2009
16
2
2
0
1

Optimizing a Joint Economic Lot Sizing Problem with PriceSensitive Demand
http://scientiairanica.sharif.edu/article_3283.html
1
This paper considers the problem of a vendorbuyer integrated productioninventory model.
The vendor manufactures the item at a nite rate and delivers the nal goods at a lotforlot shipment
policy to the buyer. We relax the assumption of uniform demand in the hitherto existing joint economic
lot sizing models and analyze the problem where the end customer demand is pricesensitive. The relation
between demand and price is considered to be linear. The model proposed, based on the integrated expected
total relevant prots of both buyer and vendor, nds out the optimal values of order quantity and markup
percentage, using an analytical approach. Some numerical examples are also used to analyze the eect of
the pricesensitivity of demand on the improvements in joint total prot over individually derived policies.
0
This paper considers the problem of a vendorbuyer integrated productioninventory model.
The vendor manufactures the item at a nite rate and delivers the nal goods at a lotforlot shipment
policy to the buyer. We relax the assumption of uniform demand in the hitherto existing joint economic
lot sizing models and analyze the problem where the end customer demand is pricesensitive. The relation
between demand and price is considered to be linear. The model proposed, based on the integrated expected
total relevant prots of both buyer and vendor, nds out the optimal values of order quantity and markup
percentage, using an analytical approach. Some numerical examples are also used to analyze the eect of
the pricesensitivity of demand on the improvements in joint total prot over individually derived policies.
Keywords: Joint economic lot sizing; Markup pricing policy; Pricesensitive demand.
0
0


M.R.
Akbari Jokar
Department of Industrial Engineering,Sharif University of Technology
Iran
email@email.com


M.
Sheikh Sajadieh
Department of Industrial Engineering,Sharif University of Technology
Iran
mcinqvwn@scientiaunknown.non
Joint economic lot sizing
Markup pricing policy
Pricesensitive demand
1

An Ecient Procedure for Computing an Optimal (R,Q) Policy in Continuous Review Systems with Poisson Demands and Constant Lead Time
http://scientiairanica.sharif.edu/article_3284.html
1
In this paper, a continuous review inventory system is considered in which an order in
a batch of size Q is placed immediately after the inventory position reaches R. Transportation time is
constant and demands are assumed to be generated by a stationary Poisson process with one unit demand
at a time. Demands not covered immediately from the inventory are backordered. In a recent paper, the
exact evaluation of batchordering policies for twolevel inventory systems was derived. This evaluation is
based on a recursive procedure for determining the exact policy costs in case of oneforone replenishment
policies. In this paper, we show how this result can be applied to nd the optimal solution of a (R;Q)
policy. To obtain the optimal policy for this system, considering a oneforone policy, we will rst solve
the base stock model by setting the inventory position at the supplier to a certain value. By considering
ordering cost, we next derive the cost function of the dened (R;Q) model and nd the optimal solution
for the exact value of the expected system costs using a search method. In demonstrating the applicability
of the proposed method, we resort to solving an example.
0
In this paper, a continuous review inventory system is considered in which an order in
a batch of size Q is placed immediately after the inventory position reaches R. Transportation time is
constant and demands are assumed to be generated by a stationary Poisson process with one unit demand
at a time. Demands not covered immediately from the inventory are backordered. In a recent paper, the
exact evaluation of batchordering policies for twolevel inventory systems was derived. This evaluation is
based on a recursive procedure for determining the exact policy costs in case of oneforone replenishment
policies. In this paper, we show how this result can be applied to nd the optimal solution of a (R;Q)
policy. To obtain the optimal policy for this system, considering a oneforone policy, we will rst solve
the base stock model by setting the inventory position at the supplier to a certain value. By considering
ordering cost, we next derive the cost function of the dened (R;Q) model and nd the optimal solution
for the exact value of the expected system costs using a search method. In demonstrating the applicability
of the proposed method, we resort to solving an example.
Keywords: Inventory; Continuous review; (R;Q) model; Base stock model; Poisson demand; Optimal
solution; Backordered demand; Constant lead time.
0
0


A.
Eshraghnia Jahromi
Department of Industrial Engineering,Sharif University of Technology
Iran
eshragh@sharif.edu


N.
Yazdan Shenas
Department of Industrial Engineering,Sharif University of Technology
Iran
nima_yazdanshenas@mehr.sharif.edu


M.
Modarres Yazdi
Department of Industrial Engineering,Sharif University of Technology
Iran
cwpzvdup@scientiaunknown.non
Inventory
Continuous review
(R
Q) model
Base stock model
Poisson demand
Optimal solution
Backordered demand
Constant lead time
1

Fuzzy Image Processing for Diagnosing In ammation in Pulmonary Biopsies
http://scientiairanica.sharif.edu/article_3285.html
1
This paper proposes a new approach to diagnose the degree of in
ammation in digital
images of pulmonary biopsies, provided by a digital camera through a microscope. Diagnosing is done by
detecting thick epithelium cell layers around the vessels and bronchus in tissue images. For analyzing the
complex images of tissue, a fuzzy image processing procedure consisting of ve main stages is presented.
The rst stage is decreasing the complexity of the images by using image preprocessing methods for
enhancement and smoothing the image with a Gaussian low pass lter in order to highlight important
details and ignore the unnecessary parts of the image. The second stage is segmentation by using a fuzzy
cmeans clustering algorithm and fuzzy canny edge detection. This step works as a data reduction method
as well as object recognition. Feature extraction, the third stage, will be done by using a fuzzy Hough
transform. After extracting features such as bronchioles and vessels from the image, the fourth stage will
be analysis and reasoning by a fuzzy inference system, which is a hybrid of the Mamdani and Logical
modeling system with a Yager parametric operator. The last stage is tuning system parameters and the
learning process with a feed forward neural network. The output of the proposed algorithm is the degree
of in
ammation inferred by the fuzzy inference system. The proposed approach is user friendly with low
computational time and the results are more precise, reliable and acceptable to experts and physicians.
0
This paper proposes a new approach to diagnose the degree of in
ammation in digital
images of pulmonary biopsies, provided by a digital camera through a microscope. Diagnosing is done by
detecting thick epithelium cell layers around the vessels and bronchus in tissue images. For analyzing the
complex images of tissue, a fuzzy image processing procedure consisting of ve main stages is presented.
The rst stage is decreasing the complexity of the images by using image preprocessing methods for
enhancement and smoothing the image with a Gaussian low pass lter in order to highlight important
details and ignore the unnecessary parts of the image. The second stage is segmentation by using a fuzzy
cmeans clustering algorithm and fuzzy canny edge detection. This step works as a data reduction method
as well as object recognition. Feature extraction, the third stage, will be done by using a fuzzy Hough
transform. After extracting features such as bronchioles and vessels from the image, the fourth stage will
be analysis and reasoning by a fuzzy inference system, which is a hybrid of the Mamdani and Logical
modeling system with a Yager parametric operator. The last stage is tuning system parameters and the
learning process with a feed forward neural network. The output of the proposed algorithm is the degree
of in
ammation inferred by the fuzzy inference system. The proposed approach is user friendly with low
computational time and the results are more precise, reliable and acceptable to experts and physicians.
Keywords: Image processing; Fuzzy modeling; Fuzzy cluster analysis; In
ammation; Pulmonary; Canny
edge detection; Hough transform; RGB image.
0
0


M.
Moeen
Department of Industrial Engineering,University of Tehran
Iran
ivsnbhxz@scientiaunknown.non


Sh.
Teimourian
Department of Industrial Engineering,University of Tehran
Iran
qvcshjxu@scientiaunknown.non


M.H.
Fazel Zarandi
Department of Industrial Engineering,Amirkabir University of Technology
Iran
zarandi@aku.ac.ir


Sh.
Norouzzadeh
Department of Industrial Engineering,Amirkabir University of Technology
Iran
dhwwbhnw@scientiaunknown.non
image processing
Fuzzy modeling
Fuzzy cluster analysis
In ammation
Pulmonary
Canny edge detection
Hough transform
RGB image
1

TwoPillar Risk Management (TPRM): A Generic Project Risk Management Process
http://scientiairanica.sharif.edu/article_3286.html
1
A conventional Risk Management Process (RMP) contains two main phases: (a) risk
assessment that includes risk identication and risk analysis, and (b) risk response that decides what, if
anything, should be done about the analyzed risks. Based on a traditional tendency, most studies in stateof
the art RMP have ample emphasis on risk assessment, but we can nd limited studies on the subject of
risk response. This paper aims to oppose the mentioned traditional view. The paper introduces a generic
RMP, namely TwoPillar Risk Management (TPRM) that considers an equivalent importance for both risk
assessment and risk response. The paper compares the TPRM with the last version of the RMP provided in
the standard of PMBoK. Application of the proposed model in projects in the construction industry shows
a tremendous total risk level improvement. We believe that applying the TPRM helps project managers
in a most eective and ecient manner in dealing with their risk management programs.
0
A conventional Risk Management Process (RMP) contains two main phases: (a) risk
assessment that includes risk identication and risk analysis, and (b) risk response that decides what, if
anything, should be done about the analyzed risks. Based on a traditional tendency, most studies in stateof
the art RMP have ample emphasis on risk assessment, but we can nd limited studies on the subject of
risk response. This paper aims to oppose the mentioned traditional view. The paper introduces a generic
RMP, namely TwoPillar Risk Management (TPRM) that considers an equivalent importance for both risk
assessment and risk response. The paper compares the TPRM with the last version of the RMP provided in
the standard of PMBoK. Application of the proposed model in projects in the construction industry shows
a tremendous total risk level improvement. We believe that applying the TPRM helps project managers
in a most eective and ecient manner in dealing with their risk management programs.
Keywords: Risk Management Process (RMP); Project risk management; Risk response.
0
0


S. M.
Seyedhoseini
Department of Industrial Engineering,Iran University of Science and Technology
Iran
seyedhoseini@yahoo.com


M. A.
Hatefi
Department of Industrial Engineering,Iran University of Science and Technology
Iran
hatefima@yahoo.com
Risk Management Process (RMP)
Project risk management
Risk response
1

A Solution for Transportation Planning in Supply Chain
http://scientiairanica.sharif.edu/article_3287.html
1
An advanced optimization system for Vehicle Routing and Scheduling Problems (VRSP),
which is one of the Supply Chain Planning modules, is introduced. An object oriented system, Computer
Aided Routing and Scheduling (CARS) can handle complicated distribution models using advanced
heuristic optimization algorithms. To classify various types of routing and scheduling problems in a
structured manner, a classication scheme is introduced based on the main objects of VRSP. Also, the
modeling and solution approach in the CARS optimization engine has been elaborated. Main static and
dynamic objects of the system as well as their relationships and interactions have been explained. The
user interface in addition to the planning and operational features of the system is described in detail.
0
An advanced optimization system for Vehicle Routing and Scheduling Problems (VRSP),
which is one of the Supply Chain Planning modules, is introduced. An object oriented system, Computer
Aided Routing and Scheduling (CARS) can handle complicated distribution models using advanced
heuristic optimization algorithms. To classify various types of routing and scheduling problems in a
structured manner, a classication scheme is introduced based on the main objects of VRSP. Also, the
modeling and solution approach in the CARS optimization engine has been elaborated. Main static and
dynamic objects of the system as well as their relationships and interactions have been explained. The
user interface in addition to the planning and operational features of the system is described in detail.
Keywords: Vehicle routing; Logistics; Supply chain planning; Advanced optimization system.
0
0


A.
Modares
School of Engineering,Sharif University of Technology
Iran
modares@sharif.edu


M.
Sepehri
Department of Engineering,Sharif University of Technology
Iran
qhrsgduo@scientiaunknown.non
Vehicle routing
Logistics
Supply chain planning
Advanced optimization system
1

Lot Sizing and Lead Time Quotations in Assembly Systems
http://scientiairanica.sharif.edu/article_3288.html
1
In this paper, a simultaneous lead time quotation and lot sizing problem in an assembly
system is investigated. We address a production system with a product that has deterministic demand over
a Tperiod planning horizon and is produced in lots because of the economy of scale. If a lot is completed
before the demand period, inventory carrying cost is incurred. On shortages, a lead time is quoted to
customers and a lead time quotation cost is incurred. Finally, if the order is delivered later than its due
date period, a tardiness cost is charged. The components supply lead time is stochastic, which follows a
discrete distribution. The problem is to decide on the lot size of products and components, supply and
production starting periods and the due date of lots (to be quoted to customers) so that relevant costs are
minimized. The objective function is the sum of the production, inventory carrying, lead time quotation
and tardiness costs. We develop a genetic algorithm to solve the proposed model. An experimental
framework is set up to test the eciency of the proposed method, which turns out to rate high, both in
terms of cost eectiveness and execution speed.
0
In this paper, a simultaneous lead time quotation and lot sizing problem in an assembly
system is investigated. We address a production system with a product that has deterministic demand over
a Tperiod planning horizon and is produced in lots because of the economy of scale. If a lot is completed
before the demand period, inventory carrying cost is incurred. On shortages, a lead time is quoted to
customers and a lead time quotation cost is incurred. Finally, if the order is delivered later than its due
date period, a tardiness cost is charged. The components supply lead time is stochastic, which follows a
discrete distribution. The problem is to decide on the lot size of products and components, supply and
production starting periods and the due date of lots (to be quoted to customers) so that relevant costs are
minimized. The objective function is the sum of the production, inventory carrying, lead time quotation
and tardiness costs. We develop a genetic algorithm to solve the proposed model. An experimental
framework is set up to test the eciency of the proposed method, which turns out to rate high, both in
terms of cost eectiveness and execution speed.
Keywords: Lotsizing; Lead time quotation; Genetic algorithms; Production planning.
0
0


F.
Kianfar
Department of Industrial Engineering,Sharif University of Technology
Iran
kianfar@sharif.edu


G.
Mokhtari
Department of Industrial Engineering,Sharif University of Technology
Iran
mokhtari@behsad.com
Lotsizing
Lead time quotation
genetic algorithms
production planning
1

Concurrent Project Scheduling and Material Planning: A Genetic Algorithm Approach
http://scientiairanica.sharif.edu/article_3289.html
1
Scheduling projects incorporated with materials ordering results in a more realistic
problem. This paper deals with the combined problem of project scheduling and material ordering. The
purpose of this paper is to minimize the total cost of this problem by determining the optimal values
of activity duration, activity nish time and the material ordering schedule subject to constraints. We
employ a genetic algorithm approach to solve it. Elements of the algorithm, such as chromosome
structure, untness function, crossover, mutation and local search operations are explained. The results
of the experimentation are quite satisfactory.
0
Scheduling projects incorporated with materials ordering results in a more realistic
problem. This paper deals with the combined problem of project scheduling and material ordering. The
purpose of this paper is to minimize the total cost of this problem by determining the optimal values
of activity duration, activity nish time and the material ordering schedule subject to constraints. We
employ a genetic algorithm approach to solve it. Elements of the algorithm, such as chromosome
structure, untness function, crossover, mutation and local search operations are explained. The results
of the experimentation are quite satisfactory.
Keywords: Project scheduling; Genetic algorithm; Material ordering
0
0


F.
Hassanzadeh
Department of Computer Engineering,Ferdowsi University of Mashhad
Iran
omchgvxe@scientiaunknown.non


M.
Sheikh Sajadieh
Department of Industrial Engineering,Sharif University of Technology
Iran
mcinqvwn@scientiaunknown.non


S.
Shadrokh
,Azad University
Iran
email@email.com
Project scheduling
Genetic Algorithm
Material ordering