An open-source simulation model for solving scheduling problems

In this study, an open-source simulation model is presented for solving scheduling problems. The model is capable of solving different benchmarks. The methods involved in the simulation are mainly based on generating dispatching rules or using them to solve problems, but there are other heuristics as well. Dispatching rules in an evolutionary process are generated using Gene Expression Programming. For this aim, a coding method, which has not been described in the literature before, is explained. Along with the explanation of the properties of the source code, information about deterministic, dynamic models, buffer states, machine breakdown states, and the methods used to deal with them is presented. Concepts are explained with visual examples. In addition, a subject that has not been investigated in the literature before is analyzed by using the simulation model. This topic is to examine the results of solving machine assignment and operation sequencing sub-problems in flexible job shop scheduling problems with different rules. Moreover, objective functions that the source code can handle are discussed. Unlike many studies in the literature, the codes are presented to the readers as open source. Also, it is open to development and can be easily modified by users to solve other types of problems. Finally, in the study, experimental results are presented on the basis of some benchmarks available in the literature, and the limits of the study and source code are explained.info:eu-repo/semantics/publishedVersio

Energy-Efficient Technologies for High-Performance Manufacturing Industries

Ph.DDOCTOR OF PHILOSOPH

An agile and adaptive holonic architecture for manufacturing control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2004. Faculdade de Engenharia. Universidade do Port

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Quality embedded intelligent remanufacturing

This thesis is motivated from the four keywords: remanufacturing, quality, multi-agent and intelligence. Recent years' environmental problems caused tightening the regulations and legislations for used products. Therefore remanufacturing is getting more attention. The quality of used products is uncertain and even dynamically changes during the remanufacturing process, and each used product should be individually handled in a different way depending on its quality. Fortunately recent developing wireless technologies like radio frequency identification (RFID) may enable remanufacturing control systems to identify, track, and control each used product and disassembled subassembly/part (PDSP) automatically. The multi-agent approach can be a good solution for the individual control of each PDSP, because a centralized control system is not eligible to managing so many elements in the remanufacturing system. The objective of this thesis is to propose a quality embedded remanufacturing system (QRS) which comprises a multi-agent framework and a scheduling mechanism. First, this thesis discusses the fundamental concepts for the proposed modeling tools and scheduling mechanism: the QRS quality characteristics and the multi-agent framework. As the second step, this thesis proposes QRS modeling tools which support the PDSP/resource quality representation and comprise: intuitive remanufacturing system representation (IRSR) and dynamic token two-level colored Petri-nets (DTPN). The former is designed from the user-side perspective and the latter is from the system-side perspective. The multi-agent framework is constructed based on the model represented with the proposed tools. Last, this thesis proposes a real-time scheduling mechanism for the QRS which enables the constructed framework to execute. The scheduling mechanism embeds a communication protocol among agents and dispatching rules formulated depending on the PDSP/resource quality. A knowledge-based approach is adopted to increase efficiency of the scheduling mechanism, where the knowledge is learned by simulations. A heuristic method is also proposed to reduce the simulation time

Some Prototype Examples for Expert Systems v.1

This report consists of the nineteen term project reports for the graduate-level course EE695G ” Expert Systems and Knowledge Engineering”, which was offered for the fall semester of 1984 in the School of Electrical Engineering. The purpose of the term project is to provide each student an opportunity of designing and implementing a prototype expert system. The application area of each of these expert systems was selected by the student(s) working on the projects. This report is published for the purpose of documenting these results for future reference by the students of the above-mentioned course and, possibly, other workers in expert systems. The nineteen reports are grouped into seven parts based on their application domains. Part 1 - Manufacturing consists of six reports, and Part II - Robotics contains three. Two reports in each of Part III - Vision and Part IV - Management, and one in each of Part V - Structural Engineering and Part VI - Automatic Programming. The last part, Part VII - Others, consists of four reports with different applications

An Application of Context-sensitive Computing for Flexible Manufacturing System Optimization

Recent advancements in embedded systems, computing, networking, WS and SOA have opened the door for seamless integration of plant floor devices to higher enterprise level applications. Semantic web technologies, knowledge-based systems, context-sensitive computing and associated application development are widely explored in this regard. Ubiquitous and pervasive computing are the main domains of interest among many researchers so far. However, context-sensitive computing in manufacturing, particularly, relevant research and development in a production environment like FMS is relatively new and growing.Dynamic job (re)scheduling and dispatching are becoming an essential part of modern FMS controls. The foremost drive is to deal with the chaotic nature of the production environment while keeping plant performance indicators unaffected. Process plans in FMS need to consider several dynamic factors, like demand fluctuations, extreme product customizations and run time priority changes. To meet this plant level dynamism, complex control architectures are used to provide an automatic response to the unexpected events. These runtime responses deal with final moment change of the control parameters that eventually influences the key performance indicators (KPIs) like machine utilization rate and overall equipment effectiveness (OEE). In response, plant controls are moving towards more decentralized and adaptive architectures, promoting integration of different support applications. The applications aim to optimize the plant operations in terms of autonomous decision making, adaptation to sudden failure, system (re) configuration and response to unexpected events for global factory optimization.The research work documented in this thesis presents the advantages of bridging the mentioned two domains of context-sensitive computing and FMS optimization, mainly to facilitate context management at factory floor for improved transparency and to better respond for real time optimization through context-based optimization support system.This manuscript presents a context-sensitive optimization approach for FMS, considering machine utilization rate and overall equipment effectiveness (OEE) as the KPIs. Runtime contextual entities are used to monitor KPIs continuously to update an ontology-based context model, and subsequently convert it into business relevant information via context management. The delivered high level knowledge is further utilized by an optimization support system (OSS) to infer: optimal job (re) scheduling and dispatching, keeping a higher machine utilization rate at runtime. The proposed solution is presented as add-on functionality for FMS control, where a modular development of the overall approach provides the solution generic and extendable across other domains. The key components are functionally implemented to a practical FMS use-case within SOA and WS-based control architecture, resulting improvement of the machine utilization rate and the enhancement of the OEE at runtime

A flexible control system for flexible manufacturing systems

A flexible workcell controller has been developed using a three level control hierarchy (workcell, workstation, equipment). The cell controller is automatically generated from a model input by the user. The model consists of three sets of graphs. One set of graphs describes the process plans of the parts produced by the manufacturing system, one set describes movements into, out of and within workstations, and the third set describes movements of parts/transporters between workstations. The controller uses an event driven Petri net to maintain state information and to communicate with lower level controllers. The control logic is contained in an artificial neural network. The Petri net state information is used as the input to the neural net and messages that are Petri net events are output from the neural net. A genetic algorithm was used to search over alternative operation choices to find a "good" solution. The system was fully implemented and several test cases are described