The maximum satisfiability problem (MAX-SAT) refers to the task of finding a variable assignment that satisfies the maximum number of clauses (or the sum of weight of satisfied clauses) in a Boolean Formula. Most loca...The maximum satisfiability problem (MAX-SAT) refers to the task of finding a variable assignment that satisfies the maximum number of clauses (or the sum of weight of satisfied clauses) in a Boolean Formula. Most local search algorithms including tabu search rely on the 1-flip neighbourhood structure. In this work, we introduce a tabu search algorithm that makes use of the multilevel paradigm for solving MAX-SAT problems. The multilevel paradigm refers to the process of dividing large and difficult problems into smaller ones, which are hopefully much easier to solve, and then work backward towards the solution of the original problem, using a solution from a previous level as a starting solution at the next level. This process aims at looking at the search as a multilevel process operating in a coarse-to-fine strategy evolving from k-flip neighbourhood to 1-flip neighbourhood-based structure. Experimental results comparing the multilevel tabu search against its single level variant are presented.展开更多
The aim of maintenance is to reduce the number of failures in equipment and to avoid breakdowns that may lead to disruptions during operations.The objective of this study is to initiate the development of a predictive...The aim of maintenance is to reduce the number of failures in equipment and to avoid breakdowns that may lead to disruptions during operations.The objective of this study is to initiate the development of a predictive maintenance solution in the shipping industry based on a computational artificial intelligence model using real-time monitoring data.The data analysed originates from the historical values from sensors measuring the vessel´s engines and compressors health and the software used to analyse these data was R.The results demonstrated key parameters held a stronger influence in the overall state of the components and proved in most cases strong correlations amongst sensor data from the same equipment.The results also showed a great potential to serve as inputs for developing a predictive model,yet further elements including failure modes identification,detection of potential failures and asset criticality are some of the issues required to define prior designing the algorithms and a solution based on artificial intelligence.A systematic approach using big data and machine learning as techniques to create predictive maintenance strategies is already creating disruption within the shipping industry,and maritime organizations need to consider how to implement these new technologies into their business operations and to improve the speed and accuracy in their maintenance decision making.展开更多
文摘The maximum satisfiability problem (MAX-SAT) refers to the task of finding a variable assignment that satisfies the maximum number of clauses (or the sum of weight of satisfied clauses) in a Boolean Formula. Most local search algorithms including tabu search rely on the 1-flip neighbourhood structure. In this work, we introduce a tabu search algorithm that makes use of the multilevel paradigm for solving MAX-SAT problems. The multilevel paradigm refers to the process of dividing large and difficult problems into smaller ones, which are hopefully much easier to solve, and then work backward towards the solution of the original problem, using a solution from a previous level as a starting solution at the next level. This process aims at looking at the search as a multilevel process operating in a coarse-to-fine strategy evolving from k-flip neighbourhood to 1-flip neighbourhood-based structure. Experimental results comparing the multilevel tabu search against its single level variant are presented.
文摘The aim of maintenance is to reduce the number of failures in equipment and to avoid breakdowns that may lead to disruptions during operations.The objective of this study is to initiate the development of a predictive maintenance solution in the shipping industry based on a computational artificial intelligence model using real-time monitoring data.The data analysed originates from the historical values from sensors measuring the vessel´s engines and compressors health and the software used to analyse these data was R.The results demonstrated key parameters held a stronger influence in the overall state of the components and proved in most cases strong correlations amongst sensor data from the same equipment.The results also showed a great potential to serve as inputs for developing a predictive model,yet further elements including failure modes identification,detection of potential failures and asset criticality are some of the issues required to define prior designing the algorithms and a solution based on artificial intelligence.A systematic approach using big data and machine learning as techniques to create predictive maintenance strategies is already creating disruption within the shipping industry,and maritime organizations need to consider how to implement these new technologies into their business operations and to improve the speed and accuracy in their maintenance decision making.
