I mabnage a transport section that moves employees from three towns to our site with a roster of 53 drivers. HR keeps flagging us for overtime that exceeds labour-act limits, so I need a solid optimisation model that tells me exactly how many drivers I truly require and how to deploy them. I can supply clean datasets covering: • drivers’ current schedules and routes • precise employee pick-up / drop-off times • historical HR overtime records The brief is to translate those inputs into an optimisation model whose primary objective is to pinpoint the minimum driver pool that still meets every route and timing commitment while eliminating excessive overtime. Labour-act hour caps and any other real-world constraints (breaks, vehicle availability, town-to-site travel windows) must be baked in. I’m open on the toolset—Python (PuLP, Pyomo), Excel Solver, or any platform that lets me run what-if scenarios and tweak assumptions without specialist coding once it’s delivered. What matters is: • A clearly documented model I can refresh with new data. • A concise report that shows recommended driver count, expected overtime reduction, and any trade-offs. • Guidance on how to adjust parameters if routes or shift patterns change. If you’ve built workforce or transport optimisation models before, I’d love to see brief examples or screenshots so I know you can hit the ground running.