8 research outputs found
Parameter selection for genetic algorithm-based simulation optimization
Cataloged from PDF version of article.Improvements on heuristic techniques with the availability of faster PC’s increase the
importance of simulation-optimization (sim/opt) applications. Sim/opt methodologies use
computer simulation integrated with an optimization sub-routine to optimize the problems of
interest. The main contribution of these methods is to make simulation as a prescriptive tool
rather than a descriptive tool, which has been widely used as the descriptive tool for
estimating the performance of complex stochastic systems. Sim/opt methodologies have been
applied on various combinatorial optimization problems, and the current trend in sim/opt area
is the use of meta-heuristic techniques. Genetic Algorithm (GA) is the well known metaheuristic,
which is a global search algorithm taking its inspiration from natural genetics. GA
has several parameters affecting its performance. Even for the GA with same structural
parameters (coding scheme, operator types, stopping criterion), the different combinations of
numerical parameters (initial population type, population size, maximum generation number
and the crossover and mutation probabilities) may lead to drastic changes in the performance
of the algorithm. This study examines the effects of numerical parameters of GA on its
performance in terms of both fitness and CPU time; and proposes guidelines for appropriate
parameter selection. A test problem of a serial assembly line taken from the literature is used
for the GA-based simulation-optimization application. A genetic algorithm coded in C is
integrated with a simulation model developed using SIMAN simulation language.
Modifications on the test problem are made to analyze the behavior of GA parameters under
different experimental conditions. The computational results reveal that in the case of a
dominant set of decision variables, for rapid convergent GA applications high mutation rates
are more useful, whereas the crossover operator does not have any significant impact on GA
performance.Boyabatlı, OnurM.S
Integrating Long-Term and Short-Term Contracting in Beef Supply Chains
Published version made available with the permission of INFORMS</p
Procurement Risk Management in Beef Supply Chains
Procurement risk management in beef supply chain
INTEGRATING LONG-TERM AND SHORT-TERM CONTRACTING IN BEEF SUPPLY CHAINS
proofs for Propositions 1 and 2 are omitted. §A provides the equivalent formulation to the stage 1 optimization problem. The optimal processing decisio