To Wave Or Not To Wave? Order Release Policies for Warehouses with an Automated Sorter

Wave-based release policies are prevalent in warehouses with an automated sorter, and take different forms depending on how much waves overlap and whether the sorter is split for operating purposes. Waveless release is emerging as an alternative policy adopted by an increasing number of firms. While that new policy presents several advantages relative to waves, it also involves the possibility of gridlock at the sorter. In collaboration with a large US online retailer and using an extensive dataset of detailed flow information, we first develop a model with validated predictive accuracy for its warehouses operating under a waveless release policy. We then use that model to compute operational guidelines for dynamically controlling the main parameter of its waveless policy, with the goal of maximizing throughput while keeping the risk of gridlock under a specified threshold. Secondly, we leverage that model and dataset to perform through simulation a performance comparison of wave-based and waveless policies in this context. Our waveless policy yields larger or equal throughput than the best performing wave-based policy with a lower gridlock probability in all scenarios considered. Waveless release policies thus appear to merit very serious consideration by practitioners. Facilities using a non-overlapping wave policy should also consider overlapping waves or a split sorter policy

Waveless picking : managing the system and making the case for adoption and change

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Global Operations Program at MIT, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 63-66).Wave-based picking systems have been used as the standard for warehouse order fulfillment for many years. Waveless picking has emerged in recent years as an alternative pick scheduling system, with proponents touting the productivity and throughput gains within such a system. This paper analyzes in more depth the differences between these two types of systems, and offers insight into the comparative advantages and disadvantages of each. While a select few pieces of literature perform some analyses of wave vs. waveless picking, this paper uses a case-study of a waveless picking system in an Amazon.com fulfillment center as a model for how to manage a waveless system once it has been adopted. Optimization methods for decreasing chute-dwell time and increasing throughput by utilizing tote prioritization are also performed using discrete-simulation modeling. The analysis concludes that managing waveless picking warehouse flow by controlling the allowable quantity of partially picked orders to match downstream chute capacity can lead to reduced control over cycle times and customer experience. Suggestions are also made on possible future research for how to optimally implement a cycle-time controlled system.by G. Todd Bishop.M.B.A.S.M

Evaluating the benefits of picking and packing planning integration in e-commerce warehouses

Motivated by recent claims on the potential value of integration in warehouse management, this study evaluates the benefits arising from integrating the planning of order picking and packing processes in e-commerce warehouses. A set of research questions are proposed for exploring various benefits under different operational conditions and an experimental study is designed to answer them. In order to have a concrete model to represent the integrated planning method, a mixed-integer nonlinear programming model is developed, and then compared against a non-integrated variation. The experimental study makes the comparisons by analysing the collected empirical data from a real-life warehouse. Our findings indicate that integrated picking and packing planning can yield improved performance in different aspects under different configurations of objectives, order quantities, order categories or workforce allocation

Evaluating the benefits of picking and packing planning integration in e-commerce warehouses

Motivated by recent claims on the potential value of integration in warehouse management, this study evaluates the benefits arising from integrating the planning of order picking and packing processes in e-commerce warehouses. A set of research questions are proposed for exploring various benefits under different operational conditions and an experimental study is designed to answer them. In order to have a concrete model to represent the integrated planning method, a mixed-integer nonlinear programming model is developed, and then compared against a non-integrated variation. The experimental study makes the comparisons by analysing the collected empirical data from a real-life warehouse. Our findings indicate that integrated picking and packing planning can yield improved performance in different aspects under different configurations of objectives, order quantities, order categories or workforce allocation

Robotized sorting systems: Large-scale scheduling under real-time conditions with limited lookahead

A major drawback of most automated warehousing solutions is that fixedly installed hardware makes them inflexible and hardly scalable. In the recent years, numerous robotized warehousing solutions have been innovated, which are more adaptable to varying capacity situations. In this paper, we consider robotized sorting systems where autonomous mobile robots load individual pieces of stock keeping units (SKUs) at a loading station, drive to the collection points temporarily associated with the orders demanding the pieces, and autonomously release them, e.g., by tilting a tray mounted on top of each robot. In these systems, a huge number of products approach the loading station with an interarrival time of very few seconds, so that we face a very challenging scheduling environment in which the following operational decisions must be taken in real time: First, since pieces of the same SKU are interchangeable among orders with a demand for this specific SKU, we have to assign pieces to suitable orders. Furthermore, each order has to be temporarily assigned to a collection point. Finally, we have to match robots and transport jobs, where pieces have to be delivered between loading station and selected collection points. These interdependent decisions become even more involved, since we (typically) do not posses complete knowledge on the arrival sequence but have merely a restricted lookahead of the next approaching products. In this paper, we show that even in such a fierce environment sophisticated optimization, based on a novel two-step multiple-scenario approach applied under real-time conditions, can be a serviceable tool to significantly improve the sortation throughput

Analysis of sorting techniques in customer fulfillment centers

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Manufacturing Program at MIT, 2003.Includes bibliographical references (leaf 95).by Sheila J. Bragg.S.M

Data Driven Efficiency for E-Warehousing: Descriptive and Prescriptive Analytics

Based on data provided by a warehouse logistics management company, we analyze the warehousing operation and its major processes of order picking and order consolidation. Without access to the actual layouts and process flow diagrams, we analyze the data to describe the processes in detail, and prescribe changes to improve the operation. We investigate the characteristics of the order preparation process and the order consolidation operation. We find that products from different orders are mixed for effective picking. Similar products from different orders are picked together in containers called totes. Full totes are stored in a buffer area, and then routed to a conveyor system where products are sorted. The contents of the totes are then consolidated into orders. This order consolidation process depends on the sequence in which totes are processed and has a huge impact on the order completion time. OCP is a new problem for both the warehouse management system and the parallel machine scheduling literature. We provide mathematical formulations for the problem and devise two solution methods. The first is a simulated annealing metaheuristic, while the second is an exact branch-and-price method. We test the solutions on both random and industry data. Simulated Annealing is found to achieve near optimal solutions within 0.01 % of optimality. For the branch-and-price approach, we use a set partitioning formulation and a column generation method where the subproblems are single machine scheduling problems that are solved using dynamic programming. We also devise a new branching rule and new dynamic programming algorithm to solve the subproblem after branching. To assess the efficiency of the proposed branch-and-price methodology, we compare against the branch-and-price approach of Chen and Powell (1999) for the parallel machine scheduling problem. We take advantage of the fact that OCP is a generalization of the parallel machine scheduling problem. The proposed, more general, branch-and-price approach achieves the same solution quality, but takes more time