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

Abstract

Purpose: The research purpose is to enhance picking performance by developing a hybrid algorithm that classifies SKU, determines slot, and routes the process for Delivery Order (DO).

Design/methodology/approach: FSN classification is used for categorizing the products into three groups: fast-moving, slow-moving, and not-moving based on the consumption rate and average stay of each SKU. The result of classification is continued with ZABLS Slotting for product placement on warehouse shelves based on the quickest to the longest picking time on each slot. Slotting results are used for VRP addresses with Ant Colony Optimization and Tabu Search hybridization algorithms.

Due to the high process and cost to move the goods storage location because of the use of FSN and ZABLS method, hybridization algorithms are compared to pre-slotting and post-slotting conditions. Method verification uses 30 random sampling DO, and based on existing storage locations, it took 757.14 seconds average picking time.

Findings: On pre-slotting condition, it reduced 17.74% to 626.34 seconds, and on post-slotting condition it reduced 25.75% to 557.64 seconds.

The reduced picking time gives PT. XYZ better performance on fulfilling delivery orders in a day; theoretically, based on standard time, PT. XYZ can fulfill 40 orders in a day, and based on current performance, PT. XYZ can only fulfill 31 DO in a day. The uses of ACO-TS hybridization algorithms on pre-slotting condition PT. XYZ can fulfill 45 DO in a day and on post-slotting condition PT. XYZ can fulfill 51 DO in a day, increasing 27.5% from current performance.

Originality/value: The novelty of this research is the use of hybridization algorithms of Ant Colony Optimization and Tabu Search (ACO-TS) to design sequential model of FSN-ZABLS to VRP to minimize picking time on each Delivery Order (DO).