An integer programming approach and implementation for an electric utility capacity planning problem with renewable energy sources

Article Abstract:

An integer programming model and algorithm are presented for an electric utility capacity expansion problem. A comparison is made between investing in non-dispatchable versus renewable energy resources. A branch-and-bound algorithm is suggested that entails continuous relaxation of an associated subproblem, with each enumeration tree node solved by means of an efficient, two-phase method. The resulting solution is then refined in a second phase, during which more accurate methods are used to show the negative load associated with the renewable resources and to calculate the probabilistic production costing. An implementation of the method is described for the Tijuana-Mexicali subsection of a Mexican utility which is not tied to the remainder of the Mexican electric system.

Electric services, Planning, Electric utilities, Mexico, Integer programming, Branch and bound algorithms, Industrial capacity

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An integrated simulation and dynamic programming approach for determining optimal runway exit locations

Article Abstract:

The efficiency of runway usage is of great concern to the Federal Aviation Administration (FAA), the NASA, and to all air carriers and air travellers. The FAA reports that airport delays in 1986 alone resulted in additional expenses worth $5.1 billion for both air carriers and passengers. The problem of improving the operational use of runways is examined. The study concentrates on establishing the geometry and location of high speed exits on a runway that allow the lowest possible weighted runway occupancy time of aircrafts with varying frequencies of runway usage and under different landing situation. An integrated simulation and optimization method for designing new runways or redesigning old ones is discussed.

Research, Industrial efficiency, Economic efficiency, Airports, Runways, Mathematical optimization

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A tight linearization and an algorithm for zero-one quadratic programming problems

Article Abstract:

A new linearization method is given for linearly constrained zero-one quadratic programming problems which are often found in situations involving strategic planning, location decisions, and economics. The new linearization method adds several constraints and yields a tighter linear or continuous programming relaxation than other methods. The usefulness of the algorithm and linearization method used are demonstrated.

Economics, Algorithms, Statistics, Statistics (Data), Quadratic programming, Economic methods, Mathematical programming, Lagrange equations, Functions, Implicit, Implicit functions

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