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Modeling and design of global logistics systems: a review of integrated strategic and tactical models and design algorithms. (English) Zbl 1073.90501
Summary: The overall focus of this research is to demonstrate the savings potential generated by the integration of the design of strategic global supply chain networks with the determination of tactical production-distribution allocations and transfer prices. The logistics systems design problem is defined as follows: given a set of potential suppliers, potential manufacturing facilities, and distribution centers with multiple possible configurations, and customers with deterministic demands, determine the configuration of the production-distribution system and the transfer prices between various subsidiaries of the corporation such that seasonal customer demands and service requirements are met and the after tax profit of the corporation is maximized. The after tax profit is the difference between the sales revenue minus the total system cost and taxes. The total cost is defined as the sum of supply, production, transportation, inventory, and facility costs. Two models and their associated solution algorithms will be introduced. The savings opportunities created by designing the system with a methodology that integrates strategic and tactical decisions rather than in a hierarchical fashion are demonstrated with two case studies.
The first model focuses on the setting of transfer prices in a global supply chain with the objective of maximizing the after tax profit of an international corporation. The constraints mandated by the national taxing authorities create a bilinear programming formulation. We will describe a very efficient heuristic iterative solution algorithm, which alternates between the optimization of the transfer prices and the material flows. Performance and bounds for the heuristic algorithms will be discussed.
The second model focuses on the production and distribution allocation in a single country system, when the customers have seasonal demands. This model also needs to be solved as a subproblem in the heuristic solution of the global transfer price model. The research develops an integrated design methodology based on primal decomposition methods for the mixed integer programming formulation. The primal decomposition allows a natural split of the production and transportation decisions and the research identifies the necessary information flows between the subsystems. The primal decomposition method also allows a very efficient solution algorithm for this general class of large mixed integer programming models. Data requirements and solution times will be discussed for a real life case study in the packaging industry.

90B05 Inventory, storage, reservoirs
Full Text: DOI
[1] Abdallah, W.M., International transfer pricing policies: decision making guidelines for multinational companies, (1989), Quorum Books New York
[2] Al-Khayyal, F.A., Jointly constrained bilinear programs and related problems: an overview, Computers and mathematics with applications, 16, 11, 53-62, (1990) · Zbl 0706.90074
[3] Al-Khayyal, F.A., Generalized bilinear programming: part I. models, applications and linear programming relaxation, European journal of operational research, 60, 306-314, (1992) · Zbl 0784.90051
[4] Arntzen, B.C.; Brown, G.G.; Harrison, T.P.; Trafton, L.L., Global supply chain management at digital equipment corporation, Interfaces, 25, 1, 69-93, (1995)
[5] Ben-Tal, A.; Eiger, G.; Gershovitz, V., Global minimization by reducing the duality gap, Mathematical programming, 63, 193-212, (1994) · Zbl 0807.90101
[6] Benders, J., Partitioning procedures for solving mixed-variables programming problems, Numerische Mathematik, 4, 238-252, (1962) · Zbl 0109.38302
[7] Brown, G.G.; Graves, G.W.; Honczarenko, M.D., Design and operation of a multicommodity production/distribution system using primal goal decomposition, Management science, 33, 11, 1469-1480, (1987)
[8] Canel, C.; Khumawala, B.M., Multi-period international facilities location: an algorithm and application, International journal of production research, 35, 7, 1891-1910, (1997) · Zbl 0940.90526
[9] Cohen, M.A.; Fisher, M.; Jaikumar, R., International manufacturing and distribution networks: A normative model framework, (), 67-93
[10] Cohen, M.A.; Kleindorfer, P.R., Creating value through operations: the legacy of elwood S. buffa, (), 3-21
[11] Cohen, M.A.; Lee, H.L., Manufacturing strategy: concepts and methods, (), 153-188, Chapter 5
[12] Cohen, M.A.; Lee, H.L., Resource deployment analysis of global manufacturing and distribution networks, Journal of manufacturing operations management, 2, 81-104, (1989)
[13] Cohen, M.A.; Moon, S., An integrated plant loading model with economies of scale and scope, European journal of operational research, 50, 3, 266-279, (1991) · Zbl 0721.90041
[14] Cole, M.H., 1995. Service considerations and the design of strategic distribution systems. Unpublished Doctoral Dissertation, Georgia Institute of Technology, Atlanta, GA
[15] Dogan, K., 1996. A primal decomposition scheme for the design of strategic production distribution systems. Unpublished Doctoral Dissertation, Georgia Institute of Technology, Atlanta, GA
[16] Geoffrion, A.M.; Graves, G.W., Multicommodity distribution system design by benders decomposition, Management science, 20, 5, 822-844, (1974) · Zbl 0304.90122
[17] Geoffrion, A.M.; Graves, G.W.; Lee, S.J., Strategic distribution system planning: A status report, (), 179-204, Chapter 7
[18] Geoffrion, A.M.; Graves, G.W.; Lee, S.J., A management support system for distribution planning, Infor, 20, 4, 287-314, (1982)
[19] Geoffrion, A.M.; Powers, R.F., Twenty years of strategic distribution system design: an evolutionary perspective. (implementation in OR/MS: an evolutionary view), Interfaces, 25, 105-128, (1995)
[20] Goetschalckx, M.; Dogan, K., A primal decomposition method for the integrated design of multi-period production – distribution systems, IIE transactions, 31, 11, 1027-1036, (1999)
[21] Goetschalckx, M.; Nemhauser, G.; Cole, M.H.; Wei, R.; Dogan, K.; Zang, X., Computer aided design of industrial logistic systems, (), 151-178
[22] Hodder, J.E.; Dincer, M.C., A multifactor model for international plant location and financing under uncertainty, Computers and operations research, 13, 5, 601-609, (1986) · Zbl 0615.90041
[23] Huchzermeier, A.; Cohen, M.A., Valuing operational flexibility under exchange rate risk, Operations research, 44, 1, 100-113, (1996) · Zbl 0847.90067
[24] Nieckels, L., Transfer pricing in multinational firms: A heuristic programming approach and a case study, (1976), John Wiley and Sons New York
[25] O’Connor, W., International transfer pricing, ()
[26] Vidal, C.J., 1998. A global supply chain model with transfer pricing and transportation cost allocation. Unpublished Doctoral Dissertation, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
[27] Vidal, C.J., Goetschalckx, M., 1996. The role and limitations of quantitative techniques in the strategic design of global logistics systems. School of Industrial and Systems Engineering Research Report, Georgia Institute of Technology, Atlanta, GA
[28] Vidal, C.; Goetschalckx, M., Strategic production – distribution models: A critical review with emphasis on global supply chain models, European journal of operational research, 98, 1-18, (1997) · Zbl 0922.90062
[29] Vidal, C.J., Goetschalckx, M., 1998. A heuristics for the design of global supply chains with transfer pricing and transportation cost allocation. Research Report, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
[30] Vidal, C.; Goetschalckx, M., Modeling the impact of uncertainties on global logistics systems, Journal of business logistics, 21, 1, 95-120, (2000)
[31] Vidal C; Goetschalckx, M., A global supply chain model with transfer pricing and transportation cost allocation, European journal of operational research, 129, 1, 134-158, (2001) · Zbl 0990.90008
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