Multiobjective Order Assignment Optimization in a Global Multiple-Factory Environment

Rong-Chang Chen,Pei-Hsuan Hung

doi:10.1155/2014/673209

Rong-Chang Chen, Pei-Hsuan Hung

Open Access

https://doi.org/10.1155/2014/673209

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Abstract

In response to radically increasing competition, many manufacturers who produce time-sensitive products have expanded their production plants to worldwide sites. Given this environment, how to aggregate customer orders from around the globe and assign them quickly to the most appropriate plants is currently a crucial issue. This study proposes an effective method to solve the order assignment problem of companies with multiple plants distributed worldwide. A multiobjective genetic algorithm (MOGA) is used to find solutions. To validate the effectiveness of the proposed approach, this study employs some real data, provided by a famous garment company in Taiwan, as a base to perform some experiments. In addition, the influences of orders with a wide range of quantities demanded are discussed. The results show that feasible solutions can be obtained effectively and efficiently. Moreover, if managers aim at lower total costs, they can divide a big customer order into more small manufacturing ones.

Highlights

To best satisfy the requirements of customers and quickly respond to changes in the market environment, some manufacturers who produce time-sensitive products, such as fashion garments or high-tech goods, have established many manufacturing sites in other countries
The issue mentioned above is concerned with order assignment [1, 2] of companies with factories spreading over the globe
In some African countries, product quantities are limited by lower skill levels, whereas plants in some Asian countries have high-level skills to produce a variety of products

Summary

Introduction

To best satisfy the requirements of customers and quickly respond to changes in the market environment, some manufacturers who produce time-sensitive products, such as fashion garments or high-tech goods, have established many manufacturing sites in other countries. The main objectives for a company are to pursue the lowest cost and the shortest total production time, in order to achieve the global optimization of their. To address the described issue, Chen et al [19] considered a two-objective optimization in a global multiple-factory environment, but they did not take production time into consideration. To prove the validity of the proposed method, real data from a famous company in Taiwan are used as the basis for some experiments Plantrelated factors such as production capacity, manufacturing costs, material costs, and transportation costs are considered. MOGA (multiobjective genetic algorithm) is employed to assign orders to optimally satisfy the objectives of a company, that is, the lowest total cost and the shortest production time.

The Problem

Modeling

The GA Structure

Results and Discussion

Conclusions