Poonam Savsani & Gaurav Banthia
PDPU, Gandhinagar, India
Title: Optimal Aggregate Production Planning by using Genetic Algorithm.
Biography
Biography: Poonam Savsani & Gaurav Banthia
Abstract
Aggregate production planning (APP) deals with the simultaneous determination of plant’s production, inventory and vocation levels over a finite time horizon. The aim of aggregate planning is to finalize overall output levels in the near to medium future in uncertain demands. This paper presents a Genetic Algorithm approach for solving aggregate production planning with different selection methods and various crossover phenomenons. Combination of four selection methods and five crossover phenomenon are taken and compared to choose the best combination for solving APP in this present work. The problem statement depicts multi-product, multi-period APP with forecasted demand. The proposed approach attempts to minimize the total cost which includes labour cost, backordering cost, subcontracting cost, inventory cost, warehouse cost, overtime cost and machine cost. Results show the outstanding performance of uniform selection procedure and two point crossover combination.