The approach of self-organized and autonomous controlled systems offers great potential to meet new requirements for the economical production of customized products with small batch sizes based on a ...distributed, flexible management of dynamics and complexity within the production and intralogistics system. To support the practical application of self-organization for intralogistics systems, a catalogue of criteria for the evaluation of the self-organization of flexible logistics systems has been developed and validated, which enables the classification of logistics systems as well as the identification and evaluation of corresponding potentials that can be achieved by increasing the degree of self-organization.
Engineer-to-order (ETO) supply chains are configured to deliver unique and complex products to serve specific market demands. If market requirements change, ETO supply chains must adapt to survive ...and thrive. This paper examines the measures ETO companies should consider to ensure that their supply chains meet the demands of changing business environments. In particular, the paper focuses on how the yard-intralogistics processes of a large supplier are affected by the current transition toward sustainable energy solutions within the oil and gas industry. A comparison between current intralogistics processes and the requirements of emerging product portfolios and project setups reveals the need for process redesign and supply-chain realignment. Current yard-intralogistics processes, which are designed for large-scale projects, lack the efficiency required by the smaller-scale, renewable projects that will be necessary in the future. Our investigation shows that intralogistics processes must be significantly changed and that such a shift will not be possible without addressing operational and organizational structures concurrently. We argue that there are critical interdependencies between operational and organizational measures. The ability of companies to manage this interdependency is crucial for ETO-based industry transitions. A framework for the transformation of ETO companies in the context of industry transitions is presented, highlighting the operational and organizational challenges involved. This framework serves as a guideline for practice and as a reference model for further research.
This paper presents a digital twin architecture for a material flow system as well as experiments to determine the sim-to-real gap between virtual models and their real counterparts. The architecture ...comprises three layers, namely the physical/logical layer, the cyber layer as well as the descriptive layer. Virtual and real assets are able to communicate on the cyber layer by means of OPCUA and the Asset Administration Shell (AAS). The virtual models are implemented in a physics-based simulation environment and allow the reuse of all control and application layer software modules of their real counterpart. In addition, the sim-to-real gap between a selected asset, namely the Transfer Unit is analyzed by means of a collision analysis of the material transfer of simple shaped objects. The results show similar behavior of the real and the virtual models. However, more effort is needed to accurately model the meshes of the collision elements to ensure realistic results.
Assembly line balancing problems (ALBP) have plagued scholars and practitioners for decades. This paper investigates a new assembly system called flexible assembly line (FAL) derived from empirical ...observations in an air-conditioner assembly workshop. FAL can avoid the ALBP itself thanks to its structural flexibility and reconfigurability. However, field investigation highlights new challenges in the FAL - the mismatch between production (assembly) and intralogistics (material supply) leads to long waiting/idle time and workflow chaos, consequently lowers productivity and increases backorders. The production-intralogistics (PiL) processes are spatiotemporally coupled and interactional. Its complexity is much higher than considering the production or intralogistics optimization solely. And the PiL processes are further complicated by uncertain events such as new job arrivals, stochastic operational time, and equipment failures. The advent of Industry 4.0 technologies shows the tremendous potentials to revolutionize the contemporary notions of production management. Massive production data can be collected and analyzed in real-time. Nevertheless, there is little methodological research regarding utilizing real-time data to support production decisions under uncertainties. Thus, how to leverage real-time data collected in Industry 4.0 environments to support the decision-making of PiL processes for achieving a matched, coordinated, and synchronous operations management under various uncertainties, is a novel research problem. This paper develops a five-phase Graduation intelligent Manufacturing System (GiMS) to achieve PiL synchronization with flexibility and resilience. The underlying principles and rationale of GiMS are formulated as a synchronization mechanism, which includes a graph-theory based clustering for planning/scheduling and real-time decentralized ticketing for execution/control. Comprehensive numerical results validate the superiority of GiMS and the benefits of visibility and traceability in various scenarios. Moreover, the effects of uncertainties and trolley capacity are investigated in the sensitivity analysis.
•A novel production-intralogistics synchronization problem in flexible assembly lines is studied.•A 5-phase implementation framework of GiMS is developed for PiL Synchronization in FAL.•A flexible and resilient real-time decision-making mechanism under GiMS is proposed.•Numerical study has validated the superiority of the proposed GiMS and real-time data.•The effects of uncertainty level and trolley capacity are studied in the sensitivity analysis.
Spare parts management is a vital supporting function in aviation Maintenance, Repair, and Overhaul (MRO). Spare parts intralogistics (SPI), the operational perspective of spare parts management, ...significantly affects performance of MRO activities. This paper proposes Cyber-Physical Spare Parts Intralogistics System (CPSPIS) to address the synchronization problems associated with the SPI business process and SPI resources. The proposed system applies Internet-of-Things technologies and unified representations to provide resources and operations traceability and visibility. Further, CPSPIS contributes several services with self-X abilities for real-time synchronization throughout the SPI process. The communication service in the physical layer provides a self-adaptive ability to coordinate between the physical environment and cyberspace. The tracking and tracing service uses self-configured SPI business-related functions to capture accurate real-time resource and operation data through spatial modeling and location-based components for system resource coordination. The self-organized data source integration service consolidates all SPI request sources as the starting point for process analysis. The initialization service has self-awareness capability and models the transformation between the request source and SPI operations. The self-adjusted execution service synchronizes every two operation steps using real-time resource and operation data. In addition, CPSPIS develops applications and visualization tools for real-time cooperation between execution and decision-making. A real-life case study is conducted in one of the largest aviation MRO organizations, and the quantitative and qualitative improvements of CPSPIS are discussed.
The interdisciplinary design of intralogistics systems (ILS) involves engineers from various disciplines, resulting in the generation of discipline-specific model files with overlapping information. ...For instance, a conveyor system can be represented from various perspectives, such as 3D-CAD models that capture its geometric information and discrete-event simulation models that depict the system's dynamic material flow performance. The growing demands for flexible reconfigurability and adaptability in intralogistics systems necessitate frequent updates to engineering models. However, these updates often result in potential model inconsistencies due to insufficient stakeholder communication. Detecting the impact of model changes and related inconsistencies is challenging in practice due to data heterogeneity and complex inter-model relations. To address these challenges, we propose an ontology-versioning approach that automates the identification of inconsistencies resulting from model changes. Our approach facilitates the integration of heterogeneous model data, enables database versioning, detects inconsistencies caused by model updates, and provides traceability for identified issues. The concept is evaluated utilizing models from a prototypical implementation on a lab-sized demonstrator. Note to Practitioners -In the industry, the current development of intralogistics systems often lacks automated synchronization of overlapping model information and consistent model interfaces, frequently leading to contradictions among the models. This has been identified as a significant source of errors in the design of both industrial and academic intralogistics systems, as revealed by a study involving intralogistics experts from different technical disciplines. Effectively managing model inconsistencies is crucial for project success, particularly when frequent model changes occur. A promising approach to tackle this issue is to systematically link model data from different disciplines, through which model inconsistencies caused by inadequate communication among engineers can be identified and prevented. However, in many cases, changes in different model versions and their resulting inconsistencies are not adequately considered. To address this issue, we propose a concept based on ontology versioning that allows for the generation, comparison, and analysis of different versions of an ontological model database. This concept automatically identifies model changes, assesses their impacts on other models, and provides information to assist engineers in problem-solving. The effectiveness of our approach is assessed through an evaluation of three representative change scenarios, simplified from real-world use cases. In future research, we plan to extend the approach to general production systems and incorporate industrial-scale models from the broad range of disciplines involved in the design process.
•Studying order picking and delivery synchronization in e-commerce warehouses.•A dynamic order picking problem with delivery decisions is presented.•A selective order picking policy integrating ...neural network prediction is proposed.•The new picking policy is effective in synchronizing order picking and delivery.
The interplay between inbound order picking and outbound delivery operations is imperative. Nevertheless, these two operations are generally studied in a separate manner, and most existing studies on inbound order picking do not consider the influence of picking operations on downstream delivery operations. This paper addresses the order picking problem in e-commerce warehouses from a new perspective, i.e., towards achieving picking and delivery synchronization. We define a Dynamic Order Picking Problem with Delivery Decisions (DOPP-DD), where picking decisions are dynamically determined, given periodically received community logistics (CL) delivery decisions, to minimize vehicle waiting time at loading docks. The DOPP-DD is modeled as a Markov Decision Process and a selective order picking (SOP) policy is proposed to solve it, leveraging a convolutional neural network (CNN) to predict near-future delivery decisions. Our numerical study showcases the CNN’s high accuracy toward predicting the CL delivery decisions. We compare the performance of the SOP against three benchmark policies across 23 instances. The results reveal that the SOP policy outperforms the others in instances with limited picking capability and demonstrates higher robustness toward limited picking resources, highlighting its potential to alleviate overstock issues and foster a more synchronized workflow in order picking and delivery operations.
The widespread adoption of Industry 4.0 technologies is revolutionising how manufacturing operations are managed and done. This revolution drives manufacturing practitioners to reevaluate their ...current manufacturing planning and control (MPC) strategies to maintain global competitiveness. The production and intralogistics (PiL) operations within traditional MPC systems are organised separately, which results in inferior overall solutions. PiL operations in a single factory are inherently coupled and interact with each other throughout the entire process, which needs synchronous organisation and operations. This paper introduces a novel concept of operations twins (OT), with vertical twinning and horizontal twining, for achieving PiL synchronisation by leveraging Industry 4.0 technologies and innovative operations management strategies. An Internet-of-Things (IoT)-based vertical twinning method is developed for real-time object-level data collection and information-sharing between PiL. A horizontal twinning mechanism is proposed to support real-time coordination of production and intralogistics operations with real-time information-sharing. A numerical study is carried out, and the results show that OT outperforms the widely used static and dynamic methods regarding the overall stability and typical measures such as makespan, average manufacturing time, and average tardiness under different levels of uncertainties.
In the field of the intralogistics industry, we present DMZoomNet, a novel architecture that combines deep learning-based detectors with distance information to enhance object detection performance. ...Evaluation of our approach is conducted using the LOCO dataset, one of the few open source datasets available specifically designed for intralogistics scenarios. By comparing DMZoomNet with existing detectors and object detection methods, we demonstrate its superiority in several object detection metrics within complex intralogistics environments, such as warehouses densely packed with objects. This work contributes to the advancement of object detection techniques in the intralogistics industry and paves the way for future research and applications in this domain.
As the number of daily transactions continues to increase, congestion frequently occurs in flower auction centers. Put system is widely applied in intralogistics operations, which includes ...distribution and redistribution areas. The uncertain arrivals of demands pose significant challenges for the efficient intralogistics operations in flower auction center. In order to improve performance of the put system, this study newly designs a demand-predictive storage assignment (DSA) mechanism in which uncertain demands are forecasted by constructing
ratio time series of each customer. Based on the demand forecasts, the customer locations within the distribution area and the number of locations within the redistribution area are easily determined. Furthermore, a paired redistribution strategy is proposed that enables two customers to share a staging block. A simulation experiment bed is constructed based on a real-life case. The experimental results indicate that the
forecasting method outperforms other demand forecast methods in literature with lower forecasting error, and the proposed DSA mechanism reduces the total travel distance compared with the closest open location.
PDF
