الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
 |  | from | 67 |  |  |
| | | from | 67 | | |
Abstract
The ultimate objective of any organization is the maximum utilization of resources and facilities available to reach the aimed at profitability and productivity. Facility layout deals with the selection of the best and appropriate arrangement of departments leading to greater working efficiency. The layout problem is a vast field of the industrial engineering practice and research with hard to define boundaries. Various models and approaches have been suggested and applied to solve the problem. These differ according to the domain of application. Yet, the common aspect joining the versatility of problem types is the subjectivity involved in the decision making process and the dependence on the experience and preferences of the decision maker. Furthermore, the decision is affected by the non- probabilistic uncertainty present in the input data which arises from erratic customer and market needs. The main objective of this study is to address the real-life, large size problem capturing the totality of criteria associated with it. The work includes a comprehensive review of literature with an emphasis laid on the multi-criteria layout problem and the emerging optimization techniques applied to it. The investigation is limited to the design stage of the block layout, which is concerned with relative arrangement of cells or departments on the shop floor. The model used is the quadratic assignment formulation considering multi-criteria and subjected to uncertain environment. It is assumed that all the departments are of equal area and that the positions of sites are known a priori. All uncertain parameters are represented in the form of triangular fuzzy numbers. A program is developed in the MATLAB environment to solve the problem. It consists of two modules forming the hybrid approach adopted in this study. The first is the fuzzy module responsible for dealing with the vaguely defined input data. The layout problem is one of the hardest combinatorial optimization problems which is known to belong to the class of NP-complete problems. Hence, the second module is built to deal with the computational complexity. A genetic algorithm is implemented to solve large size problems varying in size from 12 to 30 departments.