The Dynamic Team Orienteering Problem in Spatial Crowdsourcing: A Scenario Sampling Approach

TL;DR

This paper introduces a novel scenario sampling approach for the Dynamic Team Orienteering Problem in Spatial Crowdsourcing, effectively balancing profit maximization and computational efficiency in online request routing.

Contribution

It proposes a scenario-sampling rolling-horizon framework combined with an adaptive large neighborhood search for solving DTOP-SC, and provides a mixed-integer programming model for offline benchmarking.

Findings

The proposed policy achieves stable profit gaps compared to MIP solutions.

It maintains computational scalability with increasing problem size.

Decision times are significantly reduced to around 0.14 seconds per instance.

Abstract

In services such as retail audits and urban infrastructure monitoring, a platform dispatches rewarded, location-based micro-tasks to mobile workers traveling along personal origin-destination (OD) trips under hard time budgets. As requests with time constraints arrive online over a finite horizon, the platform must decide which requests to accept and how to route workers to maximize collected profit. We model this setting as the Dynamic Team Orienteering Problem in Spatial Crowdsourcing (DTOP-SC). To solve this problem, we propose a scenario-sampling rolling-horizon framework that mitigates myopic bias by augmenting each planning epoch with sampled virtual tasks. At each epoch, the augmented task set defines a deterministic static subproblem solved via an adaptive large neighborhood search (ALNS). We also formulate a mixed-integer programming model to provide offline reference solutions. Computational experiments are conducted on synthetic DTOP-SC instances generated from real-world road-map coordinates and on a dynamic team orienteering (DTOP) benchmark. On the map-based instances, the proposed policy exhibits stable gaps with respect to time-limited MIP solutions across the tested scales, while maintaining smooth computational scalability as the problem size increases. On the DTOP benchmark, the policy achieves an average decision time of 0.14s per instance, with 192-198s reported for multiple plan approach as an indicative reference, while maintaining competitive profit.