Approximate linear programming for networks: Average cost bounds

Abstract

This paper uses approximate linear programming (ALP) to compute average cost bounds for queueing network control problems. Like most approximate dynamic programming (ADP) methods, ALP approximates the differential cost by a linear form. New types of approximating functions are identified that offer more accuracy than previous ALP studies or other performance bound methods. The structure of the infinite constraint set is exploited to reduce it to a more manageable set. When needed, constraint sampling and truncation methods are also developed. Numerical experiments show that the LPs using quadratic approximating functions can be easily solved on examples with up to 17 buffers. Using additional functions reduced the error to 1 to 5% at the cost of larger LPs. These ALPs were solved for systems with up to 6 to 11 buffers, depending on the functions used. The method computes bounds much faster than value iteration. It also gives some insights into policies. The ALPs do not scale to very large problems, but they offer more accurate bounds than other methods and the simplicity of just solving an LP.

Energy efficient routing formation technique for hybrid ad hoc network using fusion of artificial intelligence techniques

“…[12][13][14][15][16][17][18][19][20][21] Because of it, the problem of conflicting and incommensurate always exists and may not be removed.…”

“…Michael H. Veatch 19 proposed a linear programming-based protocol (LPP). It approximated differential cost of the network with the help of linear programming and places a lower bound on average cost to achieve the optimum goal.…”

“…The characteristics of OE2R is compared with some existing methods (eg, EN-AODV 16 RECI, 17 EASRP, 18 LPP, 19 CLEA, 20 BLPP 21 ), which are shown in Table 2. The characteristics of OE2R is compared with some existing methods (eg, EN-AODV 16 RECI, 17 EASRP, 18 LPP, 19 CLEA, 20 BLPP 21 ), which are shown in Table 2.…”

“…In many real-life applications, this mapping produced a general solution to get optimal goal, which is not possible in earlier methods that are mentioned in the literature review. [12][13][14][15][16][17][18][19][20][21]…”

“…The characteristics of OE2R is compared with some existing methods (eg, EN-AODV 16 RECI, 17 EASRP, 18 LPP, 19 CLEA, 20 BLPP 21 ), which are shown in Table 2. The simulation comparison has been made with the 2 existing methods (ie, LPP 19 and BLPP 21 ). The OE2R is based on a hybrid approach that has a combined feature of several AI techniques such as MOO, GP, aspiration level, and optimset.…”