## 1 Introduction  
The definition of lot sizes represents one of the most important decisions in production planning. Lot-sizing models aim to guarantee the fulfillment of the demand requirements, establishing a balance between holding and setup costs. Complex assembly systems usually require wide and robust product structures, which may involve the use of alternate bills of materials and co-production settings. In these cases, the complexity of lot sizing decisions increases along with flexibility. Depending on the problem size and the number of considered constraints, the use of exact solution models can be inefficient in terms of computational times, especially for operational planning purposes [1, 2].  
Exact solution approaches have been widely used for the NP-hard Capacitated Lot Sizing Problem (CLSP), including cut-generation [3] and redefinition techniques [4], supported by mathematical approaches as Branch and Bound, Lagrangian Relaxation, and Wagner-Within. In the case of multi-level CLSP, the use of linear programming (LP) methods showed good results [5].  
On the other hand, the use of heuristic and meta-heuristic methods for solving lot-sizing problems has become increasingly frequent and implementations have flooded the scientific literature [2, 6, 7, 8, 9]. The advantages of these kinds of approximate procedures include reduced computational times, the ability to solve larger problems, higher flexibility, high-quality solutions, and reduced implementation costs (e.g., compared with commercial software licenses [10]).  
Among the different heuristic approaches, local and guided-search algorithms, such as the Tabu Search (TS), have demonstrated efficiency in a variety of lot-sizing problems, especially with multi-product and multi-level planning structures [11; 12; 13]. Most implementations take initial solutions from constructive algorithms, which are later improved with iterative small modifications (called moves). The search is guided by long-term and short-term memory structures within intensification and diversification phases [9; 14; 15; 16].  
The Generic Materials and Operations Planning model (GMOP) is one of the most recent exact models for solving lot-sizing problems that considers alternative bills of materials, multi-level structures, multi-site production/packaging, and co-production settings. This model was proposed in 2011 [17; 18] as a robust mixed integer programming (MIP) problem. Since 2013 [19; 20], the functioning of GMOP has been improved with the implementation of mathematical relaxation methods and its efficiency in practical applications has been demonstrated (e.g., in the automotive sector) [21; 22; 23].  
Developing heuristic approaches for solving this problem has been suggested as a research opportunity since 2012 [24; 25], but these kinds of procedures have not been extensively explored [26]. Proposing alternative solution methods and data structures represents a contribution not only to further GMOP studies, but also to the wider research field of multi-level lot-sizing problems, employing alternative product structures instead of the generalized Gozinto structure.  
The aim of this article is to propose an alternative heuristic algorithm for the GMOP, using the short-term memory principles of TS. The main considerations include: Capacity constraints, a multi-product/multi-level structure, a co-production environment, and the existence of alternate bills of materials for final products [27; 28].  
This paper is organized as follows. Section 2 shows the GMOP generalities. Section 3 shows a brief introduction to TS algorithms. Section 4 exposes the methodological framework and a functioning overview of the proposed algorithm. The obtained results and their discussion are shown, respectively, in Sections 5 and 6. Finally, conclusions and further research opportunities are highlighted in Section 7.