Yingqian Zhang

In this work, we consider learning-based applications in routing to solve a Vehicle Routing variant characterized by stochasticity and multiple objectives. Such problems are representative of practical settings where decision-makers have to deal with uncertainty in the operational environment as well as multiple conflicting objectives due to different stakeholders. We specifically consider travel time uncertainty. We also consider two objectives, total travel time and route makespan, that jointly target operational efficiency and labor regulations on shift length, although different objectives could be incorporated. Learning-based methods offer earnest computational advantages as they can repeatedly solve problems with limited interference from the decision-maker. We specifically focus on end-to-end deep learning models that leverage the attention mechanism and multiple solution trajectories. These models have seen several successful applications in routing problems. However, since travel times are not a direct input to these models due to the large dimensions of the travel time matrix, accounting for uncertainty is a challenge, especially in the presence of multiple objectives. In turn, we propose a model that simultaneously addresses stochasticity and multi-objectivity and provide a refined training mechanism for this model through scenario clustering to reduce training time. Our results show that our model is capable of constructing a Pareto Front of good quality within acceptable run times compared to three baselines.

Technical and ethical concerns impede the establishment of trust among healthcare professionals (HCPs) in developing artificial intelligence (AI)-based decision support. Yet, our understanding of trust models is constrained, and a standard accepted approach to evaluating trust in AI models is still lacking. We introduce a novel methodology to assess and quantify HCPs’ perceived trust in an interpretable machine learning model that serves as clinical decision support for diagnosing COVID-19 cases. Our approach leverages fuzzy cognitive maps (FCMs) to elicit and quantify HCPs’ trust mental models for understanding trust dynamics in clinical diagnosis. Our study reveals that HCPs rely predominantly on their own expertise when interacting with the developed interpretable clinical decision support. Although the model’s interpretations offer limited assistance in diagnostic tasks, they facilitate the HCPs’ utilization of it. However, the impact of these interpretations on the establishment of perceived trust varies among HCPs, which can lead to an increase in trust for some while decreasing it for others. To validate quantified perceived trust, we employ the degree of agreement metric, which quantitatively assesses whether HCPs lean more towards their own expertise or rely on the model’s recommendations in diagnostic tasks. We found significant alignment between the conclusions of the two metrics, indicating successful modeling and quantification of perceived trust. Plus, a moderate to strong positive correlation between the two metrics confirmed this conclusion. This means that FCMs can quantify HCPs’ perceived trust, aligning with their actual diagnostic advice shift after interacting with the model.

The increasing use of artificial intelligence (AI) models across various fields has raised concerns about whether these models can meet user trust expectations. As a result, researchers are focusing on assessing AI models’ performance relative to user expectations to determine trust levels. Evidence suggests that effective interaction with eXplainable AI (XAI) techniques can mitigate over-reliance on AI models and better align user expectations with the actual capabilities of these models in decision-making. In this study, we analyze trust from two perspectives: perceived trust, based on user self-reported trust, and demonstrated trust, which evaluates whether users, when given a choice, prefer to rely on AI or make decisions independently. We also explore how different interactions between human subjects and XAI models, along with varying levels of task risk, influence trust. Our findings reveal that these two types of trust are substantially different; human subjects do not always exhibit trust behavior in actual decision-making tasks, even when they perceive themselves as trusting the AI. Furthermore, we show that an AI model’s low error rate in making correct decisions can influence human subjects’ mental models, leading them to report a higher tendency to trust the AI. Finally, we conclude that human perceptions of trust are fragile and may change based on ongoing interactions with the model.

Combinatorial optimization (CO) problems, central to decision-making scenarios like logistics and manufacturing, are traditionally solved using problem-specific algorithms requiring significant domain expertise. While large language models (LLMs) have shown promise in automating CO problem solving, existing approaches rely on intermediate steps such as code generation or solver invocation, limiting their generality and accessibility. This paper introduces a novel framework that empowers LLMs to serve as end-to-end CO solvers by directly mapping natural language problem descriptions to solutions. We propose a two-stage training strategy: supervised fine-tuning (SFT) imparts LLMs with solution generation patterns from domain-specific solvers, while a feasibility-and-optimality-aware reinforcement learning (FOARL) process explicitly mitigates constraint violations and refines solution quality. Evaluation across seven NP-hard CO problems shows that our method achieves a high feasibility rate and reduces the average optimality gap to 1.03-8.20% by tuning a 7B-parameter LLM, surpassing both general-purpose LLMs (e.g., GPT-4o), reasoning models (e.g., DeepSeek-R1), and domain-specific heuristics. Our method establishes a unified language-based pipeline for CO without extensive code execution or manual architectural adjustments for different problems, offering a general and language-driven alternative to traditional solver design while maintaining relative feasibility guarantees.

The Job-Shop Scheduling Problem (JSP) and Flexible Job-Shop Scheduling Problem (FJSP), are canonical combinatorial optimization problems with wide-ranging applications in industrial operations. In recent years, many online reinforcement learning (RL) approaches have been proposed to learn constructive heuristics for JSP and FJSP. Although effective, these online RL methods require millions of interactions with simulated environments that may not capture real-world complexities, and their random policy initialization leads to poor sample efficiency. To address these limitations, we introduce Conservative Discrete Quantile Actor-Critic (CDQAC), a novel offline RL algorithm that learns effective scheduling policies directly from historical data, eliminating the need for costly online interactions, while maintaining the ability to improve upon suboptimal training data. CDQAC couples a quantile-based critic with a delayed policy update, estimating the return distribution of each machine-operation pair rather than selecting pairs outright. Our extensive experiments demonstrate CDQAC's remarkable ability to learn from diverse data sources. CDQAC consistently outperforms the original data-generating heuristics and surpasses state-of-the-art offline and online RL baselines. In addition, CDQAC is highly sample efficient, requiring only 10-20 training instances to learn high-quality policies. Surprisingly, we find that CDQAC performs better when trained on data generated by a random heuristic than when trained on higher-quality data from genetic algorithms and priority dispatching rules.