A New Metaheuristics For Solving Production Leveling (Heijunka) Problem: Partial Comparison Optimization

Antono Adhi

International Conference on Industrial Revolution for Polytechnic Education
Vol. 1 No. 1 (2019): International Conference on Industrial Revolution for Polytechnic Education

This article propose a new metaheuristic method for solving production leveling (heijunka) problem. This metaheuristic is called Partial Comparison Optimization (PCO). Production leveling (heijunka) is element of Toyota Production System (TPS). Those levels release production kanbans in order to achieve an even production flow over all possible types of products. PCO is used to solve combinatorial optimization such as scheduling. In this article, PCO is used to solve problem of leveling in production which is caused by fluctuation of consumer demand. PCO will execute production data of vehicle manufacturing ordered by their agent. PCO is only used to search optimum sequence of production therefore for the leveling problem, a leveling mechanism is embedded in PCO algorithm. The goal of the operation is to minimize cost that will be confronted with two opposite side of cost. In one side operation has to reduce setup cost and the other side it has to reduce indent cost. The problem also has to deal with delivery cost impacted by production leveling. PCO has capability to give optimum production leveling in order to minimize these costs.