This paper describes a solution method to the problem of processing n jobs on m non-related parallel machines. It is a linear and combinatorial generalized allocation problem that considered a sequencedependent setup time and dynamic job entry. A genetic algorithm with integer coding and random generation of population, parent selection, crossover and mutation is proposed. There are two descendants per generation that are compared against the worst existing element to enter to population. After a number of generations that is proportional to the product of nxm the solution is generated. The jobs are sequenced on each machine by due date and computational times are acceptable. It is concluded that the proposed genetic algorithm is an effective and efficient solution that focuses on reducing processing time and on meeting deadlines. [ABSTRACT FROM AUTHOR]
This paper presents a multiobjective analysis including variables of the environment of the energy system which is conditioned by the use of solid fuels, for this reason the analysis includes the external characteristics of the resource (fuel) of residue (ash) and the product (electricity/steam). The energy system used is Termocesar, a Colombian process for generating electricity from carbon. Three functions named thermoeconomic objective function, technology objective function and environmental objective function, are analyzed to establish the best location of the energy The genetic algorithm method is used for the optimization of the system. The results were analyzed for nine combinations of weights and show that for most of the cases the optimum includes coals from the Department of Cesar in Colombia. [ABSTRACT FROM AUTHOR]