## 1 Introduction  
The majority of production planning frameworks are three-tiered hierarchical frameworks that cover three fundamental time horizons: Long-, medium-, and short-term [1]. The upper two tiers are referred to as planning challenges, while the lowest level is referred to as a scheduling challenge [2]. The degree to which a product requires disaggregation increases with each level. On a long-term basis, the end item requirements are aggregated by the product family. These requirements are then subdivided into specific goods, components, and raw material requirements, and manufacturing orders are issued. Finally, these instructions are sent to machines and work centers at the lowest planning level using different scheduling algorithms to enable workload management. Delving deeper into scheduling theory, in several process industries such as metal-forming industries, the end items are produced via numerous sequential physical or chemical transformation phases (also called tasks) applied to raw materials or intermediate components. A task requires different production resources such as work centers, dedicated machines, and workers with a limited capacity [3]. Detailed production scheduling is concerned with the short-term (day or shift) allocation of tasks to resources in an efficient manner, validated by their timely completion, without violating technological and capacity constraints [4]. [PERSON] [5] identified three distinct views in order to highlight the complexity of scheduling. The theoretical view regards scheduling from a problem-solving perspective, as a hard combinatorial optimization problem isolated from the manufacturing planning and control system's area of effect. A more realistic view focuses on the decision-making aspect of the scheduling process; the production planner is burdened with allocating tasks to resources while catering to bottlenecks, uncertainties, and dynamic events. The final view is the systems-level organizational perspective, i.e., scheduling is a module of the production planning and control (PPC) system, which supports a three-level manufacturing framework that initiates from aggregate plans, proceeds to material requirement plans, and finally results in detailed schedules. The authors focus on the first two views, and by doing so, attempt to identify which theoretical mathematical techniques and algorithms are deemed appropriate for inclusion in a scheduling decision support system (DSS) geared towards complex make-to-stock environments that necessitate fast yet accurate schedule construction. From an optimization standpoint, detailed production scheduling is an extraordinarily complicated issue, with most situations being classified as being non-deterministic polynomial-time (NP)-hard [6]. Furthermore, it is worth mentioning that it is rather unlike conventional planning difficulties. Although production planning makes use of aggregate data and the resulting plans are presented on either a long- or short-term time horizon, scheduling makes use of precise shop floor data to generate schedules that are as short as a day or even a single shift. Additionally, while planning optimization techniques focus on the overall cost, the scheduling of computational methods focuses on minimizing time-based performance parameters such as the makespan, lateness, and throughput times, most of which are prone to multiple allocation limitations [7]. The unpredictability increases with a shorter time horizon and optimum methods become insoluble. Numerous techniques for estimation and optimization have already been proposed, primarily for theoretical scheduling issues. The readers are referred to by [PERSON] and [PERSON] (2018) [8] for a comprehensive examination of scheduling theories and practices. Estimation techniques are regarded as being more suited for use on real shop floors owing to their lower processing costs.