An evolutionary optimal green layout design for a production facility by simulated annealing algorithm
S. Sheik Sulaiman
a,⇑, P. Leela Jancy
b, A. Muthiah
c, V. Janakiraman
d, S. Joe Patrick Gnanaraj
aaDepartment of Mechanical Engineering, Francis Xavier Engineering College Tirunelveli, Tamil Nadu, India
bDepartment of Information Technology, Sri Sai Ram Institute of Technology, Chennai, Tamil Nadu, India
cDepartment of Mechanical Engineering, P.S.R. Engineering College, Sivakasi, Tamil Nadu, India
dDepartment of Mechanical Engineering, Vaigai Engineering College, Madurai, Tamil Nadu, India
a r t i c l e i n f o
Article history:
Received 4 April 2021
Received in revised form 9 May 2021 Accepted 11 May 2021
Available online 28 May 2021
Keywords:
Simulated annealing Detail layout design CO2emission Material handling Mat Lab
a b s t r a c t
Facility layout goal includes the physical arrangement about diverse departments, machine cells /sta- tions, machines, tools and equipments. The main objective of the Industry 4.0 is to reduce the cost and time to manufacture the products, so to reduce the product lead time, it became essential to have optimal facility layout. i.e., optimal arrangement of the machines and the work stations reduces the material movements. Thereby the productivity increases. To overcome the challenges, researchers and the indus- trialists are adopting different methodologies and tools. Even though many heuristic and hybrid method- ologies are available, but those methodologies cannot be used universally. So the primary target of this research is to identify the optimal position of the available machines and the work stations in the floor shop by satisfying the constraints using the simulated annealing algorithm. Among the various optimiza- tion, simulated annealing having better optimization formulation for the considered facility layout prob- lems in the shop floor. The experimental simulations of the existing or identified layout of the considered industry have been done with the Mat Lab. The obtained simulation results have also been validated and found that the results have the most efficient arrangement of various available machines and work cen- ters within the shop floor. As the outcome, the workflow of the materials and the goods within the work- place will be in the shortest route and in shortest time. Also the secondary objective of the research is to make user-friendly optimal green layout with the concentrated rate of emission.
Ó2021 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientific committee of the International Confer- ence on Futuristic Research in Engineering Smart Materials.
1. Introduction
This research work mainly concentrates on the arrangement of physical machines that are available within the plant of the given configuration. Within the context of producing, the target is to attenuate the entire material handling charge of moving the spec- ified material between the departments. In the initial installation stages, the various analysis have been made in the arrangement of the machines and the equipments and the arrangement were more effective, but when the changes in the manufacturing pro- cesses or any up gradation occurs, the layout need to be changed
to adopt to the up gradation. Due the enhancement in the process and with the old layout will leads to many unexpected bottlenecks.
To avoid the bottle necks, it became essential to execute the inter- nal and external changes to the possible extent Sharma and Singhal [4]. So it is clear that the re-layout is very much essential for any changes in the industries. So this challenge need to be addressed and in this research, these challenges have been taken as the fore- most objective and to identify the most efficient and more enhanced arrangement of the existing machines within the machi- nes shop to scale back the non-value added time. The problem identify within the factory includes the following: The productivity has been reduced due to improper workplace layout. Improper positioning of the machine within the workplace with reference to the sequence of the operation. Raw materials and finished prod- ucts are randomly stored with none proper allocation of space.
https://doi.org/10.1016/j.matpr.2021.05.256 2214-7853/Ó2021 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientific committee of the International Conference on Futuristic Research in Engineering Smart Materials.
⇑Corresponding author.
E-mail addresses:[email protected](S.S. Sulaiman),leela.it@sairamit.
edu.in (P. Leela Jancy), [email protected] (A. Muthiah), janakiramanven- [email protected] (V. Janakiraman), [email protected] (S. Joe Patrick Gnanaraj).
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Improper material handling design for the instant of staple to finished products. In the survey, it has been found that the 36%
of carbon di oxide emissions are only from the manufacturing industries Elbouzekri et al. [2]. Also due to industrial revolution, overall, industry energy consumption increased to 61% from the year 1971 to 2004, to meet out the rapidly increasing demand for the energy. On the other hand, the researcher found that there is a substantial opportunity available to meet the global energy demands and necessity arises to reduce the NOx and CO2emissions El Bouzekri El Idrissi et al.[3]
1.1. Material handling vehicles (Forklift’s)
The energy consumptions of the commonly used fork lift in the industrial floor shops are mainly depends on the number of hours it had been operated and also depends on the continual use in the shifts. In the year 1995, Gas Technology Institute (GTI) reported a scope in respect to annual runtimes of the fork lift in the industries with 500 to 3500 h towards battery powered forklifts, and simi- larly 1800 to 1900 h towards IC engine powered forklifts. For the class I and class II category of forklifts, I had been identified that the 69%, 16% and 15% working during only one shift, two shifts and three shifts per day respectively. In addition to it, 59% of the IC powered ice forklifts run during one shift together with 40% dur- ing two shifts. On the average 1.5 shifts per day with the assump- tion of having 5 working days a week Gaines et al. [15]. The researcher have conducted experimentation and found that the actual emissions in comparison with IC forklifts have been reported that the emission standards have been for the hydrocar- bon (HC), nitrous oxide (NOx) coupled with carbon monoxide (CO) [6]. Emission values of carbon dioxide from the engine can be quantified from the amount of fuel consumption. Conventional diesel is commonly used in the forklifts and as it is off road vehi- cles, it has not subjected to road taxes. Biodiesel have also been approved recently as the alternate for the diesel, nevertheless it produces a 20% higher CO emissions for the blend of 20-soy fuel in the 55-hp forklift operating with conventional ultra-low sulfur diesel and are related in Fig. 1. It also proved that the emissions values are comparable to the combustion related to biodiesel, Off-road emission standards formulated by the government for the diesel forklifts are provided in theTable 1. The emission stan- dards vary with respect to the rated power of the engine Gaines et al.[15].
1.2. Objectives of plant layout
Economic analyses have to be made to reduce the investment within equipments in addition to relevant handling charges.
Essential Requirement of the product design has to be satisfied without violating the norms.
Requisite concerning procedure involved in handling of equip- ment along with capacity have to be minimized.
Flexibility in the arrangements of operations and machines has to be validated.
Different categories of facility handling tools have to be installed in the process of manufacturing.
The need and the requirement of the labour existence should be provided towards the convenience and safety.
Organizational formulation and the structure have to be pro- vided with higher priority.
Utilizing the building as well as site to the maximum extent.
2. Modeling frame work
In the normal industrial framework, the various machines and the equipments are indicated on a plan with the scaled distances and exact replica of their positions. Thereby all the features exist in the plant have to be denoted. These frame work have been taken as the input to the optimization problem and the algorithm will optimize the existing layout of the considered plant to a new struc- ture[10], which can enhance the productivity, reduced material handling, higher safety, reduced lead time and better quality of production. Unnecessary movements and the efforts involved in the production process need to be avoided and thereby the plant layouts have to be optimized[11]. This various key indicators con- sidered in this research are as follows.
1 Distances to be moved by the products.
2. Distances to be moved by the equipments.
3. Distances to be moved by the operators.
Fig. 1.Emissions from ULS D and Soy Diesel (in parts per million).
Table 1
Emission Standards formulated for off road Diesel Engines, g/kWh (g/bhph).
Sl.No Rated Power (kW) NMHC + NO, PM
1. kW < 8 4.6 (3.4) 0.48(0.36)
2. 8 < kW < 19 4.5 (3.4) 0.48(0.36)
3. 19 < kW < 37 4.5 (3.4) 0.36(0.27)
4. 37 < kW < 75 4.7 (3.5) 0.24(0.18)
5. 75 < kW < 130 4.0 (3.0) 0.18(0.13)
6. 130 < kW < 560 4.0 (3.0) 0.12(0.09)
7. kW > 560 38(2.8) 0.12(0.09)
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4. Handling equipments location and movements.
5. Energy and time required for moving the features in the plant layout Harris et al.[1].
2.1. Problem definition
The finding of outcomes from the literature review is that sim- ulated annealing algorithm approaches are often suitably used for the layout design at fine blanking plant. According to working con- ditions and constraints the necessary changes are essential in the layout. The scope of this research is to find out possible improve- ments in existing plant, and to use simulated annealing algorithm approach to find optimized layout and compare the results using Mat Lab software
2.2. Analysis of the problem
The analysis has taken in a leading cooker manufacturing com- pany in Tirunelveli, Tamil Nadu. Due to inconsequence material handling and not proper layout deign the rejection rates and wastage is high. They are producing several models of household utensils like Pressure cookware, stainless steel cookware, and Non-stick cookware products.
2.2.1. Understanding of existing process sequence
For optimizing the layout, it is necessary to understand the plant layout in details and the various sequence of processes con- ducted in the plant. So the various processes and the sequence of those processes in the plant have to be studied. Also it is essential to identify the critical or bottleneck operations, so that it can be overcome while optimization process. This study will support in identifying the optimal redesigned plant features.
2.2.2. Designing and simulation of the layout
In designing the layout, various sequences and the time taken for the process and the distance to be moved have to be completed.
For simulating the layout, the available space inside the plant, rela- tion exist between the various features, importance of the various process, bottleneck operations and the capacity required to iden- tify the best desired results.
3. Procedure for problem finding Step1: Data collection.
Step2: Design and analyze the existing Layout.
Step3: Simulate using Mat Lab Software.
Step4: Select the appropriate layout that would minimize the drawbacks of existing layout.
3.1. Data collection
In general, the essential data are related to the material han- dling charges are the important criterion for the selection of any type of plant layout. For the standardization, prevailing plant lay- outs have to follow few procedures and are given as follows.
Class I Source: The design details, drawings, bill of materials, Assembly sequence, Re-design with respect to standardization?simplifications.
Class II Source: Sequence of Process, Make/outsourcing equip- ments used for the operations, process flow analysis chart Data gathering.
Class III Source: Production planning design Logistics: what to produce, where to produce, how much quantity ?product mix Marketing; forecasting and predicting the demand?rate of pro-
duction, category and number of machines required for the opera- tions Layout type continuous or of intermittent LayoutMschedule, 3.2. Design and analyze the existing layout
This study has taken in a leading cooker manufacturing com- pany in Tamil Nadu. They are producing several models of house- hold utensils like Pressure cookware, stainless steel cookware, Non-stick cookware products etc. The main raw materials used are Aluminum, stainless steel, rubber & other coating materials.
The manufacturing plant has a capacity of 550+ cores per year turnoverFigs. 2 and 3. The plant has different production facility machines of power press tools, CNC, automated powder coating plant and heavy material handling vehicles (Forklift, Cranes, AGV’S) with 300+ employees per shiftFig. 4. These products are exported to various countries and still expanding its growth towards excellence.
4. Machine Dimensions:
The machine shop has 15 departments placed in the shop floor of Overall Length = 26.80, Overall Width = 37.50 m, the dimensions are tabulated inTable 2.
4.1. Centre to centre distance between departments
Table 3 refers the centre to centre distance between each department where the centre distance of one department is mea- sured and followed by the other. We have taken the measurement using measuring equipments and noted the distance between each department (Table 4).
5. Solution methodology
5.1. 1 Simulated annealing algorithm
SA has been categorized as Meta heuristics to identification of approximate optimization solution within a large seek space. It has often aimed to hunt solution in the discrete space. For issues of finding approximate global ideal solution instead of identifying
Fig. 2.Photographic views of machine shop.
the local optimal solution with the minimal computational time, simulated annealing will be preferable by the researchers as one of the best alternatives for gradient descent Toro et al[7], The sim- ulation concerning annealing methodology having approximation
regarding to a worldwide minimum toward the function together with outsized number of attributes mathematically equal to artifi- cial multi-atomic system
5.1.1. Crossover
The crossover is the operation in which the properties of the best sequence will be inherited to the new sequence in the children chromosome. Thus the crossover operation will generate a new children chromosome which is better than the parent sequence Fig. 5Crossover is the natural operation occurs during evolution of the new generations and to avoid the divergence; it should fol- low the user definable probability Jerin Leno et al.[8].
5.1.2. Mutation
In order to identify the new generation with arbitrary features and to deviate from the local convergence, mutation of the children will be performed. Also this mutation may deviate the solution from the best convergence, so the probability have to be kept at the minimal and in this research it has been set to 0.1. There by the smaller number of the children’s only mutated i.e. making small random changes within the generated solution is shown Fig. 6. In the investigation, conducting the various trial and error methods, partial-mapped crossover Jerin Leno et al.[14], found to be better in identifying the best plant layout with the swap mutation.
5.1.3. Sequence pair
The sequence pair is not like the graph based representation, it is the pair of permutations of the N number of machines Welgama and Gibson[9], and these permutations will have the geometric relationship exist between each considered machine. During math- ematical simulation, there may be chance of overlapping of one machine over the other, but in the real time scenario machines cannot overlap among themselves. So the machines have to be positioned left or below in the sequence pair
(<. . ., a,. . ., b,. . .>,<. . ., a,. . ., b,. . .>)=>the equation denotes, Machine a is to the left side of the machine b ð1Þ
(<. . .,a,. . .,b,. . .>,<. . .,b,. . .,a,. . .>)=> the equation denotes,
Machine a is above the machine b ð2Þ
For the facility layout, the machines can be in the floor only i.e.
either vertical or horizontal direction, only these placements have been recorded for identifying the optimal positioning. Therefore, in this research, placement process of the existing machines have been aligned to generate wither in the horizontal and vertical axis e.g., x = 0 and y = 0, Jerin Leno et al.[8], same have been used for the sequence pair to find the optimal placement of various ele- ments in the department. The Fig. 7 shows the flow chart of methodology follows for computations.
6. Mathematical modeling
The mathematical modeling has been carried out after detail study of Idrissi et al. [3], here the multi objective functions to reduce the total material cost and CO2 Emission rates are taken in account. For these reasons, transportation companies and gov- ernments start taking explicitly into account emissions reduction objectives in the definition of their working plans. Then, the gener- ated working plans must minimize both cost and CO2 emissions.
These two objectives are not necessarily positively correlated and for some cases they are completely conflicting.
Fig. 3.Photographic views of machine shop.
Fig. 4.The existing shop layout.
Table 2
Machine dimensions.
S. No Name of the machine Length(m) Width(m)
1. Hydraulic holing 1.57 1.35
2. Hydraulic cutting 1.50 1.25
3. Hydraulic press 1 2.25 1.60
4. Hydraulic press 2 2.7 1.87
5. Open/close (3mash) 2.70 1.20
6. Bending 1.8 0.70
7. Holding 1.65 0.65
8. Cutting 1.6 0.95
9. Stamping 2.70 0.85
10. Fitting machine 1 1.80 0.86
11. Fitting machine 2 1.80 1.40
12. Emering 1 0.30 0.30
13. Emering 2 0.30 0.30
14. Emering 3 0.30 0.30
15. Emering 4 0.30 0.30
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6.1. Total material handling cost
Material handling cost a crucial component of a manufacturing companies profit calculation. If these overlooked when estimating production cost, the company will over estimate its potential profit. Analyzing material handling cost also help the company take measure to reduce them in the future. Costs vary by industry and location.
If a company ships more than one product at a time, it needs to split handling cost between them gets an accurate estimate to each product actual cost. It has allocate the cost to an entire batch or cal- culate a per unit peace, Depending on budgeting and forecasting needs
TMHC =P
CijFijDij Where,
Cij = Cost for the material transport between the machine.
Fij = Materials flow between the machine.
Dij = Distance between machine
6.2. Basic principles for estimating CO2the emission
The sources of emission against relevant handling vehicles exhaust gases such as hydrocarbons are due to burning of fuel.
When any IC powered engines are forced to started less than the normal functioning temperature, then the complete combustion have been occurred. As the combustion has not been done effec- tively, the pollution and the harm gases production will be nor- mally higher in rate compared to the standard desired combustion[12]. These conditions have been taken as the primary condition to reduce the emission and the empirical relational equa- tion used is given as follow.
Etotal= Estart+ Ehot+ Eevaporative
Whereas:
E is the total emission
Ehotdenotes emission during hot engine condition.
Estartdenotes emission during start of the engine at the cold.
Eevaporativedenotes emission due to evaporation.
Table 3
Centre to Centre Distance between Departments.
S.NO M15 M14 M13 M12 M11 M10 M9 M8 M7 M6 M5 M4 M3 M2 M1
1. 8.90 6.10 7.20 8.08 8.20 7.80 5.00 4.40 3.50 2.50 2.50 7.25 4.46 1.00 ——
2. 9.60 9.10 10.00 10.60 7.70 8.10 4.40 3.40 2.40 1.64 3.20 4.00 1.20 —— 1.00
3. 11.00 11.60 12.70 13.30 7.80 8.50 3.20 2.20 2.00 2.77 4.20 1.30 —— 1.20 4.45
4. 15.00 15.00 16.00 16.00 8.60 9.30 2.30 3.20 3.60 3.00 5.10 —— 1.30 4.00 7.20
5. 11.00 11.00 12.00 12.30 5.00 4.20 6.80 4.70 2.00 0.20 —— 5.10 4.20 3.20 2.50
6. 15.00 15.10 15.20 15.30 4.80 5.30 4.60 2.50 0.90 —— 0.20 3.00 2.70 1.60 2.50
7. 16.00 16.70 17.00 17.20 5.30 6.20 3.00 0.70 ——— 0.00 2.00 3.60 2.00 2.40 3.50
8. 19.00 18.90 19.50 19.60 6.20 7.00 0.60 —— 0.70 2.50 4.70 3.20 2.20 3.40 4.40
9. 2.00 19.60 20.30 21.90 7.30 8.10 ——— 0.60 3.00 4.60 6.80 2.30 3.20 4.40 5.00
10. 11.00 11.20 10.40 9.70 0.10 —— 8.10 7.00 6.20 5.30 4.20 9.30 8.50 8.10 7.80
11. 13.00 13.10 12.30 11.60 —— 8.10 7.00 6.20 5.30 4.80 5.00 8.60 7.80 7.70 8.20
12. 2.80 1.20 1.05 —— 11.00 9.70 21.00 19.00 17.00 15.30 12.00 16.90 13.30 10.00 8.00
13. 2.30 0.70 —— 1.05 12.00 10.00 20.00 19.00 17.00 15.00 12.00 16.00 12.70 10.00 7.20
14. 1.30 —— 0.70 12.00 13.00 11.00 19.00 18.00 16.00 15.10 11.00 15.00 11.60 9.10 6.10
15. —— 1.30 2.80 13.00 11.00 20.00 19.00 19.00 16.00 15.10 11.00 15.00 11.90 9.60 6.90
Table 4
Evaluation Data of CO2Emissions.
D15 D14 D13 D12 D11 D10 D9 D8 D7 D6 D5 D4 D3 D2 D1
3.30 3.20 3.20 2.60 2.80 2.10 2.90 3.00 2.10 2.80 2.30 1.00 2.75 1.00 # D1
3.40 2.40 2.60 2.60 5.00 2.40 3.80 3.40 2.30 5.00 2.50 2.00 2.00 # D2
2.60 3.40 3.20 3.40 2.60 2.70 2.80 2.60 2.40 2.60 2.30 2.90 # D3
3.00 2.30 2.60 2.80 2.60 3.40 3.00 3.20 3.60 2.30 4.00 # D4
3.10 2.10 2.40 3.20 3.60 2.30 3.40 2.30 3.60 3.40 # D5
3.20 4.10 3.40 2.30 2.60 2.80 2.60 2.40 3.60 # D6
2.90 2.60 3.20 3.40 2.60 2.80 3.40 3.20 # D7
3.10 3.20 3.40 2.90 2.60 2.80 3.40 # D8
2.90 2.80 2.40 2.60 2.80 2.30 # D9
3.60 3.50 3.20 3.00 3.20 # D10
3.00 3.20 3.10 3.20 # D11
2.50 2.70 2.90 # D12
3.20 3.80 # D13
3.10 # D14
# D15
Fig. 5.Example of 2-point crossover.
Fig. 6.Example of mutation.
The sum of all the contribution should be the overall total emission
(E hot, E start, E evaporative) are the components of the emis- sion and the standardized emission factor is given below,
Ex= ex.ax
Whereas:
ExTotal emissions
X for - hot or start or evaporative exEmission factor
axAmount of traffic activity.
6.3. CO2emissions matrix from the transport of freight
From the research perspective, the mentioned aims to test the corollary concerning to CO2emissions has not affected by the pre- viously mentioned evaporation term Elbouzekri et al. [2], In this research, the mode of transport considered for the emission control was over the off road heavy duty vehicles used in the industrial floor i.e. Forklift of 40 ton capacity[13]. For the easy computation and to reduce the complexity of the work, emissions functions for- mulated by the Hickman and Jancovici are as follows.
The assumed mean speed of the vehicle is 40 km/h
The gradient of the vehicle travel have not been considered.
The emission matrixes for the arc are given in theTables 4 and 5.
Eijðq;dÞ ¼dij efleel
Q
qijþeel
7. Results and discussions
The test problem is given in the section with this input data and the mathematical model taken from SA algorithm is developed.
The parameters used in SA are (1). Initial temperature (2). Number of iteration. The testing of these parameters is essential and there- fore, a test experiment was conducted to find out the best temper- atures for the proposed FLP
Initial temperature (Ti) = [1000, 100, 10]
Number of iteration (Ni) =[1;3;5]
8. Objective function Minimize E = W1 (TMHC)
Subject to: Non-overlap of machines and departments Where,
W1 = Weight assigned for TMHC (Total material handling cost) and Average Emission rate taken as 3
8.1. SA (Simulation Annealing) based algorithm simulation run To calculate minimum Total material handling cost and emis- sion rate the SA algorithm has run by Mat Lab software by consid- ering three different cases of Initial temperature (Ti) & Number of iteration (Ni), After running CASE 1 Initial temperature of 500 and iteration 1 the best values are tabulated inTables 6 and 6.1and the best layout are shown inFig. 8.
From the aboveTables 7 and 7.1, Ti = 1000 and Ni = 5 is selected as the best pair of combination of Ti and Ni for SA algorithm as in the CASE 2 Run and the corresponding best layout is shownFig. 9.
In CASE 3, Ti = 1000 and Ni = 10 the best pair of minimum Total material cost and emission values are tabulated inTables 8 and 8.1 and the corresponding layout is shownFig. 10.
8.2. Effective layout comparison
From the above three cases the case 3 Ni = 10 and Ti = 1000 is selected as shownFig. 11the best pair of combination of Ti and Ni for SA algorithm can be tabulated inTable 9 as the best layout based on total material handling cost with reduced emission rate.
Finally the comparison chart has shown asFig. 12for best pair of different combinations of layout design selection for three variable inputs of initial temperatures and number of iterations by SA Based algorithm run using Mat Lab Software.
Fig. 7.Flow chart.
Table 5
CO2-the Linguistic Measurement Scale.
Measure allocation scale Linguistic Terms
Low High levels Label
1. Extra low EL
2. Very low VL
3. Low L
4. Slightly low SL
5. Middle M
6. Slightly High SH
7. High H
8. Very High VH
9. Extra High EH
Table 6
Best THMC for Ni= 1.
Ti 100 500 1000
TMHC 2,583,000 2,283,700 2,583,000
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9. Concluding remarks
The paper represents an application of the simulated annealing algorithm in layout design. A case study is described in the paper referring to a machine shop designated for cooker manufacturing and Assembly Company. From the result of this study.
1 We have selected an optimal layout design with minimal total material handling cost for different initial temperature and num- ber of iteration values.
2. This study not only reducing the material handling cost for production, also identifies the unwanted movements of operator / material handling through vehicles.
3. Also to reduce emission rate (CO2) in a shop floor and re- locate the machines layout to improve production efficiency.
Table 6.1 Best THMC.
Ni Ti TMHC
1 500 2,283,700
Fig. 8.Best Layout for Ni = 1.
Table 7
Best THMC for Ni = 5.
Ti 100 500 1000
TMHC 2,321,000 2,274,500 2,190,500
Table 7.1 1-Best THMC.
Ni Ti TMHC
5 1000 2,190,500
Fig. 9.Best Layout for Ni = 5.
Table 8
Best TMHC for Ni = 10.
Ti 100 500 1000
TMHC 2,194,900 2,207,400 2,160,000
Table 8.1 Best THMC.
Ni Ti TMHC
10 1000 2,160,000
Fig. 10.est Layout for Ni = 10.
Fig. 11.TMHC Comparison chart.
Table 9 Best TMHC.
Ni Ti TMHC Average Emission Rate
1 500 2,283,700 3.0
5 1000 2,190,500
10 1000 2,160,000
10. Future scope
The major problem faced in the optimization is the plant flexi- bility, as the plants are setup by maximally utilizing the space and the resources. So in future, it has to be taken into consideration.
Also the algorithms used to forecast the optimal changes should not be infeasible solution or the algorithm should not converges at the local optimal points thereby inefficient plant layout may also be generated. So the convergence rate and the termination condi- tions have are decided at care. Also the possible plant capacity extensions have to be taken into consideration while doing the re-layout.
Multi-Objective algorithms to be studied and incorporated for best problems solution findings such as Artificial bee colony, NSGA-II (Non-dominated Sorting Genetic Algorithm), AMOSA (Archive Multi-objective Simulated Annealing).
CRediT authorship contribution statement
S. Sheik Sulaiman:Conceptualization, Methodology, Writing - review & editing. P. Leela Jancy: Software. A. Muthiah:Formal analysis, Investigation.V. Janakiraman:Supervision.S. Joe Patrick Gnanaraj:Project administration.
Declaration of Competing Interest
The authors declare that they have no known competing finan- cial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We would wish to thank all the authors of various research papers referred during scripting this paper. It was very knowledge gaining and helpful for the further research to be wiped out future.
References
[1]I. Harris, C.L. Mumford, M.M. Naim, A hybrid multi-objective approach to capacitated facility location with flexible store allocation for green logistics modelling, Transport. Res. Part E: Logist. Transport. Rev. 66 (2014) 1–22.
[2]A. Elbouzekri, M.E.S.S.A.O.U.D. Elhassania, A.E.H. Alaoui, A hybrid ant colony system for green capacitated vehicle routing problem in sustainable transport, J. Theor. Appl. Inf. Technol 54 (2) (2013).
[3]E.B.E.E. Idrissi, A. Elhilali Alaoui, Evolutionary algorithm for the bi-objective green vehicle routing problem, Int. J. Sci. Eng. Res. 5 (9) (2014) 70.
[4]P. Sharma, S. Singhal, A review on implementation of meta-heuristic approaches for layout problems in dynamic business environment, Int. J.
Multivariate Data Anal. 1 (1) (2016) 6–27.
[5]I.J. Leno, S.S. Sankar, M.V. Raj, S.G. Ponnambalam, An elitist strategy genetic algorithm for integrated layout design, Int. J. Adv. Manuf. Technol. 66 (9–12) (2013) 1573–1589.
[6]A.M. Deif, A system model for green manufacturing, J. Cleaner Prod. 19 (14) (2011) 1553–1559.
[7]O.E.M. Toro, Z.A.H. Escobar, E.M. Granada, Literature review on the vehicle routing problem in the green transportation context, Luna Azul 42 (2016) 362–
387.
[8]I.J. Leno, S.S. Sankar, S.G. Ponnambalam, An elitist strategy genetic algorithm using simulated annealing algorithm as local search for facility layout design, Int. J. Adv. Manuf. Technol. 84 (5–8) (2016) 787–799.
[9]P.S. Welgama, P.R. Gibson, A construction algorithm for the machine layout problem with fixed pick-up and drop-off points, THE Int. J. Product. Res. 31 (11) (1993) 2575–2589.
[10]F. Azadivar, J. Wang, Facility layout optimization using simulation and genetic algorithms, Int. J. Prod. Res. 38 (17) (2000) 4369–4383.
[11]S. Abdinnour-Helm, S.W. Hadley, Tabu search based heuristics for multi-floor facility layout, Int. J. Prod. Res. 38 (2) (2000) 365–383.
[12]I.D. Paul, G.P. Bhole, J.R. Chaudhari, A review on green manufacturing: it’s important, methodology and its application, Procedia Mater. Sci. 6 (2014) 1644–1649.
[13]L.M. Calvo, R. Domingo, CO2 emissions reduction and energy efficiency improvements in paper making drying process control by sensors, Sustainability 9 (4) (2017) 514.
[14]I. Jerin Leno, S. Saravana Sankar, S.G. Ponnambalam, MIP model and elitist strategy hybrid GA–SA algorithm for layout design, J. Intell. Manuf. 29 (2) (2018) 369–387.
[15] L.L. Gaines, A. Elgowainy, M.Q. Wang, Full fuel-cycle comparison of forklift propulsion systems (No. ANL/ESD/08-3). Argonne National Lab. (ANL), Argonne, IL (United States), 2008.
Fig. 12.Best TMHC Comparison chart.
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