Using simulation to analyze picker blocking in manual order picking systems

The rise of the e-commerce practice makes the warehouses be confronted with ever smaller orders that must be met ever faster, often within a 24-h period. This pressures the order picking process as the orders pickers' workload becomes higher and higher, leading subsequently to congestion in the warehouse and impacting its productivity. It is therefore crucial to determine which order batching and picking policies enhance the performance of order picking activities. This paper carries out an intensive simulation study to examine the performance of different order picking policies with batching in a wide-aisle warehouse with a low-level picker-to-parts system. The performance of the system is measured in terms of total travelled distance, number of collisions between operators (congestion) and order lead times. A full factorial design is set up and the simulation output is statistically analyzed. The results are reported and thoroughly discussed

Design and Control of Warehouse Order Picking: a literature review

Order picking has long been identified as the most labour-intensive and costly activity for almost every warehouse; the cost of order picking is estimated to be as much as 55% of the total warehouse operating expense. Any underperformance in order picking can lead to unsatisfactory service and high operational cost for its warehouse, and consequently for the whole supply chain. In order to operate efficiently, the orderpicking process needs to be robustly designed and optimally controlled. This paper gives a literature overview on typical decision problems in design and control of manual order-picking processes. We focus on optimal (internal) layout design, storage assignment methods, routing methods, order batching and zoning. The research in this area has grown rapidly recently. Still, combinations of the above areas have hardly been explored. Order-picking system developments in practice lead to promising new research directions.Order picking;Logistics;Warehouse Management

Metaheuristics for the Order Batching Problem in Manual Order Picking Systems

In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations effciently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem; the rst one is based on Iterated Local Search, the second one on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods, but provide solutions which may allow for operating distribution warehouses signicantly more effcient.Warehouse Management, Order Picking, Order Batching, Iterated Local Search, Ant Colony Optimization

Algorithms for On-line Order Batching in an Order-Picking Warehouse

In manual order picking systems, order pickers walk or ride through a distribution warehouse in order to collect items required by (internal or external) customers. Order batching consists of combining these – indivisible – customer orders into picking orders. With respect to order batching, two problem types can be distinguished: In off-line (static) batching all customer orders are known in advance. In on-line (dynamic) batching customer orders become available dynamically over time. This report considers an on-line order batching problem in which the total completion time of all customer orders arriving within a certain time period has to be minimized. The author shows how heuristic approaches for the off-line order batching can be modified in order to deal with the on-line situation. A competitive analysis shows that every on-line algorithm for this problem is at least 2-competitive. Moreover, this bound is tight if an optimal batching algorithm is used. The proposed algorithms are evaluated in a series of extensive numerical experiments. It is demonstrated that the choice of an appropriate batching method can lead to a substantial reduction of the completion time of a set of customer orders.Warehouse Management, Order Picking, Order Batching, On-line Optimization

Order batching in multi-server pick-and-sort warehouses.

In many warehouses, customer orders are batched to profit from a reduction in the order picking effort. This reduction has to be offset against an increase in sorting effort. This paper studies the impact of the order batching policy on average customer order throughput time, in warehouses where the picking and sorting functions are executed separately by either a single operator or multiple parallel operators. We present a throughput time estimation model based on Whitt's queuing network approach, assuming that the number of order lines per customer order follows a discrete probability distribution and that the warehouse uses a random storage strategy. We show that the model is adequate in approximating the optimal pick batch size, minimizing average customer order throughput time. Next, we use the model to explore the different factors influencing optimal batch size, the optimal allocation of workers to picking and sorting, and the impact of different order picking strategies such as sort-while-pick (SWP) versus pick-and-sort (PAS)Order batching; Order picking and sorting; Queueing; Warehousing;

Exact algorithms for the order picking problem

Order picking is the problem of collecting a set of products in a warehouse in a minimum amount of time. It is currently a major bottleneck in supply-chain because of its cost in time and labor force. This article presents two exact and effective algorithms for this problem. Firstly, a sparse formulation in mixed-integer programming is strengthened by preprocessing and valid inequalities. Secondly, a dynamic programming approach generalizing known algorithms for two or three cross-aisles is proposed and evaluated experimentally. Performances of these algorithms are reported and compared with the Traveling Salesman Problem (TSP) solver Concorde

Data mining framework for batching orders in real-time warehouse operations

Warehouse activities play a key role in the final customer service level. From the warehouse processes, order picking is the major contributor to this category overall expenses. Order batching is commonly employed to improve the resources efficiency. Several heuristics have been proposed for the order batching problem, most of them developed for static batching, although scarce research has been focused on dynamic batching via stochastic modeling. We present an a novel approach to the problem developing a framework based on machine learning application directly to historical order batches data; gaining valuable knowledge regarding how are the batches formed and what attributes are the most meaningful in this process. This knowledge is then translated into simple batching decision rules capable of batch orders in a real-time scenario (dynamically). The framework was compared to FCFS heuristics and single picking; the results indicate higher performance

Design and Control of Warehouse Order Picking: a literature review

Order picking has long been identified as the most labour-intensive and costly activity for almost every warehouse; the cost of order picking is estimated to be as much as 55% of the total warehouse operating expense. Any underperformance in order picking can lead to unsatisfactory service and high operational cost for its warehouse, and consequently for the whole supply chain. In order to operate efficiently, the orderpicking process needs to be robustly designed and optimally controlled. This paper gives a literature overview on typical decision problems in design and control of manual order-picking processes. We focus on optimal (internal) layout design, storage assignment methods, routing methods, order batching and zoning. The research in this area has grown rapidly recently. Still, combinations of the above areas have hardly been explored. Order-picking system developments in practice lead to promising new research directions