Computational study of a family of mixed-integer quadratic programming problems
Mathematical Programming: Series A and B
Minimum cost capacity installation for multicommodity network flows
Mathematical Programming: Series A and B - Special issue on computational integer programming
Combined Pricing and Inventory Control Under Uncertainty
Operations Research
Two large-scale network design problems
Two large-scale network design problems
Capacity expansion decision in supply chains: A control theory application
International Journal of Computer Integrated Manufacturing - THE CHALLENGES OF MANUFACTURING IN THE GLOBALLY INTEGRATED ECONOMY. GUEST EDITOR: ROBIN G. QIU
MIP modeling of incremental connected facility location
INOC'11 Proceedings of the 5th international conference on Network optimization
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In this paper we describe an efficient algorithm for solving novel optimization models arising in the context of multiperiod capacity expansion of optical networks. We assume that the network operator must make investment decisions over a multiperiod planning horizon while facing rapid changes in transmission technology, as evidenced by a steadily decreasing per-unit cost of capacity. We deviate from traditional and monopolistic models in which demands are given as input parameters, and the objective is to minimize capacity deployment costs. Instead, we assume that the carrier sets end-to-end prices of bandwidth at each period of the planning horizon. These prices determine the demands that are to be met, using a plausible and explicit price-demand relationship; the resulting demands must then be routed, requiring an investment in capacity. The objective of the optimization is now to simultaneously select end-to-end prices of bandwidth and network capacities at each period of the planning horizon, so as to maximize the overall net present value of expanding and operating the network. In the case of typical large-scale optical networks with protection requirements, the resulting optimization problems pose significant challenges to standard optimization techniques. The complexity of the model, its nonlinear nature, and the large size of realistic problem instances motivates the development of efficient and scalable solution techniques. We show that while general-purpose nonlinear solvers are typically not adequate for the task, a specialized decomposition scheme is able to handle large-scale instances of this problem in reasonable time, producing solutions whose net present value is within a small tolerance of the optimum.