2010
17
1
1
0
http://scientiairanica.sharif.edu/3317.html
An Analytic Variable Limit np Control Chart
2
2
The Shewhart np control chart is often used to monitor the quantity of nonconforming, but
it is slow in detecting small deviations. This paper proposes an efficient approach to monitor the quantity
of nonconforming. The novelty of the paper is utilization of an initial belief to construct an analytic
variable limit np control chart. The proposed method uses all gathered data, sequentially. This approach
is signicantly faster than some existent eective approaches in detecting small deviations. These charts
are mainly used for evaluation of the initial setup in the process. The simulated results for the average run
length profiles demonstrate the superiority of the new approach against the standard np chart, binomial
CUSUM, binomial EWMA and moving average approach.
2
The Shewhart np control chart is often used to monitor the quantity of nonconforming, but
it is slow in detecting small deviations. This paper proposes an efficient approach to monitor the quantity
of nonconforming. The novelty of the paper is utilization of an initial belief to construct an analytic
variable limit np control chart. The proposed method uses all gathered data, sequentially. This approach
is signicantly faster than some existent eective approaches in detecting small deviations. These charts
are mainly used for evaluation of the initial setup in the process. The simulated results for the average run
length profiles demonstrate the superiority of the new approach against the standard np chart, binomial
CUSUM, binomial EWMA and moving average approach.
Keywords: np chart; Variable limit; Quantity of nonconforming; Average run length.
0
0


M.
Aminnayeri
Department of Industrial Engineering,Amirkabir University of Technology
Iran
wxyjzzje@scientiaunknown.non


M.H.
Abooie
Department of Industrial Engineering,Amirkabir University of Technology
Iran
fdniawbt@scientiaunknown.non
np chart
Variable limit
Quantity of nonconforming
Average Run Length
http://scientiairanica.sharif.edu/3318.html
Hybrid ElectromagnetismLike Algorithm for Supplier Selection in MaketoOrder Planning
2
2
An electromagnetism algorithm is a metaheuristic proposed to derive approximate solutions
for computationally hard problems. In the literature, several successful applications have been
reported for graphbased optimization problems, such as scheduling problems. This paper presents an
application of the electromagnetism algorithm to supplier selection in a production planning process
where there are multiple products and multiple customers and also capacity constraints. We consider
a situation where the demand quantity of multiple discrete products is known over a planning horizon.
The required raw material for each of these products can be purchased from a set of approved suppliers.
Also, a demanddependent delivery time (due date) and maximum delivery time (deadline) apply for each
demand. Problems containing all these assumptions have not been addressed previously in the literature.
A decision needs to be made regarding what raw material to order and in what quantities, which suppliers
and, nally, at which periods. Numerical results indicate that the electromagnetism algorithm exhibits
impressive performances with small error ratios. The results support the success of the electromagnetism
algorithm application to the supplier selection problem of interest.
2
An electromagnetism algorithm is a metaheuristic proposed to derive approximate solutions
for computationally hard problems. In the literature, several successful applications have been
reported for graphbased optimization problems, such as scheduling problems. This paper presents an
application of the electromagnetism algorithm to supplier selection in a production planning process
where there are multiple products and multiple customers and also capacity constraints. We consider
a situation where the demand quantity of multiple discrete products is known over a planning horizon.
The required raw material for each of these products can be purchased from a set of approved suppliers.
Also, a demanddependent delivery time (due date) and maximum delivery time (deadline) apply for each
demand. Problems containing all these assumptions have not been addressed previously in the literature.
A decision needs to be made regarding what raw material to order and in what quantities, which suppliers
and, nally, at which periods. Numerical results indicate that the electromagnetism algorithm exhibits
impressive performances with small error ratios. The results support the success of the electromagnetism
algorithm application to the supplier selection problem of interest.
Keywords: Electromagnetism; Supplier selection; Total cost of ownership.
0
0


F.
Jolai
Department of Industrial Engineering,University of Tehran
Iran
gmzvcalo@scientiaunknown.non


S. M. T.
Fatemi Ghomi
Department of Industrial Engineering,Amirkabir University of Technology
Iran
fatemi@aut.ac.ir


M.
Mirabi
Department of Industrial Engineering,University of Yazd
Iran
gyfoobum@scientiaunknown.non
Electromagnetism
Supplier selection
Total cost of ownership
http://scientiairanica.sharif.edu/3319.html
Phase II Monitoring of Autocorrelated Polynomial Proles in AR(1) Processes
2
2
In many practical situations, the quality of a process or product can be characterized by
a function or prole. Here, we consider a polynomial prole and investigate the eect of the violation
of a common independence assumption, implicitly considered in most control charting applications, on
the performance of the existing monitoring techniques. We specically consider a case when there is
autocorrelation between proles over time. An autoregressive model of order one is used to model the
autocorrelation structure between error terms in successive proles. In addition, two remedial methods,
based on time series approaches, are presented for monitoring autocorrelated polynomial proles in phase
II. Their performances are compared using a numerical simulation runs in terms of an Average Run
Length (ARL) criterion. The eects of assignable cause and autocorrelation coecient on the shape of
proles are also investigated.
2
In many practical situations, the quality of a process or product can be characterized by
a function or prole. Here, we consider a polynomial prole and investigate the eect of the violation
of a common independence assumption, implicitly considered in most control charting applications, on
the performance of the existing monitoring techniques. We specically consider a case when there is
autocorrelation between proles over time. An autoregressive model of order one is used to model the
autocorrelation structure between error terms in successive proles. In addition, two remedial methods,
based on time series approaches, are presented for monitoring autocorrelated polynomial proles in phase
II. Their performances are compared using a numerical simulation runs in terms of an Average Run
Length (ARL) criterion. The eects of assignable cause and autocorrelation coecient on the shape of
proles are also investigated.
Keywords: Statistical process control; Polynomial proles; Autocorrelation; Average run length;
Assignable cause; Phase II.
0
0


R.
Noorossana
Department of Industrial Engineering,Iran University of Science and Technology
Iran
cypzpilj@scientiaunknown.non


R.B.
Kazemzadeh
Department of Industrial Engineering,Tarbiat Modares University
Iran
rkazem@modares.ac.ir


A.
Amiri
Department of Industrial Engineering,Tarbiat Modares University
Iran
a_amiri@modares.ac.ir
Statistical process control
Polynomial proles
Autocorrelation
Average Run Length
Assignable cause
Phase II
http://scientiairanica.sharif.edu/3320.html
Economic Production Quantity Model with Scrapped Items and Limited Production Capacity
2
2
In this paper, an Economic Production Quantity (EPQ) model is studied, in which the
production defectiverate follows either a uniform or a normal probability distribution. Shortages are
allowed and take a backorder state, and the existence of only one machine causes a limited production
capacity for the common cycle length of all products. The aim of this research is to determine the
optimal production quantity of each product, such that the expected total cost including holding, shortage,
production, setup and defective items cost is minimized. The mathematical model of the problem is derived,
for which the objective function is proved to be convex. Then, a derivative approach is utilized to obtain
the optimal solution. At the end, two numerical examples are provided to illustrate the practical usage of
the proposed method.
2
In this paper, an Economic Production Quantity (EPQ) model is studied, in which the
production defectiverate follows either a uniform or a normal probability distribution. Shortages are
allowed and take a backorder state, and the existence of only one machine causes a limited production
capacity for the common cycle length of all products. The aim of this research is to determine the
optimal production quantity of each product, such that the expected total cost including holding, shortage,
production, setup and defective items cost is minimized. The mathematical model of the problem is derived,
for which the objective function is proved to be convex. Then, a derivative approach is utilized to obtain
the optimal solution. At the end, two numerical examples are provided to illustrate the practical usage of
the proposed method.
Keywords: Economic production quantity; Scrapped items; Limited production capacity.
0
0


S.T.
Akhavan Niaki
Department of Industrial Engineering,Sharif University of Technology
Iran
niaki@sharif.edu


A.
Taleizadeh
Department of Industrial Engineering,Iran University of Science and Technology
Iran
mesjppkj@scientiaunknown.non


A.A.
Najafi
Department of Industrial Engineering,Qazvin Islamic Azad University
Iran
fhdlqoid@scientiaunknown.non
Economic production quantity
Scrapped items
Limited production capacity
http://scientiairanica.sharif.edu/3321.html
ResourceConstrained Project Scheduling Problem with Flexible Work Profiles: A Genetic Algorithm Approach
2
2
This paper deals with the resourceconstrained project scheduling problem with
exible
work proles. In this problem, a project contains activities interrelated by nishstarttype precedence
constraints with a time lag of zero. In many reallife projects, however, it often occurs that only one
renewable bottleneck resource is available and that activities do not have a xed prespecied duration and
associated resource requirement, but a total work content, which essentially indicates how much work has
to be performed on them. Based on this work content, all feasible work proles have to be specied for
the activities, each characterized by a xed duration and a resource requirement prole. The task of the
problem is to nd the optimum work prole and start time of each activity in order to minimize the project
makespan. We propose a procedure to nd all feasible work profiles of each activity and we use a genetic
algorithm with a new crossover operator to schedule the activities. Computational results on a randomly
generated problem set are presented.
2
This paper deals with the resourceconstrained project scheduling problem with
exible
work proles. In this problem, a project contains activities interrelated by nishstarttype precedence
constraints with a time lag of zero. In many reallife projects, however, it often occurs that only one
renewable bottleneck resource is available and that activities do not have a xed prespecied duration and
associated resource requirement, but a total work content, which essentially indicates how much work has
to be performed on them. Based on this work content, all feasible work proles have to be specied for
the activities, each characterized by a xed duration and a resource requirement prole. The task of the
problem is to nd the optimum work prole and start time of each activity in order to minimize the project
makespan. We propose a procedure to nd all feasible work profiles of each activity and we use a genetic
algorithm with a new crossover operator to schedule the activities. Computational results on a randomly
generated problem set are presented.
Keywords: Project scheduling; Heuristic; Genetic algorithm.
0
0


M.
Ranjbar
Department of Industrial Engineering,Sharif University of Technology
Iran
m_ranjbar@mehr.sharif.edu


F.
Kianfar
Department of Industrial Engineering,Sharif University of Technology
Iran
kianfar@sharif.edu
Project scheduling
Heuristic
Genetic Algorithm
http://scientiairanica.sharif.edu/3322.html
A Corporate Supply Optimizer with Flow Network
2
2
A holding or a multibusiness corporate seeks to coordinate its supply for minimum overall
costs. A Corporate Supply Optimizer (CSO), as a central entity taking advantage of the notion of
ow
networks, gathers necessary operational information from members of the corporate supply chain. The
CSO then guides supply chain members on ordering decisions for a minimum overall cost for the entire
supply chain. Its computational engine models the entire supply chain with multiple members in four
stages to satisfy customer demand. The CSO seeks a solution with minimum total costs, unlike noncooperative
supply chains where individual members compete to optimize their local costs. The existing
literature stays with restrictive assumptions on the number of supply chain stages, disallowing a case of
multiple products. Simulation results indicate an approximately 26% reduction in total costs of the supply
chain utilizing the CSO.
2
A holding or a multibusiness corporate seeks to coordinate its supply for minimum overall
costs. A Corporate Supply Optimizer (CSO), as a central entity taking advantage of the notion of
ow
networks, gathers necessary operational information from members of the corporate supply chain. The
CSO then guides supply chain members on ordering decisions for a minimum overall cost for the entire
supply chain. Its computational engine models the entire supply chain with multiple members in four
stages to satisfy customer demand. The CSO seeks a solution with minimum total costs, unlike noncooperative
supply chains where individual members compete to optimize their local costs. The existing
literature stays with restrictive assumptions on the number of supply chain stages, disallowing a case of
multiple products. Simulation results indicate an approximately 26% reduction in total costs of the supply
chain utilizing the CSO.
Keywords: Corporate supply; Coordination mechanism; Flow network; Linear programming; Optimizer.
0
0


M.
Sepehri
Department of Engineering,Sharif University of Technology
Iran
qhrsgduo@scientiaunknown.non


F.
Ghasemzadeh
School of Business,Sharif University of Technology
Iran
xoxcigox@scientiaunknown.non


K.
Fayazbakhsh
Department of Computer Science and Engineering,Amirkabir University of Technology
Iran
nfrldbky@scientiaunknown.non
Corporate supply
Coordination mechanism
Flow network
Linear programming
Optimizer
http://scientiairanica.sharif.edu/3323.html
A Heuristic Algorithm and a Lower Bound for the TwoEchelon LocationRouting Problem with Soft Time Window Constraints
2
2
The locationrouting problem is one of the most important location problems for designing
integrated logistics systems. In the last three decades, various types of objective function and constraints
have been considered for this problem. However, time window constraints have received little attention,
despite their numerous reallife applications. In this article, a new 4index mathematical model, an
ecient and fast heuristic and a lower bound for the twoechelon locationrouting problems with soft
time window constraints are presented. The proposed heuristic tries to solve the problem via creating
an initial solution, then improving it by searching on six neighborhoods of the solution, and using the
Oropt heuristic. At the end, computational results show the eciency of the proposed heuristic, using
the proposed lower bound.
2
The locationrouting problem is one of the most important location problems for designing
integrated logistics systems. In the last three decades, various types of objective function and constraints
have been considered for this problem. However, time window constraints have received little attention,
despite their numerous reallife applications. In this article, a new 4index mathematical model, an
ecient and fast heuristic and a lower bound for the twoechelon locationrouting problems with soft
time window constraints are presented. The proposed heuristic tries to solve the problem via creating
an initial solution, then improving it by searching on six neighborhoods of the solution, and using the
Oropt heuristic. At the end, computational results show the eciency of the proposed heuristic, using
the proposed lower bound.
Keywords: Locationrouting; Location; Routing; Soft time window; Heuristic algorithm.
0
0


S.H.
Zegordi
Department of Industrial Engineering,Tarbiat Modares University
Iran
email@email.com


E.
Nikbakhsh
Department of Industrial Engineering,Tarbiat Modares University
Iran
pqtrzhnk@scientiaunknown.non
Locationrouting
location
routing
Soft time window
Heuristic algorithm