EURO 2024 Copenhagen
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2166. An Adaptive Large Neighborhood Search Algorithm for Order Batching Problem

Invited abstract in session MC-45: Methods and Algorithms of Decision Support, stream Decision Support Systems.

Monday, 12:30-14:00
Room: 30 (building: 324)

Authors (first author is the speaker)

1. Ahmet Çınar
Data Science, Trendyol
2. Betül Ahat
Data Science, Trendyol
3. Mehmet Emre Altun
Trendyol
4. Bahar Umman
Operational Excellence, Trendyol
5. Meltem Sanisoğlu

Abstract

Over the past decade, amidst the rise of e-commerce enterprises and the transformative impact of the Covid-19 pandemic, the Order Batching Problem (OBP) has garnered significant attention. This heightened focus is fueled by the substantial volume of order transactions and the subsequent exponential expansion of warehouse dimensions, comprising multiple zones. These complexities have underscored the potential for over-costing in picking operations, prompting the adoption of intelligent solutions. Despite this, scant attention has been devoted to addressing OBP at the scale of Trendyol Group, a prominent player in Turkey and an increasingly influential international e-commerce entity offering a diverse array of product categories. This study particularly focuses on an OBP problem that groups orders into worklists for warehouse pickers, with the objective of maximizing the average number of items per zone. This approach seeks to minimize the duration of item picking for pickers, as a higher concentration of items per zone enables greater efficiency in item collection. To address this problem, we introduce a MIP model and implement a tailored Adaptive Large Neighborhood Search (ALNS) algorithm. Through extensive computational experiments utilizing real-life instances, our findings consistently demonstrate that ALNS yields near-optimal solutions and substantially enhances operational metrics by approximately 30%.

Keywords

Status: accepted

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