G Srinivasan

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.

The problem of scheduling n jobs on m machines with each job having a specific route has been one of considerable research over the last several decades. Branch and Bound algorithms for determining the optimal makespan have been developed and tested on small sized problems and dispatching rule based heuristic algorithms to minimize specific performance measures such as makespan, flowtime, tardiness, etc. are available to solve large sized problems. This paper addresses the same problem faced by an organization and reports the solution of this problem using genetic algorithms (GA) and a combination of dispatching rules. The proposed algorithm yields an improvement of about 30% in makespan over the present system.