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

Human-machine cooperation: optimization of drug retrieval sequencing in automated drug dispensing systems

Automated drug dispensing systems (ADDSs) are increasingly in demand in today's pharmacies, primarily driven by the growing ageing population. Recognizing the practical challenges faced by pharmacies implementing ADDSs, this study aims to optimize the layout design and sequencing issues within a human-machine cooperation environment to enhance the system throughput of ADDSs. Specifically, we develop models for drug retrieval sequencing under different system layout designs, taking into account the stochastic sorting time of pharmacists. The prescription order arrival pattern follows a successive arrival mode. To assess the efficiency of ADDSs with one input/output point and two input/output points, we propose dual command retrieval sequencing models that optimize the retrieval sequence of drugs in adjacent prescription orders. Notably, our models incorporate the stochastic sorting time of pharmacists to analyze its impact on ADDS performance. Through experimental comparisons of average picking times for prescription orders under various operational conditions, we demonstrate that a system layout design incorporating two input/output points significantly enhances the efficiency of prescription order fulfilment within a human-machine cooperation environment. Furthermore, our proposed retrieval sequencing method outperforms dynamic programming, greedy, and random strategies in terms of improving prescription order-picking efficiency. By addressing the layout design and sequencing challenges, our research contributes to the field of intelligent warehousing, particularly in smart pharmacies. The findings provide valuable insights for healthcare facilities and organizations seeking to optimize ADDS performance and enhance drug dispensing efficiency

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

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