A genetic algorithm based approach to solve process plan selection problems

Selection of a process plan is a crucial decision making problem in manufacturing systems due to the presence of alternative plans arising from the availability of several machines, tools, fixtures, etc. Because of its impact on the performance of a manufacturing system, several researchers have addressed the plan selection problem in recent years. Selecting an optimal set of plans for a given set of parts becomes an NP complete problem under multiobjective and fairly restrictive conditions. In this paper, a genetic algorithm (GA) is used to obtain a set of feasible plans, for given part types and production volume, to minimize the processing time, setup time and materials handling time constrained by not overloading the machines. Obtaining near optimal solutions by using different weights for different objectives in GA, is also studied.