Elkin Castro

An Agent Based Framework to Support Plug And Produce

The new market trends are very different, so it is crucial to the companies improve the tools and capabilities that allow themselves readjust rapidly and effectively to the news market changes and to the new requirements. In order to facilitate this process, it is proposed in this paper an agent based implementation that can provide to the existent systems the capacity to quickly adapt and reconfigure using standard technology. The proposed framework provides an intelligent tool to autonomously help the configuration when a production operator pretends to introduce a new variant of product in runtime and consult important information provided by the system to monitor execution.

Combined mathematical programming and heuristics for a radiotherapy pre-treatment scheduling problem

Demand for radiotherapy treatment services has increased due to higher cancer incidence, ageing population, and a larger number of prescribed radiotherapy fractions. This higher demand can, in turn, produce longer waiting times for radiotherapy treatment. We introduce a real-world radiotherapy pre-treatment scheduling problem at a hospital in the UK. This scheduling problem is modelled as an optimisation problem with multiple objectives. The objectives are hierarchical, thus the solution approach is based on solving a series of single-objective optimisation scheduling problems. Each of these problems is formulated as a mathematical programming model. Dispatching rules were introduced to produce an initial solution for the mathematical programming model. Their performance is compared to the mathematical programming approach.

A genetic algorithm for radiotherapy pre-treatment scheduling

This paper is concerned with the radiotherapy pretreatment patient scheduling. Radiotherapy pre-treatment deals with localisation of the area to be irradiated and generation of a treatment plan for a patient. A genetic algorithm is developed for patient scheduling which evolves priority rules for operations of radiotherapy pre-treatment. The fitness function takes into consideration the waiting time targets of patients and also the early idle time on resources. Real world data from a hospital in the UK are used in experiments.

A multi-agent architecture for plug and produce on an industrial assembly platform

Modern manufacturing companies face increased pressures to adapt to shorter product life cycles and the need to reconfigure more frequently their production systems to offer new product variants. This paper proposes a new multi-agent architecture utilising “plug and produce” principles for configuration and reconfiguration of production systems with minimum human intervention. A new decision-making approach for system reconfiguration based on tasks re-allocation is presented using goal driven methods. The application of the proposed architecture is described with a number of architectural views and its deployment is illustrated using a validation scenario implemented on an industrial assembly platform. The proposed methodology provides an innovative application of a multi-agent control environment and architecture with the objective of significantly reducing the time for deployment and ramp-up of small footprint assembly systems.

A multi-agent framework for capability-based reconfiguration of industrial assembly systems

Rapidly changing market requirements and shorter product lifecycles demand assembly systems that are able to cope with frequently changing resources, resource capabilities and product specifications. This paper presents a multi-agent framework that can adapt an assembly system in order to cope with such changes. The focus of this work is on the ability to plug resources (such as PLCs) into and out of the system, and dynamically aggregate resource capabilities to form more complex ones as resources are plugged in. In addition, an implementation of the framework on an industrial assembly system is discussed, and some insights are provided into some of the key features that product specification languages ought to have to be useful in real world assembly systems, and into the added value of using the proposed framework.

A Multi-Agent System Architecture for Self-configuration

Due to constant globalisation new trends on the market are coming up. One of the trends is the customisation of products for the customer and shorter product life cycles. To overcome the trends industries identified as key element self-reconfigurable production systems. A change to a running system means loss of time, money and manpower. A reconfigurable production system can automatically adapt to changes in terms of changing a machine or a product. The methodology behind is adapted from the office world and is called plug and produce. However, a production system has different requirements which need to be met. Due to a lack of homogeneity of industrial controllers in terms of communication and reconfigurability, as well as the interaction with the end user, the multi-agent technology was identified as a superior communicator. We present a new multilayered multi-agent architecture where the necessary agent types are introduced to fulfil the requirements for plug and produce. One scenario is shown where the architecture is employed to enable plug and produce capabilities and allow the system to adapt itself.

Automated experience-based learning for plug and produce assembly systems

This paper presents a self-learning technique for adapting modular automated assembly systems. The technique consists of automatically analysing sensor data and acquiring experience on the changes made on an assembly system to cope with new production requirements or to recover from disruptions. Experience is generalised into operational knowledge that is used to aid engineers in future adaptations by guiding them throughout the process. At each step, applicable changes are presented and ranked based on: (1) similarity between the current context and those in the experience base; (2) estimate of the impact on system performance. The experience model and the self-learning technique reflect the modular structure of the assembly machine and are particularly suitable for plug and produce systems, which are designed to offer high levels of self-organisation and adaptability. Adaptations can be performed and evaluated at different levels: from the smallest pluggable unit to the whole assembly system. Knowledge on individual modules can be reused when modules are plugged into other systems. An experimental evaluation has been conducted on an industrial case study and the results show that, with experience-based learning, adaptations of plug and produce systems can be performed in a shorter time.