Strategic Optimization of Order Batching and Storage Location Assignment in Fulfillment Centers
풀필먼트 센터의 배치 오더 피킹 및 상품 보관 위치 결정에 대한 전략적 최적화
- 주제(키워드) Order Picking , Batch Picking , Zone Picking System , Fulfillment Center , Quick Commerce
- 발행기관 고려대학교 대학원
- 지도교수 이홍철
- 발행년도 2025
- 학위수여년월 2025. 8
- 학위명 박사
- 학과 및 전공 대학원 산업경영공학과
- 원문페이지 123 p
- 실제URI http://www.dcollection.net/handler/korea/000000304880
- UCI I804:11009-000000304880
- DOI 10.23186/korea.000000304880.11009.0003055
- 본문언어 영어
초록/요약
Order picking plays a crucial role in fulfillment centers, significantly impacting throughput, efficiency, and customer satisfaction, especially in e-commerce and quick commerce (Q-commerce) sectors. This dissertation introduces two optimization strategies designed for different picking environments. The first study proposed a hybrid order picking strategy for manual systems in micro-fulfillment centers (MFCs), which dynamically switches between single-order and batch picking based on real-time order characteristics (e.g., order line distribution and arrival rates), SKU diversity, and warehouse activity profiling. Validated through agent-based simulations using operational data from a Korean MFC, this approach can reduce picker travel time by up to 30% and improve fulfillment rates by 15% compared with traditional single-order methods. The second study focused on storage location assignment in progressive zone picking systems using pick-to-light technology. A bi-objective mixed-integer programming model was developed to maximize the frequency of co-ordered dual-item picking and balance workloads across zones. Four metaheuristic algorithms, Genetic Algorithm (GA), Memetic Algorithm (MA), Iterated Local Search (ILS), and Local Search with Restart (LSR), were designed and compared with CPLEX for different problem sizes. The metaheuristic algorithms consistently outperformed CPLEX. While ILS and MA provided the highest solution quality, with improvements up to 36.2%, the LSR algorithm demonstrated exceptional computational speed, averaging under 0.11 seconds for large instances, which confirms its suitability for real-time applications. With computation times under one second, LSR is particularly well-suited for data-driven and large-scale fulfillment centers that require real-time planning. The integration of these two strategies establishes a robust and adaptable framework for enhancing order picking performance in a variety of operational settings. The hybrid approach reduces travel time and boosts fulfillment rates in manual MFCs, whereas the storage optimization model enhances dual-item picking and workload balance in zone-picking systems. Collectively, this dissertation provides a comprehensive framework that delivers innovative data-driven solutions, and contributes to the bi-objective optimization theory in warehousing logistics to enhance efficiency and scalability in modern fulfillment centers by addressing practically applicable challenges. Future work includes exploring dynamic layouts and human-robot hybrid systems.
more목차
ABSTRACT i
국문 초록 iii
PREFACE v
TABLE OF CONTENTS vi
LIST OF TABLES ix
LIST OF FIGURES x
CHAPTER 1. INTRODUCTION 1
1.1 Research background 1
1.2 Classification of order picking system 5
1.3 Research motivation and challenge 9
1.3.1 Challenges in manual order picking systems 9
1.3.2 Challenges in zone picking systems 11
1.4 Research objectives 12
1.5 Structure of the dissertation 14
CHAPTER 2. LITERATURE REVIEW 15
2.1 Overview of fulfillment center operations 15
2.2 Literature review on order batching strategies 17
2.3 Literature review on storage location assignment strategies 23
CHAPTER 3. OPTIMIZATION STRATEGY FOR ORDER BATCHING IN MANUAL PICKING SYSTEM 27
3.1 Problem statement 27
3.2 Warehouse activity profile 33
3.2.1 Order line distribution 33
3.2.2 Picking time distribution 35
3.2.3 Customer order arrival patterns 36
3.2.4 SKU pair frequency 38
3.3 Hybrid order batching framework 41
3.3.1 Rule-based criteria for single and batch picking selection 41
3.3.2 Concept of hybrid order batching strategy 44
3.4 Simulation model 45
3.4.1 Design of simulation model 45
3.4.2 Assumptions of simulation model 47
3.5 Numerical experiment 47
3.5.1 Experiment settings 47
3.5.2 Experiment results 51
CHAPTER 4. OPTIMIZATION STRATEGY FOR STORAGE LOCATION ASSIGNMENT IN PICK-AND-PASS SYSTEM 56
4.1 Problem statement 56
4.1.1 Operational flow and process in pick-and-pass systems 59
4.1.2 Key factors for optimization: Co-ordered dual-item picking and workload balance 61
4.2 Model formulation 65
4.2.1 Notation 65
4.2.2 Mathematical model 66
4.3 Solution algorithms 70
4.3.1 Genetic algorithm 71
4.3.2 Memetic algorithm 75
4.3.3 Iterated local search 77
4.3.4 Local search with restart 80
4.4 Numerical experiment 82
4.4.1 Experiment setting 83
4.4.2 Validation of optimal solution finding capability 85
4.4.3 Performance analysis on various problem sizes 87
CHAPTER 5. CONCLUSIONS 94
5.1 Summary of research findings 94
5.2 Contributions and implications 96
5.2.1 Hybrid order batching strategy for manual picking system 96
5.2.2 Storage location assignment strategy for pick-and-pass system 97
5.2.3 Overall contributions to order picking systems 99
5.3 Research limitations and future research directions 100
5.3.1 Research limitations 100
5.3.2 Future research directions 101
REFERENCES 102
