1,721,008 research outputs found
Empower fuzzy cognitive maps decision making abilities with swarm intelligence algorithms
No full textThe proposed hybrid algorithm aims at defining an FCM by the information carried by a large dataset about a specific problem, taking into consideration the BCO and FPO principles. The link between them is represented by the application of the DB-Scan clustering technique allowing to identify the right number of cluster without knowing it a priopri. The hybridisation highlights the efficacy of the algorithm in estimating the correlations among the factors involved for a specific problem, with low RMSE and computational time, demonstrated by the case study example
Performance Analysis of New Product Development Process through Timed Coloured Petri Nets
No full text
Timed coloured petri nets and project management applications
Full text linkTraditional techniques for project modelling have not adequately incorporated some factors that are essential for resource planning and management. This paper describes a new approach for project modelling that uses Timed Coloured Petri nets (TCPNs), to facilitate resource allocation in projects under constraints, commonly encountered in practice since TCPNs provide a powerful formalism for representing and analysing parallel systems. However, up to now, very little has been done to integrate this graphical and mathematical tool with the area of project management. TCPN models can be used to analyse interdependencies, criticality, substitution, conflicting resource priorities and variations in the availability of resources. This paper proposes a new model, demonstrating its usefulness for real-Time activity scheduling in a resource-constrained project environment. The analysed case study regards the construction of an Italian highway
A heuristic scheduling algorithm based on fuzzy logic and critical chain project management
No full textProject activity scheduling is one of the most important steps in numerous industrial processes, from building construction to manufacturing. The proposed paper aims at defining a multi-criteria priority indicator integrating the principles of critical chain project management (CCPM), which considers the human factor for delay in task completion, and the fuzzy logic (FL), which model human reasoning. The defined priority indicator provides a different distribution of the activity weights according to their position within the project scheduling. In particular, the fuzzy scheduling approach has been performed in order to overcome the lack in the literature about it. Results have demonstrated the efficacy and efficiency of the method improving the project makespan with a reduction equal to 40% compared to traditional approache
Reuse of honey jars for healthier bees: Developing a sustainable honey jars supply chain through the use of LCA
No full textThis study aims at improving the environmental sustainability of an existing honey production supply chain, pursuing the Sustainable Supply Chain Management philosophy and the Life Cycle Assessment principles. Focusing the attention on the packaging stage and, in particular, on the most commonly used honey packaging solution, the glass jar, this study assesses the environmental burdens associated with its manufacturing, distribution and final disposal. Once the “AS-IS” honey packaging situation of an Italian province has been analyzed, parallel packaging reuse scenarios and redistribution supply chains are modelled, involving different levels of collaboration between the honey producers and the provincial beekeeping consortium. These scenarios have been then compared to the AS-IS situation, taking into account five environmental factors important for the honeybee's survival: the carbon dioxide equivalent emission, the triethylene glycol equivalent emissions into water and soil, the sulfur dioxide equivalent emission into air and the m2 equivalent reduction of organic arable land per year. The outcomes show how the adoption of a packaging reuse policy together with a producer collaboration policy could bring, in five years, to reduce those factors on average of 16% (with a 10% packaging reuse rate), up to more than 70% (with an 85% packaging reuse rate)
An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector
Full text linkAnomaly detection plays a crucial role in preserving industrial plant health. Detecting and identifying anomalies helps prevent any production system from damage and failure. In complex systems, such as oil and gas, many components need to be kept operational. Predicting which parts will break down in a time interval or identifying which ones are working under abnormal conditions can significantly increase their reliability. Moreover, it underlines how the use of artificial intelligence is also emerging in the process industry and not only in manufacturing. In particular, the state-of-the-art analysis reveals a growing interest in the subject and that most identified algorithms are based on neural network approaches in their various forms. In this paper, an approach for fault detection and identification was developed using a Self-Organizing Map algorithm, as the results of the obtained map are intuitive and easy to understand. In order to assign each node in the output map a single class that is unique, the purity of each node is examined. The samples are identified and mapped in a two-dimensional space, clustering all readings into six macro-areas: (i) steady-state area, (ii) water anomaly macro-area, (iii) air-water anomaly area, (iv) tank anomaly area, (v) air anomaly macro-area, (vi) and steady-state transition area. Moreover, through the confusion matrix, it is found that the algorithm achieves an overall accuracy of 90 per cent and can classify and recognize the state of the system. The proposed algorithm was tested on an experimental plant at Universita Politecnica delle Marche
A Multiphase Liquid-Gas Plant Modelling Using Fuzzy Cognitive Maps: An Application to an Actual Experimental Plant
No full textAlthough the manufacturing sector now reaps the most benefits from digitization, the oil & gas sector is increasingly embracing digital technology to boost system efficiency, particularly when it comes to modeling and simulation. The oil & gas industry is a complex and multiscale system, making it more challenging to construct a complete and accurate model. This paper presents an algorithm based on the combined use of Fuzzy Cognitive Maps (FCMs) and Gray Wolf Optimization (GWO) to identify the minimal causal model for estimating the level and pressure of a vertical tank in a multiphase liquid-gas plant. Two FCMs were modelled to regress tank level and pressure separately, to analyze the minimal causal relationships among the involved variables. By choosing only simulations concerning the most usual working conditions for the plant as the training dataset, an average accuracy in the training phase of about 85% (with peaks of 99%), and 90% in the testing phase, could be achieved
- …
