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The Multiregion Dynamic Capacity Expansion Problem: An Improved Heuristic


  • C. O. Fong

    (Faculty of Economics and Administration, University of Malaya, Kuala Lumpur 22-11, Malaysia)

  • V. Srinivasan

    (Graduate School of Business, Stanford University, Stanford, California 94305)


We consider the problem of determining a schedule of capacity expansions for m producing regions and a schedule of shipments from the regions to n markets so as to meet market demands over a T-period planning horizon at minimum discounted capacity expansion and shipment costs. The proposed algorithm permits capacity expansion costs to be arbitrary nonnegative increasing functions of the expansion amounts, but the shipment (and production) costs are restricted to be proportional to the amounts shipped. The algorithm does not require market demands to be increasing over time. The cost functions are allowed to be nonstationary and the possibility of imports is considered. The proposed heuristic algorithm improves on feasible solutions by simultaneously reassigning several capacity expansions to different regions and/or time periods. A look-ahead feature prevents the algorithm from becoming myopic and a self-learning feature dynamically updates computational parameters. The heuristic algorithm was tested on both randomly generated and real-life based problems with m \le 15, and n \le 15 and T \le 25. The test problems had increasing market demands, capacity expansion costs specified in the form of a concave power function or a fixed charge plus linear function, stationary costs (aside from a constant discount factor), and no imports. Results indicate that for the class of problems tested, the heuristic algorithm is computationally efficient and provides solutions that are closer to optimum than those obtained by previous algorithms.

Suggested Citation

  • C. O. Fong & V. Srinivasan, 1986. "The Multiregion Dynamic Capacity Expansion Problem: An Improved Heuristic," Management Science, INFORMS, vol. 32(9), pages 1140-1152, September.
  • Handle: RePEc:inm:ormnsc:v:32:y:1986:i:9:p:1140-1152

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    Cited by:

    1. Martínez-Costa, Carme & Mas-Machuca, Marta & Benedito, Ernest & Corominas, Albert, 2014. "A review of mathematical programming models for strategic capacity planning in manufacturing," International Journal of Production Economics, Elsevier, vol. 153(C), pages 66-85.
    2. Francisco André & Francisco Velasco & Luis Gonzalez-Abril, 2009. "Intertemporal and spatial location of disposal facilities," Spanish Economic Review, Springer;Spanish Economic Association, vol. 11(1), pages 23-49, March.
    3. Maria Albareda-Sambola & Antonio Alonso-Ayuso & Laureano Escudero & Elena Fernández & Yolanda Hinojosa & Celeste Pizarro-Romero, 2010. "A computational comparison of several formulations for the multi-period incremental service facility location problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 62-80, July.
    4. repec:pal:jorsoc:v:56:y:2005:i:4:d:10.1057_palgrave.jors.2601826 is not listed on IDEAS
    5. Mørch, Ove & Fagerholt, Kjetil & Pantuso, Giovanni & Rakke, Jørgen, 2017. "Maximizing the rate of return on the capital employed in shipping capacity renewal," Omega, Elsevier, vol. 67(C), pages 42-53.
    6. Seifert, Ralf W. & Langenberg, Kerstin U., 2011. "Managing business dynamics with adaptive supply chain portfolios," European Journal of Operational Research, Elsevier, vol. 215(3), pages 551-562, December.


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