithm, k-chain, has an approximation ratio at most 7/9. We also analyze the longest-chain-first algorithm for the problem, characterizing graphs on which it is optimal, and showing that it has an approximation ratio no better than 5/6. Our experiments on all of these algorithms show that $$\textsc {twochain}$$ T W O C H A I N is a promising algorithm, performing nearly as well as more computationally intensive variants. We dedicate this article to the memory of Gerhard Woeginger, whose life and work greatly influenced our professional lives, as expanded upon in the Acknowledgments. Woeginger’s prolific research in scheduling, matching, bin-packing, TSP, and online algorithms in general, all served as important parts of the foundation on which our own scholarly pursuits were shaped and formed over the years. Woeginger also studied Dial-a-Ride (DARP) Problems, as DARP is a generalization both of scheduling problems and of TSP, which were two of his most active areas of research.

The pharmaceutical industry must adhere to rigorous regulations to meet specific quality standards. Additionally, the intricate nature of pharmaceutical manufacturing processes and long time to production necessitates timely detection of batch failures. AI/ML models are used for predictive maintenance in an automated and data-driven manner to detect these failures and aid timely intervention. However, these models require substantial amount of data for model training. This can lead to extended time-to-value before a predictive monitoring system can be deployed for any new process due to long process lead times. The current research proposes COSYNE, a generative AI-based approach to generate manufacturing digital twin, reducing the model development time by augmenting synthetic data with real data. The proposed solution is validated on a large pharmaceutical company’s batch manufacturing dataset, and the results are benchmarked across multiple dimensions of generation quality. Empirical results demonstrate that the proposed COSYNE outperforms the state-of-the-art approach by 2-3 times on average across all the generation quality metrics. Moreover, COSYNE enhances downstream AI/ML performance significantly through data augmentation and reduces time-to-value by creating high-fidelity digital twins with only 10% of real data and still achieve similar performance as current baseline trained on entire real data.

We study a variation of offline Dial-a-Ride, where each request has not only a source and destination, but also a revenue that is earned for serving the request. We investigate this problem for the uniform metric space with uniform revenues. While we present a study on a simplified setting of the problem that has limited practical applications, this work provides the theoretical foundation for analyzing the more general forms of the problem. Since revenues are uniform the problem is equivalent to maximizing the number of served requests. We show that the problem is NP-hard and present a 2/3 approximation algorithm. We also show that a natural generalization of this algorithm has an approximation ratio at most 7/9.

Adaptive learning has shown promise of meeting students' individual needs, and educators are interested in its affordances. However, little evidence-based practice exists to guide institutions and educators in using this pedagogical approach for teaching. In this presentation, we will share our experiences in exploring and implementing adaptive learning for first-year pharmacy students aiming to provide a tool that is adapted to students' individual learning needs. We will also share the lessons we have learned.