Differentiating households to analyze consumption patterns: a data mining study on official household budget data

Official data, administered by National Statistic Institutes (NSIs), play crucial role for being a major element of the governmental economic and social decision-making process. This strategic role raises a significant necessity for statistical authorities to adopt new data tools to shift the statistical quality of the published data to a higher level. Data mining (DM) techniques and algorithms are promising tools to provide new ways to mine the crucial, complex, and voluminous official data to complement or substitute the traditional and lagged-behind tools that NSIs have been using. This study addresses this potential utilization of DM tools on official data with a specific problem in an important survey for official statistics: Household Budget Survey. Through this study, clustering techniques are employed to characterize the household types and association rule mining technique is used to mine consumption patterns for each differentiated type. It is aimed to integrate the proposed model into data preprocessing procedure of the NSI to be able to engage in the real time analyses and to contribute exactness and timeliness of the data. (C) 2017 Wiley Periodicals, Inc

Clustering performance comparison of new generation meta-heuristic algorithms

This article addressed two new generation meta-heuristic algorithms that are introduced to the literature recently. These algorithms, proved their performance by benchmark standard test functions, are implemented to solve clustering problems. One of these algorithms called Ions Motion Optimization and it is established from the motions of ions in nature. The other algorithm is Weighted Superposition Attraction and it is predicated on two fundamental principles, which are "attracted movements of agents" and "superposition". Both of the algorithms are applied to different benchmark data sets consisted of continuous, categorical and mixed variables, and their performances are compared to Particle Swarm Optimization and Artificial Bee Colony algorithms. To eliminate the infeasible solutions, Deb's rule is integrated into the algorithms. The comparison results indicated that both of the algorithms, Ions Motion Optimization and Weighted Superposition Attraction, are competitive solution approaches for clustering problems. (C) 2017 Elsevier B.V. All rights reserved

Exploring comprehensible classification rules from trained neural networks integrated with a time-varying binary particle swarm optimizer

Purpose: Extracting comprehensible classification rules is the most emphasized concept in data mining researches. In order to obtain accurate and comprehensible classification rules from databases, a new approach is proposed by combining advantages of artificial neural networks (ANN) and swarm intelligence

Analysing the effect of flexibility on manufacturing systems performance

Purpose - In today's very competitive, dynamic and unpredictable manufacturing environments it is critical to improve manufacturing performance in order to be able to compete. Responsiveness and agility become important characteristics of manufacturing systems and organizations. Manufacturing systems must be designed optimally by taking into account responsiveness and agility related measures in order to improve effectiveness and performance. One of the important enablers of performance improvement is flexibility. It is a known fact that flexibility has a positive effect on the manufacturing system performance if it is properly utilized by the control system (usually scheduling). However, the relationship between flexibility and manufacturing system performance through scheduling is not entirely explored in the previous literature. The purpose of this paper is to investigate the effects of process plan and machine flexibilities on the scheduling performance of manufacturing job-shops

Discovering task assignment rules for assembly line balancing via genetic programming

Assembly line is one of the most commonly used manufacturing processes to produce final products in a flow line. Design of efficient assembly lines has considerable importance for the production of high-quantity standardized products. Several solution approaches such as exact, heuristic, and metaheuristics have been developed since the problem is first formulated. In this study, a new approach based on genetic programming so as to generate composite task assignment rules is proposed for balancing simple assembly lines. The proposed approach can also be applied to other types of line balancing problems. The present method makes use of genetic programming to discover task assignment rules which can be used within a single-pass constructive heuristic in order to balance a given assembly line quickly and effectively. Suitable parameters affecting the balance of the assembly line are evaluated and employed to discover highly efficient composite task assignment rules. Extensive computational results and comparisons proved the efficiency of the proposed approach in producing generic composite task assignment rules for balancing assembly lines

A hyper-heuristic approach for stochastic parallel assembly line balancing problems with equipment costs

This study addresses the stochastic parallel assembly line balancing problem with equipment costs and presents a hyper-heuristic approach based on simulated annealing for solving it. A cost-based objective function is employed to represent the incompletion, equipment, and station installation costs. The hyper-heuristic approach is utilized to search on sequencing heuristics search space, rather than a problem-specific solution space. This study focuses on the consideration of equipment costs while balancing a stochastic parallel assembly line. The performance of the solution approach is also tested on the single-model stochastic assembly line balancing problems and stochastic parallel assembly line balancing problems due to the generalizability of hyper-heuristics. The results of the benchmark problems show that in most cases the proposed algorithm provides better solutions than the best-known solutions in literature. An extensive computational study performed to determine the parameter levels derived from the problem and the solution method. The effect of the equipment costs for stochastic parallel assembly lines is also analyzed in detail

Analyzing the effect of dispatching rules on the scheduling performance through grammar based flexible scheduling system

In this paper, the effects of dispatching rules on the scheduling performance of job-shops with different flexibility levels are analyzed. Four different flexibility levels are defined for operations. Five dispatching rules are evaluated according to mean tardiness as the performance criteria for the scheduling system, Performance variations of dispatching rules among different machine flexibility levels are determined and statistically analyzed. It is found out after detailed analysis that the effect of dispatching rule selection on job shop performance weakens as the job shop flexibility increases. This important finding Should be taken into account while designing scheduling systems for job shops that are flexible. (C) 2009 Elsevier B.V. All rights reserved

Analyzing the effect of dispatching rules on the scheduling performance through grammar based flexible scheduling system

In this paper, the effects of dispatching rules on the scheduling performance of job-shops with different flexibility levels are analyzed. Four different flexibility levels are defined for operations. Five dispatching rules are evaluated according to mean tardiness as the performance criteria for the scheduling system. Performance variations of dispatching rules among different machine flexibility levels are determined and statistically analyzed. It is found out after detailed analysis that the effect of dispatching rule selection on job shop performance weakens as the job shop flexibility increases. This important finding should be taken into account while designing scheduling systems for job shops that are flexible.Flexible job shop scheduling Dispatching rules Flexibility Optimization