Using Logical Time to Ensure Liveness in Material Handling Systems With Decentralized Control

We describe a method for decentralized control of route-based material handling systems in which devices have no central controller (by definition), no common source of information, and no synchronized or common clocks with which to plan and execute their activities. The control scheme is based on the concept of \emph{logical time}, which is a means of partially ordering events in computer operating systems. We modify the concept to the domain of material handling systems and prove system liveness. We conclude by describing GridSorter, a conveyance-based sorter that uses decentralized control and logical time to sort packages. A prototype has been successfully built and tested at the Institute for Material Handling and Logistics at Karlsruhe Institute of Technology

Deployment of an Distributed Strategic Material Flow Control for Automated Material Flow Systems Consisting of Autonomous Modules

The modularisation of hardware and software is one approach to handle the demand for increasing flexibility and changeability of automated material flow systems that are, for example, utilised in flexible production systems. In such automated material flow systems, autonomous modules communicate with each other to coordinate and execute transport tasks. In this paper a strategic material flow control is introduced, which is distributed on several modules realised with a multi-agent system. The strategic material flow control agent coordinates transport tasks with advanced logistical requirements, such as sequencing. A transport task states for a transport unit the system source and sink together with arrival criteria at the sink, e.g. sequence In order to fulfil the arrival criteria the strategic material flow agent selects additional destinations within the automated material flow system to buffer a transport unit. For the selection of suitable buffer modules, several strategies are proposed and evaluated in a simulation study

Throughput Analysis for Layout Optimisation of Modular Conveyor Systems

In this paper, objective functions for the optimisation of modular conveyor systems will be introduced. Modular conveyor systems consist of conventional as well as modular conveyor hardware, which are arranged in form of matrix-like layouts. The aim of an ongoing research project is to provide small and medium-sized enterprises with a user-friendly decision support for the selection and planning of modular conveyor systems. For this purpose, the conveyor systems should be evaluated according to the objectives throughput and space requirement. Therefore, mathematical equations have been developed, which enable a fast and precise evaluation of layouts. The paper focuses mainly on the efficient calculation of the throughput. The result quality of the evaluation equations regarding the throughput was proven by a simulation of example systems

GridHub: a grid-based, high-density material handling system.

In the past twenty years, the share of e-commerce has increased (FRED, 2019). Since more and more activities, such as picking and sorting customers’ orders, are done in warehouses, high efficiency warehouses are in demand. Furthermore, the efficiency of warehouses is related to customer satisfaction (Colla and Lapoule, 2012). Storage systems are key components in warehouses, which are related to the efficiency of warehouse operations. In this dissertation, we address an automatic puzzle-based storage system under decentralized control. We call this system GridHub. GridHub meets standards of Industry 4.0 (Lasi et al., 2014), and it features high throughput with parallel order processing. In the first part of this research, we describe a GridHub which can handle unit-sized items; that is, one box only occupies one conveyor module. The GridHub is capable of moving boxes in all cardinal directions. It can complete multiple material handling tasks, such as sorting, sequencing, retrieving, and storing without changing the control algorithms. To move the active boxes to their targets, we developed a decentralized control algorithm to arrange box movements. The algorithms are executed by conveyor modules cyclically, and all actions in the execution process are one iteration of the algorithm. There are three phases in one iteration (assess, negotiation and convey), and several steps consist of one phase. The conveyor modules execute the algorithm simultaneously and synchronize at every step. The goal of the control algorithms is to move active boxes into their immediate destinations, and the key idea of the algorithm is to move away other boxes for the active boxes through message passing process. Negotiation behaviors are patterns of action generated by the conveyor modules while executing the algorithms. We describe these behaviors and explain how they affect the transfer process of active boxes. Some of those behaviors and other actions, which can prevent the transferring processes of boxes, are listed and discussed. These actions are related to deadlock and livelock in the GridHub. We prove that GridHub is deadlock free, and it is also livelock free under certain conditions. In the second part of this research, we extend the unit-sized GridHub by enabling it to handle non-unit-sized boxes meaning every box can occupy more than one conveyor module. We name the new GridHub the NU GridHub. The control algorithms of the NU GridHub are developed based on the unit-sized GridHub’s algorithms by adding new rules. Performance of the NU GridHub is also measured and discussed. GridHub is the first grid-based material handling system to offer four-way movement of stored items with a rich set of material handling task – storage, retrieval, sorting, and sequencing. GridHub is also the first grid-based system to implement a decentralized control algorithm based on “nested attempts,” a feature the guarantee deadlock free operation. Finally, the NU GridHub is the first grid-based solution to handle bigger boxes, which have not been done for a grid-based system under the virtual aisle method

Logical Time for Decentralized Control of Material Handling Systems

The fourth industrial revolution aims to transform production systems. In this work, Logical Time which is a control principle for distributed systems is transferred to material handling systems with decentralized control. The GridSorter, a modular sorter with grid-like structure, is chosen as showcase system. The system is proven to be deadlock-free and is robust against varying transport times. The time-window-based route reservation process is described as Iterative Deepening A*

Enhancing service-oriented holonic multi-agent systems with self-organization

Multi-agents systems and holonic manufacturing systems are suitable approaches to design a new and alternative class of production control systems, based on the decentralization of control functions over distributed autonomous and cooperative entities. However, in spite of their enormous potential they lack some aspects related to interoperability, migration, optimisation in decentralised structures and truly self-adaptation. This paper discusses the advantages of combining these paradigms with complementary paradigms, such as service-oriented architectures, and enhancing them with biologically inspired algorithms and techniques, such as emergent behaviour and self-organization, to reach a truly robust, agile and adaptive control system. An example of applying a stigmergy-based algorithm to dynamically route pallets in a production system is also provided

Bio-inspired multi-agent systems for reconfigurable manufacturing systems

The current market’s demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature’s insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufactur- ing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets

Industrial agents in the era of service-oriented architectures and cloudbased industrial infrastructures

The umbrella paradigm underpinning novel collaborative industrial systems is to consider the set of intelligent system units as a conglomerate of distributed, autonomous, intelligent, proactive, fault-tolerant, and reusable units, which operate as a set of cooperating entities (Colombo and Karnouskos, 2009). These entities are forming an evolvable infrastructure, entering and/or going out (plug-in/plugout) in an asynchronous manner. Moreover, these entities, having each of them their own functionalities, data, and associated information are now connected and able to interact. They are capable of working in a proactive manner, initiating collaborative actions and dynamically interacting with each other in order to achieve both local and global objectives.info:eu-repo/semantics/publishedVersio