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

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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