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ЭКОНОМИКА, ПРЕДПРИНИМАТЕЛЬСТВО И ПРАВО
Том 9 • Номер 4 • Октябрь-декабрь 2019 ISSN 2222-534Х
>
Journal of Economics, Entrepreneurship and Law
Первое
экономическое издательство
г.
-J
Inbound-Assembly Shop Logistics (INASHLO) Optimization by locating temporary storage (stock) with Fuzzy - six sigma approach
Khamisabadi J. 12 3, Rouhina M. 4 5, Rahmani B. 4
1 Islamic Azad University,
2 Middle East BALABAN Supply Chain Engineering Co,
3 World Logistics Engineering Institute,
4 Iran Khodro Corporation (IKCO),
5 University of Tehran,
The main objective of this study was to determine the best location for temporary storage to facilitate assembly lines feeding operation in the field of Inbound-Assembly Shop Logistics (INASHLO) Optimization and supply chain management in automobile assembly Tondar 90 Iranian companies. In the first step, the initial study, the assembly hall was divided into four areas .This division is based on the opinions of experts and the establishment of assembly lines. Each area separately studied by the three groups of Industrial Engineering and also introduced three proposed location in each area for establishing the temporary storage. Then, by the expert group, supplying key indicators in each of the proposed location in each area were calculated. Supply key indicators studied in this study include the transportation cost and standard time. Using a set of control data for data matching trapezoidal fuzzy numbers to classify data obtained from undergraduate studies were performed on seven floors. Then, with the use of fuzzy Topsis, priority areas and proposed locations to establishing a temporary storage in the assembly hall were determined. Finally, after two stages of research, to finally establishing a temporary storage, the second area and first proposed location in this area were selected.
КЛЮЧЕВЫЕ СЛОВА: INASHLO, supply chain management, logistics, Fuzzy TOPSIS, stock
1. Introduction and Problem Statement
These days, daily increasing of competitive conditions in markets, customer services and essential progress in information technology and communication industries caused to satisfying the customers in appropriate quality of product or service, low price in comparison to other competitive and on time delivery of product or service, has the essential role in remaining of organizations at markets and getting the market's proportion. For this reason the concept of supplying chain management is posed during these two decades. Facility, which is a function of the existence any work easy. The facilities may be a machine tool the service center, cell manufacturing,
machine shop, Department, warehouse and .... Facilities layout represents a
special order physical facility In fact, the purpose of study facility location
АННОТАЦИЯ:
is: Minimize delays in material handling, supply flexibility, more efficient use of space and labor, and increased staff morale. Locating facilities in the factory environment often makes "facilities layout issues". These issues have particular importance in production costs, process, delayed time and productivity. Proper location of facilities has efficiency in total performance of operation and it can even reduce operating costs by 50 percent and also to increase the value of an organization's products [7] (Mehrmanesh, Khamisabadi, 2013).The main objective of logistics and supply chain management systems in an organization is an optimal supply of assembly lines. The implementation of an integrated logistics system in the organizations is very important. Several factors influence the establishment of an integrated logistics system Including the manpower, transportation equipment, the location chosen for the maintenance and storage of raw materials to produce of final product in assembly lines. The main objective in this study, choosing the best location for establishing the temporary storage in assembly shop for short-term storage of raw materials in each of the workstations on the assembly line are located. How to placement and location of temporary storage, it is very important. For a more detailed description can be noted that the determination of the
ABSTRACT:_
The main objective of this study was to determine the best location for temporary storage to facilitate assembly lines feeding operation in the field of Inbound-Assembly Shop Logistics (INASHLO) Optimization and supply chain management in automobile assembly Tondar 90 Iranian companies. In the first step, the initial study the assembly hall was divided into four areas .This division is based on the opinions of experts and the establishment of assembly lines. Each area separately studied by the three groups of Industrial Engineering and also introduced three proposed location in each area for establishing the temporary storage. Then, by the expert group, supplying key indicators in each of the proposed location in each area were calculated. Supply key indicators studied in this study include the transportation cost and standard time. Using a set of control data for data matching trapezoidal fuzzy numbers to classify data obtained from undergraduate studies were performed on seven floors. Then, with the use of fuzzy Topsis, priority areas and proposed locations to establishing a temporary storage in the assembly hall were determined. Finally, after two stages of research, to finally establishing a temporary storage, the second area and first proposed location in this area were selected.
KEYWORDS: INASHLO, supply chain management, logistics, Fuzzy TOPSIS, stock
JEL Classification: Mil, M16, M21 Received: 01.12.2019 / Published: 30.12.2019
© Author(s) / Publication: PRIMEC Publishers
For correspondence: Khamisabadi J. (Javad_Khamisabadi@yahoo.com)
CITATION:_
Khamisabadi J., Rouhina M., Rahmani B. (2019) Inbound-Assembly Shop Logistics (INASHLO) Optimization by locating temporary storage (stock) with Fuzzy - six sigma approach [Inbound-Assembly Shop Logistics (INASHLO) Optimization by locating temporary storage (stock) with Fuzzy - six sigma approach]. Ekonomika, predprinimatelstvo i pravo. 9. (4). - 639-658. doi: 10.18334/epp.9.4.41525
optimal location for the establishment of a new stock in assembly shop. The results are very effective in a production system Such that the results would be as follows:
1. Facilitate the flow of materials.
2. Matching supply assembly lines by coefficient consumption of Parts.
3. Reduced assembly line stoppages due to parts shortages and lack of timely supply.
4. Matching Parts transportation programming with production programming.
5. Reduce unemployment of transportation operators.
Much research has been done in this issue, some of which that: Introduce a mathematical programming model in order to locating the warehouses in the vehicle routing [7] (Mehrmanesh, Khamisabadi, 2013). A New Exact Algorithm for the Vehicle Routing Problem Based on Q-Path and K-Shortest path relaxation [1] (Alinaghian, Behrouzi, 2010) .Solution of a Large Scale Traveling Salesman Problem. An integrated model of the periodic delivery problems for vending-machine supply chains [2] (Christofides, Hadji constantinou, Mingozzi, 1993). Heuristic solutions to multi-depot location-routing problems [3] (Dantzig, Fulderson, Johnson, 2016). Introduce a Machinery optimal layout method using a mathematical model [6] (Tolouei ashalaghi, Mojrian, 2010). Introducing a Model in Order to Logistics Balance with the Aim of Improving for Total Expected Cost [7] (Mehrmanesh, Khamisabadi, et al., 2013). Integrating purchasing and routing in a propane gas supply chain [8] (Wen-Chyuan Chiang, Russell, 2002).
2. literature review
2.1. Supply Chain
Supplying chain consists of material stream, money and information between supplier's network, transportation, producer, distribution network and final customer. 2.1. Supply chain management
Supply chain management (SCM) is the management of a network of interconnected businesses involved in the provision of product and service packages required by the end customers in a supply chain. Supply chain management spans all movement and storage of raw materials, work-in-process inventory, and finished goods from point of origin to point of consumption [4] (Rusdiansyah, De-bi Tsao 2004).
ОБ АВТОРАХ:_
Khamisabadi Javad, PhD in Industrial Management, Islamic Azad University; CEO of Middle East BALABAN Supply Chain Engineering Co (Tehran, Iran); Founder & CEO of World Logistics Engineering Institute (Erzurum, Turkey) (Javad_Khamisabadi0yahoo.com)
Rouhina Mohammad, Systems Director, Iran Khodro Corporation (IKCO) (Tehran, Iran); DBA, University of Tehran (Tehran, Iran)
Rahmani Babak, TPS Deputy Iran Khodro Corporation (IKCO) (Tehran, Iran)
ЦИТИРОВАТЬ СТАТЬЮ:_
Khamisabadi J., Rouhina M., Rahmani B. Inbound-Assembly Shop Logistics (INASHLO) Optimization by locating temporary storage (stock) with Fuzzy - six sigma approach // Экономика, предпринимательство и право. - 2019. - Том 9. - № 4. - С. 639-658. doi: 10.18334/epp.9.4.41525
2.3. Logistics
The keyword "logistic" has been used in the U.S.A military forces for more than one century and gradually has been accepted by the other military forces of English language countries. In the recent decades this is also developed in trade market and civil industrial. Logistic has originally come from a Greek work "logistics" and it means science of computation and skill in computerizing. Antoine Henry Jomini had the first systematic try to definition this word with low accordance and connected to the other war elements. He was a French commentator and war writer. He defined the logistic in his book "the brief art of war" in 1838 like this: logistic is the scientism art of militaries movement. Based on his definition, the logistic apparently consists of all supporting and moving activities of militaries such as, planning.
2.4. Facility location
Facility location, also known as location analysis or k center problem, is a branch of operations research and computational geometry concerning itself with mathematical modeling and solution of problems concerning optimal placement of facilities in order to minimize transportation costs, avoid placing hazardous materials near housing, outperform competitors' facilities, etc. Although originated from location problems, the study also applies to data clustering, which in turn is related to unsupervised learning, classification, databases, spatial range-searching, data-mining etc. [5] (Tai-Hsi Wu, Chinyao Low, Jiunn-Wei Bai, 2001).
3. Research Methodology
Case Study
This study has been conducted in Tondar 90 assembly shop in Iran khodro Co. Car Tondar 90 is the same car L90 that is produced in Renault Co in France. For this study, the assembly shop has been segmented to four areas and each area is included three proposed location for establishing the temporary storage (stock). This assembly shop has eight assembly lines.
3.1. Segmentation and determination the proposed location of each area in Tondar 90 assembly shop in accordance with the opinion of experts
In this step of study, based on experts comments and their researches, has been taken this decision that be segmented Tondar 90 assembly shop to four areas and each area three proposed location to establishing temporary storage (stock). Assembly shop layout and four areas and three proposed locations in each area to establishing temporary storage, are shown in figure 2.
3.2. Calculation of main transportation indexes by teams of experts in each area for establishing temporary storage in Tondar 90 assembly shop
In this step of study, the main transportation indexes (Average of transportation standard time, transportation cost) has been calculated by three teams of industrial engineering experts in each area and proposed locations in each area to establishing stock in assembly
Figure 1. Research Practical Model Source: (Khamisabadi and etc., 2019)
shop. Data obtained from three expert groups are as follows:
3.2.1. The data obtained about four areas and proposed locations in each area from first expert team is as follows.
Area 2
(4)
itill
II ?-1
UJJJiSii (6.
(io"
Area 4
fmiiiit;
-—— i
i
13Ö
(12.
(2 Area (0
Ii 9 20 „ (3 i i i i i i i i s
S 10
S 1 II II i! 1
B B r H B B
i«. . i . _<■
i ! ! ! I | Area 3
1 £ I ST 3d 20 CA09267I E1 110 CA g (9
s ! i III ! § II 1
BEE! E — a E3R3E
Figure 2. Segmentation of Tondar 90 Assembly shop Layout Source: (Khamisabadi and etc., 2019)
Attention that:
t: average of transportation standard time for each pallet (seconds), c: transportation cost for each pallet (Taman per hours), PL: (number of) proposed location to establishing stock in assembly shop. Table 1 shows that:
1. The amounts of each main transportation indexes that obtained by first expert team in each area and each proposed location to establishing temporary storage (stock) in assembly shop.
Tablel
The data obtained about four areas and proposed locations in each area
from first expert team
Index First area Second area Third area Fourth area
PL: 1 PL: 2 PL: 3 PL: 4 PL: 5 PL: 6 PL: 7 PL: 8 PL: 9 PL: 10 PL: 11 PL: 12
t 76 81 83 63 74 58 68 72 76 86 79 66
c 295 281 258 248 263 287 283 266 273 297 275 286
Source: (Khamisabadi and etc., 2019)
2. Average of transportation standard time for each pallet (seconds) calculated by first expert team by using of
Stop Watch technique.
3. Transportation cost has been calculated according to optimum total expected cost (TEC) by using of linear programming, by first industrial engineering expert team and their experts in department of financial management (Taman per hours).
3.2.2. The data obtained about four areas and proposed locations in each area from second expert team is as follows.
Table 2
The data obtained about four areas and proposed locations in each area from second expert team
Index First area Second area Third area Fourth area
PL: 1 PL: 2 PL: 3 PL: 4 PL: 5 PL: 6 PL: 7 PL: 8 PL: 9 PL: 10 PL: 11 PL: 12
t 65 79 90 67 81 74 72 82 68 82 73 64
c 269 277 282 253 271 274 279 286 266 287 265 270
Source: (Khamisabadi and etc., 2019)
Table 2 shows that:
1. The amounts of each main transportation indexes that obtained by second expert team in each area and each proposed location to establishing temporary storage (stock) in assembly shop.
2. Average of transportation standard time for each pallet (seconds) calculated by second expert team by using of Stop Watch technique.
3. Transportation cost has been calculated according to optimum total expected cost (TEC) by using of linear programming, by second industrial engineering expert team and their experts in department of financial management (Taman per hours).
3.2.3. The data obtained about four areas and proposed locations in each area from third expert team is as follows.
Table 3
The data obtained about four areas and proposed locations in each area
from third expert team
Index First area Second area Third area Fourth area
PL: 1 PL: 2 PL: 3 PL: 4 PL: 5 PL: 6 PL: 7 PL: 8 PL: 9 PL: 10 PL: 11 PL: 12
t 79 74 83 72 80 71 87 75 73 68 76 82
c 272 275 286 284 280 273 283 277 275 270 287 279
Source: (Khamisabadi and etc., 2019)
Table 3 shows that:
1. The amounts of each main transportation indexes that obtained by third expert team in each area and each proposed location to establishing temporary storage (stock) in assembly shop.
2. Average of transportation standard time for each pallet (seconds) calculated by third expert team by using of
Stop Watch technique.
3. Transportation cost has been calculated according to optimum total expected cost (TEC) by using of linear programming, by third industrial engineering expert team and their experts in department of financial management (Taman per hours).
3.3. Determination the control limits for amounts of the transportation main indexes based on obtained data by each expert team
The main aim in this step of study is determination the control limits for amounts of the transportation main indexes in each area to classifying the obtained data by each expert team. The aim of classifying these data is adjustment obtained data with fuzzy weights in next steps for final decision making.
Rt = Rt (max) — Rt (min) , Rc = Rc (max) — Rc (min)
= y ti - y Rt - y Ri
, t = , Rt = ^ ' , Rc
c' c' ' n n
y c -_yti __y c ÍUCLJ = t + A2Rt ÍUCLc = c + A2Rt
t =
m
m ' [LCLt = t - A2Rt ' (LCLc = c - A2Rt n : number of Area = 4 , A2 = 0.729
3.3.1. Determination the control limits for amounts of the transportation main indexes based on obtained data by first expert team.
Table 4
The average of transportation standard time and transportation cost, Range of obtained data by first expert team
Area t, c. Rti RCi
1 80 278 7 37
2 65 266 16 39
3 72 274 8 17
4 77 286 20 22
Source: (Khamisabadi and etc., 2019)
t, = 195 = 65 , c, = 798 = 266, Rt = 60 = 15, Rc == 28.75 , Rt = 74- 58 = 16
1 3 1 3 t 4 c 4 tl
= 1104 = 297 iUCLt = 73.5 + 0/729 x12.75 = 82.794 fUCLc = 276 + 0.729 x 28.75 = 296.96
c =-= 276 , t =-= 73.5 -! , -!
4 4 [LCLt = 73.5 - 0.729 x17.75 = 64.205 [LCLc = 276 - 0.729 x 28.75 = 255.04
Table 4 shows that:
1. The average of transportation standard times of three proposed location in each area.
2. The average of transportation costs of three proposed location in each area.
3. The Range of transportation standard times of three proposed location in each area.
4. The Range of transportation standard costs of three proposed location in each area. 3.3.2. Determination the control limits for amounts of the transportation main indexes
based on obtained data by second expert team.
Table 5
The average of transportation standard time and transportation cost, Range of obtained data by second expert team
Area t, c, Rti RCi
1 78 276 25 13
2 74 266 14 21
3 74 277 14 20
4 73 274 18 22
Source: (Khamisabadi and etc., 2019)
222 798 _ 71 _ 76
t. =-= 74 , c1 =-= 266 , Rt = 81-67 = 14 , Rc = 274- 253 = 21 , Rt = — = 17.75 , Rc = — = 19
1 3 3 1 4 4
- 1093 = 299 [UCLt = 74.75 + 0.729 x17.75 = 87.69 iUCL? = 273.25 + 0.729 x 19 = 287.101
c =-= 273.25 , t =-= 74.75 i , i c
4 4 [LCLt = 74.75 - 0.729 x17.75 = 61.81 |LCLc = 273.25 - 0.729 x 19 = 259.4
Table 5 shows that:
1. The average of transportation standard times of three proposed location in each area.
2. The average of transportation costs of three proposed location in each area.
3. The Range of transportation standard times of three proposed location in each area.
4. The Range of transportation standard costs of three proposed location in each area. 3.3.3. Determination the control limits for amounts of the transportation main indexes
based on obtained data by third expert team.
Table 6
The average of transportation standard time and transportation cost, Range of obtained data by third expert team
Area ti c, Rti RCi
1 78.666 277.666 9 14
2 74.333 279 9 11
3 78.333 278.333 14 8
4 75.333 278.666 14 17
Source: (Khamisabadi and etc., 2019)
Rt = 46 = 11.5, Rc = 12.5, C = 1113:665 = 278.416 = 306665 = 76.666 ' 4 c 4 4
fUCLt = 76.666 + 0.729 x11.5 = 85.05 [UCLc = 278.416 + 0.729 x12.5 = 287.428
|LCLt = 76.666 - 0.729 x11.5 = 68.283 , [LCLc = 278.416 - 0.729 x12.5 = 269.304
Table 6 shows that:
1. The average of transportation standard times of three proposed location in each area.
2. The average of transportation costs of three proposed location in each area.
3. The Range of transportation standard times of three proposed location in each area.
4. The Range of transportation standard costs of three proposed location in each area.
3.4. Classifying transportation main indexes based on obtained data by expert teams
to adjustment amounts of transportation main indexes in each area with fuzzy weights
In this step of study, by using of the obtained amounts of upper control limits and lower control limits for each transportation main indexes, data classification is done. The main aim of this data classification, adjustment the calculated amounts of each transportation main index by each expert team with fuzzy weights. In fact, determination the levels that calculated amounts of each transportation main index, be placed in it. The number of levels in this classification is seven levels. In fact, the number of levels is the same of number of fuzzy weights.
r r r , mu data-min data
.
numbEC of ]evris
Table 7
How to calculate the levels length to data classifying
Index Information First expert team Second expert team Third expert team
ti Cj ti Cj ti Cj
Min data 64.205 255.04 61.81 259.4 68.283 269.304
Max data 82.794 296.96 87.69 287.101 85.05 287.428
Number of levels 7 7 7 7 7 7
Levels length 2.655 5.988 3.697 3.957 2.395 2.589
Source: (Khamisabadi and etc., 2019)
Table 8
Linguistic variables to determine the weight (trapezoidal fuzzy numbers) of each
negative index
Very low VL (8, 9, 10, 10)
Low L (7, 8, 8, 9)
Lower than average LA (5, 6, 7, 8)
Average A (4, 5, 5, 6)
Mora than average MA (2, 3, 4, 5)
High H (1, 2, 2, 3)
Very high VH (0, 0, 1, 2)
Source: (Chen, 2000)
Table 9
Classifying transportation main indexes based on obtained data by each expert
team
Classifying transportation main indexes based on obtained data by each expert team
Level obtained data by first expert team obtained data by second expert team obtained data by third expert team
Classifying of tj Classifying of C; Classifying of tj Classifying of C; Classifying of t; Classifying of C;
1 64.205 < t; < 66.86 255.04 < Cj < 261.028 61.81 < tj < 65.507 259.4 < Cj < 263.357 68.283 < t; < 70.678 269.304 < C; < 271.893
2 66.86 < t; < 69.515 261.028 < Cj < 267.016 65.507 < ti < 69.204 263.357 < Cj < 267.314 70.678 < t; < 73.073 271.893 < Cj < 274.482
3 69.515 < t; < 72.17 267.016 < Cj < 273.004 69.204 < < 72.901 267.314 < Cj < 271.271 73.073 < t; < 75.468 274.893 < C < 277.071
4 72.17 < t; < 74.825 273.004 < Cj < 278.992 72.901 < tj < 76.598 271.271 < Cj < 275.228 75.468< t; < 77.863 277.071 < Cj < 279.66
5 74.825 < t; < 77.48 278.992 < Cj < 284.98 76.598< t; < 80.295 275.228 < Cj < 279.185 77.683 < t; < 80.258 279.66 < Cj < 282.249
6 77.48 < t; < 80.135 284.98 < Cj < 290.968 80.295 < t; < 83.992 279.185 < Cj < 283.142 80.258 < t; < 82.653 282.249 < C; < 284.838
7 80.135 < t; < 82.794 290.968 < Cj < 296.96 83.992 < t; < 87.69 283.142< C; < 287.101 82.653 < t; < 85.05 284.838 < Cj < 287.428
3.5. Adjustment amounts of transportation main indexes in each area with fuzzy weights
In this step of study, be adjusting amounts of transportation main indexes in each area with fuzzy weights. In fact, the average of transportation standard times and also the average of transportation cost in each area, be adjusting with fuzzy weights.
Table 10
Adj ustment amounts of transportation main indexes in each area with fuzzy
we ig h ts
Adjustment amounts of transportation main indexes in each area with fuzzy weights based on obtained data by first expert team
area ti level Status trapezoidal fuzzy numbers Ci level status trapezoidal fuzzy numbers
1 80 6 High (1, 2, 2, 3) 278 4 Average (4, 5, 5, 6)
2 65 1 Very low (8, 9, 10, 10) 266 2 Low (7, 8, 8, 9)
3 72 3 Lower than average (5, 6, 7, 8) 274 4 Average (4, 5, 5, 6)
4 77 5 More than average (2, 3, 4, 5) 286 6 High (1, 2, 2, 3)
Adjustment amounts of transportation main indexes in each area with fuzzy weights based on obtained data by second expert team
area ti level Status trapezoidal fuzzy numbers Ci level status trapezoidal fuzzy numbers
1 78 5 More than average (2, 3, 4, 5) 276 5 More than average (2, 3, 4, 5)
2 74 4 Average (4, 5, 5, 6) 266 2 low (7, 8, 8, 9)
3 74 4 Average (4, 5, 5, 6) 277 5 More than average (2, 3, 4, 5)
4 73 4 Average (4, 5, 5, 6) 274 4 Average (4, 5, 5, 6)
Adjustment amounts of transportation main indexes in each area with fuzzy weights based on obtained data by third expert team
area ti level Status trapezoidal fuzzy numbers Ci level status trapezoidal fuzzy numbers
1 78.666 5 More than average (2, 3, 4, 5) 277.666 4 Average (4, 5, 5, 6)
2 74.333 3 Lower than average (5, 6, 7, 8) 279 4 Average (4, 5, 5, 6)
3 78.333 5 More than average (2, 3, 4, 5) 278.333 4 Average (4, 5, 5, 6)
4 75.333 3 Lower than average (5, 6, 7, 8) 278.666 4 Average (4, 5, 5, 6)
Source: (Khamisabadi and etc., 2019)
3.6. Make the decision matrix based on fuzzy data obtained by teams of experts in each area
In this step of study, according to the obtained data by three expert teams and table 10, make the decision matrix.
Table 11
Fuzzy data according to the obtained data by three expert teams to make the decision matrix
Index alternative First expert team second expert team Third expert team
Transportation Standard Time First area (1, 2, 2, 3) (2, 3, 4, 5) (2, 3, 4, 5)
Second area (8, 9, 10, 10) (4, 5, 5, 6) (5, 6, 7, 8)
Third area (5, 6, 7, 8) (4, 5, 5, 6) (2, 3, 4, 5)
Fourth area (2, 3, 4, 5) (4, 5, 5, 6) (5, 6, 7, 8)
Transportation Cost First area (4, 5, 5, 6) (2, 3, 4, 5) (4, 5, 5, 6)
Second area (7, 8, 8, 9) (7, 8, 8, 9) (4, 5, 5, 6)
Third area (4, 5, 5, 6) (2, 3, 4, 5) (4, 5, 5, 6)
Fourth area (1, 2, 2, 3) (4, 5, 5, 6) (4, 5, 5, 6)
Source: (Khamisabadi and etc., 2019)
Table 12
Make the decision matrix
~ ■—-—Index alternative " -—^^^ Transportation standard Time transportation Cost
First area (2, 3, 4, 5) (4, 5, 5, 6)
Second area (5, 6, 7, 8) (7, 8, 8, 9)
Third area (4, 5, 5, 6) (4, 5, 5, 6)
Fourth area (4, 5, 5, 6) (4, 5, 5, 6)
Source: (Khamisabadi and etc., 2019)
3.7. Decision making about selection of the best area for establishing temporary storage (stock) by using of Fuzzy TOPSIS Technique
In this step of study, final decision making about selection of the best area for establishing the temporary storage (stock) in Tondar 90 assembly shop by using of fuzzy Topsis technique is done.
Technique for order performance by similarity to ideal solution (TOPSIS), one of known classical MCDM method, was first developed by Hwang and Yoon (1981) for solving MCDM problems. TOPSIS is known as one of the most classical MCDM methods, which is based on the idea, that the selected alternative should have the shortest distance from the positive ideal solution and on the other side the farthest distance of the negative ideal solution (Chen and Hwang, 1982). The TOPSIS-method will be applied to a case study, which is described in detail. In classical MCDM methods, the ratings and the weights of the criteria are known precisely (Jahanshahlou et al, 2006), Decision making process steps by fuzzy TOPSIS technique are shown below:
Step 1: calculating weights vector w~j.
Step 2: normalizing the calculated matrix:
* =[].
(1)
B c {1,...,n} is related to benefit-based indices and C c {1,...,n} is related to cost-based indices.
r.. =
'j
f a., b.. c.. d.. ^
j j j j
d* d* d* d*
d'j c'j b'j a'j
j e B
j 6 C
Step 3: so normalized weighted matrix is calculated as formula 4:
^ =1^ ] m
' = 1,2,..., m, j = 1,2,
, n V ,
i w
(2)
(3)
(4)
'j 'J J
Step 4: determining the fuzzy positive ideal solution vj. (FPIS) and fuzzy negative ideal solution (FNIS) (formulas 5, 6):
mm v.,
; j 6 B
j i max v j; j 6 c
\ max ~. ; j 6 B
=f=1,--m~j C
j I min v ; j 6 C
I i=1,...,m J
(5)
FNIS = {v: | j = 1,..., n}
FPIS = | j = 1, ...,
(6)
Step 5: calculating the alternatives from positive and negative ideal by applying formulas 7 and 8:
d* = i = 1, m (7)
j=l
n
di = Y.d(yjV)' i =1' ...' m (8)
j=i
Step 6: Calculating the relative closeness to the ideal solution:
cc. = -d_ (9)
i + di
In real-world situation, because of incomplete or non-obtainable information, the data (attributes) are often not so deterministic, there for they usually are fuzzy /imprecise. So, we try to extend TOPSIS for fuzzy data to categorize the driving factors affecting on intellectual capital. The expert team's comments about importance of each transportation main index are:
Table 13
The expert team's comments about importance of each transportation main index
Index The first expert team's comments the second expert team's comments the third expert team's comments
status trapezoidal fuzzy numbers status trapezoidal fuzzy numbers status trapezoidal fuzzy numbers
Transportation Standard Time High (7, 8, 8, 9) High (8, 9, 10, 10) high (7, 8, 8, 9)
Transportation Cost Very high (8, 9, 10, 10) High (8, 9, 10, 10) Very high (8, 9, 10, 10)
Source: (Khamisabadi and etc., 2019)
Table 14
Fuzzy weights matrix
Index Fuzzy weight
Transportation Standard Time (7, 8, 8, 9)
Transportation Cost (8, 9, 10, 10)
Source: (Khamisabadi and etc., 2019)
Table 15
Fuzzy weighted normalized matrix
Index alternative -------- transportation standard time transportation Cost
First area (0.31, 0.43, 0.57, 1) (0.23, 0.35, 0.35, 0.5)
Second area (0.19, 0.24, 0.28, 0.4) (0.154, 0.175, 0.19, 0.24)
Third area (0.25, 0.35, 0.35, 0.5) (0.23, 0.35, 0.35, 0.5)
Fourth area (0.25, 0.35, 0.35, 0.5) (0.23, 0.35, 0.35, 0.5)
Source: (Khamisabadi and etc., 2019)
And finally by applying formulas 7, 8 and 9, fuzzy positive ideal solution, negative ideal solution and the relative closeness to the ideal solution were calculated which are shown in table 17.
Table 16
Final indices ranks
Alternative di+ di- Cci Ranks
First area 0.52 0 0 4
Second area 0 0.3 1 1
Third area 0.256 0.254 0.507 2
Fourth area 0.256 0.254 0.507 2
Based on table 17, second area in layout of Tondar 90 assembly shop is the best area for establishing the temporary storage (stock).
3.8. Determination the control limits for amounts of the transportation main indexes for each proposed location in selected area
Table 17
the average of transportation standard time and transportation cost, range of obtained data by three expert teams in proposed location in second area
Proposed location tt Rt Rc
in second area
4 67.33 261.66 9 36
5 78.33 271.33 7 17
6 67.66 278 16 14
Source: (Khamisabadi and etc., 2019)
- 63 + 72 + 67 _ 248 + 284+253 = 213.32 = - -
t1 =---= 67.333, C1 =---= 261.666, t = —-— = 71.106 , c = 270.33 Rt = 10.660 , Rc = 22.33
UCLC = 270.33 +1.023 x22.33 = 293.173
c n = 3 ^ A2 = 1.023
LCLC = 27.033 -1.023 x 22.33 = 247.486 2
Table 17 shows that:
1. The average of transportation standard times of three proposed location in the second area that obtained by three expert teams.
2. The average of transportation costs of three proposed location in the second area that obtained by three expert teams.
3. The Range of transportation standard times of three proposed location in the second area that obtained by three expert teams.
4. The Range of transportation standard costs of three proposed location in the second area that obtained by three expert teams.
3.9. Classifying transportation main indexes in each proposed location to adjustment mounts of transportation main indexes in each proposed location in second area with fuzzy weights
In this step of study, by using of the obtained amounts of upper control limits and lower control limits for each transportation main indexes, data classification is done. The main aim of this data classification, adjustment the calculated amounts of each transportation main index by each expert team with fuzzy weights. In fact, determination the levels that calculated amounts of each transportation main index, be placed in it. The number of levels in this classification is seven levels. In fact, the number of levels is the same of the number of fuzzy weights.
Table 18
How to calculate the levels length to data classifying
Index
Information^^^^^^^ ti Ci
Min data 60.201 247.486
Max data 82.011 293.173
Number of levels 7 7
Levels length 3.115 6.526
Source: (Khamisabadi and etc., 2019)
Table 19
Classifying transportation main indexes based on obtained data by each expert
team
Level obtained data by three expert teams
Classifying of tj Classifying of c;
1 60.201 < t; < 63.316 247.486 < q < 254.012
2 63.316 < tj < 66.431 254.012 < q < 260.538
3 66.431 < tj < 69.546 260.538 < Cj < 267.064
4 69.546 < tj < 72.661 267.064 < c; < 273.6
5 72.661 < tj < 75.776 273.6 < c; < 280.126
6 75.776 < t; < 78.891 280.126 < cj < 286.652
7 78.891 < t; < 82.011 286.652 < c; < 293.173
Source: (Khamisabadi and etc., 2019)
3.10. Adjustment amounts of transportation main indexes in each proposed location in second area with fuzzy weights
In this step of study, be adjusting amounts of transportation main indexes in proposed locations of second area with fuzzy weights. In fact, the average of transportation standard times and also the average of transportation cost in second area, be adjusting with fuzzy weights.
Table 20
Adjustment amounts of transportation main indexes in proposed locations of second area with fuzzy weights based on obtained data by each expert team
Proposed level Status trapezoidal level status trapezoidal
location ti fuzzy num- Ci fuzzy num-
bers bers
4 67.33 3 Lower than (5, 6, 7, 8) 261.66 3 Lower than (5, 6, 7, 8)
average average
5 78.33 6 High (1, 2, 2, 3) 271.33 4 Average (4, 5, 5, 6)
6 67.66 3 Lower than (5, 6, 7, 8) 278 5 more than (2, 3, 4, 5)
average average
3.11. Make the decision matrix based on fuzzy data obtained by teams of experts in proposed locations in second area
In this step of study, according to the obtained data by three expert teams and table20, make the decision matrix to selection of the best proposed location in the second area to establishing the temporary storage (stock).
Table 21
Make the decision matrix
" "—-—Index Alternative "—-—^^ Transportation standard Time transportation Cost
Proposed location 4 (5, 6, 7, 8) (5, 6, 7, 8)
Proposed location 5 (1, 2, 2, 3) (4, 5, 5, 6)
Proposed location 6 (5, 6, 7, 8) (2, 3, 4, 5)
Source: (Khamisabadi and etc., 2019) Table 22 Fuzzy weighted normalized matrix
"—-—^^^ Index alternative " -— transportation standard time transportation Cost
Proposed location 4 (0.096, 0.124, 0.144, 0.2) (0.087, 0.1, 0.128, 0.174)
Proposed location 5 (0.254, 0.435, 0.435, 1) (0.116, 0.14, 0.154, 0.217)
Proposed location 6 (0.096, 0.124, 0.144, 0.2) (0.14, 0.175, 0.254, 0.435)
Source: (Khamisabadi and etc., 2019)
3.12. Decision making about selection of the best proposed locations in second area for establishing temporary storage (stock) by using of Fuzzy TOPSIS Technique
And finally by applying formulas 7, 8 and 9, fuzzy positive ideal solution, negative ideal solution and the relative closeness to the ideal solution were calculated which are shown in table 23.
Table 23
Final indices ranks
alternative di+ di- Cci Ranks
Proposed location 4 1.375 0.88 0.39 1
Proposed location 5 1.05 0.121 0.107 3
Proposed location 6 1.237 0.216 0.148 2
Source: (Khamisabadi and etc., 2019)
Based on table 23, first proposed location of second area in layout of Tondar 90 assembly shop is the best location for establishing the temporary storage (stock).
4. Conclusion and suggestion
According to the results obtained in this study, the role of determination the optimum location to establishing the temporary storage in Tondar 90 assembly shop, to on time and
optimum supplying of Raw material required for each assembly lines, is very importance. The optimum establishing of temporary storage (stock) in assembly shop, cause to increasing efficiency of supplying operators, efficient use of transport equipments and in general , supply and logistics system performance is improved. Performance optimization of logistics and supply systems in organization, cause to facilitate the production process, reduced assembly lines stoppages due to material shortage and ultimately, cause to reduce the total expected cost of productions.
In this study, the layout of Tondar 90 assembly shop to establishing the temporary storage (stock) is segmented to four main areas and each area is segmented to three proposed location. According to the results obtained in this study, the best location for establishing the temporary storage in Tondar 90 assembly shop is second area and the best proposed location in the second area is proposed location 4.
This results of study, is obtained based on studies of three group of industrial engineering and also supply main indexes (transportation cost and transportation standard time).
Because of variety of variations, it is not possible to control the total variations that mean that some impressive variations on the result of research are out of control. So, it is suggested that the related researches in this filed should be done by all impressive variations.
As for the optimization of supply indexes in supply chain management and how to establishing the temporary storage, how to locating it and it's relating to the costs is a new issue in Iranian organizations, so in considering of its indexes and in organizations and filed study, this research has been faced with the previous researches limitations. Also it is suggested that research in this field should be done by impressive various indexes in supply chain management systems and different organizations.
ИСТОЧНИКИ:
1. Alinaghian M., Borouzi M. Introduce a mathematical programming model in order to
locating the warehouses in the vehicle routing // Journal of science. - 2010. - № 49. - p. 286-99.
2. Christofides N., Hadji constantinou E, Mingozzi A. Have presented a model for determin-
ing the order point and optimizing the size of order considering the transportation costs. - The Management School imperial Collage, London, 405(1), 1993. - 248-220 p.
3. Dantzig G.B, Fulderson R., Johnson S.M. Solution of a Large Scale Traveling Salesman
Problem // Management Science. - 2016. - № 6. - p. 91-80.
4. Rusdiansyah A., De-bi Tsao An integrated model of the periodic delivery problems for
vending-machine supply chains // Management Science. - 2004. - № 5. - p. 191-180.
5. Tai-Hsi Wu, Chinyao Low, Jiunn-Wei Bai Heuristic solutions to multi-depot location-
routing problems // Management Science. - 2001. - № 200(2). - p. 77-100.
6. Tolouei ashalaghi Abbas, Mohadeseh M. Introduce a Machinery optimal layout method
using a mathematical model // Management Researches. - 2010. - № 21(87). - p. 81-94.
7. Mehrmanesh H., Khamisabadi J., et al. Introducing a Model in Order to Logistics Balance
with the Aim of Improving for Total Expected Cost // Case Study: Tondar 90 Assembly
shop - Iran khodro Co: Journal of Basic and Applied Scientific Research 3(3). 2013. - p.
285-292.
8. Wen-Chyuan Chiang, Russellas R.A. Integrating purchasing and routing in a propane gas
supply chain // Management Science. - 2002. - № 30(3). - p. 88-101.
REFERENCES:
Alinaghian M., Borouzi M. (2010). Introduce a mathematical programming model in order to locating the warehouses in the vehicle routing Journal of science. (49). 286-99.
Christofides N., Hadji constantinou E, Mingozzi A. (1993). Have presented a model for determining the order point and optimizing the size of order considering the transportation costs
Dantzig G.B, Fulderson R., Johnson S.M. (2016). Solution of a Large Scale Traveling Salesman ProblemManagement Science. (6). 91-80.
Mehrmanesh H., Khamisabadi J., et al. (2013). Introducing a Model in Order to Logistics Balance with the Aim of Improving for Total Expected Cost Case Study: Tondar 90 Assembly shop - Iran khodro Co. 285-292.
Rusdiansyah A., De-bi Tsao (2004). An integrated model of the periodic delivery problems for vending-machine supply chains Management Science. (5). 191-180.
Tai-Hsi Wu, Chinyao Low, Jiunn-Wei Bai (2001). Heuristic solutions to multi-depot location-routingproblemsManagement Science. (200(2)). 77-100.
Tolouei ashalaghi Abbas, Mohadeseh M. (2010). Introduce a Machinery optimal layout method using a mathematical model Management Researches. (21(87)). 81-94.
Wen-Chyuan Chiang, Russellas R.A. (2002). Integrating purchasing and routing in a propane gas supply chainManagement Science. (30(3)). 88-101.
