Approaches to Optimizing the Loading of Production Lines in the Alcohol Industry

Abstract

Purpose: This article aims to synthesize empirical evidence from fifteen peer-reviewed studies on production and warehouse planning in multi-product environments, with particular focus on line loading, batch sizing, filtration staging, and buffer management. The objective is to identify conditions under which sequencing, capacity representation, and buffer design improve overall equipment effectiveness (OEE), throughput, and service performance.

Methodology: A structured literature review was conducted, extracting and comparing modeling approaches (mixed-integer programming, heuristics, metaheuristics, simulation), decision variables (SKU grouping, changeover policies, capacity constraints), buffer policies, and performance indicators (OEE, throughput, service level). Findings were synthesized into a cross-study analytical framework highlighting recurring design patterns and operational trade-offs.

Findings: Three consistent conclusions emerged. First, SKU grouping policies that reduce sequence-dependent changeovers significantly increase OEE and stabilize flow, particularly in high-mix environments. Second, explicitly modeling filtration/processing capacity and dynamic constraints prevents micro-stoppages and protects throughput, whereas ignoring such constraints leads to systematically optimistic plans. Third, moderate upstream buffering improves delivery reliability, but benefits diminish rapidly beyond a threshold range. Decomposition- or cooperation-based algorithms outperform monolithic optimization models when product variety is wide and planning horizons are long.

Unique contribution to theory, practice and policy (recommendations):
The study contributes an integrated decision checklist for APS/MES implementation, linking model choice, data requirements, KPI alignment, and bottleneck diagnostics within one operational framework. Practically, it provides managers with a structured method for selecting planning algorithms and buffer policies based on product mix complexity and capacity volatility. For policy and organizational governance, it emphasizes the importance of standardized data structures and cross-functional KPI harmonization to avoid suboptimal planning. Future research should extend analysis to end-to-end models connecting production lines and warehouse systems under real operational datasets.