الفهرس 
LI’-rERATURE REVIEWOnly 14 pages are availabe for public view
 |  | from | 510 |  |  |
| | | from | 510 | | |
Abstract
Research in the field of the Job-Shop Scheduling was given little attention
as compared to that given to the Flow-Shop Scheduling Problems. In
addition, most of researches, which tackle the scheduling problems, aim at
minimizing the total make-span and in most of the cases with no-wait
allowed for the products. Little attention has been given to consider the
role of cost on determining such schedules. In the present work the
Common Cycle Scheduling Problem (CCSP) and the Cyclic Scheduling
Problem (CSP) in the job-shop environment are considered. This
production problem is concerned with determining the optimal production
schedule and lot sizes for a given set of products using a number of
machines. The CCSP is based on scheduling a number of products using a
common (base) cycle time, so that the lot size for each product is the
forecast demand of certain product over the base cycle time. The CSP is
based on allocating different products to different basic cycles, so that each
product is produced in equal lot sizes spaced equally apart, by a period that
is an integer of the basic cycle time. The produced quantities should satisfy
the expected demand in that period, until the product is reproduced. When
producing the same products repetitively, it is preferred to minimize the
production costs rather than the make-span. The costs considered are the
setup, in-process inventory, queuing, finished products inventory holding,
as well as machine delay and lost-sales costs. Genetic Algorithm is used to
solve the CCSP for a multi-stage, multi-product, job-shop environment
under deterministic and stationary conditions, allowing the products to
wait between the different stages. The same Genetic Algorithm is used
with an allocation procedure, to allocate the products to the different basic cycles, for the same problem. Applying the proposed model to minimize
the make-span of benchmark instances was used as a factorial experiment
to adjust the Genetic Algorithm’s parameters. The model was proved to be successful in determining the common cycle and cyclic schedules that
minimize the total scheduling cost per unit time in a job-shop environment.
The keywords: Common Cycle Scheduling, Cyclic Scheduling, Job-Shop
Scheduling, Lot Sizing, Genetic Algorithm.