Two-stage storage assignment to minimize travel time and congestion for warehouse order picking operations
Introduction
Most studies of the order picking operation focus only on the travel time and/or distance of order pickers as a criterion to evaluate the order picking performance. Less attention has been paid to traffic congestion even though it cannot be ignored because congestion significantly affects the warehouse productivity by slowing down the order picking process, leading to more work stress and safety problems, and increasing picker’s turnover rate (Chiang et al., 2002, De Koster and Yu, 2006, Gue et al., 2006, Tompkins et al., 2010, Pan and Ming-Hung, 2012). The main reason for traffic congestion is the imbalanced traffic flow that is caused when some order pickers work in the same place at the same time, which is inevitably found in practice (Gu, Goetschalckx, & McGinnis, 2007).
As the order picking operation accounts for 50–75% of the total operating cost of a warehouse (Coyle et al., 1996, Jewkes et al., 2004, De Koster et al., 2007), it has been considered as a key to improve warehouse performance and minimize the cost and, therefore, comprehensive studies have been conducted over the past decades (Petersen and Schmenner, 1999, Petersen and Aase, 2004, De Koster et al., 2007, Scholz et al., 2017). The main topics can be categorized into routing, storage location assignment, batching, zoning, and order release mode. In particular, the storage location assignment has been studied as a fundamental method to reduce the workload of order pickers because it is closely related to order picking processes. Order picking is an activity to retrieve stock keeping units (SKUs) from storage locations to fulfill customer orders (Van Den Berg et al., 1999). Hence, storage locations have direct effects on the productivity of order picking operations, which can be a key performance indicator in warehouse management (Frazelle, 2002).
The traditional correlated storage assignment strategies have contributed to reducing the picker travel time by considering correlation among SKUs. That is, SKUs frequently picked up together have the high correlation and they are stored close to each other. However, this may create the imbalanced traffic flow and traffic congestion, which in turn, increase picking delays. As it is obvious that picking delay can significantly compromise the warehouse productivity, the need to consider both travel time and picking delay cannot be overemphasized.
This research proposes a systematic and integrated approach that extends the correlated storage assignment strategy to improve the efficiency of warehouse order picking operations by simultaneously considering both the travel time and picking delays. The contribution of this research is summarized as follows:
- •
A new storage assignment method is proposed to resolve the conflict between the travel time and picking delays.
- •
The proposed method is evaluated by simulation studies based on real data collected from a local warehouse.
- •
The experimental results from the simulation studies provide practical implications of the storage location assignment for order picking operations.
The correlated and traffic balanced storage assignment (C&TBSA) consists of two stages: clustering and assignment. In the clustering stage, a bi-objective optimization model is formulated to group SKUs with the consideration of both the travel efficiency and traffic flow balance; i.e., maximizing correlation among SKUs in the same cluster to increase the travel efficiency while evenly distributing the amount of traffic volumes across clusters to alleviate potential picking delays. Multi-objective evolutionary algorithms (MOEAs) are employed to solve this bi-objective optimization problem with the capability of searching various solutions in a single simulation run (Fernandez, Lopez, Bernal, Coello, & Coello, 2010). Three widely used MOEAs are compared to discover the most suited MOEA for the optimization model for SKU clustering: (1) non-dominated sorting genetic Algorithm 2 (NSGA2), (2) Pareto evolutionary Algorithm 2 (SPEA2), and (3) Pareto envelope-based selection Algorithm 2 (PESA2). In the assignment stage, SKUs grouped into each cluster are stored in available locations within a storage area assigned to the cluster. It is preferred to assign highly demanded clusters to more accessible locations to minimize the travel time. A case study is conducted, based on real data obtained from a local warehouse where forklifts are mainly used as order picking devices, to evaluate C&TBSA compared with random, class-based, and correlated storage assignment methods.
The remainder of this research is organized as follows. In Section 2, a literature review on both warehouse storage assignment methods and MOEAs is presented. Section 3 introduces the clustering stage where the bi-objective optimization model for SKU clustering is formulated and solved using MOEAs. Section 4 focuses on the assignment stage where SKUs in each cluster are assigned to empty storage locations. In Section 5, the simulation-based case study is presented, and experimental results are discussed. Section 6 concludes the research.
Section snippets
Literature review
Storage location assignment is a fundamental way to improve warehouse order picking operations because the storage location directly affects travel time that takes up to 55% of order picking activities (De Koster et al., 2007). A variety of warehouse storage assignment methods such as random, dedicated, class-based, and correlated storage assignment have been proposed and studied in recent decades (Frazelle, 1989, Pan et al., 2012).
The random storage assignment allocates SKUs randomly to empty
Optimization model and MOEAs in the clustering stage
The overall flowchart of C&TBSA implementation is depicted in Fig. 1. The demand of each SKU is obtained from customer order data and the correlation based on the SKU co-appearance in the same customer orders is also computed. Then, a bi-objective optimization model is formulated for SKU clustering. Finally, SKUs in each cluster are assigned to available storage locations in the assignment stage.
SKU storage location decision in the assignment stage
The solution, obtained from the bi-objective optimization model, represents a set of clusters where SKUs are grouped based on different preferences to each Objective function (1) or (2).
In the assignment stage, SKUs grouped into each cluster are randomly stored in empty storage locations within a storage area assigned to the cluster; i.e., the storage location is a unit of space where one SKU can be stored and the storage area consists of multiple storage locations as shown in Fig. 4.
It is also
Simulation-based case study
The case study conducted in this research is based on the real warehouse transaction data obtained from a local warehouse (partner company) where forklifts are mainly used by order pickers for material handling. Several storage assignment scenarios are created and tested in a single-level rack simulation model based on the local warehouse layout. The travel time and picking delays found in order picking operations to fulfill customer orders are used as key performance indicators to compare the
Conclusion and future work
In this research, a systematic and integrated approach is proposed, which extends the correlated storage assignment strategy to improve the efficiency of warehouse order picking operations. Different from other analytical storage assignment methods mostly focusing on minimizing travel expenses in the literature, this research considers both travel time and picking delays caused by traffic congestion as criteria to evaluate the performance of order picking operations. This is because the
Acknowledgement
This research is partly supported by Toyota Material Handling North America (TMHNA) through its University Research Program.
References (62)
- et al.
A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms
Computers & Operations Research
(2016) - et al.
Defining order picking policy: A storage assignment model and a simulated annealing solution in AS/RS systems
Applied Mathematical Modelling
(2013) - et al.
Storage location assignment: Using the product structure to reduce order picking times
International Journal of Production Economics
(1996) - et al.
Design and control of warehouse order picking: A literature review
European Journal of Operational Research
(2007) - et al.
Evolutionary multiobjective optimization using an outranking-based dominance generalization
Computers & Operations Research
(2010) - et al.
Multi-objective optimization of vehicle passive suspension system using NSGA-II, SPEA2 and PESA-II
Procedia Technology
(2016) - et al.
Research on Warehouse operation: A comprehensive review
European Journal of Operational Research
(2007) - et al.
Product location, allocation and server home base location for an order picking line with multiple servers
Computers & Operations Research
(2004) - et al.
A new multi-objective particle swarm optimization method for solving reliability redundancy allocation problems
Reliability Engineering & System Safety
(2013) - et al.
A novel hybrid multi-objective immune algorithm with adaptive differential evolution
Computers & Operations Research
(2015)