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论文类型：期刊论文
发表时间：2014-01-01
发表刊物：INTERNATIONAL JOURNAL OF LOGISTICS MANAGEMENT
收录刊物：SSCI
卷号：25
期号：1
页面范围：133-159
ISSN号：0957-4093
关键字：Uncertainty; Pareto front; Outsourcing; Dynamic programming; Machine scheduling; Population-based heuristics
摘要：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.