This paper considers a new variant of a multi-objective flexible job-shop scheduling problem, featuring multisubset selection of manufactured recipes. We propose a novel associated chromosome encoding and customise the classic MOEA/D multi-objective genetic algorithm with new genetic operators. The applicability of the proposed approach is evaluated experimentally and showed to outperform typical multiobjective genetic algorithms. The problem variant is motivated by realworld manufacturing in a chemical plant and is applicable to other plants that manufacture goods using alternative recipes.
the paper is one of the few attempts to develop a Product Service System (PSS) ontology aiming to facilitate Knowledge Management in collaborative PSS design, focusing upon machine industry. The PSS ontology includes concepts such as products, services, PSS, PSS lifecycle, process and stakeholders, including direct customers, consumers and their feedback. The context sensitivity approach is proposed to fully support the use of different tools for PSS development by various stakeholders. The context model includes both PSS ontology and a so-called usercentric ontology. The process to develop the ontologies is described. The approach to build the PSS ontology is based on the so-called Basic Formal Ontology (BFO). The foreseen applications of both ontologies in industrial practice of machine vendors and the expected benefits are being elaborated.
A new approach for the realisation of self-learning production systems based on a context aware approach, allowing self-adaptation of flexible manufacturing processes in production systems, is presented. The usage of dynamically changing context for adaptation of flexible manufacturing lines/processes is proposed. The presented solution includes services for context extraction, adaptation and self-learning allowing high adaptation of production systems depending on the identified context. A generic architecture following Service Oriented Principles is presented allowing for integration of the proposed solution into various production systems. A successful application of the developed solution in real world manufacturing environment is presented.
The work presented in this paper demonstrates how flexible manufacturing systems (FMS) combined with context awareness can be used to allow for an improved decision support in manufacturing industry. Thereby manufacturing companies shall be supported in a continuous process of increasing efficiency and availability of their production machines. Such optimization has to be embedded in the processes allowing for run time adaptation of the process to various dynamically changing external conditions. Context awareness, based on the information obtained from cyber physical systems, is a promising approach to allow for efficient building of such embedded optimization solutions. The objective of the research presented is to explore how context awareness, using the information from cyber physical systems integrated in the processes, can be applied to build a solution for selfoptimization of discrete, flexible manufacturing processes.
Intelligent products, having cyber physical features, are best candidates for building Intelligent Product Service Systems (IPSS), in which integrated products and services provide a higher level of intelligence. Such IPSS may actively provide feedback on their use, which in turn may support the development of new IPSS. The objective is to develop a set of tools to establish a Collaborative Network, where both human actors and products themselves can collaborate and contribute to the development of such IPSS. The tools support involvement of various stakeholders within the Collaborative Networks. Several tools, such as a tool to select sensors and intelligent features at the products, a tool to model context under which IPSS is used, as well as tools to provide feedback on the IPSS use are defined. The paper presents as well the application of the proposed concept in machine industry.
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