Selection of a reconfigurable assembly systems strategy using fuzzy multi criteria decision making

CONTENTS
INTRODUCTION
PRESENT MANUFACTURING & MARKET SCENARIO
LITERATURE REVIEW
OBJECTIVE
CASE STUDY
OBSERVATION
IDENTIFICATION OF ASSEMBLY STRATEGIES
IMPLEMENTATION OF FUZZY MCDM TOOLS
RESULT
CONCLUSION
LIST OF PUBLICATIONS
REFERENCES

PRESENT MANUFACTURING & MARKET SCENARIO
In the recent years, the competition among the manufacturing industries have been
increased enormously in the global market.
The aggressive market competition: a minor change occurred in the product variety and
volume of products to fulfil the need of the customer, can induce a huge difference in
the long term survival of the industry. It also affects its reputation among the existing as
well as prospective clients.
The demand of the customers is not fixed, and also the market shares changes very
quickly in the global economic system. It becomes unmanageable for the manufacturing
industries to adapt quick changes in the market in terms of quantities and varieties by
utilizing the existing production systems.
So the manufacturing industries are now adapting new technologies for manufacturing
as well as assembling in the production sector to cope up with the fluctuating market
demand.

INTRODUCTION YO RECONFIGURABLE MANUFACTURING
SYSTEMS
Both DML and FMS are static; RMS are dynamic, with capacity and functionality changing in response to market changes.
Source: Y. Koren and M. Shpitalni, “Design of Reconfigurable Manufacturing Systems,” CIRP - Journal of Manufacturing Systems, pp. 130-141, 2010.

ASSEMBLY SYSTEMS
GENERALLY CLASSIFIED INTO THE FOLLOWING:
• Manual Assembly Systems : It consists of human assemblers with the help of simple tools and fixtures
• Flexible Assembly Systems : It consists of automated machines, robots as well as human assemblers
• Dedicated Assembly Systems : It consists of fully automated assembly systems for mass productions
ISSUES IN TRADITIONAL ASSEMBLY SYSTEMS
• Less Functionality ( Product type within part family)
• Variability including different model versions in a single assembly line
• Mass customization along with customizing each versions with addition and removal of
alternative components is very difficult
• Over or under producing of products due to change in demand.
• Retooling within a certain period of time to make different variety products is expensive.
• Lengthy change over time
• Low responsiveness to market fluctuations

INTRODUCTION RECONFIGURABLE ASSEMBLY
SYSTEMS
• RAS are those type of assembly systems which can rapidly change their :
Capacity (quantities that are assembled) and
Functionality (product type, within a product family)
in order to adapt the fluctuating and uncertainty in the market demand.
• This system can operate asynchronously and be reconfigured to achieve
a large variety of component choices according to which the different
variety of products can be assembled

(Continued)
• It also allows to quick rearrangement of assembly equipment to alter the
process flow according to the desired product.
• When the market demand or production task varies, the assembly line
will often reconstruct accordingly
• It is as an integrated, computer-controlled system of assembly robots,
modular components and tools that can be used for assembling a variety
of similar type of product

Modular RAS – A Model
Modular reconfigurable assembly system
Actuators Module
Rigid Link Connectors
and Tools
Assembled into
robots system with
random degree of
freedom and
geometry, which
can be reconfigured
in assembly
systems as per
requirement

RECONFIGURABLE ASSEMBLY LINE DEDICATED ASSEMBLY LINE
Product 3 Product 4
Product 1
Product 2
MODULES

KEY CHARACTERISTICS OF RAS
CUSTOMIZATION CONVERTIBILITY
SCALABILITY
RECONFIGURABLE
HARDWARE
RECONFIGURABLE
SOFTWARE

LITERATURE REVIEW
25 Papers have been surveyed, which are published between 1999 – 2016 from International Journal of
Production Research, CIRP Annals - Manufacturing Technology, Journal of Manufacturing Systems,
Journal of intelligent manufacturing, International Journal of Advance Manufacturing Technology, CIRP
Journal of Manufacturing Science and Technology and IEEE - Automation Science and Engineering
Finally 6 of them were made as base papers for further research work.
Paralikas, J., Fysikopoulos, A., Pandremenos, J., & Chryssolouris, G. (2011), “Product modularity and
assembly systems: An automotive case study”, CIRP Annals, 60, 165-168.
• Investigation regarding the influence of product design modularity, configuration and operation of assembly system
in a single line in which the modular design from the automotive industry was proposed and compared with the
traditional assembly line having three parallel assembly lines for 3 different variants
• These assembly configurations are compared according to perspective of assembly system responsiveness to
demand, cumulative delays, product delay (in days) with respect to shortest processing time, estimation of assembly
line utilization and investment costs per product for 3 different alternatives.
• This concluded that modular design has high product flexibility, more product delay due to single assembly line and
less cost per part.

Gyulaia, D., Véna, Z., Pfeiffera, A., Vánczaa, J., & Monostoria, L. (2012).
“Matching Demand and System Structure in Reconfigurable assembly
systems”, 45th CIRP Conference on Manufacturing Systems, 579-584.
• A simulation based technique was introduced which defined the limitations and
components of RAS according to historical order streams by learning through a real
production facility in the automotive industry.
• Two main issues were identified, defining product mix for a given time horizon which
could be produced in RAS, its configuration as well as the operational conditions of the
manufacturing system were formulated.
• This methodology separates the low volume and high volume products and product
family, thus refining production capacity. This study also showed when the technological
aspects of a production system is considered, it might support capacity design decisions.
• Furthermore, formal model of the system was employed in which determined the optimal
solutions for the product mix problems.

Benkamoun,N., Huyet,L. & Kouiss,K. ;(2013), “Reconfigurable Assembly System
configuration design approaches for product change”, Proceedings of 2013
International Conference on Industrial Engineering and Systems Management
(IESM).
• This paper reviews the main strategies dealing with variety and product change in assembly
system design
• Focusing on assembly system level, different configurations levels of representations, and also
configuration perspective definitions are highlighted, like physical layout (arrangement of
workstations) and logical layout (task assignment, with or without resources selections).
• For these different viewpoints, common strategies of configuration design for variety are
identified and optimal system configuration for a mix of different products were found.
• The interest of reconfigurability paradigm for product change has been pointed out as essential
for current and future market context

Kashkoush, M., & ElMaraghy, H. (2014). “Product family formation for reconfigurable
assembly systems”,Variety Management in Manufacturing – Proceedings of the 47th
CIRP Conference on Manufacturing Systems, 17, 302 – 307.
• A product family formation method was proposed to a RAS which results in improving its utilization and
productivity and therefore reinforcing cost effective production.
• In this approach product assembly sequence was used as well as product demand, commonality as
similarity coefficients, this commonality coefficient measures the level by which components are shared
among the group of products.
• It is represented in the form of binary rooted trees. Moreover, average hierarchical clustering was applied
to construct different clusterings based on the similarity coefficient as well as product demand and
commonality.
• The proposed method was employed in a suitable example having 8 products, in which according to the
level of similarity, number of product families clustered were determined and thus improvement in system
efficiency and productivity were determined.

Abdi, M. R., & Labib, A. W. (2010). A design strategy for reconfigurable
manufacturing systems (RMSs) using analytical hierarchical process (AHP): A
case study. International Journal of Production Research, 2273–2299.
• A design strategy for Reconfigurable Manufacturing System (RMS) was proposed having various
criteria and alternatives which were identified for design strategy for implementing RMS.
• The strategical approach was carried out through case study in a manufacturing environment.
As a result, multi-criteria decision-making problem was developed in which the experts related
to different departments rated their opinions.
• The developed MCDM was solved using Analytical Hierarchical Process (AHP) tool, which also
highlighted the manufacturing responsiveness that was taken as a new economic objective as
well as considering the traditional objectives like high quality and low cost.
• The results were also analysed through sensitivity analysis by changing the priorities of criteria
which helps to determine the best solutions among the alternatives in that existing
manufacturing industry.

Colledani, M., Monostori, L. & Unglert, J.,;(2016), “Design and
management of reconfigurable assembly lines in the automotive
industry”, CIRP-Annals Manufacturing Technology- . 441–446
• A methodology was proposed for the design and reconfiguration management of
modular assembly systems.
• It addresses the selection of the technological modules, their integration in the
assembly cell, and the reconfiguration policies to handle volume and lot size
variability.
• It is aimed for efficient design and management of modular reconfigurable
assembly systems and also at reducing the overall design time.
• The applicability of the proposed method is justified by an industrial case study
of an automotive supplier of body parts

RESEARCH GAP
• Very few simulation tools tools are available for its optimization to get better
results and for its design and operation purposes, still lacks transparency for
low volume products or all of a sudden change in demand to lower volumes in
the same assembly system.
• In some cases, if there is a single assembly line having fully RAS or partial RAS,
some of the issues arises such as bottle neck and system over-utilization as
well as under-utilization during an assembling sequence of a particular
product. Therefore, it causes unavoidable delay and buffers at the preceding
assembly stations.
• A robust Mathematical model is needed to be developed for market
uncertainties, customization and sudden changes in demand for
implementation of RAS.

Continued.
• An optimized configuration of assembling tools in RAS is needed to
be developed for quick change over of assembling a particular type of
product to other type maintaining the required system throughput as
per the demand of the customer.
• A strategical approach for implementation of RAS in the Industry
using Multi-Criteria Decision Making was not found in the present
literature survey

IDENTIFICATION OF VARIOUS RESEARCH ISSUES OF RAS
DESIGN ISSUES
PRODUCT FAMILY FOR RAS
DESIGN OF MODULAR
PRODUCTS
DESIGN OF OPTIMUM
CONFIGURATION FOR RAS
OPTIMUM DESIGN FOR
ASSEMBLY
IDENTIFYING AND
QUANTIFYING THE DESIGN
REQUIREMENTS
OPERATIONAL ISSUES
USE OF PROPER
SIMULATION TOOL FOR RAS
IDENTIFYING QUALITY
PROBLEMS
FIXTURE-LESS ASSEMBLY
FOR RAS
OPTIMAL SCHEDULING OF
RAS

OBJECTIVES
1. Studying and analysing the existing assembly line for the case
study by industrial visits, collecting relevant information, their
present technology, products, competitors etc. and finally
identifying the issues present in their existing assembly systems.
2. Identification of the appropriate criteria and the strategies
related to assembly systems which can be applied to their
existing assembly systems with the help of the expert’s
opinion. This is, followed by formation of a Multi-Criteria
Decision Making (MCDM) problem.
3. Finally, identifying and selecting the suitable Multi-Criteria
Decision Making tool for solving the MCDM Problem and
determining the best alternative strategy which can be
implemented in their existing assembly systems.

GANTT CHART FOR THE WORK 2016 2016 2017 2017
WORK PLAN Jul Aug Sep Oct Nov Dec Jan Feb Mar April May June
Literature Survey
Identification of Literature Gap
Identification of Objectives
Going Through Various MCDM Tools
Industrial Visits
Issues Identifiction from Industrial Visit
Identification and Formulation of Multi-Criteria Problem
Application of a suitable MCDM tool to the identified problem in
RAS
Result
Thesis Writing
Submission
Completed
Projected

CASE STUDY
• Case study industry: TATA CUMMINS PRIVATE
LIMITED
• Tata Cummins Private Limited (TCL),
Jamshedpur: It is a 50:50 joint venture between
Cummins Inc. USA, the world’s largest
independent designer and manufacturer of
diesel engines. The main application of this
Engines includes in Busses, Trucks, Tractors
Trailers, Trippers etc.
• Area of interest in visit: Engine Assembly line
• Purpose: To collect relevant data about
assembly line and identifying some of the
present issues. Selection of optimum
assembling strategy from possible alternatives
available in the case study taken.
A survey was carried out regarding their methods
of assembly line moreover the varieties of
products assembled, assembly time, similar as
well as variant components, issues were studied

ENGINE TYPE CONTROL
SYSTEMS
DISPLACEMENT
( IN LITRES)
MODEL NAME
FUEL SYSTEMS
HORSE POWER
MECHANICAL
MECHANICA
LLY CONTROL
OF FUEL
SYSTEM
5.9
BS-1
BOSCH FUEL SYSTEM
130
BS-2 150
BS-3 180
ELECTRONIC
(ISBe)
ELECTRONIC
ALLY FUEL
CONTROL
5.9
BS-3
BOSCH FUEL SYSTEM
150
BS-4 180
6.7
BS-3 150
BS-4 180
230 300DAYTONA*
SPECIAL (ISBe*)
ELECTRONIC
ALLY FUEL
CONTROL
5.9
BS-4 (UMBRELLA)
(CFS) CUMMINS FUEL
SYSTEM AND PISTON
180 230
BRAKE
NON BRAKE
ENGINES TYPES AND VARIETIES PRODUCED AT TATA CUMMINS:
* - Special Purpose and Prototype Installed

FIGURES OF VARIANT MODELS
Mechanical BS-2 Mechanical BS-3 Electronic BS-4
Image Source: Department of After Testing Products, TATA CUMMINS Pvt. Ltd.

SOME KEY OBSERVATIONS:
• Various types of engines are assembled having some common/basic
components and some specific components.
• Variant components includes the type of fuel control systems, cylinder head,
engine blocks, connecting rods, fasteners, gear cover type etc.
• Capacities of assembling systems per shift : 200 engines/shift with 85%
running efficiencies ( includes break, down time, parts movement time [avg.
20 sec], etc.)
• Average Cycle time for producing one engine is 159 Minutes.
• In every 2.25 minutes (average), one finished engine is produced from the
assembly.
• The core assembly process time for one engine is on an average 141.75 mins
• Total Number of Assembly Stations is 93 including leak test and quality test
inspection.

SOME ISSUES IDENTIFIED FROM EXISTING
ASSEMBLY SYSTEMS:
• The existing assembly system is not flexible enough to handle sudden increase in
demand, that is it lacks scalability.
• To meet more demand, an auxiliary assembly line is activated along side the original
assembly line with increase in man power to ramp up production maximum up to
10%
• Presently, line balancing is applied to increase the utilization.
• Lack of changes in hardware components in assembly line for quick change over
(functionality) and for quick scalability.
• The system does not have diagnosability at assembling stations. Separate inspection
stations are present to identify the faults/errors and if found then sent back for
rework.
• During changeover from one product to another, bottleneck issues and increase in
waiting time occurs at some assembling station.

STRUCTURE OF MCDM PROBLEM
LEVEL 1. SELECTION OF
EXPERTS
CRITERIA
ALTERNATIVES
MCDM TOOL
LEVEL 5. APPLICATION OF
FUZZY-VIKOR and FUZZY-
TOPSIS METHOD TO THE
MCDM PROBLEM

LEVEL 1. SELECTION OF EXPERTS
Experts belonging to different Departments of
the industry are selected for the MCDM problem
EXPERT 1 – Assistant
General Manager,
Department of
Manufacturing
Engineering (ME)
EXPERT 2 –
Application Manager,
Department of
Industrial and
Information
Technology
EXPERT 3 – Shop
Floor Manager,
Department of After
Testing Products (ATP)
EXPERT 4 – Assistant
General Manager,
Department of
Quality Control (QC)
IDENTIFICATIONOFSTRATEGIESRELATEDTO
RAS

The following criteria were selected with the help
of experts
SCALABILITY RESPONSIVENESS FUNCTIONALITY DIAGNOSABILITY
CHANGEOVER
EFFORT AND
TIME
RESOURCE
UTILIZATION
IDENTIFICATIONOFSTRATEGIESRELATEDTORAS
LEVEL 2. SELECTION OF CRITERIA

FUZZY MCDM
In the present work the ratings and weights are
collected from the experts in terms of
Trapezoidal Fuzzy Numbers (TrFN) in order to
implement Fuzzy based MCDM tools like Fuzzy
VIKOR and Fuzzy TOPSIS.
As the experts opinions are always
subjective in nature, hence to counter the
vagueness in the ratings and weights,
Fuzzy based MCDM tool is preferred to
conventional MCDM

VL L M H VH
µÃ(X)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Fig. Linguistic variables for importance weight of each criteria as well as alternatives
TRAPEZOIDAL FUZZY NUMBERS (TrFN) USED IN THE PRESENT WORK

CRITERIA
DECISION
MAKER 1
(P1)
DECISION
MAKER 2
(P2)
DECISION
MAKER 3
(P3)
DECISION
MAKER 4
(P4)
C1 SCALABILITY H H VH H
C2 RESPONSIVENESS H H VH H
C3 FUNCTIONALITY M M L M
C4 DIAGNOSABILITY M H H VH
C5
CHANGEOVER EFFORT
AND TIME
H H VH M
C6 RESOURCE
UTILIZATION
M M H H
IMPORTANCE OF EACH CRITERION BY DECISION MAKERS

IDENTIFICATION OF ALTERNATIVE STRATEGIES:
• Feasible alternatives (assembling choices) may differ from one company to another
because of influencing factors such as the available technology and adaptation to new one,
budget, existing system type and the volume and type of products to be assembled.
• According to the planning horizons’ priorities selection of alternatives:
(i) Short Term (ST): (under two years).
(ii) Medium Term (MT): (between 2 and 5 years).
(iii) Long Term (LT): (over 5 years)
The alternatives are selected based on long term planning horizon:
• The market fluctuation increases
• Demand of the engines varies ( Requirement of Scalability)
• Requirement of high variety of engines increases ( Requirement of Functionality)
• Requirement to produce and assemble latest European standard increases i.e. BS-4 Type or
BS-5 type of engines (Requirement of Mass Production)
• Forecasting about more requirement of highly customized engines for various heavy
vehicles with high efficiencies and less emission for the environment concern
(Requirement of Customization and quick re-configuration)
• Likelihood for changes in future through introduction of newer products in the market.

The following alternatives were identified with the help
of experts based on Long Term Planning Horizon
ALTERNATIVE 1 (A1)
– EXISTING
ASSEMBLY SYSTEMS
ALTERNATIVE 2 (A2)
– RECONFIURABLE
ASSEMBLY SYSTEMS
ALTERNATIVE 3 (A3)
– PARTIAL
RECONFIGURABLE
ASSEMBLY SYSTEMS
ALTERNATIVE 4 (A4)
– FULLY
AUTOMATED
ASSEMBLY SYSTEMS
IDENTIFICATIONOFSTRATEGIESRELATEDTORAS
LEVEL 3. SELECTION OF ALTERNATIVES

DECISION MAKERS ALTERNATIVES
CRITERIA
C1 C2 C3 C4 C5 C6
P1
A1 L M M M H M
A2 H VH H H L H
A3 M H H H L M
A4 M M L H H H
P2
A1 L M M M M M
A2 H H H VH L M
A3 M H H H L M
A4 M M M H VH H
P3
A1 VL M M M M M
A2 VH VH VH H VL M
A3 H H H H M M
A4 M H L H H H
P4
A1 L M M L M M
A2 H H H H L H
A3 H H H M M H
A4 M M L H H H

MULTI CRITERIA DECISION MATRIX
CRITERIA
C1 C2 C3 C4 C5 C6
P1 P2 P3 P4 P1 P2 P3 P4 P1 P2 P3 P4 P1 P2 P3 P4 P1 P2 P3 P4 P1 P2 P3 P4
WEIGHT H H VH H H H VH H M M L M M H H VH H H VH M M M H H
ALTERNATIVES
A1 L L VL L M M M M M M M M M M M L H M M M M M M M
A2 H H VH H VH H VH H H H VH H H VH H H L L VL L H M M H
A3 M M H H H H H H H H H H H H H M L L L M M M M H
A4 M M M M M M H M L M L L H H H H H VH H H H H H H

REPRESENTATION OF FUZZY NUMBERS OF EACH CRITERION WITH RESPECT TO EACH ALTERNATIVES
CRITERIA
C1 C2 C3 C4 C5 C6
P1 P2 P3 P4 P1 P2 P3 P4 P1 P2 P3 P4 P1 P2 P3 P4 P1 P2 P3 P4 P1 P2 P3 P4
WEIGHT
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.7,0.8,0.9,1.0)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.7,0.8,0.9,1.0)
(0.5,0.6,0.7,0.8)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.1,0.2,0.3,0.4)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.7,0.8,0.9,1.0)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.7,0.8,0.9,1.0)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
ALTERNATIVES
A1
(0.1,0.2,0.3,0.4)
(0.1,0.2,0.3,0.4)
(0.0,0.0,0.1,0.2)
(0.1,0.2,0.3,0.4)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.1,0.2,0.3,0.4)
(0.1,0.2,0.3,0.4)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
A2
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.7,0.8,0.9,1.0)
(0.5,0.6,0.7,0.8)
(0.7,0.8,0.9,1.0)
(0.5,0.6,0.7,0.8)
(0.7,0.8,0.9,1.0)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.7,0.8,0.9,1.0)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.7,0.8,0.9,1.0)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.7,0.8,0.9,1.0)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.5,0.6,0.7,0.8)
A3
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.3,0.4,0.5,0.6)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.5,0.6,0.7,0.8)
A4
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.3,0.4,0.5,0.6)
(0.5,0.6,0.7,0.8)
(0.3,0.4,0.5,0.6)
(0.1,0.2,0.3,0.4)
(0.3,0.4,0.5,0.6)
(0.1,0.2,0.3,0.4)
(0.1,0.2,0.3,0.4)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.1,0.2,0.3,0.4)
(0.0,0.0,0.1,0.2)
(0.1,0.2,0.3,0.4)
(0.1,0.2,0.3,0.4)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)
(0.5,0.6,0.7,0.8)

THE AGGREGATED FUZZY RATINGS:
The Aggregated Fuzzy ratings (Xijk) with respect to each criterion Cj can be
calculated as {Xijk= (Xijk1, Xijk2, Xijk3, Xijk4) such that i=1,2,…,m, j= 1,2, . . . ,n and
k=1,2,…,K} and
Xijk1 = mink{ Xijk1}
• Xijk2 =
k
=
1
K
Xijk2
• Xijk3 =
k
=
1
K
Xijk3
• Xijk4 = maxk{Xijk4}
Similarly Aggregated Fuzzy weights (Wjk) with respect to each criterion can be
calculated as {(Wjk1, Wjk2, Wjk3, Wjk4) such that j=1,2,…,n and k=1,2,…,K } and
• Wjk1 = mink{ Wjk1}
• Wjk2 =
k
=
1
K
Wjk2
• Wjk3 =
k
=
1
K
Wjk3
• Wjk4 = maxk{ Xijk4}

AGGREGATED FUZZY RATINGS
CRITERIA
C1 C2 C3 C4 C5 C6
WEIGHT (0.50,0.65,0.7
5,1.0)
(0.50,0.65,0.
75,1.0)
(0.10,0.35,0.
45,0.60)
(0.30,0.60,0.
70,1.0)
(0.30,0.60,0.
70,1.0)
(0.30,0.50,0.
60,0.80)
ALTERNATIVES
A1
(0.00,0.15,0.2
5,0.40)
(0.30,0.40,0.
50,0.60)
(0.30,0.40,0.
50,0.60)
(0.10,0.35,0.
45,0.60)
(0.10,0.35,0.
45,0.60)
(0.30,0.40,0.
50,0.60)
A2
(0.50,0.65,0.7
5,1.0)
(0.50,0.70,0.
80,1.0)
(0.50,0.65,0.
75,1.0)
(0.50,0.65,0.
75,1.0)
(0.50,0.65,0.
75,1.0)
(0.30,0.50,0.
60,0.80)
A3
(0.30,0.50,0.6
0,0.80)
(050,0.60,0.7
0,0.80)
(050,0.60,0.7
0,0.80)
(0.30,0.55,0.
65,0.80)
(0.30,0.55,0.
65,0.80)
(0.30,0.45,0.
55,0.80)
A4
(0.30,0.40,0.5
0,0.60)
(0.30,0.45,0.
55,0.80)
(0.10,0.25,0.
35,0.60)
(0.50,0.60,0.
70,0.80)
(0.00,0.15,0.
25,0.40)
(050,0.60,0.7
0,0.80)

DEFUZZIFICATION (CENTRE OF AREA METHOD)
 


µ(x)dx
.xµ
)X( ij
xdx
Defuzz
  
  










































 ij2
ij1
ij3
ij2
ij4
ij3
ij2
ij1
ij3
ij2
ij4
ij3
X
X
X
X
X
X ij3ij4
ij4
ij1ij2
ij1
X
X
X
X
X
X ij3ij4
ij4
ij1ij2
ij1
XX
X
dx
XX
X-X
XX
X
xdx
XX
X-X
xdx
X
xdx
xdx
X
xdx
   
4321
2
12
2
344321
3
1
3
1
ijijijij
ijijijijijijijij
XXXX
XXXXXXXX




DEFUZZIFIED CRISP VALUES
CRITERIA
C1 C2 C3 C4 C5 C6
WEIGHT 0.7305 0.7305 0.3694 0.6500 0.6500 0.5500
ALTERNATIVES
A1 0.2000 0.4500 0.4500 0.3694 0.3694 0.4500
A2 0.7305 0.7500 0.7305 0.7305 0.7305 0.5500
A3 0.5500 0.6500 0.6500 0.5694 0.5694 0.5305
A4 0.4500 0.5305 0.3305 0.6500 0.2000 0.6500

LEVEL 4. SELECTION OF MCDM TOOL
• Contains many conflicting criteria and alternatives
• Contains a lot of uncertainty in evaluating the alternatives and the
conflicting criteria
• It was assumed that a compromising solution is acceptable to resolve
the conflicts between the alternatives
• The compromised solution will be considered as the feasible solution
(close to ideal) which will help the decision makers to conclude the final
decision
• Extension of VIKOR and TOPSIS method were implemented in the Fuzzy
environment to find out the best feasible alternatives (FUZZY-VIKOR
and FUZZY-TOPSIS)

LEVEL 5. APPLICATION OF FUZZY-VIKOR
METHOD TO THE MCDM PROBLEM

1. FUZZY VIKOR METHODOLOGY
a. Determine the best f*
and the worst f-
values of all criterion ratings that is j=1,2,…,n
f*
=max (Xij)
f-
=min (Xij)
b. Now determine the utility measure Si and the regret measure Ri by the following
relation.
Si =

n
j
jW
1 i
iji
)f-*(f
)f-*(f
.
Ri = Max 






)f-*(f
)f-*(f
.
i
iji
jW
c. After that determine the VIKOR Index, Qj by using the following relation:
Qj =
 
 
 
 *
*
i
*
*
i
R
R
)1(
S
S
R
R
v
S
S
v





 
Where S*
= Min Si, S*
= Max Si, R*
= Min Ri, R*
= Max Ri and v is taken as a weight of
strategy of maximum group utility and 1-v is taken as weight of individual group regret.
d. Arrange the alternatives according to S,R and Q as per the increasing order.

DEFUZZIFIED CRISP VALUES
CRITERIA
C1 C2 C3 C4 C5 C6
WEIGHT 0.7305 0.7305 0.3694 0.6500 0.6500 0.5500
ALTERNATIVES
A1 0.2000 0.4500 0.4500 0.3694 0.3694 0.4500
A2 0.7305 0.7500 0.7305 0.7305 0.7305 0.5500
A3 0.5500 0.6500 0.6500 0.5694 0.5694 0.5305
A4 0.4500 0.5305 0.3305 0.6500 0.2000 0.6500
The Best (fi
*) and Worst Values (fi
-)

RESULT
ALTERNA
TIVES
fi
* fi
- Si Ri Qi
A1 0.45 0.20 1.1496 0.7305 0.6458
A2 0.75 0.55 0.7838* 0.55* 0
A3 0.65 0.5305 2.0381 0.6113 0.6698
A4 0.65 0.20 1.4305 0.65 0.4516
fi
* = The Best
fi
- = Worst Values
Si = Utility Factor Measure
Ri = The Regret Factor Measure
Qi = VIKOR Index

RANKING THE RESULT
ALTERNA
TIVES
fi
* fi
- Si Ri Qi
A1 0.45 0.20 1.1496 0.7305 0.6458
A2 0.75 0.55 0.7838* 0.55* 0
A3 0.65 0.5305 2.0381 0.6113 0.6698
A4 0.65 0.20 1.4305 0.65 0.4516
fi
* = The Best
fi
- = Worst Values
Qi = VIKOR Index
4
3
2
111
2
2
3
3
4
4

RANKING THE RESULTS
Ranking by Increasing
Order 1 2 3 4
S A2 A1 A4 A3
R A2 A3 A4 A1
Q A2 A4 A1 A3

CONDITIONS:
Finally propose a compromise solution, the alternative (A(1)
) which is the best solution
as ranked by the measure of VIKOR Index Q (minimum) if the following two
conditions are satisfied:
C1: Acceptable advantage:
Q(A(2)
)-Q(A(1)
) ≥ DQ
Where Q(A(2)
) is the alternative with second position in the ranking list
As per Q; DQ
)1(
1


j
C2: Acceptable stability in decision making:
Under this condition the alternatives are also ranked by S and R and compared with the
ranking of Q. This compromise solution is stable within a decision-making process,
which could be the strategy of maximum group utility (when v > 05 is needed), or ‘‘by
consensus” (v ≈ 0.5), or ‘‘with veto” (v < 0.5). If the ranking of the (A(1)
) for the
measures that is as per S and R are same, then (A(1)
) is the best solution.
If one of the conditions is not satisfied, then a following set of compromise solution is
proposed which includes:
 The alternatives, (A(1)
) and (A(2)
) if only condition C2 not satisfied,
Or
 Alternatives (A(1)
), (A(2)
),…, (A(M)
) if the condition C1 is not satisfied, where
(A(M)
) is determined by Q(A(M)
)- Q(A(1)
) ˂ DQ for maximum M.

• CONDITION 1
Q(A(2))-Q(A(1)) ≥ DQ
DQ=1/(J-1) = 1/(4-1) = 0.33
Q(A(2))-Q(A(1)) = 0.4516 – 0 = 0.4516 ≥ 0.33
Q(A(2)) = VIKOR INDEX VALUE OF THE 2ND RANKED
Q(A(1)) = VIKOR INDEX VALUE OF THE 1ST RANKED

RESULT
ALTERNA
TIVES
fi
* fi
- Si Ri Qi
A1 0.45 0.20 1.1496 0.7305 0.6458
A2 0.75 0.55 0.7838* 0.55* 0
A3 0.65 0.5305 2.0381 0.6113 0.6698
A4 0.65 0.20 1.4305 0.65 0.4516
fi
* = The Best
fi
- = Worst Values
Qi = VIKOR Index
4
3
2
111
2
2
3
3
4
4

• CONDITION 1
Q(A(2))-Q(A(1)) ≥ DQ
DQ=1/(J-1) = 1/(4-1) = 0.33
Q(A(2))-Q(A(1)) = 0.4516 – 0 = 0.4516 ≥ 0.33
THUS, CONDITION 1 SATISFIES

• CONDITION 1
Q(A(2))-Q(A(1)) ≥ DQ
DQ=1/(J-1) = 1/(4-1) = 0.33
Q(A(2))-Q(A(1)) = 0.4516 – 0 = 0.4516 ≥ 0.33
• CONDITION 2
If the ranking of the (A(1)) for the measures that is as per S and R are
same, then (A(1)) is the best solution.

4. RANKING THE RESULTS
Ranking by Increasing
Order
1 2 3 4
S A2 A1 A4 A3
R A2 A3 A4 A1
Q A2 A4 A1 A3

• CONDITION 1
Q(A(2))-Q(A(1)) ≥ DQ
DQ=1/(J-1) = 1/(4-1) = 0.33
Q(A(2))-Q(A(1)) = 0.4516 – 0 = 0.4516 ≥ 0.33
• CONDITION 2
If the ranking of the (A(1)) for the measures that is as per S and R are
same, then (A(1)) is the best solution.

ANALYSING CONDITIONS:
1. It is observed that according to the VIKOR Index (Q), the best
alternative is A2 and also the condition C1 (Q(A(2))-Q(A(1)) ≥ DQ)
2. C2 (A2 is ranked best as per the measures S and R) are satisfied
Thus, the best choice of alternative is A2 that is, the alternative
Reconfigurable Assembly System

2. APPLICATION OF FUZZY TOPSIS

NORMALIZED DECISION MATRIX USIX TOPSIS
METHODOLODY
rij =

j
ij
ij
x
x
1
2
for each Criterion Ci

NORMALIZED DECISION MATRIX
CRITERIA
C1 C2 C3 C4 C5 C6
WEIGHT 0.7305 0.7305 0.3694 0.6500 0.6500 0.5500
ALTERNATIVES
A1 0.1925 0.3713 0.3996 0.3103 0.3632 0.4092
A2 0.7033 0.6188 0.6488 0.6137 0.7183 0.5002
A3 0.5295 0.5363 0.5773 0.4783 0.5598 0.4825
A4 0.4333 0.4377 0.2935 0.5460 0.1966 0.5911

WEIGHTED NORMALIZED DECISION MATRIX
The Weight of each criterion is multiplied to their corresponding
normalized ratings to get the weighted normalized decision matrix as
shown in the table

WEIGHT DECISION MATRIX OF EACH COLUMN
C1 C2 C3 C4 C5 C6
A1 0.1406 0.2712 0.1476 0.2017 0.2360 0.2250
A2 0.5137 0.4520 0.2396 0.3989 0.4669 0.2751
A3 0.3867 0.3917 0.2132 0.3109 0.3638 0.2653
A4 0.3165 0.3197 0.1084 0.3549 0.1278 0.3251

REPRESENTATION OF THE TYPES IDEAL SOLUTION
CRITERIA
C1 C2 C3 C4 C5 C6
PIS 0.5137 0.4520 0.2396 0.3989 0.4669 0.3251
NIS
0.1406 0.2712 0.1084 0.2017 0.1278 0.2250

Separation between the alternatives and the
ideal solutions (Positive and Negative):

D   

m
j ij PISx1
2
and

D   

m
j ij NISx1
2
Relative Closeness To Ideal Solution, C*:







 

DD
D
C*

SEPARATIONS AND RELATIVE CLOSENESS FOR EACH ALTERNATIVE
ALTERNATIVES
A1 (EAS) A2 (RAS) A3 (PRAS) A4 (FAS/DAS)
D+ (+VE)IDEAL SOLN 0.5034 0.05 0.22 0.4362
D- (-VE)IDEAL SOLN
0.1150 0.5938 0.3937 0.2584
C*(RELATIVE CLOSENESS TO
IDEAL SOLUTION) 0.1859 0.9223 0.6415 0.6279
RANKING 4 1 2 3
RESULT

RANKING OF ALTERNATIVES AS PER THE
FOLLOWING METHODS
FUZZY-
MCDM
TOOL
ALTERNATIVES
VIKOR A2 A4 A1 A3
TOPSIS A2 A3 A4 A1
A1 EAS
A2 RAS
A3 PRAS
A4 FAS

CONCLUSION
• The criteria and feasible alternatives, related to RAS strategies were
considered in an industrial case study and rated as linguistic values as
per the decision makers who are the experts related to various
departments of that industry.
• MCDM was developed based on RAS strategy for a long-term planning
horizon with the help of experts and was solved using Fuzzy VIKOR and
Fuzzy TOPSIS methodology to find out the best alternative solution.
• Thus, the alternative A2 that is RAS, is obtained as the best alternative in
upcoming years for that case study.
• Therefore, RAS are highly recommended for the present assembling
systems as well as in future in order to sustain in the global market
competition.

FUTURE WORKS
• The research work presented in this thesis can be extended to develop a
generic model for Reconfigurable Assembly Systems strategies selection for
many other assembly systems of manufacturing industries with the aid of
different MCDM tools with modifications. This will be useful in considering as
the potential alternatives to the manufacturing and assembly based
industries at large.
• Automobile manufacturer and other consumer electronics goods
manufacturer are likely to utilize these strategies selections for
Reconfigurable Assembly Systems developed in the present work
• The different enabling technologies for RAS like Modular software,
hardware and fixtures can be included in the future research, during the
selection of optimized RAS strategy related to configuration, reconfigurable
hardware, software etc.

LIST OF PUBLICATIONS
1. L.N. Pattanaik and Abinash Jena, “Research Issues in Design and Operation of Reconfigurable
Assembly Systems”, Proceedings of International Conference on Evolutions in manufacturing:
Technologies and Business Strategies for Global Competitiveness, Pages 215-219, 12th – 13th November
2016, BIT, Mesra.
2. L.N. Pattanaik and Abinash Jena, “Identification of Strategies for Reconfigurable Assembly Systems: A
Case Study”, Proceedings of National Conference on “Research Challenges in Mechanical Engineering
for National Development”, Pages 3-12, 28th March 2017, BIT, Durg.
3. L.N. Pattanaik and Abinash Jena, “Selection of Reconfigurable Assembly Systems Strategy using Fuzzy
TOPSIS”, Proceedings of International Conference on Advances in Mechanical Engineering
Sciences, Pages 105, Publisher: IFERP – Institute for Engineering Research and Publication, 21st –
22nd April 2017, P.E.S. College of Engineering, Mandya.

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