Order picking optimization with order assignment and multiple workstations in KIVA warehouses

We consider the problem of allocating orders and racks to multiple stations and sequencing their interlinked processing flows at each station in the robot-assisted KIVA warehouse. The various decisions involved in the problem, which are closely associated and must be solved in real time, are often tackled separately for ease of treatment. However, exploiting the synergy between order assignment and picking station scheduling benefits picking efficiency. We develop a comprehensive mathematical model that takes the synergy into consideration to minimize the total number of rack visits. To solve this intractable problem, we develop an efficient algorithm based on simulated annealing and dynamic programming. Computational studies show that the proposed approach outperforms the rule-based policies used in practice in terms of solution quality. Moreover, the results reveal that ignoring the order assignment policy leads to considerable optimality gaps for real-world-sized instances

Robotized Warehouse Systems: Developments and Research Opportunities

Robotized handling systems are increasingly applied in distribution centers. They require little space, provide flexibility in managing varying demand requirements, and are able to work 24/7. This makes them particularly fit for e-commerce operations. This paper reviews new categories of robotized handling systems, such as the shuttle-based storage and retrieval systems, shuttle-based compact storage systems, and robotic mobile fulfillment systems. For each system, we categorize the literature in three groups: system analysis, design optimization, and operations planning and control. Our focus is to identify the research issue and OR modeling methodology adopted to analyze the problem. We find that many new robotic systems and applications have hardly been studied in academic literature, despite their increasing use in practice. Due to unique system features (such as autonomous control, networked and dynamic operation), new models and methods are needed to address the design and operational control challenges for such systems, in particular, for the integration of subsystems. Integrated robotized warehouse systems will form the next category of warehouses. All vital warehouse design, planning and control logic such as methods to design layout, storage and order picking system selection, storage slotting, order batching, picker routing, and picker to order assignment will have to be revisited for new robotized warehouses

Automated Order Picking Systems and the Links between Design and Performance: A Systematic Literature Review

With new market developments and e-commerce, there is an increased use of and interest in automation for order picking. This paper presents a systematic review and content analysis of the literature. It has the purpose of understanding the relevant performance aspects for automated, or partly automated, OPSs and identifying the studied links between design and performance, i.e. identifying which combinations of design aspects and performance aspects have been studied in previous research. For this purpose, 74 papers were selected and reviewed. From the review, it is clear that there has been an increased number of papers dealing with the performance of automated, or partly automated, OPSs in recent years. Moreover, there are differences between the different OPS types, but, overall, the performance categories of throughput, lead time, and operational efficiency have received the most attention in the literature. The paper identifies links between design and performance that have been studied, as well as links that appear to be under-researched. For academics, this paper synthesises the current knowledge on the performance of automation in OPSs and identifies opportunities for future research. For practitioners, the paper provides knowledge that can support the decision-making process of automation in OPSs

Beurteilung unterschiedlicher Use Case-Konfigurationen in einem Robotic Mobile Fulfilment System

In recent years, hybrid order picking systems like Robotic Mobile Fulfilment Systems (RMFS) have become established and widely used in ecommerce. Companies from other logistics areas with different use cases often decide against investing in RMFS due to high investment risks or unknown performance benefits. This work contains a performance evaluation of three different use case configurations based on logistics areas in e-commerce and production conducted by a simulation model for multi-level RMFS with an integrated rolling planning approach. The model leads to a demonstrator supporting logistics managers in their decisionmaking. Those and other users can vary input parameters in the demonstrator, create different use case configurations, and run the simulation model to evaluate performance by key performance indicators (KPIs). The work depends on several discussions and interviews with logistics experts to define realistic use cases the logistics manager can identify

A two-stage approach for order and rack allocation in a mobile rack environment

In this paper we investigate a problem associated with operating a robotic mobile fulfilment system (RMFS). This is the problem of allocating orders and mobile storage racks to pickers. We present a two-stage formulation of the problem. In our two-stage approach we, in the first-stage, deal with the orders which must be definitely fulfilled (picked), where the racks chosen to fulfil these first-stage orders are chosen so as to (collectively) contain sufficient product to satisfy all orders. In the second-stage we restrict attention to those racks chosen in the first-stage solution in terms of allocating second-stage orders. We present three different strategies for first-stage order selection; one of these strategies minimises the requirement to make decisions as to the rack sequence (i.e. the sequence in which racks are presented to each picker). We present a heuristic procedure to reduce the number of racks that need to be considered and too present a heuristic for order and rack allocation based on partial integer optimisation that makes direct use of our two-stage formulation. Extensive computational results are presented for test problems that are made publicly available

RoboPlanner: Towards an Autonomous Robotic Action Planning Framework for Industry 4.0

Autonomous robots are being increasingly integrated into manufacturing, supply chain and retail industries due to the twin advantages of improved throughput and adaptivity. In order to handle complex Industry 4.0 tasks, the autonomous robots require robust action plans, that can self-adapt to runtime changes. A further requirement is efficient implementation of knowledge bases, that may be queried during planning and execution. In this paper, we propose RoboPlanner, a framework to generate action plans in autonomous robots. In RoboPlanner, we model the knowledge of world models, robotic capabilities and task templates using knowledge property graphs and graph databases. Design time queries and robotic perception are used to enable intelligent action planning. At runtime, integrity constraints on world model observations are used to update knowledge bases. We demonstrate these solutions on autonomous picker robots deployed in Industry 4.0 warehouses

On the performance of robotic parts-to-picker order picking systems

Order picking is the activity in which a number of items are retrieved from a warehousing system to satisfy a number of customer orders. Automating order picking systems has become a common response to the wide variety of products and components stored in today’s warehouses and the short delivery lead times requested by today’s customers. As a result, new technical solutions have reached the market, including robotic parts-to-picker order picking systems such as robot-based compact storage and retrieval systems (RCSRSs) and robotic mobile fulfilment systems (RMFSs).\ua0Despite the increased use of robotic parts-to-picker order picking systems, knowledge about how they perform in terms of throughput, order lead time, human factors, quality, flexibility, operational efficiency, and investment and operational costs needs to be further developed, as does knowledge about how their performance is affected by the order picking system’s design and context. Accordingly, the purpose of this thesis is to expand knowledge about the performance of robotic parts-to-picker order picking systems by investigating how their design and context influence their performance. \ua0The thesis is built upon three studies: a systematic literature review study focusing on automated order picking systems, a multiple-case study on RCSRSs, and a single-case study on RMFSs. First, the systematic literature review study on the performance of automated order picking systems provides an overview of literature on order picking systems to date, aspects of their performance, and how their performance relates to their design. Second, the multiple-case study sheds light on characteristics of the performance of RCSRSs and the relationships between their performance and design. Third and last, the single-case study affords insights on how the context of RMFSs affects their performance.\ua0The thesis contributes to practice by providing guidance to decision makers within industry in terms of the performance to expect of robotic parts-to-picker OPSs depending on their design and context. In turn, such knowledge can facilitate the selection and design of an OPS or else the redesign of a current system. At the same time, the thesis contributes to theory by providing a synthesis of literature addressing the performance of automated OPSs and by outlining the relationships between their design and performance