Optimisation of a machine loading problem using a genetic algorithm-based heuristic

In the present work, apart from operating on the structure of a conventional genetic algorithm (GA), a heuristic which uses techniques like differential mutation probability, elitism and local search is used to produce near optimal solutions for large machine loading problems with less computational intensity. Two variants of the machine loading problem are analysed in the present work: single batch model and the multiple batch models. The sensitivity of the problem with respect to the tool capacity constraint is evaluated to find that moderately restricted problems requiring greater computational resources in comparison to lesser restricted and tightly restricted class of problems. The performance of various dispatching rules was compared to infer that the least slack principle fares better than the other tested dispatching rules. It is observed from the results, that the proposed heuristic is efficient in handling large and complex machine loading problems.