A Decision Support System for Multi-Objective Porcelain Container Loading Problem Based on Genetic Algorithm

Abstract

The multi-objective genetic algorithm approach for solving the Porcelain Container Loading Problem (PCLP) has a vital role in the global logistics industry. In this paper, a logistical problem with one constrained container loading problem that has to be filled with a set of boxes has been the focus. This study addresses a real-life problem that exports departments in the international porcelain industry face. Our objective is to maximize product profitability and delivery priority. Since the CLP is known as an NP-hard problem, the Genetic Algorithm (GA) approach is proposed to solve the problem based on these objective functions. The parameters of the GA affect the obtained results. We made tuning by using an experimental design in order to determine the appropriate parameters. The main contribution of the study is to present a new decision support system taking into account the objectives of the delivery time and profit rate priority of the manufacturer in the porcelain sector. Thus, loading according to the company’s priority and distribution in the shortest distance has been successfully achieved. The results show the efficiency of the proposed decision support system, which solves the CLP with up to 12 different products in boxes of different volumes.