This paper presents a review of the state-of-the-art literature and a subsequent taxonomy of mixed linear programming models (MILP) developed for production planning. Specifically, single-stage models are analysed due to their relevance to various scenarios. One of the most common scenarios involves multi-station processes that include a main stage as a bottleneck that limits the production rate of the whole plant, and is therefore of critical importance for the plant. Another situation is the multi-stage processes that can be decomposed into multiple, much simpler single-stage problems considered as independent or weakly linked. And, naturally, it is also applicable to real single-station plant scenarios. To this end, a total of 120 papers have been reviewed. 29 of them have been analysed in detail. This review presents 30 models in total that have been analysed in terms of their most significant characteristics.This document explains each of these characteristics and, consequently, specifies which models possess each characteristic. In particular, it specifies whether the models are formulated in continuous, discrete or hybrid time; whether their objective function is cost-based (maximising profit or minimising cost) or time-based (minimising makespan, tardiness, earliness, lateness, number of delayed orders, total time invested in setups or total processing time); whether the model includes the task of selecting batch sizes for each product among its objectives; whether setup times are taken into account and what types of setups (sequence-dependent, equipment-dependent, resource-dependent or operation-dependent); whether deadlines are considered, whether these deadlines are strict or regulated by penalties and what types of deadlines are included (order completion, order availability or equipment availability); finally, whether stop periods for breaks, maintenance, inspections or defect correction are included in the planning.The final result of this work is a clear and concise tabular representation of the information discussed, which provides a framework for future developments in this field. In particular, we identify two possible lines of future research that stand out for their interest in real problems and whose presence is limited in the current state of the art. The first of these research lines concerns the inclusion of maintenance task planning and the second concerns the inclusion of the autonomous batch size selection. Both tasks are within the MILP model itself.