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Smart Manufacturing Systems: Image

The flexible robotic cell scheduling problem

Industry based Research Project

A flexible robotic cell (FRC) is a manufacturing unit in which CNC machines perform all the processes of a product, and the robot is in charge of loading and unloading the parts to the machines and from the machines. FRC is more welcomed by the firms working under the mass customization paradigm since the system is more flexible, agile and the production rate, as well as the quality, is much fine comparing with job shops. In an FRC, the same group of processes is performed on all the machines. Hence, each item is processed only on one machine. Depends on the order of the input parts, the state of a cell (i.e., machines and robots) might be reversible. Thus, in a reversible FRC, the state of the cell’s components can always get back to the initial state or might back to some home state, which will appear as the initial state. The duration of the reversible cycle is called cycle time, and the scheduling problem is cyclic. In a cycle, each machine processes one part and as a fact, declining the cycle time of a cyclic production system means increasing the production rate. The cycle time depends on the order of the actions. Thus, determining the order of the actions to minimize the cycle time is an optimization problem.
In this project, the scheduling problem of a flexible robotic cell is considered. At the cell, machines are identical and parallel, and in line. There is an input buffer for the raw materials and an output buffer for the products and intermediate buffers. A robot is in charge of loading and unloading the items from the input buffer to machines and from machines to the output buffer.

Smart Manufacturing Systems: Research

Dynamic multi-agent-based scheduling

Research Project

In modern manufacturing systems with computational complexities, decision-making with respect to dynamic rescheduling and reconfiguration in case of internal disturbances is an important issue. This project introduces a multiagent-based dynamic scheduling system for manufacturing flow lines (MFLs) using the Prometheus methodology (PM) considering the dynamic customer demands and internal disturbances. The PM is used for designing a decision-making system with the feature of simultaneous dynamic rescheduling. The developed system is implemented on a real MFL of a small- and medium-sized enterprise where the dynamic customer demands and internal machine break downs are considered. The application has been completely modeled using a Prometheus design tool, which offers full support to the PM, and implemented in JACK agent-based systems. Each agent is autonomous and has an ability to cooperate and negotiate with other agents. The proposed decisionmaking system supports both static and dynamic scheduling. A simulation platform for testing the proposed multi-agent system (MAS) is developed, and two real scenarios are defined for evaluating the proposed system. The analysis takes into account the comparisons of the overall performances of the system models using the MAS scheduling and conventional scheduling approaches. The result of simulation indicates that the proposed MAS could increase the uptime productivity

Smart Manufacturing Systems: Research

Flexible testing platform for flexible manufacturing system

Research Project

The success of flexible manufacturing systems depends on efficacious scheduling and control architecture. However, the scheduling and control architectures currently employed suffer from the flexibility and reconfiguration capacity to manage disturbances when they occur. Consequently, the system performance rapidly degrades when the system operation is interrupted. The objective of this project is to develop a simulation test platform for the examination of the distributed control system in  FMS.
The merits and benefits of the developed simulation test platform in comparison to existing tools are as follows:
•This platform is a general use real-time simulation test platform that can be applied to any type of manufacturing system. The test platform tool can be used to validate the system performance improvements before any expensive investments.
•The simulation test platform is flexible and modular. The modular architecture allows the integration of any other control approaches (e.g., Holonic manufacturing).

Smart Manufacturing Systems: Research

RFID-enabled distributed manufacturing control and monitoring system

Research Project

Flexible manufacturing systems are complex, stochastic environments requiring the development of innovative, intelligent control architectures that support flexibility, agility, and reconfigurability. Distributed manufacturing control system addresses this challenge by introducing an adaptive production control approach supported by the presence of autonomous control units that are cooperating. Most of the currently distributed control systems still suffer from a lack of flexibility and agility when the product verity is high and is not reconfigured in case of ad hoc events. To overcome this limitation, a drawback of excessive dependence on up-to-date information about the products and other elements that move within the system is essential. Radiofrequency Identification (RFID) is a new emerging technology which uses radiofrequency waves to transfer data between a reader and movable item for identification, tracking, and categorization purpose.

 The Project aims are:  

  • Requirement analysis of the RFID-enabled distributed control and monitoring system

  • Develop an architecture to deploy RFID-enabled distributed control and monitoring system using a set of agents that are responsible for the realization of different tasks to enhance agility, flexibility, and reconfigurability of the manufacturing system.

  • Test and employing different algorithms aiming to boost the communication power among the agent   

Smart Manufacturing Systems: Research

Enterprise competency modeling

Research Project

In recent years, some enterprise engineering researchers have outlined the theoretical case for enterprise knowledge management. It is claimed that with product life cycles shortening and technologies becoming increasingly imitable, enterprise knowledge emerges as a major source of competitive advantage by its inimitability and immobility. Enterprise engineering is an approach for easy-to-understand definitions of the enterprise’s business entities and relationships; processes and planning; organizational structure; (d) market details and products/services; (e) and high-level planning and preferences. The artificial intelligence and enterprise modeling communities have developed important enterprise models and/or ontologies, including the Toronto Virtual Enterprise (TOVE), the Open Information Model (OIM), Computer Integrated Manufacturing Open System Architecture (CIMOSA), IDEON, Business Process Modelling Language (BPML), and Collaborative Network Organisation (CNO). Meanwhile, the global market encourages organizations to have a clear understanding of their area of expertise to maintain a competitive advantage. In addition to the enterprise model, it is important to capture and manage the knowledge and skills of enterprises’ internal competencies. Some professionals and researchers refer to these areas of organizational expertise as competency. Enterprise Competency is a crucial factor in business scenarios, in that it provides a more nuanced description of an enterprise’s or individual’s profile. Such a profile demonstrates the knowledge, skills, experience, and attributes necessary to effectively implement a defined function That competency is an essential component of enterprise engineering, acting as a new means to consider knowledge capitalization, associated with a new vision of performance, as well as new forms of ontology. First, the understanding and auditing of competencies acquired, required, and desired by a company, and second, representing them in a structured manner, are beneficial steps for enhancing the company’s performance.
We believe that the first step in successfully identifying and exploiting an enterprise’s core-competencies is creating a universal model for the competency, capability end organizational resources. Capability is referred to as; enterprise’s ability to exploit its resources. For better exploitation of the resources, information about activates which are realizable at the resource and the knowledge about how these resources and processes can work together are useful and essential issues. Minimizing risk and acquiring sufficient enterprise information while reducing costs and time-to-market are the main plans leading to the storage, management, and maintenance of organizational competencies. A competency model is a knowledge model that describes the skills and abilities of a particular organization. Organizations need a comprehensive competency model for the successful management of internal resources/activities and corresponding to their inter-related activities. For an organization to participate in Virtual Organization Breeding Environment (VBE) activities, prior submission of the competency model is necessary. On the other hand, competency models are an essential tool for improving organizational core competency. In small and medium-sized enterprises, the competency models can be developed from oral information while, in a more complex organization, the collection and modeling of competency by a human actor is not any more effective. In such cases, computer-based mechanisms are required. Available literature reviewed emphasizes the competency model as a paradigm that depends on modeling purpose that varies from one model to another. Furthermore, collection, analysis, and management of competencies for modeling purposes are complex tasks involving many aspects of the manufacturing and business environment. Despite the plausibility of these arguments, however, relatively few studies have provided empirical insights into how companies identify, represent, and manage ‘enterprise competency’ through the interplay between organizational context and information technology. Indeed, much of the existing literature is concerned with an ontological debate about the conceptual nature of competency and therefore tends to promote particular approaches as panaceas. More specifically, with the development of the field of ‘competency management,’ there has been a massive outpouring of articles dealing with these issues from a prescriptive standpoint. Their relatively weak empirical base notwithstanding, many of these contributions confidently define enterprise competency as a kind of economic asset or commodity, or as a purely cognitive phenomenon. These theoretical arguments are difficult to relate to the experience of business organizations. We also know comparatively little about the actual organizational processes through which enterprise competency is valorized in competitive outcomes.
In an attempt to shed some light on the above-mentioned issues, the main objective of this project is to examine the dynamics of successful competency modeling practices and to consider the extent to which such practices can be generalized and adapted by others. Therefore, the overall effect of this theoretical approach is to bridge a gap between the abstract concepts that we employ to understand enterprise competency and the practical, context-dependent realities facing business organizations. The main aims of this research thesis are: (a) understand capability and competency concepts (b) introduce an approach to store, manage, represent, and maintenance capability and competency of an organization at different levels of abstraction (c) suggest some criteria for using competency as an ontology for organization integration.

Smart Manufacturing Systems: Research
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