Ayrıntılı Bilimsel Program ve Bildiri Özetleri - YAEM2010

YAEM 2010
YÖNEYLEM ARAÞTIRMASI VE ENDÜSTRÝ MÜHENDÝSLÝGI 30. ULUSAL KONGRESÝ
Capacitated lot sizing problem with setup carryover deals with
multiple products produced on a single machine. A setup is assumed
to be carried over from one period to the next and the partial
sequencing of the first and last product is incorporated. When the
demand of a period cannot be satisfied, it is backordered. In this
study, a heuristic hybrid approach combining Genetic Algorithms
(GAs) and Fix-and-Optimize heuristic is proposed to solve the
capacitated lot sizing problem with set-up carryover and
backordering. The first phase of the study involves optimizing the
control parameters such as the combination of the population size and
the number of generations, rate of crossover and rate of mutation.
Next, the performance of the proposed hybrid approach is compared
to the pure GAs using various problem instances.
Periodic Capacity Management under a Lead Time Performance
Constraint
Nasuh Cagdas Buyukkaramikli
Department of Industrial Engineering and Innovation Sciences, TU/e,
Eindhoven, Hollanda
In this paper, we study a production system that operates under a
lead time performance constraint which guarantees the completion of
a job order before a pre-determined lead time
with a certain probability. The demand arrival times and service
requirements for the jobs are random. In order to reduce capacity
related costs, system decides to use flexible capacity policies. Most of
the flexible capacity practices in real life are inherently periodic due to
several reasons. Motivated by this observation, we focus on periodic
capacity policies and model the production system as a queuing
system that changes its service rate periodically. We assume two
levels of capacity available: permanent and contingent capacity levels.
The contingent capacity is supplied if needed at the start of a period,
and is available during that period, at a cost rate that decreases in
period length. We develop a procedure to create a set for candidate
period lengths and permanent/contingent capacity levels and search
for the capacity levels and the threshold point that minimizes the
capacity related costs for a given period length.
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