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Machine scheduling with outsourcing : Coping with supply chain uncertainty with a second supplying source

Feng Liu (School of Management Science and Engineering, Dalian University of Technology, Dalian, P.R. China and Department of Supply Chain Management & Marketing Sciences, Rutgers Business School, Rutgers University, Newark, New Jersey, USA)
Jian-Jun Wang (School of Management Science and Engineering, Dalian University of Technology, Dalian, P.R. China)
Haozhe Chen (College of Business, East Carolina University, Greenville, North Carolina, USA)
De-Li Yang (School of Management Science and Engineering, Dalian University of Technology, Dalian, P.R. China)

The International Journal of Logistics Management

ISSN: 0957-4093

Article publication date: 6 May 2014

934

Abstract

Purpose

The purpose of this paper is to study the use of outsourcing as a mechanism to cope with supply chain uncertainty, more specifically, how to deal with sudden arrival of higher priority jobs that require immediate processing, in an in-house manufacturer's facility from the perspective of outsourcing. An operational level schedule of production and distribution of outsourced jobs to the manufacturer's facility should be determined for the subcontractor in order to achieve overall optimality.

Design/methodology/approach

The problem is of bi-criteria in that both the transportation cost measured by number of delivery vehicles and schedule performance measured by jobs’ delivery times. In order to obtain the problem's Pareto front, we propose dynamic programming (DP) heuristic solution procedure based on integrated decision making, and population-heuristic solution procedures using different encoding schemes based on sequential decision making. Computational studies are designed and carried out by randomly generating comparative variations of numerical problem instances.

Findings

By comparing several existing performance metrics for the obtained Pareto fronts, it is found that DP heuristic outperforms population-heuristic in both solutions diversity and proximity to optimal Pareto front. Also in population-heuristic, sub-range keys representation appears to be a better encoding scheme for the problem than random keys representation.

Originality/value

This study contributes to the limited yet important knowledge body on using outsourcing approach to coping with possible supply chain disruptions in production scheduling due to sudden customer orders. More specifically, we used modeling methodology to confirm the importance of collaboration with subcontractors to effective supply chain risk management.

Keywords

Acknowledgements

The authors are grateful for three anonymous referees for their helpful comments on earlier version of the article. This research was supported by the National Natural Science Foundation of China (71271039, 70902033), New Century Excellent Talents in University (NCET-13-0082), Changjiang Scholars and Innovative Research Team in University (IRT1214), the Fundamental Research Funds for the Central Universities (DUT14YQ211).

Citation

Liu, F., Wang, J.-J., Chen, H. and Yang, D.-L. (2014), "Machine scheduling with outsourcing : Coping with supply chain uncertainty with a second supplying source", The International Journal of Logistics Management, Vol. 25 No. 1, pp. 133-159. https://doi.org/10.1108/IJLM-12-2012-0142

Publisher

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Emerald Group Publishing Limited

Copyright © 2014, Emerald Group Publishing Limited

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