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

Design and optimization of an explosive storage policy in internet fulfillment warehouses

This research investigates the warehousing operations of internet retailers. The primary physical process in internet retail is fulfillment, which typically involves a large internet fulfillment warehouse (IFW) that has been built and designed exclusively for online sales and an accompanying parcel delivery network. Based on observational studies of IFW operations at a leading internet retailer, the investigations find that traditional warehousing methods are being replaced by new methods which better leverage information technology and efficiently serve the new internet retail driven supply chain economy. Traditional methods assume a warehouse moves bulk volumes to retail points where the bulks get broken down into individual items and sold. But in internet retail all the middle elements of a supply chain are combined into the IFW. Specifically, six key structural differentiations between traditional and IFW operations are identified: (i) explosive storage policy (ii) very large number of beehive storage locations (iii) bins with commingled SKUs (iv) immediate order fulfillment (v) short picking routes with single unit picks and (vi) high transaction volumes with total digital control. In combination, these have the effect of organizing the entire IFW warehouse like a forward picking area. Several models to describe and control IFW operations are developed and optimized. For IFWs the primary performance metric is order fulfillment time, the interval between order receipt and shipment, with a target of less than four hours to allow for same day shipment. Central to achieving this objective is an explosive storage policy which is defined as: An incoming bulk SKU is exploded into E storage lots such that no lot contains more than 10% of the received quantity, the lots are then stored in E locations anywhere in the warehouse without preset restrictions. The explosion ratio Ψo is introduced that measures the dispersion density, and show that in a randomized storage warehouse Ψoo\u3e0.40. Specific research objectives that are accomplished: (i) Develope a descriptive and prescriptive model for the control of IFW product flows identifying control variables and parameters and their relationship to the fulfillment time performance objective, (ii) Use a simulation analysis and baseline or greedy storage and picking algorithms to confirm that fulfillment time is a convex function of E and sensitive to Ǩ, the pick list size. For an experimental problem the fulfillment time decrease by 7% and 16% for explosion ratios ranging between Ψo=0.1 and 0.8, confirming the benefits of an explosive strategy, (iii) Develope the Bin Weighted Order Fillability (BWOF) heuristic, a fast order picking algorithm which estimates the number of pending orders than can be filled from a specific bin location. For small problems (120 orders) the BWOF performes well against an optimal assignment. For 45 test problems the BWOF matches the optimal in 28 cases and within 10% in five cases. For the large simulation experimental problems the BWOF heuristic further reduces fulfillment time by 18% for Ǩ =13, 27% for Ǩ =15 and 39% for Ǩ =17. The best fulfillment times are achieved at Ψo=0.5, allowing for additional benefits from faster storage times and reduced storage costs

Adaptive Resource Management for Uncertain Execution Platforms

Embedded systems are becoming increasingly complex. At the same time, the components that make up the system grow more uncertain in their properties. For example, current developments in CPU design focuses on optimizing for average performance rather than better worst case performance. This, combined with presence of 3rd party software components with unknown properties, makes resource management using prior knowledge less and less feasible. This thesis presents results on how to model software components so that resource allocation decisions can be made on-line. Both the single and multiple resource case is considered as well as extending the models to include resource constraints based on hardware dynam- ics. Techniques for estimating component parameters on-line are presented. Also presented is an algorithm for computing an optimal allocation based on a set of convex utility functions. The algorithm is designed to be computationally efficient and to use simple mathematical expres- sions that are suitable for fixed point arithmetics. An implementation of the algorithm and results from experiments is presented, showing that an adaptive strategy using both estimation and optimization can outperform a static approach in cases where uncertainty is high

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Design and Control of Efficient Order Picking Processes

Binnen een logistieke keten dienen producten fysiek te worden verplaatst van de ene locatie naar de andere, van producenten naar eindgebruikers. Tijdens dit proces worden producten gewoonlijk opgeslagen op bepaalde plaatsen (magazijnen) voor een bepaalde periode. Orderverzameling – het ophalen van producten uit de opslaglocatie in het magazijn naar aanleiding van een specifieke klantorder – is het meest kritieke magazijnproces. Het is een arbeidsintensieve operatie in handmatig bestuurde systemen, en een kapitaalintensieve operatie in geautomatiseerde systemen. Een niet optimaal functionerend orderverzamelingsproces kan leiden tot onbevredigende service en hoge operationele kosten voor het magazijn, en dientengevolge voor de hele keten. Om efficiënt te kunnen functioneren dient het orderverzamelingsproces robuust te zijn ontworpen en optimaal te worden bestuurd. Dit proefschrift heeft als doel analytische modellen te ontwerpen die het ontwerp en de besturing van efficiënte orderverzamelingsprocessen ondersteunen. Verschillende methoden worden voorgesteld voor het schatten van de route langs de locaties van de te verzamelen producten, het bepalen van de optimale grenzen van zones in het magazijn die bestemd zijn voor opslag, de indeling van het magazijn, het aantal producten die tegelijk (in één ronde) worden verzameld (de batch size) en het aantal zones in het magazijn die worden ingericht voor het verzamelen en gereedmaken van orders. De methoden worden getest middels simulatie experimenten en worden inzichtelijk gemaakt met behulp van rekenexperimenten.Tho Le-Duc was born in 1974 in Quang Ninh, Vietnam. He received a Bachelor degree in Navigation Science from the Vietnam Maritime University in 1996 and a Postgraduate Diploma in Industrial Engineering from the Asian Institute of Technology Bangkok Thailand (AIT) in 1998. Thanks to the financial support from the Belgian Development and Co-operations, he obtained his master degree in Industrial Management from the Catholic University of Leuven in 2000. Since May 2001, he started as a Ph.D. candidate (AIO) at the RSM Erasmus University (formerly Rotterdam School of Management/Faculteit Bedrijfskunde), the Erasmus University Rotterdam. For about more than four years, he performed research on order picking in warehouses. As the results, Tho Le-Duc has been presented his research at several conferences in the fields of operations research, material handling, logistics and supply chain management in both Europe and North America. His research papers have been published or accepted for publication in several refereed conference proceedings, scientific books and international journals.Within a logistics chain, products need to be physically moved from one location to another, from manufacturers to end users. During this process, commonly products are buffered or stored at certain places (warehouses) for a certain period of time. Order picking - the process of retrieving products from storage (or buffer area) in response to a specific customer request - is the most critical warehouse process. It is a labour intensive operation in manual systems and a capital intensive operation in automated systems. Order picking underperformance may lead to unsatisfactory service and high operational cost for the warehouse, and consequently for the whole chain. In order to operate efficiently, the order picking process needs to be designed and optimally controlled. Thesis Design and Control of Efficient Order Picking Processes aims at providing analytical models to support the design and control of efficient order picking processes. Various methods for estimating picking tour length, determining the optimal storage zone boundaries, layout, picking batch size and number of pick zones are presented. The methods are tested by simulation experiments and illustrated by numerical examples

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development