Sequential model of FSN classification with ZABLS slotting and vehicle routing problem using hybridization of ant colony optimization and tabu search to reduce picking time
DOI: 10.3926/jiem.7333
archive: archived pipeline: cataloged verified
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Summary
This study addresses the inefficiency of warehouse picking processes at PT. XYZ, a Fast-Moving Consumer Goods (FMCG) company, where picking performance achieved only 78% of the standard time. The research aims to reduce picking time by developing a sequential optimization model that integrates Fast, Slow, and Non-moving (FSN) classification, ZABLS slotting, and Vehicle Routing Problem (VRP) optimization. The motivation stems from operational bottlenecks caused by lack of Warehouse Management System data, operator training deficits, and high warehouse occupancy, which necessitate a methodological approach rather than capital investment. The methodology employs a three-stage process using 2021 historical data. First, SKUs are classified using FSN analysis based on consumption rate and average stay. Second, ZABLS slotting allocates SKUs to warehouse shelves by calculating rectilinear distances and picking times per slot, prioritizing fast-moving items in locations with the shortest retrieval times. Third, a hybrid algorithm combining Ant Colony Optimization (ACO) and Tabu Search (TS) solves the VRP to determine optimal picking routes. The ACO provides an initial solution for the TS algorithm to refine, aiming to minimize total travel and picking time while respecting vehicle capacity constraints. The model was validated using 30 randomly sampled Delivery Orders (DOs), comparing performance under pre-slotting (current layout) and post-slotting (optimized layout) conditions. The results demonstrate significant improvements in picking efficiency. The baseline average picking time was 757.14 seconds. Under the pre-slotting condition with the ACO-TS hybrid routing, picking time decreased by 17.74% to 626.34 seconds. Under the post-slotting condition, which incorporates both optimized slotting and routing, picking time decreased by 25.75% to 557.64 seconds. Consequently, the daily fulfillment capacity increased from 31 DOs (current performance) to 45 DOs in the pre-slotting scenario and 51 DOs in the post-slotting scenario. This represents a 27.5% increase in daily order fulfillment capability compared to current operations, exceeding the theoretical standard of 40 DOs per day. The significance of this research lies in its novel integration of FSN classification, ZABLS slotting, and ACO-TS hybridization for VRP. It provides a practical framework for FMCG warehouses to enhance logistics performance without significant infrastructure investment. By systematically optimizing product placement and routing, the model offers a scalable solution to reduce operational costs and improve service rates, addressing critical inefficiencies in material handling and order fulfillment.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-20 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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