International Journal of Management and Fuzzy Systems 2015; 1(2): 15-20 Published online July July, 2015 (http://www.sciencepublishinggroup.com/j/ijmfs) doi: 10.11648/j.ijmfs.20150102.11 A Novel Strategy for Comparative Points in Facility Layout Problem with Fuzzy Logic Erfan Ghasem Khani 1, * , Mostafa Ali Beigi 2 1 Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin, Iran 2 Young Researchers and Elite Club, Islamic Azad University, Firoozkooh, Iran Email address: Erfanghasemkhani1@gmail.com (E. Ghasem Khani), mostafaalibeigi@gmail.com (M. Ali Beigi) To cite this article: Erfan Ghasem Khani, Mostafa Ali Beigi. A Novel Strategy for Comparative Points in Facility Layout Problem with Fuzzy Logic. International Journal of Management and Fuzzy Systems. Vol. 1, No. 2, 2015, pp. 15-20. doi: 10.11648/j.ijmfs.20150102.11 Abstract: Distance measure is one of the most important component in facility layout problems. Many distance approaches have been proposed so far. However, there is no method that can always give a satisfactory solution to every situation. In this paper, first we review on some distance methods, then we present a new strategy for comparative points in facility layout with fuzzy logic, which it is very useable, specifically when it is hard (or impossible) to use other methods to solve uncertain points. Finally, some numerical examples illustrate the presented method as well as comparing it with other various ones. Keywords: Multi Attribute Decision Making (MADM), Facility Layout (FL), Distance Measure, Fuzzy Logic, Uncertain Points, MOER Method, Decision Making (DM) 1. Introduction Nowadays the concept of Facility Layout (FL) is acquiring more and more attention in the representation of intelligent automation. In many cases it is necessary to known in what manner which method selecting for distance measure, how various data differ or agree with each other, and what the measure of their comparison is. A good placement of facilities is dependent on data in problem and selecting method of measure distance. Facility Layout (FL) Problem is one of the classical problems in which the planning for the placement of different types of facilities such as machines, employee workstations, utilities, customer service areas, restrooms, material storage areas, lunchrooms, drinking fountains, offices, and internal walls is discussed [Francis, White and McGinnis, 1992]. Many distance method for facility plant have been proposed, such as Euclidean distance, Squared Euclidean distance, Chebyshev distance, rectilinear distance. Sometimes we receive lots of information from factories and most of them are approximate. FL sometimes occurs in a fuzzy environment where the available information is imprecise/uncertain which may confuse the designer in the FL problem. There are many misconceptions about fuzzy logic. To begin with, fuzzy logic is not fuzzy. Basically, fuzzy logic is a precise logic of imprecision and approximate reasoning [Zade, 1975- Zade, 1979]. More specifically, fuzzy logic may be viewed as an attempt at formalization/mechanization of two remarkable human capabilities. First, the capability to converse, reason and make rational decisions in an environment of imprecision, uncertainty, incompleteness of information, conflicting information, partiality of truth and partiality of possibility – in short, in an environment of imperfect information. And second, the capability to perform a wide variety of physical and mental tasks without any measurements and any computations [Zade, 2008]. Applications of fuzzy numbers for indicating uncertain and vague information in facility layout, Multiple-criteria decision analysis, linguistic controllers, data mining, and etc. Fuzzy distance can be widely usage in attribute importance. Many fuzzy distance indices have been proposed since 1965. Some of the methods used crisp number to calculate the distance between two trapezoidal fuzzy numbers [Bloch, 1999- Saha, Wehrli, Gomberg, 2002- Pedrycz, 2007]. Human intuition says that the distance between two uncertain numbers should as a collection of points with different degrees of belongingness, then the distance between two fuzzy numbers is noting but the collection of pairwise distance between the elements of the respective fuzzy numbers [Voxman, 1998- Jahantigh and Hajighasemi, 2012]. Therefore, in this study, we pay to other methods for fuzzy distance, which used fuzzy distance to calculate the distance between two fuzzy numbers and introduce a fuzzy distance