Planning has achieved significant progress in recent years. Among the various approaches to scale up plan synthesis, the use of macro-actions has been widely explored. As a first stage towards the development of a solution to learn on-line macro-actions, we propose an algorithm to identify useful macro-actions based on data mining techniques. The integration in the planning search of these lear...

The exploration of hybrid metaheuristics — combination of metaheuristics with concepts and processes from other research areas — has been an important trend in combinatorial optimization research. In this work, we developed a hybrid version of the GRASP metaheuristic which incorporates the path-relinking procedure — a memory-based intensification strategy — and a data mining module. Computation...

Many technologies have life cycles that are shorter than the life cycle of the product they are in. Life cycle mismatches caused by the obsolescence of technology (and particularly the obsolescence of electronic parts) results in high sustainment costs for long field life systems, e.g., avionics and military systems. This paper describes a new methodology for forecasting electronic part obsoles...

In the open-pit mining, one of the first decisions that must be made in production planning stage, after completing the design of final pit limits, is determining of the processing plant cut-off grade. Since this grade has an essential effect on operations, choosing the optimum cut-off grade is of considerable importance. Different goals may be used for determining optimum cut-off grade. One of...

We consider the problem of mining high-utility plans from historical plan databases that can be used to transform customers from one class to other, more desirable classes. Traditional data mining algorithms are focused on finding frequent sequences. But high frequency may not imply low costs and high benefits. Traditional Markov Decision Process (MDP) algorithms are designed to address this is...

his article considers the supermarket manager’s problem of forecasting demand for a product as a function of the product’s attributes and of market control variables. To forecast sales on the stock keeping unit (SKU) level, a good model should account for product attributes, historical sales levels, and store specifics, and to control for marketing mix. One of the challenges here is that many v...

Supervised clustering is a data mining technique that assigns a set of data to predefined classes by analyzing dataset attributes. It is considered as an important technique for information retrieval, management, and mining in information systems. Since customer satisfaction is the main goal of organizations in modern society, to meet the requirements, 137 call center of Tehran city council is ...