A Genetic Algorithm Approach to Solve the General Lot Sizing and Scheduling Problem.

This paper presents preliminary results found by a genetic algorithm solving the general lot sizing and scheduling problem (GLSP) with single machine, sequence dependent setup cost and penalties for demands not satisfied. A set of instances are generated for this problem where solutions are first obtained using the solver CPLEX. Next, these solutions are used as benchmark to evaluate the GA results. The results found by GA shows its promising performance for the set of instances solved.