HKUST Business Review

rom navigating our personal priorities to determining business strategy at the highest level, many tasks require us to balance competing goals. In some cases, this optimization is fairly straightforward: When one goal is clearly more important than the other, and when the environment stays static over time, it’s easy enough to see what to do. But in other cases, the importance of different goals may vary, or be difficult to determine. What does it take to balance competing goals in such a dynamic, opaque environment? Our recent research explored this critical question in the context of an industry that’s tasked with balancing numerous conflicting goals every day: ride-sharing platforms. Ride-share companies like Uber, Lyft, and Didi have developed complex algorithms to decide which passengers to match with which drivers in real time. In an ideal world, these matching algorithms would balance short-term considerations such as maximizing total revenue and minimizing passenger pick-up distances with long-term goals such as fostering a reliable community of drivers and ensuring consistently high service quality for passengers. But in practice, we’ve found that many commonly-used algorithms simply assign passengers to the nearest available driver, to reduce wait times and increase the chances that the match is accepted by both parties. Reliably Balancing Conflicting Goals Isn’t Easy Part of the reason for this typical strategy is that balancing the conflicting goals of revenue gain, service excellence, and operational efficiency in the dynamic context of ride-share matching is far from trivial. It’s not just a matter of short-termism — creating systems that can reliably optimize for multiple, non-static metrics is a complicated technical challenge. That’s where our study comes in. Through our extensive research, we developed an innovative framework for real-time decision-making designed to help anyone strike a balance between conflicting objectives by dynamically adjusting how those different metrics are weighted. Here’s how it works: A Dynamic Framework for Multi-Objective Optimization Step 1: Establish Aspirational Targets First, define your ideal target for each objective. For example, in the case of ride-share matching, what is your ideal revenue target, service quality score, and pick-up wait time? The ideal target can be designated as the utopia target, constructed by optimizing each objective function separately, yet unachievable simultaneously. Step 2: Track Performance Gaps Next, use automated tools to continuously measure the extent to which current outcomes deviate from these targets. In particular, we measure the performance gap between the achieved solution and the ideal target using a specific convex distance metric, such as the Euclidean distance function. Step 3: Update Weight Functions Finally, adaptively adjust the relative importance of each objective based on real-time tracking of the performance gaps. For example, if service quality starts to lag, the algorithm would temporarily prioritize dispatching higher-rated drivers. Conversely, if revenue begins to fall short, it would prioritize higher-value trips. We use the performance gaps, which are updated adaptively over time, as the priority weights to solve the multi-objective optimization problems. This approach guarantees that the achieved solution stays closest to the utopia target in the dynamic environment, achieving a delicate balance among the conflicting goals. The framework was inspired by a natural, human phenomenon known as the Gaze Heuristic. 9 HKUST Business Review