Abstract
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.
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References
Aldakhilallah K. A., Ramesh R. (1999) Computer-integrated process planning and scheduling (CIPPS): Intelligent support for product design, process planning and control. International Journal of Production Research 37(3): 481–500
Baganha M. P., Cohen M. A. (1998) The stabilizing effect of inventory in supply chains. Operations Research 46(3): 72–83
Bongaerts L., Monostori L., McFarlane D., Kadar B. (2000) Hierarchy in distributed shop floor control. Computers in Industry 43: 123–137
Brandimarte P., Calderni M. (1995) A hierarchical bicriterion approach to integrated process plan selection and job shop scheduling. International Journal of production Research 33(1): 161–181
Cai N., Wang L., Fenga H. Y. (2009) GA-based adaptive setup planning toward process planning and scheduling integration. International Journal of Production Research 47(10): 2745–2766
Chen Y. M., Liao C. C., Prasad B. (1998) A Systematic approach of virtual enterprising through knowledge management techniques. Concurrent Engineering-Research and Applications 6(3): 225–244
Chen F., Drezner Z., Ryan J. K., Simchi-Levi D. (2000) Quantifying the bull-whip effect in a simple supply chain: The impact of forecasting, lead-times and information. Management Science 46(1): 436–443
Chen M. C., Yang T., Yen C. T. (2007) Investigating the value of information sharing in multi-echelon supply chains. Quality and Quantity 41(3): 497–511
Cho H., Son Y., Jones A. (2006) Design and conceptual development of shop-floor controllers through the manipulation of process plans. International Journal of Computer Integrated Manufacturing 19(4): 359–376
Computer Sciences Corporation. (1996 December 18) Healthcare industry study reveals potential supply chain savings. Business Wire.
Dilts D. M., Boyd N. P., Whorms H. H. (1991) The evolution of control architectures for automated manufacturing systems. Journal of Manufacturing Systems 10(1): 79–93
Grean M., Shaw M. J. (2002) Supply –chain partnership between P&G and Wal-Mart. Integrated Series in Information Systems, E-Business Management, Springer-US, 1: 155–171
Guo Y. W., Li W. D., Mileham A. R., Owen G. W. (2009) Optimisation of integrated process planning and scheduling using a particle swarm optimisation approach. International Journal of Production Research 47(14): 3775–3796
Hicks T. G. (2006) Handbook of material engineering calculations (2nd ed.). McGraw-Hill, New York
Joshi S., Chang T. C., Liu C. R. (1986) Process planning formalization in an AI framework. Artificial Intelligence in Engineering 1(1): 45–53
Kim Y. K., Park K., Ko J. (2003) A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Computers & Operations Research 30: 1151–1171
Kurt Salmon Associates Incorporation: (1993) Efficient consumer response: Enhancing consumer value in the grocery industry. Food Marketing Institute, Washington, D.C.
Lambert D. M., Cooper M. C. (2000) Issues in supply chain management. Industrial Engineering Management 29(1): 65–83
Lancioni R. A., Smith M. F., Olica T. A. (2000) The role of the internet in supply chain management. Industrial Engineering Management 29(1): 45–56
Lee H. L., So K. C., Tang C. S. (2000) The value of information sharing in two-level supply chain. Management Science 46(5): 626–643
Lee H. L., Padmanabhan V., Whang S. (1997) Information distortion in a supply chain: The bullwhip effect. Management Science 43(4): 546–558
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): 119–150
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).
Ma G. H., Zhang Y. F., Nee A. Y. C. (2000) A simulated annealing-based optimization algorithm for process planning. International Journal of Production Research 38(12): 2671–2687
Moon C., Kim J., Hur S. (2002) Integrated process planning and scheduling with minimizing total tardiness in multi-plants supply chain. Computers & Industrial Engineering 43: 331–339
Moon C., Seo Y. (2005) Evolutionary algorithm for advanced process planning and scheduling in a multi-plant. Computers & Industrial Engineering 48: 311–325
Nau D. S., Chang T. (1983) Prospects for process selection using artificial intelligence. Computers in Industry 4: 253–263
Premier Alliance. (2004 December 9). Lessons Learned from Premier’s Third Annual Supply Chain Collaborative Breakthrough Series. Business Wire.
Samaddar S., Nargundkar S., Dayley M. (2006) Inter-organizational information sharing: The role of supply network configuration and partner goal congruence. European Journal of Operations Research 174(2): 744–765
Sherali H. D., Desai J., Glickman T. S. (2008) Optimal allocation of risk-reduction resources in event trees. Management Science 54(7): 1313–1321
Siems T. F. (2005) Supply chain management: The science of better, faster, cheaper. Southwest Economy 2(March/April): 6–12
Sormaz D., Khoshnevis B. (2003) Generation of alternative process plans in integrated manufacturing systems. Journal of Intelligent Manufacturing 14: 509–526
Sterman J. D. (1989) Modelling managerial behaviour: Misperceptions of feedback in a dynamic decision making experiment. Management Science 35(3): 321–339
Stevenson M. (1994) The store to end all stores. Canadian Business Review 67(5): 20–26
Venkateswaran J., Son Y. J. (2004) Impact of modeling approximations in supply chain analysis-an experimental study. International Journal of Production Research 42(15): 2971–2992
Yan H. S., Xia Q. F., Zhu M. R., Liu X. L., Guo Z. M. (2003) Integrated production planning and scheduling on automobile assembly lines. IIE Transactions 35: 711–725
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Celik, N., Nageshwaraniyer, S.S. & Son, YJ. Impact of information sharing in hierarchical decision-making framework in manufacturing supply chains. J Intell Manuf 23, 1083–1101 (2012). https://doi.org/10.1007/s10845-010-0430-3
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DOI: https://doi.org/10.1007/s10845-010-0430-3