1,007 research outputs found
Robust manufacturing system design using petri nets and bayesian methods
Manufacturing system design decisions are costly and involve significant
investment in terms of allocation of resources. These decisions are complex, due to
uncertainties related to uncontrollable factors such as processing times and part
demands. Designers often need to find a robust manufacturing system design that meets
certain objectives under these uncertainties. Failure to find a robust design can lead to
expensive consequences in terms of lost sales and high production costs. In order to find
a robust design configuration, designers need accurate methods to model various
uncertainties and efficient ways to search for feasible configurations.
The dissertation work uses a multi-objective Genetic Algorithm (GA) and Petri net
based modeling framework for a robust manufacturing system design. The Petri nets are
coupled with Bayesian Model Averaging (BMA) to capture uncertainties associated with
uncontrollable factors. BMA provides a unified framework to capture model, parameter
and stochastic uncertainties associated with representation of various manufacturing
activities. The BMA based approach overcomes limitations associated with uncertainty representation using classical methods presented in literature. Petri net based modeling is
used to capture interactions among various subsystems, operation precedence and to
identify bottleneck or conflicting situations. When coupled with Bayesian methods, Petri
nets provide accurate assessment of manufacturing system dynamics and performance in
presence of uncertainties. A multi-objective Genetic Algorithm (GA) is used to search
manufacturing system designs, allowing designers to consider multiple objectives. The
dissertation work provides algorithms for integrating Bayesian methods with Petri nets.
Two manufacturing system design examples are presented to demonstrate the proposed
approach. The results obtained using Bayesian methods are compared with classical
methods and the effect of choosing different types of priors is evaluated.
In summary, the dissertation provides a new, integrated Petri net based modeling
framework coupled with BMA based approach for modeling and performance analysis
of manufacturing system designs. The dissertation work allows designers to obtain
accurate performance estimates of design configurations by considering model,
parameter and stochastic uncertainties associated with representation of uncontrollable
factors. Multi-objective GA coupled with Petri nets provide a flexible and time saving
approach for searching and evaluating alternative manufacturing system designs
Scheduling Algorithms: Challenges Towards Smart Manufacturing
Collecting, processing, analyzing, and driving knowledge from large-scale real-time data is now realized with the emergence of Artificial Intelligence (AI) and Deep Learning (DL). The breakthrough of Industry 4.0 lays a foundation for intelligent manufacturing. However, implementation challenges of scheduling algorithms in the context of smart manufacturing are not yet comprehensively studied. The purpose of this study is to show the scheduling No.s that need to be considered in the smart manufacturing paradigm. To attain this objective, the literature review is conducted in five stages using publish or perish tools from different sources such as Scopus, Pubmed, Crossref, and Google Scholar. As a result, the first contribution of this study is a critical analysis of existing production scheduling algorithms\u27 characteristics and limitations from the viewpoint of smart manufacturing. The other contribution is to suggest the best strategies for selecting scheduling algorithms in a real-world scenario
ZASTOSOWANIE SIECI PETRIEGO W SYSTEMACH WSPOMAGANIA DECYZJI OPARTYCH NA INTELIGENTNEJ WIELOŹRÓDŁOWEJ ANALIZIE DANYCH
The paper deals with the design of data analysis systems for business process automation. A general scheme of decision support system was developed in which one of the modules is based on Petri Nets. The way of implementation of Petri Net model in optimization problem regarding service-oriented decision support system was shown. The Petri Net model of distribution workflow was presented and simulation experiments was completed. As a result the optimal solution as a set of parameters was emerged.Artykuł dotyczy problematyki projektowania zautomatyzowanych systemów analizy danych biznesowych. Opracowano ogólny model systemu wspomagania decyzji, w którym jeden z modułów funkcjonuje w oparciu o sieci Petriego. Zaprezentowano sposób implementacji sieci Petriego do realizacji zadań optymalizacyjnych dotyczących zorientowanego na usługi systemu wspomagania decyzji. Przeprowadzono szereg eksperymentów symulacyjnych wykorzystując model przepływu pracy utworzony na bazie sieci Petriego. Rezultatem badań było wyłonienie optymalnego zbioru parametrów procesu biznesowego
Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor
The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities
Survey on assembly sequencing: a combinatorial and geometrical perspective
A systematic overview on the subject of assembly sequencing is presented. Sequencing lies at the core of assembly planning, and variants include finding a feasible sequence—respecting the precedence constraints between the assembly operations—, or determining an optimal one according to one or several operational criteria. The different ways of representing the space of feasible assembly sequences are described, as well as the search and optimization algorithms that can be used. Geometry plays a fundamental role in devising the precedence constraints between assembly operations, and this is the subject of the second part of the survey, which treats also motion in contact in the context of the actual performance of assembly operations.Peer ReviewedPostprint (author’s final draft
An agent-based simulator for quantifying the cost of uncertainty in production systems
Product-mix problems, where a range of products that generate different incomes compete for a
limited set of production resources, are key to the success of many organisations. In their
deterministic forms, these are simple optimisation problems; however, the consideration of stochasticity may turn them into analytically and/or computationally intractable problems. Thus,
simulation becomes a powerful approach for providing efficient solutions to real-world productmix problems. In this paper, we develop a simulator for exploring the cost of uncertainty in these
production systems using Petri nets and agent-based techniques. Specifically, we implement a
stochastic version of Goldratt’s PQ problem that incorporates uncertainty in the volume and mix
of customer demand. Through statistics, we derive regression models that link the net profit to the
level of variability in the volume and mix. While the net profit decreases as uncertainty grows, we
find that the system is able to effectively accommodate a certain level of variability when using a
Drum-Buffer-Rope mechanism. In this regard, we reveal that the system is more robust to mix
than to volume uncertainty. Later, we analyse the cost-benefit trade-off of uncertainty reduction,
which has important implications for professionals. This analysis may help them optimise the
profitability of investments. In this regard, we observe that mitigating volume uncertainty should
be given higher consideration when the costs of reducing variability are low, while the efforts are
best concentrated on alleviating mix uncertainty under high costs.This article was financially supported by the State Research Agency of the Spanish Ministry of Science and Innovation (MCIN/AEI/ 10.13039/50110 0 011033), via the project SPUR, with grant ref. PID2020–117021GB-I00. In addition, the authors greatly appreciate the valuable and constructive feedback received from the Editorial team of this journal and two anonymous reviewers in the different stages of the review process
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