Impact of information sharing in hierarchical decision-making framework in manufacturing supply chains
- 345 Downloads
This paper presents a comprehensive framework for the analysis of the impact of information sharing in hierarchical decision-making in manufacturing supply chains. In this framework, the process plan selection and real-time resource allocation problems are formulated as hierarchical optimization problems, where problems at each level in the hierarchy are solved by separate multi-objective genetic algorithms. The considered multi-objective genetic algorithms generate near optimal solutions for NP-hard problems with less computational complexity. In this work, a four-level hierarchical decision structure is considered, where the decision levels are defined as enterprise level, shop level, cell level, and equipment level. Using this framework, the sources of information affecting the achievement of best possible decisions are then identified at each of these levels, and the extent of their effects from sharing them are analyzed in terms of the axis, degree and the content of information. The generality and validity of the proposed approach have been successfully tested for diverse manufacturing systems generated from a designed experiment.
KeywordsMulti-objective optimization Supply chain Information sharing Shop floor control Hierarchical decision-making
Unable to display preview. Download preview PDF.
- Computer Sciences Corporation. (1996 December 18) Healthcare industry study reveals potential supply chain savings. Business Wire.Google Scholar
- Hicks T. G. (2006) Handbook of material engineering calculations (2nd ed.). McGraw-Hill, New YorkGoogle Scholar
- Kurt Salmon Associates Incorporation: (1993) Efficient consumer response: Enhancing consumer value in the grocery industry. Food Marketing Institute, Washington, D.C.Google Scholar
- Legner C., Schemm J. (2008) Toward the inter-organizational product information supply chain—Evidence from the retail and consumer goods industries. Journal of the Association for Information Systems 9(3–4): 119–150Google Scholar
- Li, X., Gao, L., Shao, X., Zhang, C., & Wang, C. (2009). Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling. Computers and Operations Research, (article in press).Google Scholar
- Premier Alliance. (2004 December 9). Lessons Learned from Premier’s Third Annual Supply Chain Collaborative Breakthrough Series. Business Wire.Google Scholar
- Siems T. F. (2005) Supply chain management: The science of better, faster, cheaper. Southwest Economy 2(March/April): 6–12Google Scholar
- Stevenson M. (1994) The store to end all stores. Canadian Business Review 67(5): 20–26Google Scholar