Although the Simplex Method (SM) developed for Dantzig is efficient for solving many linear programming problems (LPs), there are constructions of hard linear programs as the Klee-Minty cubes and another deformed products, where this method has an exponential behavior. This work presents the integration of genetic algorithms (GA) and SM to fastly reach the optimum of this type of problems. This...

In the Stanford Linear Collider (SLC) each beam, after leaving a damping ring, is compressed in the Ring-to-Linac (RTL) transfer line before entering the linear accelerator. At a bunch population of 4:0 10 particles, due to the limited energy acceptance of the RTL, approximately 15% of the beam has normally been lost. During the 1996 run, however, to eliminate this loss the bunch was partially ...

This paper develops a decentralized leader-follower game for network design of a competitive supply chain problem in which a new chain as the leader enters a market with one existing supply chain as a follower. Both chains produce an identical product, customer demand is inelastic and customer utility function is based on Huff gravity-based model. The leader wants to shape his network and set a...

For some of the EOS models the dimension of equilibrium problem can be reduced. Stability and difficulties in implementation are among the problems of flash calculation. In this work, a new reduction technique is presented to prepare a reduced number of equilibrium equations. Afterwards, a number of appropriate solution variables are selected for the prepared equation system to solve the equati...

In routing games, agents pick their routes through a network to minimize their own delay. A primary concern for the network designer in routing games is the average agent delay at equilibrium. A number of methods to control this average delay have received substantial attention, including network tolls, Stackelberg routing, and edge removal. A related approach with arguably greater practical re...

Modeling the purposeful behavior of imperfect agents from a small number of observations is a challenging task. When restricted to the single-agent decision-theoretic setting, inverse optimal control techniques assume that observed behavior is an approximately optimal solution to an unknown decision problem. These techniques learn a utility function that explains the example behavior and can th...