The literature on the product mix decision (or master production scheduling) under the Theory of Constraints (TOC), which was developed in the past two decades, has addressed this problem as a static operational decision. Consequently, the developed solution techniques do not consider the system's dynamism and the associated challenges arising from the complexity of operations during the implementation of master production schedules. This paper aims to address this gap by developing a new heuristic approach for master production scheduling under the TOC philosophy that considers the main operational factors that influence actual throughput after implementation of the detailed schedule. We examine the validity of the proposed heuristic by comparison to Integer Linear Programming and two heuristics in a wide range of scenarios using simulation modelling. Statistical analyses indicate that the new algorithm leads to significantly enhanced performance during implementation for problems with setup times. The findings show that the bottleneck identification approach in current methods in the TOC literature is not effective and accurate for complex operations in real-world job shop systems. This study contributes to the literature on master production scheduling and product mix decisions by enhancing the likelihood of achieving anticipated throughput during the implementation of the detailed schedule.