2021-04-12T05:55:13Z
http://jiei.azad.ac.ir/?_action=export&rf=summon&issue=1134709
Journal of Industrial Engineering, International
1735-5702
1735-5702
2014
10
2
A DMAIC approach for process capability improvement an engine crankshaft manufacturing process
G. V. S. S.
Sharma
P. Srinivasa
Rao
The define–measure–analyze–improve–control
(DMAIC) approach is a five-strata 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 stub-end-hole boring operation of the
manufacture of crankshaft. This statistical process control
study starts with selection of the critical-to-quality (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.
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
2014
06
01
http://jiei.azad.ac.ir/article_676504_445960a07cd3d356d6ad644fb0d649ba.pdf
Journal of Industrial Engineering, International
1735-5702
1735-5702
2014
10
2
An iterative method for forecasting most probable point of stochastic demand
J.
Behnamian
S. M. T.
Fatemi Ghomi
B.
Karimi
M. Fadaei
Moludi
The demand forecasting is essential for all
production and non-production 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 n-dimensional 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 demand-state
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.
Uncertainty First
order Taylor series expansion State space models Most probable point Forecasting practice Demand forecasting
2014
06
01
http://jiei.azad.ac.ir/article_676505_3e2e4059628b311fa2783959ef948761.pdf
Journal of Industrial Engineering, International
1735-5702
1735-5702
2014
10
2
More efficiency in fuel consumption using gearbox optimization based on Taguchi method
Masoud
Goharimanesh
Aliakbar
Akbari
Alireza Akbarzadeh
Tootoonchi
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
signal-to-noise ratio as well as analysis of variance.
Fuel consumption Driving cycle Design of
experiment optimization Taguchi ANOVA
2014
06
01
http://jiei.azad.ac.ir/article_676506_0c2f10259c57aeb49f219fcb18820b45.pdf
Journal of Industrial Engineering, International
1735-5702
1735-5702
2014
10
2
An archived multi-objective simulated annealing for a dynamic cellular manufacturing system
Hossein
Shirazi
Reza
Kia
Nikbakhsh
Javadian
Reza
Tavakkoli-Moghaddam
To design a group layout of a cellular manufacturing
system (CMS) in a dynamic environment, a
multi-objective mixed-integer non-linear 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
multi-objective simulated annealing (AMOSA) algorithm
is designed to find Pareto-optimal solutions. Matrix-based
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 well-known 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
multi-objective model.
Dynamic cellular manufacturing systems
Group layout Production planning Archived multiobjective
Simulated Annealing
2014
06
01
http://jiei.azad.ac.ir/article_676507_4f987037f6633c29a489b8d5c5b79d11.pdf
Journal of Industrial Engineering, International
1735-5702
1735-5702
2014
10
2
A novel hybrid genetic algorithm to solve the make-to-order sequence-dependent flow-shop scheduling problem
Mohammad
Mirabi
S. M.
T. Fatemi Ghomi
F
. Jolai
Flow-shop 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 NP-hard, there
is no efficient algorithm to reach the optimal solution of the
problem. To minimize the holding, delay and setup costs of
large permutation flow-shop scheduling problems with
sequence-dependent 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
make-to-order 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.
Hybrid genetic algorithm Scheduling Permutation flow
shop Sequence dependent
2014
06
01
http://jiei.azad.ac.ir/article_676508_d82ec82d599846f01f6bc0555a46c29e.pdf
Journal of Industrial Engineering, International
1735-5702
1735-5702
2014
10
2
Solving Fractional Programming Problems based on Swarm Intelligence
Osama
Abdel Raouf
Ibrahim
M. Hezam
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.
Swarm intelligence Particle swarm
optimization Firefly algorithm Fractional programming
2014
06
01
http://jiei.azad.ac.ir/article_676509_79d8e98c5dca427a8673229ad3671af1.pdf
Journal of Industrial Engineering, International
1735-5702
1735-5702
2014
10
2
Application of TPM indicators for analyzing work time of machines used in the pressure die casting
Stanisław
Borkowski
Agnieszka
Czajkowska
Renata
Stasiak-Betlejewska
Atul
B. Borade
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.
TPM Pressure die casting The correlation
coefficient Quality
2014
06
01
http://jiei.azad.ac.ir/article_676510_daf3ef4130e3447b75f161c2790d5956.pdf
Journal of Industrial Engineering, International
1735-5702
1735-5702
2014
10
2
Distribution network design under demand uncertainty using genetic algorithm and Monte Carlo simulation approach: a case study in pharmaceutical industry
Arman
Izadi
Ali Mohammad
Kimiagari
Distribution network design as a strategic
decision has long-term 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 real-world 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.
Distribution network design Facility
location Genetic algorithms Monte Carlo simulation
2014
06
01
http://jiei.azad.ac.ir/article_676511_44c1fe8b3de70c05fe56f3c6748b8571.pdf