Discovering Assignment Rules in Workforce Schedules Using Data Mining
نویسنده
چکیده
Discovering hidden patterns in large sets of workforce schedules to gain insight into the potential knowledge in workforce schedules are crucial to better understanding the workforce dispatching decision making process, thereby improve workforce allocation and optimization. In this paper, a conceptual framework of the scheduling pattern discovery system is proposed. Association rule extraction methodologies are applied to explore the patterns in workforce schedules generated by a genetic algorithm (GA) based method through maximizing system throughput and machine utilization in a parallel production environment. A rule set scheduler is developed which approximates the genetic algorithm’s functionality furthermore yields problem solutions by means of rules of thumb. Numerical examples illustrate that the discovered scheduling patterns can unveil the relationships existing between the characteristics of workers and machine operations, facilitate managers to enhance workforce assignment and predict system production.
منابع مشابه
Association Rules Discovery in Workforce Schedule Database
Discovering hidden patterns in large sets of workforce schedules to gain insight into the potential knowledge in workforce schedules are crucial to better understanding the workforce dispatching decision making process, thereby improve workforce allocation and optimization. In this paper, a conceptual framework of the scheduling pattern discovery system is proposed. Association rule extraction ...
متن کاملAn Integrated DEA and Data Mining Approach for Performance Assessment
This paper presents a data envelopment analysis (DEA) model combined with Bootstrapping to assess performance of one of the Data mining Algorithms. We applied a two-step process for performance productivity analysis of insurance branches within a case study. First, using a DEA model, the study analyzes the productivity of eighteen decision-making units (DMUs). Using a Malmquist index, DEA deter...
متن کاملKnowledge Acquisition tool for Classification Rules using Genetic Algorithm Approach
Classification Rule Mining (CRM) is a data mining technique for discovering important classification rules from large dataset. This work presents an efficient genetic algorithm for discovering significant IF-THEN rules from a given dataset. The proposed algorithm consists of two main steps. First step generates set of classification rules and the second step deletes the weak rules and selects o...
متن کاملKnowledge Acquisition tool for Classification Rules using Genetic Algorithm Approach
Classification Rule Mining (CRM) is a data mining technique for discovering important classification rules from large dataset. This work presents an efficient genetic algorithm for discovering significant IF-THEN rules from a given dataset. The proposed algorithm consists of two main steps. First step generates set of classification rules and the second step deletes the weak rules and selects o...
متن کاملDiscovering Temporal Relation Rules Mining from Interval Data
In this paper, we propose a new data mining technique that can address the temporal relation rules of temporal interval data by using Allen’s theory. We present two new algorithms for discovering temporal relationships: one is to preprocess an algorithm for the generalization of temporal interval data and to transform timestamp data into temporal interval data; and the other is to use a tempora...
متن کامل