OPTIMIZATION OF MATERIAL FLOW
IN FLEXIBLE MANUFACTURING
SYSTEM
J.V.S. BHASKAR* Associate Professor Narayana Engineeirng College, Muthukur road, Nellore-524004
Andhra pradesh,India bhaskarjvs@rediffmail.com
Dr.B.DATTATRAYA SARMA**
Principal
Narayana Engineeirng College, Muthukur road, Nellore
Andhra pradesh,India bdsarma@rediffmail.com
Dr. K. HEMA CHANDRA REDDY***
Director (I/C) Academic & Planning JNTU Anantapur, Anantapur-515002
Andhra pradesh,India konireddy@gmail.com
Dr.V.VEERANNA**** Principal
Sreenivasa Engineering & technology Kurnool – 518002
Andhra pradesh,India veerannav@rediffmail.com
Abstract
Flexible manufacturing systems have evolved as a solution to efficient mid-volume production of a variety of part types with low setup time, low work-in-process, low inventory, short manufacturing lead time, high machine utilization and high quality. Flexible manufacturing system (FMS) is a computer controlled manufacturing system composed of separate workstations that are inter-connected by automatic material handling system. FMS can produce a number of different parts concurrently. Each part requires different operations in a certain sequence and workstations can typically perform a variety of operations. In this work, a material and information flow analysis as well as an analysis of the department and machines layout is made using genetic algorithm and Tabu search. This method reduces the manufacturing lead-time to produce the components and in-turn gives monetary benefits to the industry.
Keywords:Flexible manufacturing system, Lead-time, Genetic algorithm, Tabu search.
1.Introduction
achieve a high degree of customization. CIM has recently received a great deal of attention as an effective strategy to improve manufacturing responsiveness and quality. In this work it is focused on the arrangement of machines in a FMS layout to produce selected part family of components. Initially the configuration of FMS under study has been established. Four cases of layouts have been considered with scheduling as a constraint. An object-oriented methodology is adopted for modeling.
2.Layout Configurations Of Flexible Manufacturing System
Flexible manufacturing system brings rewards in actual manufacture of products, as the process is designed for several products to be run on different machines within a manufacturing facility allowing for greater growth and stability with more diversity in output. A Flexible Manufacturing System is designed to provide an effective operation sequence to fulfill the production requirements and reasonably allocate the resources. The objectives of the system are to shorten the throughput time and reduce the resource requirements, which includes avoiding deadlock in material flow, decreasing in-process inventory, balancing the workload of all machines and make good use of the bottleneck devices. The different types of FMS layouts are line, loop, ladder, open field and circular machine layout.
3.Scheduling Of Flexible Manufacturing System
Flexible material-handling systems for manufacturing have the capability of moving articles or carriers between process stations in different sequences. The traditional method for controlling the routing of carriers is to determine, in advance, all of the useful paths within the system, and store the information in a central computer until needed. Flexible manufacturing system scheduling could be considered as a static scheduling problem where fixed sets of orders are to be scheduled either using optimization or priority scheduling heuristics. Alternatively, this could also be viewed as dynamic scheduling problem, where orders arrive periodically for scheduling as daily orders are released from a material requirement planning system or as individual customer’s orders. For these types of scheduling, performance criteria minimizing the make span or the mean order lateness and tardiness are usually considered. For the first decision, factors such as system congestion and part fixture availability are considered. The second decision concerns about choosing the type of part to enter the FMS at the loading station when it is time for a new part to enter the system, considering such factors as part characteristics and machine workload conditions. The dispatching decision is concerned about routing parts through the FMS at the time of actual production, and also sequencing parts at the individual machines in an FMS.
4.Genetic Algorithms
Genetic Algorithms are computerized search and optimization algorithms based on mechanics of natural genetics and natural selection. They operate on the principle of “The survival of the fittest”, where weak individuals die before reproducing, while stronger ones live longer and bear many off spring and breed children, who often inherit the qualities that enabled their parents to survive. The reproduced children are in most cases stronger than their parents. Professor John Holland of the university of Michigan and Ann Arbor envisaged the concept of these Algorithms in the mid sixties. Thereafter a number of investigators and other researchers have contributed in developing this field. Genetic Algorithms are empirically proven to provide robust search in complex spaces. Many papers and dissertations establish the validity of the technique in function of optimization and control of application. Genetic Algorithms are finding widespread applications in business, scientific and engineering circles. The reason behind the growing number of applications is clear. These Algorithms are computationally simple, yet powerful in their search for improvement.
5.Tabu Search
as a source of error but can also prove to be source of gain. The Tabu method operates in this way with the exception that new courses are not chosen randomly. Instead that Tabu Search proceeds according to the supposition that there is no point in accepting a new (poor) solution unless it is to avoid a path already investigated. This ensures new regions of a problem solution space will be investigated in which the goal of avoiding local minima and ultimately finding the desired solution.
6.Case Problem
In this work the arrangement of machines are considered for line layout, loop layout, ladder layout and open field layout The input data required is number of machines, number of jobs, batch sizes, inter slot distance, load unload distances and unit transportation costs, Processing times for different jobs, processing sequence of jobs on different machines, loading unloading and transportation cost. The same was shown below in tables 1 to 10 for the above four layouts.
Number of machines = 6 Number of jobs = 6
Table 1 Batch size of the components
Table 2 processing time of parts & processing sequence of machines O= operation, M= Machine number, T= Time
B1 O=1 M=1 T=8 O=2 M=2 T=7 O=3 M=3 T=14 O=4 M=4 T=9 O=5 M=5 T=3 O=6 M=6 T=4 B2 O=1 M=2 T=10 O=2 M=3 T=17 O=3 M=5 T=6 O=4 M=6 T=13 O=5 M=4 T=4 O=6 M=1 T=3 B3 O=1 M=4 T=18 O=2 M=3 T=16 O=3 M=6 T=11 O=4 M=1 T=12 O=5 M=5 T=3 O=6 M=2 T=2 B4 O=1 M=4 T=16 O=2 M=1 T=7 O=3 M=2 T=11 O=4 M=3 T=4 O=5 M=5 T=4 O=6 M=6 T=13 B5 O=1 M=3 T=12 O=2 M=2 T=15 O=3 M=4 T=9 O=4 M=1 T=11 O=5 M=6 T=3 O=6 M=5 T=4 B6 O=1 M=6 T=8 O=2 M=5 T=7 O=3 M=4 T=9 O=4 M=1 T=6 O=5 M=2 T=11 O=6 M=3 T=12
Table 3: Inter slot Distance for Line layout
Slots S1 S2 S3 S4 S5 S6
S1 0 4 6 8 10 12
S2 4 0 4 6 8 10
S3 6 4 0 4 6 8
S4 8 6 4 0 4 6
S5 10 8 6 4 0 4
S6 12 10 8 6 4 0
Table 4: Load, Un load Matrices for Line layout
Table 5: Load, Un load Matrices for Loop layout
Table 6: Inter slot Distance for Loop layout
Table 7: Inter slot distance for ladder layout
Table 8: Load, Un load Matrices for Ladder layout
Table 9: Load, Un load Matrices for open field layout
Table 10: Inter slot distance for open field layout Slots S1 S2 S3 S4 S5 S6
Load 3 5 7 9 11 13
Un load 13 11 9 7 5 3
Slots S1 S2 S3 S4 S5 S6 S1 0 4 6 8 10 12 S2 4 0 4 6 8 10 S3 6 4 0 4 6 8 S4 8 6 4 0 4 6
S5 10 8 6 4 0 4
S6 12 10 8 6 4 0
Slots S1 S2 S3 S4 S5 S6
Load 4 6 8 10 12 14
Un load 14 12 10 8 6 4
Slots S1 S2 S3 S4 S5 S6
S1 0 6 8 10 12 14
S2 6 0 6 8 10 12 S3 8 6 0 6 8 10 S4 10 8 6 0 6 8
S5 12 10 8 6 0 6
S6 14 12 10 8 6 0
Slots S1 S2 S3 S4 S5 S6
Load 1 5 7 9 11 13
Un load 13 11 9 7 5 1
Slots S1 S2 S3 S4 S5 S6
S1 0 4 4 6 10 8
S2 4 0 4 4 8 10
S3 4 4 0 6 4 4
S4 6 4 6 0 4 6
S5 10 8 4 4 0 4
S6 8 8 4 6 4 0
Slots S1 S2 S3 S4 S5 S6
Load 4 6 6 8 8 6
7.Objective Function
The objective is to minimize the make-span [65], and to obtain an optimal layout plan for the machines by minimizing the total transportation cost incurred in the system [48]. In designing FMS, multi objective functions are used [6].
i) Minimize Make-span = Cnm n = Number of batches m = Number of machines
Mi = Machine in slot n1 Mj = Machine in slot nN N = Number of Slots
Cm1m2 = Unit Material handling between machines m1 and m2 (m1, m2 = 1, 2, 3, ………...M)
Dkm1m2 = Rectangular distance between machinery locations n1 and n2 (n1, n2 = 1, 2, 3, ……….N )
Fm1m2 = Amount of material flow among machines m1 and m2 (m1, m2 = 1, 2, 3, ……….M)
M = Total number of machines contained in the manufacturing system LCmi = Load cost for machine
ULCmj = Unload cost for machine
8.Results
Different parameters are considered in this problem for the four layouts i.e. line, loop, ladder and open field layouts, to solve the problem based on GA, Tabu search.The results are computed in the tables 11 to 18.
Table 11: GA Job sequence
Table 12 :GA Job waiting time & make span
B1 B2 B3 B4 B5 B6 Make span
2160 2290 690 2850 2790 700 4410
Table 13 :GA Machine sequence
Type of Layout
Machine sequence Line layout M3 M2 M5 M6 M1 M4 Loop layout M6 M3 M5 M2 M1 M4 Ladder layout M6 M5 M4 M2 M3 M1 Open field Layout M6 M4 M5 M3 M1 M2
Machine Number
Job ( in Batches) sequence to be manufactured
M1 B1 B4 B6 B5 B3 B2 M2 B2 B1 B5 B4 B6 B3 M3 B5 B2 B3 B1 B4 B6
M4 B4 B3 B6 B5 B1 B2
M5 B6 B2 B4 B1 B3 B5
M6 B6 B3 B2 B5 B1 B4
M M
ii)
Total cost = Ψ = Σ Σ ( Fm1m2 X Cm1m2 X Dk (m1) (m2)) + LCmi + ULCmj
Table 14: GA Machine-waiting time &Transportation cost
M1 M2 M3 M4 M5 M6 Transportation cost 1637 1347 177 1361 2537 1417 8334
Table 15: Tabu Job sequence
Table 16: Tabu Job waiting time & make span
Table 17: Tabu Machine sequence
Type of Layout
Machine sequence Line layout M2 M3 M1 M4 M5 M6 Loop layout M2 M3 M1 M4 M5 M6 Ladder layout M2 M3 M1 M4 M5 M6 Open field Layout M6 M4 M5 M1 M2 M3
Table 18: Tabu Machine-waiting time &Transportation cost
M1 M2 M3 M4 M5 M6 Transportation Cost
5300 5080 3930 4430 5910 4830 5300
9. Conclusions
The FMS layout is optimized, by using the Genetic algorithm and Tabu search, with scheduling as the constraint. The various parameters like the flow time, Job sequence to be manufactured, machine sequence, total transportation cost, machine and job waiting times are determined for line, loop, ladder and open field layouts. The result shows that the GA gives the optimum results and open field layout is advisable. The necessary program is developed in C++ language.
REFERENCES
[1] Arun Kumar, Dr.A.K.S.Choudhary & Dr. D.Sigh “Heuristic Approach to Production Scheduling in Job-shop Industries – A Case Study”. Industrial Engineering Journal, vol 28(1999) pp 4-6.
[2] Chuda Basnet, Joe H. Mize, “Scheduling and Control of Flexible Manufacturing System : A Critical Review”. Journal of Intelligent Manufacturing, Special issue on Production Planning and Scheduling, (1995) pp 1-41.
[3] David E. Goldberg, “Genetic Algorithms in search & Machine Learning” Addison Wesley Longman (1999). [4] Fred Glover., Manuel Laguna., “Tabu Search” Kulwer academic Publisher. (1997).
[5] John Hubbard, “Programming with C++” Mcgraw-Hill International Edition. (1996).
[6] Karthikeyan T. “Modelling and Analysis of Scheduling in Computer Integrated Manufacturing”, Ph.D. Dissertation, Bharathidasan University, Tiruchirappalli. (2003).
[7] Lam F.S.C, Lin B.C, Sriskandarajaiah C, Yan H, “Scheduling to minimize product design time using Genetic algorithm”, International Journal of production research, vol 2(1999) pp 83-99.
Machine Number
Job ( in Batches) sequence to be manufactured
M1 B5 B1 B2 B3 B6 B4
M2 B5 B3 B4 B1 B2 B6
M3 B3 B4 B6 B1 B2 B5
M4 B2 B4 B1 B6 B3 B5
M5 B6 B3 B2 B1 B4 B5
M6 B1 B5 B4 B2 B3 B6
B1 B2 B3 B4 B5 B6 Make span
[8] Lee C.Y, Piramuthu S, Tsai Y.K, “Job shop scheduling with a Genetic algorithm and machine learning” International Journal of production research, vol 4 (1997) pp 48-56.
[9] Mikell P. Groover, “Automation, Production systems, and Computer Integrated Manufacturing”, Pearson education (1987). [10] R.Panneerselvam, “Production and Operations Manegement” Prentice-hall India. (1999).
[11] Robert Lafore, “Object Oriented Programming Turbo C++”, Galgotia Publication,Pvt. Ltd., New Delhi.(1991).
