Locating Post Offices Using Fuzzy Goal Programming and Geographical Information System (GIS)

This paper deals with the problem of locating new post offices in a megacity. To do so, a combination of geographicalinformation system (GIS) and fuzzy goal programming (FGP) is used. In order to locate new offices, first six types of servicefacilities with high levels of interactions with post offices are defined. Then, aspiration level of proximity for each servicefacility is determined. Based on these values, a fuzzy goal programming model is constructed to find potential locations offacilities. In order to determine the optimal locations among potential facilities, a maximal covering location problem(MCLP) is solved and results are reported. Results show that although the current state is near-optimal, for future expansionsof the network, the government should spend money on central and southern parts of this megacity

Aggregate System Analysis for Prediction of Tardiness and Mixed Zones of Continuous Casting with Fuzzy Methodology

grantor: University of TorontoThis thesis presents an aggregate system analysis with fuzzy methodology for interpretation, diagnosis and prediction of the behavior of the complex systems. The proposed systematic fuzzy modeling has three significant characteristics: (a) an improved fuzzy clustering approach with covariance-norm matrix, (b) an improved strategy for input variable selection and assignment of input-output membership functions, and (c) an appropriate parametrized reasoning mechanism. Initially, we surveyed the literature on fuzzy system modeling and discussed different approaches to fuzzy cluster analysis. Some of these procedures revealed shortcomings in with real-world data. Having developed the proposed model and its related algorithms, we tested it on four sets of data from real-world case studies. We found our approach better suited the real-world problems, including the interactions and correlations among complex sets of data and variables. It also presented a suitable strategy for determining the number of clusters and the degree of fuzziness of the system. We then introduced the index and methodology for significant input selection and assignment of input membership functions and considered possible correlations between input variables, using a Mahalanobis distance measure. The parametrized inference mechanism determined the actual parameters of the system based on the data. We tuned the input-output membership functions through a supervised-learning procedure to reduce the system's error. The proposed fuzzy methodology then was applied for system analysis, diagnosis and prediction of three complex problems in continuous casting: tardiness, mixed-zone effects, and total costs of tardiness and mixed zones. In each case, we compared the results with those of previous fuzzy models with identity-norm matrices and Euclidean distance measures and with a classical multiple-regression model. The results show that the proposed fuzzy methodology is superior with respect to identifying the critical rules, critical variables, and error minimization.Ph.D

The single-allocation hierarchical hub median location problem with fuzzy demands

Although, many papers have appeared in the literature of hub location problem, most of them deal with the problem in a crisp environment. In this paper, the single-allocation hierarchical hub median problem (SA-H-MP) with fuzzy demands is addressed. The structure of the model is derived from Yaman (2009) and consists of a three-level network of demand nodes, non-central hubs, and central hubs. It has been assumed that the demands are not known precisely and are estimated using fuzzy variables. In order to solve the problem, a simulation-embedded variable neighborhood search (VNS) is applied. The results of running the proposed approach on the well-known CAB dataset verify that it is able to solve test problems with less than one percent of error

Application of Rough Set Theory in Data Mining for Decision Support Systems (DSSs)

Abstract Decision support systems (DSSs) are prevalent information systems for decision making in many competitive business environments. In a DSS, decision making process is intimately related to some factors which determine the quality of information systems and their related products. Traditional approaches to data analysis usually cannot be implemented in sophisticated Companies, where managers need some DSS tools for rapid decision making. In traditional approaches to decision making, usually scientific expertise together with statistical techniques are needed to support the managers. However, these approaches are not able to handle the huge amount of real data, and the processes are usually very slow. Recently, several innovative facilities have been presented for decision making process in enterprises. Presenting new techniques for development of huge databases, together with some heuristic models have enhanced the capabilities of DSSs to support managers in all levels of organizations. Today, data mining and knowledge discovery is considered as the main module of development of advanced DSSs. In this research, we use rough set theory for data mining for decision making process in a DSS. The proposed approach concentrates on individual objects rather than population of the objects. Finally, a rule extracted from a data set and the corresponding features (attributes) is considered in modeling data mining

Risk-based Analysis of Construction Accidents in Iran During 2007-2011-Meta Analyze Study.

The present study aimed to investigate the characteristics of occupational accidents and frequency and severity of work related accidents in the construction industry among Iranian insured workers during the years 20072011.The Iranian Social Security Organization (ISSO) accident database containing 21,864 cases between the years 2007-2011 was applied in this study. In the next step, Total Accident Rate (TRA), Total Severity Index (TSI), and Risk Factor (RF) were defined. The core of this work is devoted to analyzing the data from different perspectives such as age of workers, occupation and construction phase, day of the week, time of the day, seasonal analysis, regional considerations, type of accident, and body parts affected.Workers between 15-19 years old (TAR=13.4%) are almost six times more exposed to risk of accident than the average of all ages (TAR=2.51%). Laborers and structural workers (TAR=66.6%) and those working at heights (TAR=47.2%) experience more accidents than other groups of workers. Moreover, older workers over 65 years old (TSI=1.97%> average TSI=1.60%), work supervisors (TSI=12.20% >average TSI=9.09%), and night shift workers (TSI=1.89% >average TSI=1.47%) are more prone to severe accidents.It is recommended that laborers, young workers, weekend and night shift workers be supervised more carefully in the workplace. Use of Personal Protective Equipment (PPE) should be compulsory in working environments, and special attention should be undertaken to people working outdoors and at heights. It is also suggested that policymakers pay more attention to the improvement of safety conditions in deprived and cold western regions