In real world production systems, uncertain events such as random machine breakdown and processing time can occur anytime. These events lead to disruption of normal activities and consequently invalidate the initial schedule. Considering uncertainty in the scheduling process enables organisations to resume their activities effectively after uncertain events occur. The focus of this paper is proactive scheduling approach with an objective of minimising the total cost (lateness/earliness penalty and tooling cost). Robust optimisation is used to solve the scheduling problem considering processing time, setup times and tooling cost as uncertain parameters. Numerous scenarios are solved using data from local job shop. Multiple performance measurement criteria are evaluated to assess the significance of results obtained using robust and deterministic models. Design of experiment (DOE) has been implemented to evaluate the effects of different factors on the total cost and computational times.