An Improved Genetic Algorithm for Production Planning and Scheduling Optimization Problem

Abstract

This paper proposes an improvement of genetic algorithm (GA) for optimization of production planning and scheduling in the manufacturing industry. The problem is dynamic, combinatorial and multidimensional in nature with factors like production selection, production line allotment, manufacturing sequence, order quantity, etc., to be solved in sync. The efficiency and effectiveness of the proposed GA are demonstrated by a case study. Selection of parameters of the proposed GA is done using Taguchi experiment method. Performance comparison is done using six optimization solvers, namely pattern search solver, simulated annealing, tabu search, stochastic gradient descent, ant colony optimization and traditional genetic algorithm method.