With a large network of more than 1,000 buses, 4,000 bus stops and 1,800 drivers, Greyhound requires feasible driver schedules to staff every bus while addressing driver shortages, regulatory constraints and driver lifestyle preferences. Driver scheduling had always been a tedious, manual and myopic process, resulting in high costs for extra board drivers and irregular assignments, contributing to poor driver retention. Greyhound collaborated with Optym to create and implement DriverPlan, a driver schedule optimization system that automates the scheduling process, reduces the need for extra board drivers and improves the quality of driver assignments.

We developed a three-stage approach to solve the problem efficiently: 1) we created the possible sequences of bus driving assignments, called tours; (2) we created favorable tour patterns for different on-duty and off-duty arrangements; and (3) we chose the optimal selection of patterns to cover all driving assignments.

At each stage, we considered multiple objective function factors, union and regulatory rules and implementability requirements. We worked in close collaboration with the driver planning group at Greyhound to ensure that tacit knowledge was included in the optimization model, the model solutions were implementable and the system was easy to configure. We illustrated results to end users with a business intelligence tool for detailed analysis and agreed on the final configuration.

1. Our model demonstrated a 60% reduction in six-day work weeks without compromising on driver wages, leading to higher driver satisfaction and retention.
2. The model created more regular driver schedules, resulting in a 33% reduction in extra boards.
3. Our model reduced layover and driver idle time costs by 5%.

As a long time user of HaulPlan, Saia knew their way around the system when the unexpected struck. While purpose-built for linehaul planning and optimization, Optym’s technology enabled Saia to maintain advanced agility, which easily transferred over as an emergency planning tool.

Saia took advantage of various optimization modes, which allowed for studies for diverting shipments from one terminal to another specific terminal, selecting the next best terminal in hub skip mode. As terminals would quickly need to be dropped from the network, Saia was able to quickly adjust.

As a long time user of HaulPlan, Saia knew their way around the system when the unexpected struck. While purpose-built for linehaul planning and optimization, Optym’s technology enabled Saia to maintain advanced agility, which easily transferred over as an emergency planning tool.

Saia took advantage of various optimization modes, which allowed for studies for diverting shipments from one terminal to another specific terminal, selecting the next best terminal in hub skip mode. As terminals would quickly need to be dropped from the network, Saia was able to quickly adjust.