Facility Layout Optimization Using Simulation and Genetic Algorithms

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This article was downloaded by: [Brunel University] On: 21 July 2010 Access details: Access Details: [subscription number 908327975] Publisher Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 3741 Mortimer Street, London W1T 3JH, UK

International Journal of Production Research

Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t713696255

Facility layout optimization using simulation and genetic algorithmsFarhad Azadivar John (Jian) Wang

To cite this Article Wang (Jian) , Farhad Azadivar John(2000) 'Facility layout optimization using simulation and genetic

algorithms', International Journal of Production Research, 38: 17, 4369 4383 To link to this Article: DOI: 10.1080/00207540050205154 URL: http://dx.doi.org/10.1080/00207540050205154

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INT. J. PROD. RES., 2000, VOL. 38, NO. 17, 4369 4383

Facility layout optimization using simulation and genetic algorithmsFARHAD AZADIVAR{* and JOHN (JIAN) WANG{Traditionally, the objective of a facility layout problem has been to minimize the material handling cost of the manufacturing system. While it is important to reduce the amount of material handling, the traditional methods do not address the actual time at which the material is transported. In todays short cycle time production environments, the timing of material movement may have a bigger impact on the productivity of the system than its cost. In this paper, a facility layout optimization technique is presented that takes into consideration the dynamic characteristics and operational constraints of the system as a whole, and is able to solve the facility layout design problem based on a systems performance measures, such as the cycle time and productivity. Each layout solution is presented in the form of a string that is suitable for analysis by a genetic algorithm technique. These solutions are then translated into simulation models by a specially designed automated simulation model generator. Genetic algorithms are used to optimize the layout for manufacturing e ectiveness while simulation serves as a system performance evaluation tool. Combined with a statistical comparison technique to reduce the simulation burden, the test results demonstrate that the proposed approach overcomes the limitations of traditional layout optimization methods and is capable of nding optimal or near optimal solutions.

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Introduction The facility layout problem in a manufacturing setting is de ned as the determination of the relative locations for, and allocation of, the available space among a given number of workstations. Although most facility layout solutions have, in the past, focused on minimizing the amount of transportation , the e ect of a given layout design on the production function of a manufacturing system is much more than just the cost of material handling. While material handling cost remains critical, shorter cycle times have become much more important in todays manufacturing systems. In other words, when a certain material is moved is as important, if not more important, as how much it costs to move it. Rapid development of new products, coupled with short delivery times demanded by customers, are the bases of the time-based competitive strategies rapidly being adopted by leading rms in many industries. Responsive delivery without ine cient excess inventory and short manufacturing cycle times are the practical considerations that have strong impacts on the layout design and should be incorporated into the layout design process as genuine concerns.

{ Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, KA 66506, USA. { Talus Solutions Inc., Waterstone, Suite 300, 4751 Best Road, Atlanta, GA 30337-5609, USA. * To whom correspondence should be addressed. Present address: College of Engineering, University of Massachusetts, Dartmouth, MA 02747-2300 , USA. e-mail: fazadivar@umassd.eduInternational Journal of Production Research ISSN 0020 7543 print/ISSN 1366 588X online # 2000 Taylor & Francis Ltd http://www.tandf.co.uk/journals

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Because of complexity of the manufacturing systems, usually closed-form analytical expressions for objective functions do not exist. During the past three decades, a variety of approaches have been proposed to deal with facility layout optimization problems. In order to come up with analytical objective functions, most of these approaches limited themselves within the assumption that the volume of material ow between workstation pairs is xed and resources are always available. In some cases, there have been a few attempts to take the dynamic characteristics of systems into considerations as well. Techniques such as dynamic programming (Rosenblatt 1986), and fuzzy logic theory (Cheng and Gen 1996) have been used to model such uncertainties. However, we believe that, in order to account for all the impacts a layout design has on the performance of a system, a more detailed model of the system needs to be considered for evaluation of the performance measures. To accomplish this, we use computer simulation. The problem with computer simulation models is that they do not yield themselves easily to optimization processes. In this paper, it is proposed to use an integrated solution procedure that optimizes facility layout designs using simulation as the means of evaluating the objective function. This provides an additional exibility in optimization because, in addition to the usual quantitative variables, evaluation by simulation allows consideration of qualitative decision variables that analytical objective functions are not equipped to incorporate. One of the promising methods of optimizing problems whose performances are evaluated by a simulation model, especially when qualitative variables are involved, is the use of Genetic Algorithms (GA). Azadivar and Tompkins (1999) proposed a simulation model generator with a GA-based optimum seeking algorithm capable of optimizing simulation models whose performances are functions of qualitative and structural decision variables of the system. Zhang (1997) extended the technique to more general exible manufacturing systems. The work presented here is a methodology that is based on this approach for facility layout design where the objective function is a measure of an actual system performance rather than just the volume of materials handled. 2. Problem statement Consider a manufacturing system consisting of m workstations in which n types of parts, each requiring a set of tasks (operations), are to be processed. A workstation may consist of a single machine, a cell of several machines, an inspection centre, a paint booth, etc. The parts require processing on di erent subsets of the m workstations and have di erent processing times in each workstation. Each workstation has its own queuing discipline and breakdown distribution. The system is either a pull or a push type. In addition, let the area of the shop oor, the area required by each workstation, the time delay in each workstation, capacity and speed of the material handling devices, and the precedence constraints of tasks be given. A desired design for the system requires an arrangement of these workstations into the shop oor such that a certain measure of performance is optimized. The main assumptions for this problem are as follows. . The work areas of workstations are rectangular in shape and their orientations are known. . Every workstation works only one part at a time. . Every transporter carries only one type of part at a time.

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. The operations are not pre-emptable. . The operating sequences of tasks are the same for the same part types. . The objective of the facility layout design is to minimize some measure of the system performance (e.g. production completion time of the parts produced in the system, while preserving the stated constraints). Although the procedure being described in this paper is suitable for all types of layouts, here we describe the procedure for free layout problems, which are the most general (and the most di cult) facility layout systems. A free layout is de ned as follows. There is a set of m workstations, denoted by fMig, i 1; 2; . . . ; m. The area that each workstation occupies is restricted to be rectangular and is characterized by its length li, width wi and length and width clearances of cli and cwi, respectively. A facility layout solution for a given m-workstation plant consists of a bounded rectangle, R, partitioned by horizontal and vertical line segments into m non-overlapping rectangular regions, denoted by fri, i 1; 2; . . . ; m. Each region ri, with width xi and length yi, must be large enough to accommodate one workstation Mi plus its clearances. 3. Use of genetic algorithm in facility layout design Genetic Algorithms (GA), proposed by Holland (1975), are heuristic search and optimization techniques that imitate the natural selection and biological evolutionary process. In a GA approach to optimization, feasible solutions to the problem are encoded in data structures in the form of a string of decision choices that resemble chromosomes. The algorithm maintains a population of individuals or chromosomes (solutions) that evolve as chromosomes are created and discarded. Each chromosome comprises a number of genes (decision choices), that describe various aspects of a particular solution. The layout design corresponding to each chromosome is characterized by its tness, which is measured by its objective function value. A generation consisting of surviving individuals of the previous population and new individuals or o spring is generated through reproduction by means of crossover, mutation, and selection of their parents chromosomes. An e ective layout of workstations can signi cantly cut down manufacturing lead times. Unfortunately, the complexity of this task increases exponentially as the number of workstations increases. There are n! di erent ways of arranging n workstations into n locations. If all workstations are of equal area, or can be physically interchanged without altering the overall adjacency or distance relationships among the remaining workstations, it is easy to specify, in advance, a nite number of potential sites for these workstations to occupy. Given this, the layout problem can be modelled as a quadratic assignment problem (QAP). If we allow workstations unequal areas, their respective dimensions and the clearance requirements between them will determine the distance between two workstations. In such a case, since distances between locations are not equal and cannot be predetermined, it becomes extremely di cult even to describe feasible solutions. During the past three decades, numerous heuristic methods have been developed to obtain some good, rather than optimal, solutions for layout problems. The primary di culties associated with these problems are the vast number of possible physical layouts, and the existence of many relatively poor local optima. For such a problem, one might expect parallel search methods to perform better than strictly

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serial searches, and randomized search methods to perform better than greedy or enumerative searches. Genetic algorithms combine both of these attributes in a parallel, stochastic heuristic. As a powerful and broadly applicable stochastic search and optimization technique, genetic algorithms have successfully been applied in various areas of industrial engineering, such as production scheduling and sequencing, reliability design, vehicle routing and scheduling, group technology, transportation , and many others. The technique has also been applied to the facility layout problem (Tate and Smith 1995, Cheng and Gen 1996, Meller and Gau 1996, Tam 1992). However, these published works are mostly material handling cost driven and do not put enough emphasis on the performance measures that are time driven and are complex functions of the layout design (e.g. production rate, cycle time). To evaluate these complex measures, simulation modelling is often the only feasible method. The approach proposed here, which combines simulation modelling and a genetic algorithm, provides a unique opportunity to address this issue.Downloaded By: [Brunel University] At: 20:56 21 July 2010

3.1. String representation of a layout design Most of the concepts in modelling layout problems for application of genetic algorithms in this work have been adopted from Cheng and Gen (1996). A brief description of their approach in representing these problems and using genetic algorithm operators is presented here. A free layout type facility design can be represented as a slicing structure. Slicing is the process of cutting a rectangular region into two smaller rectangular regions by either a horizontal or a vertical line segment ( gure 1(a)). The line segment is called the cut-line. The slicing operations are repeated for each newly formed rectangle, with the slice-line direction chosen to be perpendicular to the previous slice line. A slicing structure is constructed by recursively partitioning a rectangle R (i.e. the oor plan) in such a way that each rectangular partition in the slicing structure corresponds to the space allocated to a workstation. An equivalent representation of a slicing structure is a slicing tree. A slicing tree is a binary tree, which shows the recursive partitioning process that generates a slicing

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