Abstract: This paper presents a genetic algorithm based loading methodology for a capacity constrained job-shop with the consideration of alternative process plans for each part to be produced. Performance analysis of the proposed methodology is carried out for two case studies by considering two different manufacturing scenarios. Results obtained indicate that the methodology is quite effective in improving the shop load balance, and hence, it can be included in the frameworks of manufacturing planning systems of job-shop oriented industries.
Abstract: This paper presents optimization of makespan for ‘n’
jobs and ‘m’ machines flexible job shop scheduling problem with
sequence dependent setup time using genetic algorithm (GA)
approach. A restart scheme has also been applied to prevent the
premature convergence. Two case studies are taken into
consideration. Results are obtained by considering crossover
probability (pc = 0.85) and mutation probability (pm = 0.15). Five
simulation runs for each case study are taken and minimum value
among them is taken as optimal makespan. Results indicate that
optimal makespan can be achieved with more than one sequence of
jobs in a production order.