2014
10
2
2
0
1

A DMAIC approach for process capability improvement an engine crankshaft manufacturing process
http://jiei.azad.ac.ir/article_676504.html
1
The define–measure–analyze–improve–control
(DMAIC) approach is a fivestrata approach, namely
DMAIC. This approach is the scientific approach for
reducing the deviations and improving the capability levels
of the manufacturing processes. The present work elaborates
on DMAIC approach applied in reducing the process
variations of the stubendhole boring operation of the
manufacture of crankshaft. This statistical process control
study starts with selection of the criticaltoquality (CTQ)
characteristic in the define stratum. The next stratum constitutes
the collection of dimensional measurement data of
the CTQ characteristic identified. This is followed by the
analysis and improvement strata where the various quality
control tools like Ishikawa diagram, physical mechanism
analysis, failure modes effects analysis and analysis of
variance are applied. Finally, the process monitoring charts
are deployed at the workplace for regular monitoring and
control of the concerned CTQ characteristic. By adopting
DMAIC approach, standard deviation is reduced from
0.003 to 0.002. The process potential capability index (CP)
values improved from 1.29 to 2.02 and the process performance
capability index (CPK) values improved from
0.32 to 1.45, respectively.
0

0
0


G. V. S. S.
Sharma
Department of Mechanical Engineering, GMR Institute of Technology, Rajam, 532127, AP, India
Iran


P. Srinivasa
Rao
Department of Industrial Engineering, GITAM University, Visakhapatnam, 530045, AP, India
Iran
Critical to quality (CTQ) characteristic
Cause and effect diagram Statistical process control
(SPC) Process monitoring charts (PMC) Failure modes
and effects analysis (FMEA) Analysis of variance
(ANOVA) Physical mechanism (PM) analysis
1

An iterative method for forecasting most probable point of stochastic demand
http://jiei.azad.ac.ir/article_676505.html
1
The demand forecasting is essential for all
production and nonproduction systems. However, nowadays
there are only few researches on this area. Most of
researches somehow benefited from simulation in the
conditions of demand uncertainty. But this paper presents
an iterative method to find most probable stochastic
demand point with normally distributed and independent
variables of ndimensional space and the demand space is a
nonlinear function. So this point is compatible with both
external conditions and historical data and it is the shortest
distance from origin to the approximated demandstate
surface. Another advantage of this paper is considering ndimensional
and nonlinear (nth degree) demand function.
Numerical results proved this procedure is convergent and
running time is reasonable.
0

0
0


J.
Behnamian
Department of Industrial Engineering, Faculty of Engineering, BuAli Sina University, Hamedan, Iran
Iran


S. M. T.
Fatemi Ghomi
Department of Industrial Engineering, Amirkabir University of Technology, Hafez Avenue No. 424, 1591634311, Tehran, Iran
Iran


B.
Karimi
Department of Industrial Engineering, Amirkabir University of Technology, Hafez Avenue No. 424, 1591634311, Tehran, Iran
Iran


M. Fadaei
Moludi
Department of Industrial Engineering, Amirkabir University of Technology, Hafez Avenue No. 424, 1591634311, Tehran, Iran
Iran
Uncertainty First
order Taylor series expansion State space models Most probable point Forecasting practice Demand forecasting
1

More efficiency in fuel consumption using gearbox optimization based on Taguchi method
http://jiei.azad.ac.ir/article_676506.html
1
Automotive emission is becoming a critical
threat to today’s human health. Many researchers are
studying engine designs leading to less fuel consumption.
Gearbox selection plays a key role in an engine design. In
this study, Taguchi quality engineering method is
employed, and optimum gear ratios in a five speed gear box
is obtained. A table of various gear ratios is suggested by
design of experiment techniques. Fuel consumption is
calculated through simulating the corresponding combustion
dynamics model. Using a 95 % confidence level,
optimal parameter combinations are determined using the
Taguchi method. The level of importance of the parameters
on the fuel efficiency is resolved using the analysis of
signaltonoise ratio as well as analysis of variance.
0

0
0


Masoud
Goharimanesh
Department of Mechanical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad (FUM) campus, Azadi Sq., P.O. Box: 9177948974, Mashhad, Khorasan Razavi, Iran
Iran


Aliakbar
Akbari
Department of Mechanical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad (FUM) campus, Azadi Sq., P.O. Box: 9177948974, Mashhad, Khorasan Razavi, Iran
Iran


Alireza Akbarzadeh
Tootoonchi
Department of Mechanical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad (FUM) campus, Azadi Sq., P.O. Box: 9177948974, Mashhad, Khorasan Razavi, Iran
Iran
Fuel consumption Driving cycle Design of
experiment optimization Taguchi ANOVA
1

An archived multiobjective simulated annealing for a dynamic cellular manufacturing system
http://jiei.azad.ac.ir/article_676507.html
1
To design a group layout of a cellular manufacturing
system (CMS) in a dynamic environment, a
multiobjective mixedinteger nonlinear programming
model is developed. The model integrates cell formation,
group layout and production planning (PP) as three interrelated
decisions involved in the design of a CMS. This
paper provides an extensive coverage of important manufacturing
features used in the design of CMSs and enhances
the flexibility of an existing model in handling the fluctuations
of part demands more economically by adding
machine depot and PP decisions. Two conflicting objectives
to be minimized are the total costs and the imbalance
of workload among cells. As the considered objectives in
this model are in conflict with each other, an archived
multiobjective simulated annealing (AMOSA) algorithm
is designed to find Paretooptimal solutions. Matrixbased
solution representation, a heuristic procedure generating an
initial and feasible solution and efficient mutation operators
are the advantages of the designed AMOSA. To demonstrate
the efficiency of the proposed algorithm, the performance
of AMOSA is compared with an exact algorithm
(i.e., [constraint method) solved by the GAMS software
and a wellknown evolutionary algorithm, namely NSGAII
for some randomly generated problems based on some
comparison metrics. The obtained results show that the
designed AMOSA can obtain satisfactory solutions for the
multiobjective model.
0

0
0


Hossein
Shirazi
Department of Industrial Management, Qom Branch, Islamic Azad University, P.O. Box 3749113191, Qom, Iran
Iran
hossein.shirazi63@gmail.com


Reza
Kia
Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, P.O. Box 148, Firoozkooh, Iran
Iran


Nikbakhsh
Javadian
Department of Industrial Engineering, Mazandaran University of Science and Technology, P.O. Box 734, Babol, Iran
Iran


Reza
TavakkoliMoghaddam
School of Industrial Engineering and Engineering Optimization Research Group, College of Engineering, University of Tehran, P.O.Box 111554563, Tehran, Iran
Iran
tavakoli@ut.ac.ir
Dynamic cellular manufacturing systems
Group layout Production planning Archived multiobjective
Simulated Annealing
1

A novel hybrid genetic algorithm to solve the maketoorder sequencedependent flowshop scheduling problem
http://jiei.azad.ac.ir/article_676508.html
1
Flowshop scheduling problem (FSP) deals
with the scheduling of a set of n jobs that visit a set of
m machines in the same order. As the FSP is NPhard, there
is no efficient algorithm to reach the optimal solution of the
problem. To minimize the holding, delay and setup costs of
large permutation flowshop scheduling problems with
sequencedependent setup times on each machine, this
paper develops a novel hybrid genetic algorithm (HGA)
with three genetic operators. Proposed HGA applies a
modified approach to generate a pool of initial solutions,
and also uses an improved heuristic called the iterated swap < /div>
procedure to improve the initial solutions. We consider the
maketoorder production approach that some sequences
between jobs are assumed as tabu based on maximum
allowable setup cost. In addition, the results are compared
to some recently developed heuristics and computational
experimental results show that the proposed HGA performs
very competitively with respect to accuracy and efficiency
of solution.
0

0
0


Mohammad
Mirabi
Group of Industrial Engineering, Ayatollah Haeri University of Meybod, P.O. Box 8961955133, Meybod, Iran
Iran


S. M.
T. Fatemi Ghomi
Department of Industrial Engineering, Amirkabir University of Technology, P.O. Box 1591634311, Tehran, Iran
Iran


F
. Jolai
Department of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box 14395515, Tehran, Iran
Iran
Hybrid genetic algorithm Scheduling Permutation flow
shop Sequence dependent
1

Solving Fractional Programming Problems based on Swarm Intelligence
http://jiei.azad.ac.ir/article_676509.html
1
This paper presents a new approach to solve
Fractional Programming Problems (FPPs) based on two
different Swarm Intelligence (SI) algorithms. The two
algorithms are: Particle Swarm Optimization, and Firefly
Algorithm. The two algorithms are tested using several
FPP benchmark examples and two selected industrial
applications. The test aims to prove the capability of the SI
algorithms to solve any type of FPPs. The solution results
employing the SI algorithms are compared with a number
of exact and metaheuristic solution methods used for
handling FPPs. Swarm Intelligence can be denoted as an
effective technique for solving linear or nonlinear, nondifferentiable
fractional objective functions. Problems with
an optimal solution at a finite point and an unbounded
constraint set, can be solved using the proposed approach.
Numerical examples are given to show the feasibility,
effectiveness, and robustness of the proposed algorithm.
The results obtained using the two SI algorithms revealed
the superiority of the proposed technique among others in
computational time. A better accuracy was remarkably
observed in the solution results of the industrial application
problems.
0

0
0


Osama
Abdel Raouf
Operations Research and DSS Department, Menofia University, Shebien Elkoum, Menofia , 32511, Egypt
Iran


Ibrahim
M. Hezam
Department of Mathematics and computer, Faculty of Education, Ibb University, Ibb city, Yemen
Iran
Swarm intelligence Particle swarm
optimization Firefly algorithm Fractional programming
1

Application of TPM indicators for analyzing work time of machines used in the pressure die casting
http://jiei.azad.ac.ir/article_676510.html
1
The article presents the application of total
productive maintenance (TPM) to analyze the working
time indicators of casting machines with particular
emphasis on failures and unplanned downtime to reduce
the proportion of emergency operation for preventive
maintenance and diagnostics. The article presents that the
influence of individual factors of complex machinery
maintenance (TPM) is different and depends on the
machines’ modernity level. In an original way, by using
correlation graphs, research findings on the impact of
individual TPM factors on the castings quality were presented
and interpreted. The examination results conducted
for machines with varying modernity degrees allowed to
determine changes within the impact of individual TPM
factors depending on machine parameters. These results
provide a rich source of information for the improvement
processes on casting quality of the foundry industry that
satisfies the automotive industry demand.
0

0
0


Stanisław
Borkowski
Czestochowa University of Technology, Institute of Production Engineering, Poland, Częstochowa, Poland
Iran


Agnieszka
Czajkowska
Kielce University of Technology, Faculty of Civil and Architecture, Poland, Kielce, Poland
Iran


Renata
StasiakBetlejewska
Czestochowa University of Technology, Institute of Production Engineering, Poland, Częstochowa, Poland
Iran


Atul
B. Borade
Jawaharlal Darda Institute of Engineering and Technology, Mechanical Engineering Department, Maharashtra, India
Iran
TPM Pressure die casting The correlation
coefficient Quality
1

Distribution network design under demand uncertainty using genetic algorithm and Monte Carlo simulation approach: a case study in pharmaceutical industry
http://jiei.azad.ac.ir/article_676511.html
1
Distribution network design as a strategic
decision has longterm effect on tactical and operational
supply chain management. In this research, the location–
allocation problem is studied under demand uncertainty.
The purposes of this study were to specify the optimal
number and location of distribution centers and to determine
the allocation of customer demands to distribution
centers. The main feature of this research is solving the
model with unknown demand function which is suitable
with the realworld problems. To consider the uncertainty,
a set of possible scenarios for customer demands is created
based on the Monte Carlo simulation. The coefficient of
variation of costs is mentioned as a measure of risk and the
most stable structure for firm’s distribution network is
defined based on the concept of robust optimization. The
best structure is identified using genetic algorithms and
14 % reduction in total supply chain costs is the outcome.
Moreover, it imposes the least cost variation created by
fluctuation in customer demands (such as epidemic diseases
outbreak in some areas of the country) to the logistical
system. It is noteworthy that this research is done in
one of the largest pharmaceutical distribution firms in Iran.
0

0
0


Arman
Izadi
Department of Industrial Engineering, Amirkabir University of Technology, Hafez Street, NO 424, 158754413, Tehran, Iran
Iran


Ali Mohammad
Kimiagari
Department of Industrial Engineering, Amirkabir University of Technology, Hafez Street, NO 424, 158754413, Tehran, Iran
Iran
Distribution network design Facility
location Genetic algorithms Monte Carlo simulation