A holonic workforce allocation model for labour-intensive manufacturing

This paper presents a new model for workforce allocation in labour-intensive industries. In such industries where production processes mostly include manual assembly operations, performance is highly influenced by the availability of skilled workers. Sudden unavailability of skilled labour has signi...

Full description

Bibliographic Details
Main Authors: Mozafar Saadat, Salman Saeidlou, Melissa C. L. Tan
Format: Article
Language:English
Published: Taylor & Francis Group 2017-01-01
Series:Cogent Engineering
Subjects:
Online Access:http://dx.doi.org/10.1080/23311916.2017.1324747
Description
Summary:This paper presents a new model for workforce allocation in labour-intensive industries. In such industries where production processes mostly include manual assembly operations, performance is highly influenced by the availability of skilled workers. Sudden unavailability of skilled labour has significant adverse effects on production. Furthermore, as competition intensifies, production becomes more sensitive to changing market demands. Such disturbances can be attenuated by introducing flexibility in the production planning process. Workforce allocation plays a significant role in the planning process. Thus, this paper focuses on workforce allocation, and a support system is developed from the concepts of holonic manufacturing systems and PROSA reference architecture. The system was designed in unified modelling language and was tested using an object-oriented software developed in C++. The use of the holonic methodology to develop the system has helped to identify the shortfalls of the conventional method adopted in industry and develop algorithms to improve the workforce allocation process. The proposed system was simulated using production data from a computer manufacturer case study. The paper then presents a comparison of the factory’s conventional method of workforce allocation with the proposed holonic workforce allocation system. The results suggest an improved manufacturing throughput performance.
ISSN:2331-1916