الفهرس 
LITERATURE REVIEWOnly 14 pages are availabe for public view
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Abstract
The learning curve concept has widely been applied to many industrialwith different setup to holding cost ratios. .apaciry levels and learning
parameters.
The results obtained are analyzed to study the setup learnmg effects on
production schedules and production costs.
The results obtained for both single and multi product cases are compared
together to illustrate cost savings that can be aclueved
applications as a control tool to predict costs and time requirements .
The problem of lot size determinanon IS one of the most important
industrial applications that the learning concept was applied. In this thesis.
the work is focused on the study of the effects of setup learning on
capacity constraints lot sizing problem with variable demand and tnne
varying capacity for both single and multi product cases.
The present work is divided into four parts:
The first part is concerned with the learning concept and Its
applications. Some of the researches dealing with lot Sizing problem
under process and setup learning are also mentioned.
The main topics discussed in this part are’
I. Types of learning and remission.
2. Learning curve concept and its applications
3. Effect of learning on lot size dererminanou
The econd and tlurd pans are concerned with the capacity constrained
lot sizing problem under setup learning for both ”Ingle and multi product
case. The work is devoted to study the learmng effects on producuon
schedules and production costs.
A mathematical approach to find the optimal lor size under learning
effects is used. A nonlinear integer programming model is developed to find the opnrnal lot sizes for the capacuy constrained single product
problem under setup learning.
The model developed consists of.
Minimized objective function : that IS the total costs l inventory costs.
setup costs and constant costs) . to be minimized.
2. Decision variables. those are the production volume. inventory level
and number of setups .
.:;. Constraints. those are the capacity available and the units produced
must be greater or equal [0 the demands
An efficient heuristic ( Planning with Setup Learning PS L) algorithm is
used to solve the mathematical model with comparable computation time,
especially . when the number of variables is large. A computer program
for the PSL heuristic is developed to find the production schedules and
production costs.
The mathematical model developed for the single product case is
modified to express the more complex and most probable case for multi
product capacity constrained lot sizing problem with learning . The
planning with setup learning ( PSL ) heuristic algorithm is modified to be
used as nn efficient tool LO solve the problem In comparable computation
time as the number of variables becomes large compared with the mgle
product one. The computer program esrabhshed for the single product is
also adapted to suit the modified ( PSL ) heunsnc algorithm for the multi
product case.
Experimental works are carried out in the forth part to validate the
models de eloped for both single and multi product cases using products