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Manual order picking, the process of retrieving stock keeping units from their storage location to fulfil customer orders, is one of the most labour-intensive and costly activity in modern supply chains. To improve the outcome of order picking systems, automated and robotized components are increasingly introduced creating hybrid order picking systems where humans and machines jointly work together. This study focuses on the application of a hybrid picker-to-parts order picking system, in which human operators collaborate with Automated Mobile Robots (AMRs). In this paper a warehouse with a two-blocks layout is investigated. The main contributions are new mathematical models for the optimization of picking operations and synchronizations. Two alternative implementations for an AMR system are considered. In the first one handover locations, where pickers load AMRs are shared between pairs of opposite sub-aisles, while in the second they are not. It is shown that solving the mathematical models proposed by the meaning of black-box solvers provides a viable algorithmic optimization approach that can be used in practice to derive efficient operational plannings. The experimental study presented, based on a real warehouse and real orders, finally allows to evaluate and strategically compare the two alternative implementations considered for the AMR system.

Scattered storage is a storage assignment strategy where single items are isolated and distributed all around the shelves of a warehouse. This way, the probability of always having some items per stock-keeping units close-by is increased, which is intended to reduce the unproductive walking time during order picking. Scattered storage is especially suited if each order line demands just a few items, so that it is mainly applied by business-to-consumer online retailers. This paper formulates a storage assignment problem supporting the scattered storage strategy. We provide and test suited solution procedures and investigate important managerial aspects, such as the frequency with which refilling the shelves should be executed. The online appendix is available at https://doi.org/10.1287/trsc.2017.0779 .

This study proposes a method for time-efficient order picking based on a structural stability assessment (SSA) when target boxes inside box-stacking storage (BSS) on multi-layer racks are removed. This method performs optimal order picking by generating a path to directly pick the target box without first picking the upper boxes in the BBS, if it is possible to pick the target box directly. The SSA algorithm generates images of the complement structure by removing the target box within BBS and uses them as input data for the CNN model to evaluate the stability of the structure. To create the CNN model, we generated a dataset using CoppeliaSim simulation, considering the size and shape of the overall structure of the BBS, the size and number of each box, and the number of target boxes. The accuracy of the generated CNN model was 95.1% on test data, while it achieved 97% accuracy when using real-world data. This validation process confirmed that the algorithm can be effectively applied to real BBS logistics environments to perform optimal order picking.

Restaurants are increasingly opting for technological innovations for food ordering. While digital modes of ordering, such as kiosks, tablets, and apps, embrace emerging innovations, can there be unintended consequences regarding the foods purchased and total spending? The findings from a series of studies, including six studies conducted in the field, demonstrate that a digital (vs. non-digital) ordering mode leads consumers to have a more automatic decision making mode and lower cognitive involvement, which results in more indulgent outcomes in the form of unhealthy food orders and higher overall spending. This effect attenuates for consumers with a high degree of technology acceptance and for orders placed earlier in the day. These findings suggest that restaurant managers with the goal of selling healthier options would benefit from having non-digital ordering modes, while managers desiring more indulgent purchases would benefit from having digital ordering modes available.

Previous studies have shown that individuals make suboptimal decisions in a variety of supply chain and inventory settings. We hypothesize that one cause is that individuals are overconfident (in particular, overprecise) in their estimation of order variation. Previous work has shown theoretically that underestimating the variance of demand causes orders to deviate from optimal in predictable ways. We provide two experiments supporting this theoretical link. In the first, we elicit the precision of each individual's beliefs and demonstrate that overprecision significantly correlates with order bias. We find that overprecision explains almost one-third of the observed ordering mistakes and that the effect of overprecision is robust to learning and other dynamic considerations. In the second, we introduce a new technique to exogenously reduce overprecision. We find that participants randomly assigned to this treatment demonstrate less overprecision and less biased orders than do those in a control group. This paper was accepted by Peter Wakker, decision analysis.

The logistics market has been impacted by increase of e-commerce, mass customization, omni-channel distribution, and just-in-time philosophy. In order to attend to this dynamic change, automation has been applied in warehouses. Although, some researches point out the lack of flexibility as a bottleneck. Therefore, a comprehensive literature review of recent papers is vital to draw a framework of the past and to shed light on future directions. This paper aims to review published papers in the last ten years related to flexible automation in warehouses and to construct a framework that could guide future researchers in the construction of an innovative conceptual model that may be applied at warehouses in the future. A total of 113 papers published between January 2008 and December 2018 have been selected, reviewed, and categorized to construct a useful foundation of past research. Results showed the key point to achieve a flexible automated warehouse is the combination of automated equipment, data collection technologies, and management solutions. Finally, based on the reviewed papers, an innovative framework of a flexible automated warehouse is proposed.

An MIAPP-NALT system is an order picking system in which cases are picked at multiple in-the-aisle pick positions (MIAPP) and storage and retrieval operations are performed by a narrow aisle lift truck (NALT). In this paper, the operation of such a system involving three classes of stock keeping units with random demands for storage and retrieval operations is modeled as an M/G/1 queue, where “customers” are storage and retrieval requests, the “server” is the NALT, and retrieval requests have non-preemptive priority over storage requests. Our goal is to explore a methodology and solution method to obtain the optimal layout design of a class-based MIAPP-NALT system with stochastic demands and priority service. To this end, an operation time model of the system is developed and the first two moments for the operation time are derived. To overcome the challenge in finding the desired optimal layout, a near-optimal layout obtained via a heuristic approach is obtained at first and is improved afterwards. Based on the optimal layout, some valuable queueing results demonstrate the benefit of using a priority-based discipline. Moreover, some useful insights regarding the selection of dedicated versus random storage policies are obtained.

Currently, companies are called to meet variable market demand whilst having to comply with tighter delivery times, also due to the growing spread of e-commerce systems in the last decade. As never before, it is therefore mandatory to increase the efficiency within distribution centers to minimize operating costs and increase environmental and economical sustainability. The picking process is the most expensive task in a warehouse, both for the required resources and time for completing all the operations, which is typically carried out manually. Several policies can be identified, such as discrete or batch picking. Many studies tend to optimize both policies, treating them distinctly and integrating them with other factors including, for instance, the logic of product allocation. This article stands on a higher decision-making level: starting from a database obtained with a simulative approach that contains the average distances covered by pickers in different warehouse configurations, the aim is to provide an analysis of which factors have the greatest impact in preferring a discrete order picking policy over the batch one. The factors in question are shape factor, input–output point, routing and storage location assignment policies. Results can be useful for industrial practitioners in defining the most efficient managerial strategies.

It has been shown that Augmented Reality improves the efficiency and well-being of order pickers; however, the adoption of AR Headsets in real contexts is hindered by comfort, safety, and battery duration issues. AR Glasses offer a lightweight alternative, yet they are seldom addressed in the current literature, and there is a lack of user studies exploring suitable visualization designs for these devices. Therefore, this research designs three AR visualizations of target position for order picking: Numeric Code, 2D Map, and 3D Map. They take into account the layout of the repository and the constraints of a small, low-resolution monocular display. These visualizations are tested in a within-subject user study with 30 participants employing AR Glasses in a simulated order-picking task. The Numeric Code visualization resulted in lower Task Completion Time (TCT) and error rates and was also rated as the least cognitively demanding and most preferred. This highlights that, for lightweight devices, simpler graphical interfaces tend to perform better. This study provides empirical insights for the design of innovative AR interfaces in logistics, using industry-relevant devices such as AR Glasses and conducting the evaluation in an extensive laboratory setup.

Due to the convenience of online grocery apps and home delivery, online grocery shopping has become popular in recent years. Globally, consumer behavior has significantly changed the consumption and purchase patterns of online grocery shopping. This study aimed to develop an efficient model for online grocery order fulfillment that both reduces costs and increases supply chain efficiency and sustainability. This study first aimed to develop the current picking model by adopting real-world data from a store in Riyadh, Saudi Arabia. Subsequently, four proposed models were developed to improve the efficiency and sustainability of the online grocery order fulfillment process. The results show a significant improvement in all models over the current picking model. The percentage improvements in fulfillment time per product are as follows: single order picking—8.33%; batch order picking—6.78%; zone order picking—3.08%; and hybrid order picking—13.20%, which combines zone and batch order picking. Retailers and online grocery apps could adopt these models to increase efficiency and sustainability. Also, these models have great potential for future research and improvement by optimizing product placement, in addition to picking methods and picking routes, which are the focus of this study.