Developing a hybrid data mining approach based on multi-objective particle swarm optimization for solving a traveling salesman problem

A traveling salesman problem (TSP) is an NP-hard optimization problem. So it is necessary to use intelligent and heuristic methods to solve such a hard problem in a less computational time. This paper proposes a novel hybrid approach, which is a data mining (DM) based on multi-objective particle swarm optimization (MOPSO), called intelligent MOPSO (IMOPSO). The first step of the proposed IMOPSO is to find efficient solutions by applying the MOPSO approach. Then, the GRI (Generalized Rule Induction) algorithm, which is a powerful association rule mining, is used for extracting rules from efficient solutions of the MOPSO approach. Afterwards, the extracted rules are applied to improve solutions of the MOPSO for large-sized problems. Our proposed approach (IMOPSP) conforms to a standard data mining framework is called CRISP-DM and is performed on five standard problems with bi-objectives. The associated results of this approach are compared with the results obtained by the MOPSO approach. The results show the superiority of the proposed IMOPSO to obtain more and better solutions in comparison to the MOPSO approach

Proposing an approach to calculate headway intervals to improve bus fleet scheduling using a data mining algorithm

Abstract The growth of AVL (Automatic Vehicle Location) systems leads to huge amount of data about different parts of bus fleet (buses, stations, passenger, etc.) which is very useful to improve bus fleet efficiency. In addition, by processing fleet and passengers' historical data it is possible to detect passenger's behavioral patterns in different parts of the day and to use it in order to improve fleet plans. In this research, a new approach is developed to use AVL data to investigate relationship between headway change and passenger downfall rate. For this purpose, a new method is developed that is called Intelligent Headway Selection (IHS) approach. The aim of this approach is finding similar days from passengers' behavior perspective in the dataset and by focusing on unusual patterns of each group, headway changes effects on passenger downfall rate is being studied. In this approach, in the first step, each day is classified into specific time periods (like half of hours) and the passengers' behavior pattern is detected for each day during the specified time periods. Then, in the K-Means algorithm, Euclidian distance measure is replaced with Dynamic Time Warping (DTW) algorithm to enable the K-Means to compare time series. The modified K-Means algorithm is used to compare days in the dataset and categorize similar days in the same clusters. Then, headway -passenger per minute plot is created for each time period to detect unusual patterns. Then, a Headway Interval Detection Procedure (HIDP) is developed to use these unusual patterns to find suitable headway values for each time period. Afterwards, these plots merged and the final headways are calculated

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The use of resource allocation approach for hospitals based on the initial efficiency by using data envelopment analysis

Introduction: Recourse allocation is very important in today’s highly competitive environment to enhance the quality and reduce costs due to limited resources and unlimited needs of the society. The aim of this study was to implement resource allocation in order to improve the efficiency of hospital. Method: This is a mixed method study. The data used in this paper are secondary data related to the 30 large acute and general hospitals in the US. Bed, service mix, full-time equivalent (FTE), and operational expenses are input indicators in hospital, and adjusted admissions and outpatient visits are output indicators. Using goal programming (GP) model and data envelopment analysis (DEA) model with a common weights, we suggest three scenarios for resource allocation and budget allocation. “Resource allocation based on efficiency”, “budget allocation based on efficiency” and “two stage allocation of budget”. The first scenario was used for allocating the resources and the second and third ones for allocating budget to decision making units (DMUs). The data were analyzed by LINGO software. Results: Before the allocation, four hospitals were efficient and the efficiency of six hospitals was less than 50%, but after allocation, in the first case of the first scenario 14 hospitals, 11 hospitals in the second case of the first scenario, 24 hospitals in the second scenario and 17 hospitals in the third scenario were efficient, and it is an important point that after the allocation, efficiency of all hospitals increased. Conclusion: This study can be useful for hospital administrators; it can help them to allocate their resource and budget and increase the efficiency of their hospitals

Analysis of the Impact of Residential Property and Equipment on Building Energy Efficiency and Consumption—A Data Mining Approach

Human living could become very difficult due to a lack of energy. The household sector plays a significant role in energy consumption. Trying to optimize and achieve efficient energy consumption can lead to large-scale energy savings. The aim of this paper is to identify the equipment and property affecting energy efficiency and consumption in residential homes. For this purpose, a hybrid data-mining approach based on K-means algorithms and decision trees is presented. To analyze the approach, data is modeled once using the approach and then without it. A data set of residential homes of England and Wales is arranged in low, medium and high consumption clusters. The C5.0 algorithm is run on each cluster to extract factors affecting energy efficiency. The comparison of the modeling results, and also their accuracy, prove that the approach employed has the ability to extract the findings with greater accuracy and detail than in other cases. The installation of boilers, using cavity walls, and installing insulation could improve energy efficiency. Old homes and the usage of economy 7 electricity have an unfavorable effect on energy efficiency, but the approach shows that each cluster behaved differently in these factors related to energy efficiency and has unique results