1
SURVEY ON ASSEMBLY LINE BALANCING IN SMALL AND MEDIUM SCALE INDUSTRIES
MANIVANNAN.R,SATHISH.S Sri Ramakrishna engineering college,
Coimbatore. [email protected]
Abstract:-Manufacturing engineers are now a day’s force to accomplish the production tasks with minimization of idle time and each workstation queue time. An accomplish the production tasks means must sequence the operations and schedule the machines in proper manner. Sequencing generally queue the work parts but scheduling is calendar the work part with ease of complete the task. Sequencing is achieving the quality products but the scheduling will achieve the quantity products will lead to accomplish the production targets. While achieving production targets the important thing is classification of parts and groups the machines based on the part character. The part character will decide based on the process route or manufacturing attributes. A part classification and coding is one of the major tasks in cellular manufacturing for accomplish the production targets through the scheduling and line balancing. A common problem in line balancing is allocation of parts to the machines to the proper machine. Important point is concern in line balancing is the allocation of part in the cell which is complete their process with in the cell no exceptional process where found in the line balancing. In this survey paper we discuss about the various methodologies and algorithms are used to group the machines with their similar character of the machine part sequence.
INTRODUCTION
Assembly line balancing is the assignment of tasks to the work stations to minimize the idle time of the each workstation unit. Assigning tasks to the work station is based on the similar processing routes or similar manufacturing characteristics. The task assignment is in line balancing is complete the part with the cell is the ultimate aim of the line balancing engineers. A complete line balancing cell consists of minimum number of exceptional elements and zero bottleneck components in the cell. An assembly line balancing which is under the family of group technology (GT). After the line balancing the similar character or processing route machines are placed (or) grouped in single cell which reduce the queue time of the components and the total time consumption of the workstation processing time. The line balancing software is used as MASCOT which is not commonly used but the software this helps to solve line balancing problem as the cell formation using matrix solvent.In a line balancing approaches the formation of matrix (machine*part) is classically based on two approaches such as family formation cell design and algorithm based approach. Family formation cell design which under PFA where matrix row
represents the operation plan code and column represents the component. An Algorithm based approach which is the pre-defined set of procedures or formulas derived by experts of line balancing engineers or production managers. Above two approaches are commonly used to enhance the productivity but rather than productivity but improve the machine call efficiency the similarity co-efficient method has commonly employed. This similarity co-efficient method which is based on the similar processing character of the machines is described and calculations are done. Line balancing is commonly employed in the electrical industries to route their complicated parts to easy and optimum production results.Before going to study about line balancing we must study about the CELLULAR ANUYFACTURING and their types. Cellular manufacturing is the vital role in the line balancing. Cellular manufacturing is the application of group technology in which the similar process route or manufacturing route; machines are grouped based on this each cell which depends on the process characters of the parts. Cellular manufacturing has implemented by automated or manual methods. When implement group technology in automated method has going to flexible manufacturing system.
2 Flexible manufacturing system has adopted in group technology by PFA. The PFA which is based on the process sheets which contains the process details based on the machine part matrix. Grouping of parts based on the types such as visual inspection, part classification and coding, PFA. A Close relationship with the assembly line balancing with cellular manufacturing is the PFA. A Production flow analysis which describes the grouping based on the production sheets rather than the part design.
ABOUT SURVEY
An ultimate aim of line balancing is to schedule the work pieces and alter the machine sequence as their cell based configuration. Various algorithms are applied to solve line balancing problem for the cell formation. A literature survey was conducted regarding this. Survey was classified into three. They are based on scheduling for calendar the work piece for minimizing idle time of the part, based on cell formation techniques, based on meta heuristic techniques problems such as p and np type problems were considered for line balancing industries.
1. Survey based on scheduling and sequencing in line balancing
Shyh-Chang Lin, Erik D. Goodman, William F. Punch[1], investigated in his paper titled, Shortest-route formulation of mixed-model assembly line balancing problem, In the paper, describes about the genetic algorithm for scheduling problem where discussed in that approach where commonly employed in the job shop scheduling problem. In this article Genetic algorithm approach only used for the static JSSPs. A static JSSP are commonly started with the time as zero but the starting time is unknown for the dynamic JSSP.Apinanthana Udomsakdigool1,
VoratasKhachitvichyanukul[2], conducted study on Ant colony algorithm for multi- criteria job shop scheduling to minimize make span, mean flow time and mean tardiness. A make span and mean flow time criteria are focused on improving the resource utilization and productivity. The JSP is generally defined as the aim of optimizing one or more scheduling objectives. A JSP is a strong NP hard problem. Problem has described on a set
of jobs J from 1 to n jobs where machined M machines from 1 to n for a period of P.Mahanim Omar, Adam Baharum, Yahya Abu Hasan[3], conducted study based on the applying genetic algorithm for scheduling as JSSP. A cross over and mutation criteria is used to evaluate the schedules. Genetic algorithms are solving the problem by the principle of evaluation.
In search process it will generate a solution using the cross over and mutation. In hill climbing approach the search procedure will stop once it detects no improvement in next iteration. A job shop problem is described by the author is where jobs are processed by the machines, in this process the machine which is not stop until the finish without finish the jobs and also no constraint is allowed.
2. Cell formation techniques in group technology
P.Krishnanda Rao and A.M.
Chincholkar[4] studied about various cell formation techniques used for making machine part matrix. Group technology is a modern manufacturing philosophy which has emerged as an important scientific principle involving for improving the productivity of manufacturing systems. As present common cell formation techniques generate machine groups (cells) is the first phase of cell formation and component families are assigned to each other is the second part of the cell formation technique.
V.Venugopal and T.T.Narayanan[5]
studied about application of genetic algorithm with the multiple objectives for machine component grouping. In a competitive environment and ever- changing customer preference manufactures are follow just in time capability with increased flexibility, productivity and also the quality. In a computer integrated manufacturing system the dissimilar machines are grouped to form a cell the formed cell is representation of the whole manufacturing process.S.K. Khator and S.A.Iran [6] studied about the cell formation techniques to maintain an optimum output.Common approaches as classification and coding and machine component group analysis. Other existing approaches to matrix formulation as Rank order clustering algorithm, Direct
3 clustering algorithm and module synthesis. Rank order clustering algorithm represents route card data as a binary matrix. Using a weighted positional method rearrange the rows and column based on their ranked position value.
After the arrangement the 1 s are grouped then the o s are formed in the one side of the cell. The efficiency of the ROC depends on the exceptional elements and bottlenecks present in the cell. An exceptional elements are 1 s are formed outside of the cell but the bottlenecks are the parts are processed more than one cells in the work cell. Direct clustering algorithm is a poor version of ROC algorithm. An arrangement of cell is the total count of the positive entities in the initial part*machine matrix and then the arrangements are the rows in descending order and columns are ascending order as the rank is called K. In this ROC approximate cluster is formed then iterations are done until the cluster formation takes place.C-H CHU [7] et al studied approaches to cellular manufacturing in a group technology. An eye balling method is to examine the clusters using an eye. In this method which is commonly employed and also easy to understand. Later adopting this method a classification and coding method employed which is highly depends on the process route characteristics. A production flow analysis (PFA) is the other hand for grouping parts into families based on their operating sequence or routing information. Tarungupta [8] et al clustering algorithms are discussed and also study their performance. In a clustering chaining problem is emerged while solving SLINK approach as very small groups are formed succeeding under the single large group. A student t test statistical performance conducted with 5 percent level of acceptance or rejection is carried based on the operation. While chaining two common terms are used such as PPERH and PERL which are measure the chaining severity.
3. Survey based on matrix and cluster analysis
Andrew Kusiak, Wing S. Chow [9] et al studies show that the efficient solving group technology problem under two categories such as standard and augmented formulation. A standard formulation based on the 0-1
machine*part matrix which does not consider any cost but augmented formulation shows that the dependence of various parameters such as part, cost, number of machines present is limited. To solve above two formulations having a two different approaches are employed are cluster identification algorithm and cost analysis algorithm. In a cluster identification algorithm with the computational time complexity detects the machine cells and part families are provided that the machine incidence matrix. In the machine*part matrix the large matrix is decomposed in to the several small matrices while decomposing a matrix formation of diagonal structure is the main problem in the machine*part matrix. V.Satheesh kumar, et al [10] et al evaluation of cell formation algorithms and also the key implementations in MOD-SLC algorithm. The case study was done by an industry for manufacturing a mono block and submersible pumps.
Compare the matrix with the previous result. From the comparison of the various types of clustering grouping efficiency for SLC is 84.26%, ROC is 80.66%, ROC-2 is 79.18%, DCA is 79.18% and finally MOD-SLC is 91.33%
grouping efficiency. The MOD-SLC is more grouping efficiency than the other clustering technique. In an application of cellular manufacturing material handling distance effectively reduce by 51.25% and can improve productivity by 29.04%.Larry E.Stanfel [11] et al studies shows that achieving economic production how the machine becomes cluster and also discuss about various clustering problems. Production scheduling is accomplished by the formation of machine cells would lead to reduce the inventories. Another advantage over cell formation is humanistic, A cell formation is achieves the proper communication with the workers lead to job satisfaction and employees attitude also improved.
Machines are grouped to form cells within the cells the group of operations are forms the cluster. Generally two forms of clusters formed such as clusters of components and clusters of machines.
Impact and matrix size in group efficiency measurements
sSimon Li, HoumanMehrabadi [12] et al A practical numerical example is shown has contains the 14 machines and 24 parts
4 are present. Initially inspect the nonzero entities in the matrix has inspected and make the cell in the shape of rectangular shape. A machine*part size matrix is 14*24 has forms the 7 machine cells. The grouping efficiency is 71.89%. The main draw back as hierarchical clustering is cannot produce the results better than the global optimization technique or approaches.
Comparison of grouping techniques based on cell size
Miin-Shen Yang, Jennn-Hwai Yang [13] et al machine cell formation in group technology using a modified ART1 method. Recently neural networks are mostly used to cell formation in group technology. Neural networks are robust and adaptive in nature properties. Author propose a modified ART1 method adopts the neural network algorithm. Statistical process control (SPC) has several merits ane demerits are present, in a SPC the control chart monitors the process deviation and other process related parameters are monitored. Control chart has not only minimal the waste but it has also increase the efficiency. The production process involves the most sophisticated procedures and also the parts have been moved from one place to another place inside the plant or workstation unit which increase the idle time of the machine and also the manpower requirement. Group technology adopts the similarities between the parts which has utilize the part or product quality compensation. Neural networks are the information processing unit which reflects the result of process with the aid of mathematical modelling. There are three neural networks are widely used in the applications such as feed forward, feedback and competitive neural network.
ART1 is a competitive learning network which flexible number of neural models are used in GT. ART1 algorithm is used to solve problem as 15*15 machine*part matrix has applied in various stages efficiency of the machine*part is 100%
efficiency with zero exceptional elements and second 15*15 matrix produce the 97.7% efficiency with eight exceptional elements then similar size 91.7%
efficiency with nine voids. From above observation generally voids and exceptional elements are decrease the efficiency of grouping and as fall as cell.
Another example we have studied that the 35 parts and 28 machines are taken to solve under ART1 method. The Dagli and Huggahalli method is best suitable for cell efficiency based on their respective of the groups. If c=5 means the cell efficiency is 75.08% the c=6 means the cell efficiency is 87.81%. The cell group has c=7 means the efficiency become 89.11%. From the above discussion the number of cell is greatly influence the efficiency of the system, more cell has present means efficiency has also increased. The last example is 105 parts with 46 machines as taken into account which produce the efficiency as 87.54% with number of cell is 7.
CONCLUSION
Various cell formation techniques are discussed in this paper and also comparison has done based on various clustering methods.W.K.Low, M.R.Osman and RosnahM.Yusuff [14] et al shows that the comparative study of some cellular manufacturing techniques. The small-lot manufacturing industries are use job- shop production with using inefficient rules are followed lead to failure to attain the production target. Improper scheduling leads to increase the setup time rather than the production time.
This paper concerns the treatments of bottleneck, exceptional elements, or voids using the part subcontracting or machine duplication. Various algorithms are developed and make comparison shows the better algorithm for suitable machine*part size matrix. Sustain the production targets produce the more components with the aid of small batch size makes the variety of components.
This paper discussed about the seven cell formation techniques are used and six reference matrixes are discussed 6*5, 9*7, 40*24, 24*14, 24*18, 9*7, 40*24. Above matrices are rearrange their sequence until the machine*part clustering is formed. The machine*part choice and efficiency depends on the exceptional elements, voids, size of the matrix or combination of above all. A second objective of the study is maximum machine utilization and minimum number of exceptional elements. However the part*machine cells were developed, treatments of bottlenecks, exceptional elements or voids. Above mentioned
5 matrix sizes where the 6*7 matrix forms two cells in all mentioned cell techniques.
The 9*7 matrix where forms more cells in MODROC, DCA is six cells are formed.
The 24*14 matrix size which shows the MODROC is five cells are formed. Detailed study shows that the MODROC is only the maximum cells formed rather than the other techniques. The cluster identification algorithm is difficult to form the cells in the mentioned algorithm.
There is no direct relationship between the selection of the cell formation
techniques and the type of matrices has the cluster type.
SCOPE FOR FUTURE WORK
Search time depends on matrix size and type of cell formation technique. In this paper has clearly described that the output of various matrix sizes and their efficiencies. W.K.Low, M.R.Osman and RosnahM.Yusuff [14] studies described about the cell formation techniques and their cell countings. Below table shows the cell efficiency for cell formation is directly propotional to the number of cells present.
Author Matrix size Cell
efficiency No of cell M.R.Osman and
RosnahM.Yusuff
6*5(ROC) 73.33% 2
9*7(ROC) 50.8% 3
24*14(ROC) 64.63% 4 24*18(ROC) 48.59% 9 40*24(ROC) 24.30% 13 REFERENCE
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