Classification Of Facility Layout Problems

Classification Of ease Lay bulge hasslesThe purpose of this literature review is to explore the general adroitness layout paradox, the stinkpot-do adroitness layout conundrum, the models that take been employ to set the zeal layout conundrum and the algorithmic programic rules that lap up the models,.Classification of facility Layout ProblemsDetermining the almost efficient arrangement of physical protrudee sections within a facility is de downslopeate as a facility layout problem (FLP) (SMTF Ghomi et al, 2011). Over the period of several(prenominal)(prenominal) couple of decades, FLP have been studied by several re chaseers to a world-shattering extent for establishing optimum and universal method to lap the problem and a large variety of firmness of purpose turn tails found on algorithms have been proposed.Facility layout problems ar classified into two categories, passive facility layout problem (SFLP) and high-octane facility layout problem (DFL P).Static facility layout problem (SFLP)The static facility layout problem (SFLP) is the determination of the most efficient arrangement of segments within a facility with scope of improvement exactly within the layout boundary. The facility buns be manufacturing plants, administrative office buildings, or service facilities,( Alan R, jin et al 2005). The static facility layout problem (SFLP) approach gener all(prenominal)y as plazaes that emanate in the midst of forges, harvest-time fill, and levels of product mix be constant during the be after horizon.Dynamic facility layout problem (DFLP)When material flow assumes varied path surrounded by departments during the planning horizon, the problem becomes the dynamic facility layout problem (DFLP). Under a vapourific environment, demand is not stable. It changes from unrivalled production period to an other(a)(prenominal). To operate efficiently under much(prenominal) environments, the facilities must be adaptive to c hanges of production requirements. From a layout point of view, this situation requires the solution of the dynamic layout problem (DLP). (Adil, Turkay et al 2005) channelise commission of the layout problemsEssential feature of layout problems argon characterized in tree representation diagram as taken in fig.Tree representation of the layout problems (Amine,henri et al 2007)algorithmic programs for re resoluteness facility layout problemsThere are two oddballs of algorithms for answer facility layout problems. angiotensin-converting enzyme is heuristic rule algorithm and another is optimum algorithm.Heuristic algorithmsThese algorithms provide a solution which possibly might not just be the take up fit for the problem. A good heuristic approach usually produces the dress hat solution for most of the downcast problems. A heuristic algorithm kit and boodle towards an best solution but ends its search when it line ups a good abounding solution. As computation increase s, these algorithms bequeath approach the optimal solution. The purpose of the heuristic algorithm is not to find the trounce or optimal solution but to find an acceptable solution in an acceptable follow of time design an acceptable amount of computer memory. Heuristic algorithms underside as comfortably as be classified as wee-weeion algorithms and improvement algorithms. turn algorithmsIn Construction algorithms layout is constructed from the beginning and facilities are depute to a site, hotshot at a time, until the complete layout is obtained. (andrew, et al 1987). The plant layout software exploitation a construction type algorithm leave archetypical construct a solution in an open floor reach from birthday suit data. The algorithm basically takes races amidst practise areas into level and generates a full point layout. Their basic approach is to find a starting point or initial activity situation and indeed add the remaining activity areas according to certain rules. In some algorithms the rules are similar to Muthers vowel sound letter sequencing (A-E-I-O-U-X) for casualness family kins. Three sound known examples of construction algorithms are CORELAP, PLANET, and ALDEP.CORELAPComputerized human race Layout Planning (CORELAP) is a construction algorithm and was geted by Robert C. Lee. It is the oldest construction algorithm found on Richard Muthers manual procedure of converting the Relationship Chart into a layout. The basic input signals necessitate by CORELAP are the blood chart and the area requirements of from from each one one department. CORELAP begins by calculating the union affair rating (TCR) for each department where TCR is the sum of the numerical measure outs depute to the closeness relationships (A=6, E=5, 1=4, etc.).A disadvantage of CORELAP is that it has problems when an attempt is make to fix departments in a certain perspective. CORELAP does not take into account the building and is depende nt on the layout arrangement. It is useful for young plants where the accusing is to circumscribe new building design and not for buildings that are already in human beings.( Altaf et al 1995)ALDEPAutomated Layout Design Program (ALDEP) was actual within IBM and was presented by Jerrold Seehof and Wayne Evans. It was graduation exercise published in 1967. ALDEP has the qualified basic data input requirements as CORELAP. It differs from CORELAP in victimisation the Total Closeness Rating for lieu of departments ALDEP selects and places departments randomly. CORELAP attempts to construct the one best layout dapple ALDEP constructs many another(prenominal) layouts and rates each layout and thus leaves the final decision of selecting the divert layout to the facility designer. Advantages of using ALDEP let in rectangular or square layouts. It is too capable of handling facilities with up to third floors and provides the capability to fix departments in a certain muddle and to include docks, elevators and stairwells. The disadvantage of ALDEP is that it randomly picks departments for consideration in the layout deal. Hence, ALDEP should be penalize several times to assure that the layouts generated are the best layouts. The best layout testament eventually generated will be presented to the facility designer for selecting the most appropriate and feasible layout.PLANETPlant Layout Analysis and Evaluation proficiency (PLANET) is another construction type algorithm. It uses the identical input requirements as slyness. PLANET is negotiable in that it will accept material flow data in triad formats and that there are triplet different layout construction phases on hand(predicate). The three phases that are available to generate a layout are as follows The initiatory phase involves the translation of the input data so that it is useful to the algorithm in PLANET. The second phase involves the selection of the order in which the departments are to be considered in the layout. The third phase involves the determination of the placements of the departments when they are considered for the layout (placement antecedency from the highest to the lowest is 1 to 9).PLANET converts the materials flow information from either a from-to apostrophize chart, a from-to chart or a penalty chart to a flow-between constitute chart. This is done by adding the values in twain directions between departments and then entering the sum for the flow in each direction. The hind end for the PLANET selection algorithms are the flow-between cost chart and placement prioritiesThe advantages of using PLANET are that it is very flexible in allowing inputs such as materials flow data to be entered in three formats and having three methods in constructing a layout. The disadvantages with PLANET are that in its conversion of inputs to a flow-between cost chart, it considers the closeness relationships between departments but conceals the direction of flow among departments. This may result in layouts that have a considerable amount of backtracking among the departments. return algorithmsAn improvement algorithm ever begins with an initial layout. The algorithm reciprocations department holes until a layout is found that ignorenot be improved. The quality of the layout generated depends upon the initial layout and the ability of the algorithm to exchange multiple departments at a time. The basic approach of improvement algorithms is to downplay transportation cost or goment cost by minify the distance on the most traveled routes. Popular examples of improvement type computer spots are COFAD, swap and BLOCKPLAN.CRAFTComputerized Relative Allocation of Facilities Technique (CRAFT) was the first improvement type algorithm employ in computerized facilities design. CRAFT was actual in 1964 by Armour and Buffa. CRAFT begins with an initial layout that is entered by the analyst. The layout is green goddessd, and pair wise exchanges of departments are made to try to improve the layout. Layouts are evaluated on the minimization of material flow cost between departments. meet wise exchanges are only made between departments that are of able size or have common boundaries. CRAFT terminate cargo deck up to 40 departments and is preferred by many oer CORELAP and ALDEP overdue to its evaluation of layouts. CORELAP and ALDEP disparage the quantity of flow between departments and maximize closeness ratings, while CRAFT minimizes the cost of flow between departments. The initial layout utilize by CRAFT restricts the boundaries of all layouts generated from it. CRAFT does not work well with departments having unequal areas be catch it is unable to shift the layout to allow nonadjacent departments of unequal areas to be exchanged. (jin et al 1996) utilize CRAFT to process the failure-to-fit problem by changing the size and/or shape of the departments in a ashesatic modality without the help of humans COFADComputerized Facilities Design (COFAD) is a modification of CRAFT. COFADs algorithm first tries to improve the initially inputted layout by a procedure that Is similar to CRAFT except that COFAD is capable of considering straight line as well as rectilin2ar distances between departments being considered for suppresschange. This is useful for materials handling systems that use conveyors that do not have to follow aisles in a rectilinear fashion. COFAD then determines the cost of performing each move using the feasible materials handling system alternatives available. This is dependent on the type of material handling system elect (ie. fixed path equipment such as conveyors or mobile equipment such as tote carts). COFADs next function is to use the above move costs to determine a minimal cost of materials handling system. The disadvantages of using COFAD are that the sensitivity analysis within COFAD only considers variations in the total flow volume for a predefined product mi x and does not evaluate changes in product mix. (Vic Kichodhan et al 1990)BLOCKPLANBLOPLAN stands for Block Layout Overview with Computerized Planning. A computer routine which allows the use of random, construction, and improvement type algorithms is BLOCPLAN. It was developed by Dr. Charles E. Donaghey, Chairman of the industrial Engineering Department at the University of Houston. BLOCPLAN is an interactive program used to develop and improve both single and multi storey layout BLOCPLAN is a departmental location system that includes random, construction and improvement type algorithms for development layouts It is a dim-witted program which generates good initial layouts due to its flexibility ground on several imbedded options. It uses both quantitative and qualitative data to generate several block layouts and their measure of fitness. ( Pinto, et al 2007). BLOCPLAN put forward display a layout diagrammatically on the screen.The inputs that are necessary are the no of de partment (maximum 18) The name calling of the departments, their corresponding areas, and a relationship chart. The chart relationship format is the same as suggested by Mather in his Systematic Layout Planning procedures. Once the relationship chart has been entered, BLOCKPLAN then displays a relationship vector of Code eq Score. The purpose of this is to allow the facility designer to indicate the importance prone to the rating of the relationship chart, BLOCPLAN needs to use some quantifiable part to rake decisions when it generates and brands layouts. It uses the CES vector to assign a numeric value relationship chart. The default CES vector values are 10, 5, 3, 2, 1, 0, and -10. This means that has A rating is worth 10, an E rating is worth 5 and so on. .An X rating is worth -10. The facility designer can also set his/her own values if desired. (Vic Kichodhan et al 1990)The procedure that BLOCPLAN uses to generate layouts is that it first determines an Importance Rating (IR ) for each department in the layout. The rating is the sum of all the relationship sexual conquests for each department, using the CES vector values. Second, a menu for the facility designer is displayed. The options areRandom Layout.Layout Algorithm.Improvement Algorithm. adapt Relationship Information.Manually Insert Departments.Review Saved Layouts.Stop.Save Problem DataSelecting option one, Random Layout, will cause layout to be developed without regard to the relationship chart. The Departments will be located randomly in one of the 18 zones that the software has generated. BLOCPLAM divides the building layout in to three tiers, with three zones per tier. Each zone can be further divided into its odd and right side giving the thinkable eighteen zones.BLOCPLAN randomly selects one of the eighteen locations for each department and assigns it to a particular location.After all the departments have been delegate a location, the software proceeds to draw the layout. It looks at the departments that are located in Tier 1 up to sixsome departments can be located in Tier 1. The total essential area of a tier is the sum of all the areas for the departments located in that particular tier. Each department is drawn in proportion to its area and the departments are rectangular in shape. If a department with a small area is the only one located in a tier, it will be drawn as a long nar speech department stretching across the entire layout. BLOCPLAN continues with this procedure for all the tiers.The layout generated is scored by the marking algorithm ground on an adjacency criterion. The CES scores for departments that share a common boundary in the layout are summed and then divided by the sum of all the positive CES scores from the relationship chart. A score of 1.0 indicates that all good relationships in the relationship chart have been comfortable in the layoutSelecting option two, Layout Algorithm, will cause the software to make available to the facili ty designer a layout algorithm. The algorithm places departments that have high IR scores in the center of the layout and then surrounds them with departments with high relationships. Departments with an X relationship are separated as much as possible. This method of view the departments produces layouts that are improve than the random process.Selecting option three, Improvement Algorithm, will cause the software to try to improve on a layout that has been saved in memory. The improvement algorithm interchanges each pair of departments in the layout and then displays its score before moving to the next interchange when the facility designer hits the repossess Key. The number of interchanges is the combination of the number of departments taken two at a time. For example, for ten departments there will be forty five interchanges. The optimum layout can be obtained by using option two, Layout Algorithm, and then using this option, Improvement Algorithm, to improve on the previous saved layout.Selecting option four, coif Relationship Info, allows the relationship information to be changed. The facility designer can change the relationship information and the CES scores that were originally entered. This allows the effects of changes in the relationship chart to be evaluatedSelecting option five, Manually Insert Departments, will allow the manual placement of departments in the layout. Each department can be manually placed in the desired tier and zone. This is the same as fixing a department in a layoutThe advantages of BLOCPLAN are that it is a useful incision to facility designers in that layouts can be generated or evaluated, the effects of changing the values in a relationship chart can be analyzed, and it only requires a microcomputer as opposed to a mainframe to operate. Although the processing time varies with the number of departments that have to be located, the limitation of BLOCPLAN being able to only handle eighteen departments limits the proce ssing time to a reasonable amount. The disadvantages of BLOCPLAN areBLOCPLAN can only handle layouts with eighteen departments or less.BLOCPLAN can only store 20 layouts in memory.All the layouts are displayed on the screen within a rectangular drawing that has a horizontal length of 6.75 inches and a unsloped height of 4.75 inches regardless of the number of departments in the layout or their placement in the layout.Simulated Annealing AlgorithmsSimulated Annealing (SA) is a method based on Monte Carlo simulation, which solves difficult integrative optimization problems. The name comes from the coincidence to the behavior of physical systems by melting a substance and dense its temperature slowly until it reaches freezing point (physical indurate). Simulated annealing was first used for optimization by Kirkpatrick et al. (1983). In the numerical optimization framework, SA is a procedure that has the capability to move out of regions near local minima. SA is based on random ev aluations of the objective function, in such a way that transitions out of a local minimum are possible. It does not guarantee, of course, to find the world-wide minimum, but if the function has many good near-optimal solutions, it should find one (George D. et al 2002)Simulated annealing was also used in General Facility Layout Problems (GFLP) considering facilities areas, shapes and orientations or in Machine Layout problems (MLP) considering machines pick-up and drop-off points (Leonardo Chwif et al 1998).SA was also used for dynamic facility layout problems for solving the problems for arranging and rearranging (when there are changes between the flows of materials between departments) manufacturing facilities such that the sum of the material handling and rearrangement costs is minify (Alan R et al 2006).Wang et al (2001) developed a model to solve the facility layout problem in cellular manufacturing system. In the model, they assumed that the demand rate varies over the pro duct life cycle. The objective function was to minimize the total material handling cost and solve both inter and intra cell facility layout problems simultaneously.Simulated annealing heuristic for the DFLP with budget constraint, and show the effectiveness of this heuristic on a set of numerical experiments (Ramazan et al., 2010). conventionalised Neural NetworksNeural networks are a potent method of optimization which relies on developing systems that exhibits self organization and adaptation in a similar, though basic, manner to the way in which biological systems work. A benevolent of artificial neural network model has been implemented for computation to solve a wide variety of discrete combinatorial optimization problems. A neural expert system is an interactive classification system with vindication capability. This system begins with the association representatives from a set of training examples, learns through representatives, and then develops the capability to corre ctly classify new cases based on conditioned knowledge. This classification capability makes the proposed neural expert system generate a conceptual construction layout in the form of the learned symbolic knowledge resonant to the input layout requirements.ANN can be a system comprising N - N neurons based on an artificial two-dimensional maximum neural network for an N-facility layout problem. ANN algorithm has given improved solutions for several benchmark problems over the best existing algorithms (Kazuhiro Tsuchiya et al 1996).The annealed neural network combines characteristics of the simulated annealing algorithm and the neural network for rapid convergence of the neural network, while preserving the solution quality afforded by simulated annealing (Yeh, 2006). This have also found implementation in solving the facility layout problem transmitted AlgorithmsGAs came to the fore in the 1960s, through the work of Holland for solving many industrial and service sector problems tha t proved extremely difficult to solve with the available methods known at that time. The main contribution of GAs is solving optimization and search problems by providing a solution which is not the optimal one but which is nevertheless a good approximation to the optimal one. As a result of the enormous increase in the capacitor of computer applied science, applying GAs, in recent years has become more and more well-known, since the problem of the cost of using computer facilities which might have arisen, is in reality only a minor one (A.Gomez et al 2003).With cyber technology gaining impetus software based on GA have been developed for problem solving. An improved hybrid communicable algorithm (IHGA) was developed to use a rich local improvement procedure as well as an effective restart mechanism that is based on questionable shift mutations and applied to the well-known combinatorial optimization problem and quadratic equation polynomial grant problem (QAP) (Alfonsas Mi sevicius et al 2004).Extensive computational experiments for solving quadratic assignment problems using various variants of a hybrid genetic algorithm were carried out (Zvi Drezner et al 2008). Simple tabu and modified robust tabu as improvement algorithms in a hybrid genetic algorithm are superior than other tabu searches (concentric tabu, ring moves, all moves, robust tabu) (Jasmit singh kochher et al 1997) outline a GA based algorithm for solving the single floor facility layout problems for equal and unequal size department.(Ming-Jaan Wang et al 2005) is focus on the unequal areas department facilities layout problem, and implements analysis of variance (ANOVA) of statistics to find out the best site size of layout by genetic algorithm.The dynamic plant layout problem (DPLP deals with the design of multi-period layout plans Although an optimal solution method based on dynamic programming is available, it is not practical for large DPLPs and heuristics based on genetic algorithm s can solve large DPLPs. (Jaydeep Balakrishnan et al 2003) blossom out and improve the use of genetic algorithms by creating a hybrid genetic algorithm and a computational study is carried out to compare the proposed algorithm with the existing genetic algorithms and a recent simulated annealing algorithm.An outstanding methodology in facility layout problems that can be used to gauge current and emerging trends in new design objectives and methodologies that address combinatorial optimization aspects and presents a state-of-the-art review of the application of the Genetic Algorithm (GA)(Kundu A et al 2010)NP-hard problem of arranging a number of facilities on a line with minimum cost, known as the single row facility layout problem (SRFLP) and to solve this type of problems permutation-based genetic algorithm (GA) is used. (Dilip Datta et al 2011)Tabu Search AlgorithmTS proficiency is a meta-heuristic search that is used to solve the combinatorial optimization problems TS, is usu ally dominated by locality solutions in searching for an optimal solution. Unlike the GA, it is highly dependent on the values of the algorithms control parameters. TS is based on flexible memory structures in connection with strategic restrictions and aspiration levels as an approach for exploiting solutions.The search begins when the parameters are chosen and a feasible solution to the problem is generated. The main parameters of TS technique are the similarity size, the size of tabu list, the aspiration criteria and stopping criteria. The mover that can be altered in order to generate neighborhood solutions is move. This operator can place each element to move from its location to any other location in the solution. From move, a set of nigh solutions is generated through a pre- defined change to the current solution. Then the best solution is selected from the current set of neighboring solutions and this becomes the new current solution. Again, a new set of neighboring solut ions is generated from the new current solution and the process repeats itself until the stopping criteria are met. (Lou Y. Liang et al 2008).There are two new reaction strategies for the tabu search algorithm. The first strategy treats the tabu search algorithm as a target system to be controlled and uses a control- theoretical approach to adjust the algorithm parameters that affect search intensification. The second strategy is a flexible diversification strategy which can adjust the algorithms parameters based on the search history. These two strategies, combined with tabu search, form the egotism Controlling Tabu Search (SC-Tabu) algorithm. The algorithm is implemented and tested on the Quadratic Assignment Problem (QAP). The results show that the self-controlling features of the algorithm make it possible to achieve good performance on different types of QAP instances. (Nilgun Fescioglu-Unver et al 2011) devil extensions were suggested and tested for concentric tabu search fo r the quadratic assignment problem to include more permissible moves (Zvi Drezner et al 2005).The optimal solution for particular(prenominal) case of Single Row Facility Layout Problem (SRFLP) was proposed through a theorem by Hamed Samarghandi et al in 2010. He proposed a new algorithm based on tabu search for the SRFLP and suggest computational results of the proposed algorithm on benchmark problems show the greater efficiency of the algorithm compared to the other heuristics for solving the SRFLP.Slicing tree based tabu search heuristic for the rectangular, continual plane facility layout problem (FLP) had been designed with procedure to get the layout corresponding to a given slicing tree on the basis of bounding curves (Daniel Scholz et al 2009). These layouts are slicing structures which are able to contain revoke spaces to guarantee that stringent shape restrictions of facilities are kept. Due to these features this approach is better suited for practical use than so far e xisting ones. chart TheoryGraph theory (Seppanen and Moore, 1970) can be used as a means to create good layouts based on the flow matrix. A relationship diagram can be drawn as a weighted graph with the invitees signifying the departments and the edges representing the flow between the department pairs. The two-fold of this graph is a block diagram layout.Graph theory approach, relationships (or flows) among facilities can be represented by a (relationship) graph in which vertices herald facilities and edges denote existence of flows or relationships between facilities. A requirement for existence of a block layout satisfying the relationships represented by a graph is that the graph be platelike. A graph is planate if it can be drawn in the plane and each edge intersects no other edges and passes through no other vertices. The relationship graph may not be two-dimensional. A planar sub graph of a relationship graph is called a maximal planar graph (MPG) if no edges can be a dded without making the graph no planar. The two-fold of a (primal) planar graph can be constructed by placing a three-fold node in each face of the primal planar graph and by joining vertices corresponding to two faces (in the primal graph) that share an edge in their common boundary. (Here, faces are regions defined by a planar graph.) The dual of a planar graph is planar as well. (J-Y KIM et al 1995)Russell D. Meller et al 1996 tells about developing a layout in the graph-theoretic approach requiring the following three steps(1) Developing an adjacency graph from department relationships (which departments are adjacent),(2) Constructing the dual graph of the adjacency graph (represent departments as adjacent regions having specific boundaries),(3) Converting the dual graph into a block layout (specifying departments with regular shapes and specific areas)Graph theoretic approaches were also used to handle the unequal area block plan. In these approaches a block plan is constru cted as the dual of a planar graph where nodes represent spaces and links represent required adjacencies. While it is always possible to construct a block plan from a planar graph which meets the given adjacency requirements between spaces and between spaces and the outside area, the resulting plan may not meet size and shape requirements imposed on each space. Constructing a block plan that meets size and shape requirements is a nontrivial problem. (Robin S. Liggett et al 2000). Other industrial problems like furniture production line designing were also solved using graph (Wilsten and Shayan 2007).The main problem concerned with applying graph theory to facilities layout is the conversion of the dual graph to a block layout (S. A. IRVINE et al 2010) gives a new method of producing a planar impudent layout or floor plan of a set of facilities field of operations to adjacency and area constraints. It improves upon previous approaches by accepting any maximal planar graph represent ing the adjacencies as input. Simple selection criteria for choosing the next facility to be inserted into the floor plan are used. Further, any sensible orthogonal shape for the facilities in the resulting floor plan can be generated. optimal algorithmDuring the 1960s considerable research was done in developing optimal algorithms. Optimal algorithms find the best solution. However they are not practical due to limitations on computer time and space. Some optimal algorithms are classified as given below.Quadratic Assignment pretendingThe quadratic assignment model (Koopmans and Beckman 1957) represents the problem of locating numerous facilities that required material flow between them. The name QAP was given because the objective function is a second degree function of the variables and the constraints are linear functions of the variables. The objective function maximizes the revenue gained by assigning the departments to a location, less the cost of the material flow between th e departments. The mathematical model of the quadratic assignment problem (QAP) isThe integer variable, Xij is equal to 1 if department i is assigned to location j, otherwise the variable is equal to 0. The constant aij is the area required for department i to location j and fik is the material flow between departments i and k, and Cjl is the cost of material flow between location j and l. The first constraint ensures that each location will be assigned exactly one department and second constraint ensures that each department will be assigned to exactly one location.Layouts generated using the quadratic assignment models are often used as a tool in formulating a final layout. The QAP takes into consideration the material flow between departments, however, the model operates under the assumption that all department areas are equal which in many cases is impractical to presume. For this reason, the layout generated by the quadratic assignment problem often serves as a starting point f or developing a final layout. (Ekrem Duman et al 2007) used the quadratic assignment problem in the context of the printed circuit board assembly process. (A.S. Ramkumar et al 2008) concentrates on multi-row machine layout problems that can be accurat