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Capacity planning in the service industry
Abstract
Capacity expansion planning in distributed service organizations differs substantially from capacity expansion planning in the manufacturing industry. A major difference originates from the nature of the service industries, whose products can neither be shipped to other demanding markets nor be stored for future demand. Thus, there is a tendency toward building many small Independent Capacity Units (ICUs), each serving a local demand. In the manufacturing environment, however, only a relatively small number of facilities are considered for manufacturing towards both satisfying local markets and exporting to remote markets. Extensive research has been conducted on the capacity expansion problem in the manufacturing environment as compared to almost no research on the service industry. An investigation of the Service Capacity Problem (SCP) is conducted. Different features are included in the SCP formulation. The SCP objective is to maximize profits, a more realistic goal than to minimize costs. Moreover, the feasible region of solutions is increased substantially, and therefore, complicating the search for an optimal solution. The SCP model is motivated by the fact that most organizations experience a budget constraint which limits their capacity expansion implementation only to the most profitable plans. This is in contrast to the manufacturing capacity expansion model which explicitly assumes unlimited available funds for capacity expansion. The ability to solve efficiently problems constituting of many ICUs, is the intention of this research. Models, exact algorithms and heuristics are developed congruent to three different organizational decision making approaches, denoted in this thesis by: a price allocation approach, a resource allocation approach and a central approach. These models, exact algorithms and heuristics deal with capacity expansion problems in which it is assumed that all terms and costs are linear, and with more complicated formulations in which the capacity expansion costs are non-linear. In the second case, the cost function has a fixed charge paid any time the capacity is expanded. The computational tests of the exact algorithms developed for linear costs, indicate computation time savings of up to 96% compared to a general purpose linear programming code. The heuristics developed for non-linear costs, solved the problems in very short computation times compared to the prohibitive computation time needed by a general purpose integer programming code. The exact algorithm for the linear costs and the heuristic for the non-linear costs for which we provided the above computational results, have complexity O(n log n), making them suitable for large scale problems.
Subject Area
Industrial engineering|Operations research|Business administration
Recommended Citation
Ganz, Zvi, "Capacity planning in the service industry" (1991). Doctoral Dissertations Available from Proquest. AAI9120882.
https://scholarworks.umass.edu/dissertations/AAI9120882