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ABSTRACTRecent advances in mathematical programming methods and computing technology have the potential for overcoming the many formidable barriers to the widespread solution of nonlinear spatial models. In particular, the recent public availability of the software package MINOS/AUGMENTED has major implications for both policy and theoretical research involving spatial models. In this paper, mathematical programming formulations of two highly nonlinear spatial models are provided, model solutions presented, and associated computational experience discussed. In addition, related work is discussed briefly and suggestions are advanced concerning the potential role of the package in furthering the objectives of this related work.Subject Areas: Mathematical Programming, Transportation, and Urban and Regional Planning.

Large‐scale mixed‐integer linear programming (MILP) models may easily prove extraordinarily difficult to solve, even with efficient commercially implemented MILP solution codes. Drawing on experience gained in solving and analyzing three intertemporal investment planning MILP models for electric power supply, this note offers several practical suggestions for reducing computer solution times for general production‐allocation MILP models. Solution time reduction stems from judicious use of the powerful computational capabilities of existing commercial linear programming codes in conjunction with information known or to be learned by the practitioner about the model's structure.