As the number of aircraft and ground transportation vehicles increases, airport surface operations may benefit from automated decision support systems to provide effective conflict detection and resolution (CD&R). A conflict between aircraft and/or ground vehicles can be life-threatening and cause severe economic loss. CD&R is of great importance to safe and efficient airport surface operations. This research designs a CD&R algorithm based on the collaborative control theory to overcome the risks and threats resulting from the control of many aircraft and vehicles. Two types of conflicts in airport surface operations are identified, and a methodology is defined to model and analyze three-dimensional and time-based (4D) airport surface constraints. The CD&R algorithm uses 4D constraints and takes advantage of properties of conflict networks for effective CD&R. A hybrid conflict resolution strategy is applied to prioritize conflicts for global and local resolutions. The methodology is demonstrated and validated with the case of the Hartsfield Atlanta International Airport. Further research will validate the CD&R algorithm with real surface operations data.