Fabio Sgarbossa, Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim
Mirco Peron, Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim
Giuseppe Fragapane, Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim
Axel Vislie Mikkelsen, Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim
August Heiervang Dahl, Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim
Extended Abstract
Summary. In this extended abstract we will present a new kind of paradigm in intralogistics, called Cloud Material Handling System (CMHS), which has been introduced and developed at Logistics 4.0 Lab at NTNU (Norway). The idea is based on commonly used transportation service providers and platforms like Uber or Lyft. An Intelligent Cognitive Engine operating in cloud schedules and assigns the handling requests made by the customers (unit loads) to the available cars and drivers (material handling equipment). After an introduction explaining the industrial challenges of improving performance of material handling systems and how the emerging new technologies can be used to overcome them, CMHS concept and functioning are described. Then the results of some simulation from a case study are briefly presented and discussed, showing that CMHS has great potential in improving performance of intralogistics.
1. Introduction and Background
Nowadays, the implementation and usage of cloud technologies represents the avantgarde in production systems. Thereby, the so-called cloud manufacturing affects the management of the production system due to possibility of sharing real-time information about the status of product and all production services. Cloud technologies have been mainly analyzed and discussed on shop-floor level connecting machines, however, material handling systems have been left behind.
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The typical structure of today’s material handling systems is a mix of different equipment with various levels of automation (Furmans and Gue, 2018). Manual and mechanized systems, such as manual carts and industrial vehicles (i.e. pallet trucks, forklifts), in which humans still play an important decisional role, still dominate to a large extend the transportation of goods. In the last years, automated solutions, such as Automated Guided Vehicles (AGVs) or Automated Storage and Retrieval Systems (AS/RS), are implemented with their own decentralized control systems. Since these systems are not connected with each other, a multilevel hierarchical control system is necessary to coordinate the different sub-systems and allows the products to be moved from one point to the next within the production system.
The efficient utilization of the Material Handling Equipment (MHE) has a strong effect on the productivity, profitability, and flexibility of the production systems. Some examples are a machine waiting for the product to process since the forklift driver is not available, or a machine being blocked because the unit loads in the unloading station are still waiting to be transported to the next production phase.
The availability of industry 4.0 technologies, such as Indoor Positioning Technologies (IPT) as part of Internet of Things (IoT), motion tracking and control, and cloud computing is making MHE one of the most feasible solutions for increasing the flexibility of production systems.
By extending the definition of cloud manufacturing to handling activities, a new kind of paradigm, called Cloud Material Handling System (CMHS), has been introduced and developed by the authors in the Logistics 4.0 Laboratory at the Norwegian University of Science and Technology (Sgarbossa et al, 2020).
2. CMHS Concept
The concept of the CMHS can be compared to the transportation service provider and platform called Uber. In this case, the ‘consumers’ are the unit loads, called Smart Objects (SOs) which require a specific service from the system (typically to be transported from one point to another), while the ‘cars’ and ‘drivers’ are the MHE (forklifts, manual trolleys, conveyors, etc.) with differ-ent capabilities (capacity, cost, speed, time, service level, etc.), called Material Handling Modules (MHMs). The core of the CMHS platform is the Intelligent Cognitive Engine (ICE) that can dynamically schedule and assign the handling requests to the available MHE resources based on techniques using Artificial Intelligence (AI).
The real-time localization of the SOs and MHMs due to the IPT implementation, and the sharing of their attributes/functions along with positions, are enabling new decision-making processes for scheduling and control of all the components in the system.
Figure 1 depicts the operation model developed by the authors at the base of the CMHS. It consists of three categories of stakeholders: SOs, MHMs, and ICE, sharing a common knowledge of the system.
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Figure 1. Operation model of Cloud Material Handling System (adapted from Liu et al, 2019)
Aligning with the concept of cloud manufacturing, the CMHS has the scope to satisfy consumers’
requests (SOs) through the available resources (MHMs) in a cloud environment (ICE), reducing the complexity of a multilevel hierarchical control system and increasing the overall flexibility and productivity of the manufacturing system. The CMHS has been primarily developed for applications within a factory and production system. Nevertheless, the concept can be also extended to a multi-factory environment where the logistics activities are, for example, external transportations. With CMHS, the scheduling of the Material Handling Modules (MHMs) can be optimized, increasing the flexibility and productivity of the overall manufacturing system.
3. Preliminary Performance Analysis
From a theoretical perspective, the CMHS can bridge the gap between conventional material handling systems and fully automated ones. In pursuing a dynamic, flexible, and automated material handling system, few scientific contributions are dedicated to human-operated MHMs for material handling activities. Manual solutions often rely on conventional dispatching methods with low flexibility, restricting MHMs to predefined areas and/or material flows. In contrast, the CMHS provides automation capabilities for all MHMs, enabling increased freedom of movement.
Flexibility is further enhanced as the CMHS is deployable for any facility, regardless of layout configuration and type of material flows.
In order to analyze the profitability and performance of the CMHS from a practical perspective, the CMHS has been analyzed in a fully cooperative multi-agent shop floor for the management of a forklift fleet using queuing theory. The basis of comparison measures fleet utilization and product throughput, serving as a foundation to develop general guidelines and variable thresholds for businesses to decide whether to implement the CMHS.
The fleet’s performance has been evaluated by comparing two different dispatching methods:
1) fixed assignment policy where the forklifts are dedicated to specific areas and 2) on-demand
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policy where CMHS is used to manage the fleet. The latter approach investigates heuristic rules like Longest Waiting Time (LWT) and Shortest Travel Distance (STD), which have been used as benchmarks to assess the performance of deep reinforcement learning (DRL).
An extensive simulation model based on a case study has been developed with functionality for multiple scenario testing where the CMHS policies are compared to conventional fixed assign-ments. The scenarios are designed to test how the CMHS performs in situations when demand for material handling changes stochastically over time, in the event of workstation delays or when unforeseen maintenance is required.
The simulation results have shown that the CMHS returns higher total throughput (on average 25-30%) with a 40% decrease in the number of required MHMs in all scenarios compared to the fixed assignment. These results reinforce the hypothesis that the CMHS is well suited for dynamic shop-floor environments and that increased freedom of movement for MHMs can significantly increase material handling efficiency.
Furthermore, an analysis of how the MHM fleet should be dispatched optimally has been carried out. The results suggest that DRL can adapt well to stochastic material handling demand and outperforms the best heuristic by 10% in mean product throughput with a significantly lower deviation (8% compared to 16%). The performance of the DRL approach relative to the heuristics is consistently enhanced with higher variations in material flow demand and work-station delays, indicating that a machine learning implementation will be especially advan-tageous in such scenarios.
References
Furmans, Kai, and Kevin Gue. 2018. “A Framework for Modeling Material Han-dling with Decentralized Control.” Progress in Material Handling Research, January. https://
digitalcommons.georgiasouthern.edu/pmhr_2018/23.
Liu, Yongkui, Lihui Wang, Xi Vincent Wang, Xun Xu, and Lin Zhang. 2019. “Scheduling in Cloud Manufacturing: State-of-the-Art and Research Chal-lenges.” International Journal of Production Research 57 (15–16): 4854–79.
Sgarbossa, Fabio, Mirco Peron, Giuseppe Fragapane. 2020. Cloud Material Handling Systems:
conceptual model and cloud-based scheduling of handling activities, In: Sokolov, Ivanov, Dolgui (eds): Scheduling in Industry 4.0 and Cloud Manufacturing. International Series in Operations Research & Management Science, vol 289. Springer, Cham. https://doi.org/
10.1007/978-3-030-43177-8_5.
The 2021 International Scientific Symposium on Logistics is a joint event of Bundesvereinigung Logistik
and Fraunhofer Institute for Material Flow and Logistics.