2013
5
2
2
0
On Bottleneck Product Rate Variation Problem with Batching
2
2
The product rate variation problem minimizes the variation in the rate at which different models of a common base product are produced on the assembly lines with the assumption of negligible switchover cost and unit processing time for each copy of each model. The assumption of significant setup and arbitrary processing times forces the problem to be a two phase problem. The first phase determines the size and the number of batches and the second one sequences the batches of models. In this paper, the bottleneck case i.e. the minmax case of the problem with a generalized objective function is formulated. A Pareto optimal solution is proposed and a relation between optimal sequences for the problem with different objective functions is investigated.
1

477
491


Shree
Khadka
Central Department of Mathematics, Tribhuvan University, Kathmandu, Nepal
Central Department of Mathematics, Tribhuvan
Nepal
shreeramkhadka@gmail.com


Tanka
Dhamala
Central Department of Mathematics, Tribhuvan University, Kathmandu, Nepal
Central Department of Mathematics, Tribhuvan
Nepal
dhamala@yahoo.com
Product rate variation problem
batching
sequencing problem
nonlinear integer programming
Mapping Sequence diagram in Fuzzy UML to Fuzzy Petri Net
2
2
This ability in fuzzy UML, practically leaves the customers and market’s need without response in this important and vital area. Here, the available sequence diagrams in fuzzy UML will map into fuzzy Petri net. However, the formal models ability will be added to the Semiformal fuzzy UML. This formalization will add the automatic processing ability to the Semiformal fuzzy UML. Further more, the other advantages of this mapping is: access to nonfunctional parameters such as reliability automatically to the considering systems, study the verification of the designed plan and also decrease the expenses because of satiety to make lab sample before its implementation. Using the fuzzy UML mapping into fuzzy Petri net in control, critical and realtime systems will be more applicable.
1

492
505


E.
Akbari
Department of Mathematics, Islamic Azad University, Sari Branch, Sari, Iran
Department of Mathematics, Islamic Azad University
Iran


R.
Noorian Talooki
Department of Computer Engineering, Islamic Azad University, Sari Branch, Sari, Iran
Department of Computer Engineering, Islamic
Iran


H.
Motameni
Department of Computer Engineering, Islamic Azad University, Sari Branch, Sari, Iran
Department of Computer Engineering, Islamic
Iran
motameni@iausari.ac.ir
nonfunctional parameters
fuzzy UML
sequence diagram
fuzzy Petri net
Formalization
On the averaging of differential inclusions with Fuzzy right hand side with the average of the right hand side is absent
2
2
In this article we consider the averaging method for differential inclusions with fuzzy righthand side for the case when the limit of a method of an average does not exist.
1

506
517


Andrej
Plotnikov
Odessa State Academy of Civil Engineering and Architecture, Odessa, Ukraine
Odessa State Academy of Civil Engineering
Ukraine
aplotnikov@ukr.net


Tatyana
Komleva
Odessa State Academy of Civil Engineering and Architecture, Odessa, Ukraine
Odessa State Academy of Civil Engineering
Ukraine


Lilia
Plotnikova
Odessa National Polytechnic University, Odessa, Ukraine
Odessa National Polytechnic University, Odessa,
Ukraine
differential inclusion
averaging method
fuzzy set
Rsolution
AnalyticApproximate Solution For An Integro Differential Equation Arising In Oscillating Magnetic Fields Using Homotopy Analysis Method
2
2
In this paper, we give an analytical approximate solution for an integro differential equation which describes the charged particle motion for certain configurations of oscillating magnetic fields is considered. The homotopy analysis method (HAM) is used for solving this equation. Several examples are given to reconfirm the efficiency of these algorithms. The results of applying this procedure to the integrodifferential equation with timeperiodic coefficients show the high accuracy, simplicity and efficiency of this method.
1

518
535


H.
SaberiNik
Department of Applied Mathematics, Ferdowsi University of Mashhad, P.O. Box 1159, Mashhad, Iran.
Department of Applied Mathematics, Ferdowsi
Iran
saberi_hssn@yahoo.com


S.
Effati
Department of Applied Mathematics, Ferdowsi University of Mashhad, P.O. Box 1159, Mashhad, Iran.
Department of Applied Mathematics, Ferdowsi
Iran


R.
Buzhabadi
Department of Applied Mathematics, Ferdowsi University of Mashhad, P.O. Box 1159, Mashhad, Iran.
Department of Applied Mathematics, Ferdowsi
Iran
Homotopy analysis method
integrodifferential equations
approximateanalytic solution
homotopyderivative
Homotopy perturbation method