Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

Theory and practice of uncertain programming

An interactive television program guide system is provided. An interactive television program guide provides users with an opportunity to select programs for recording on a remote media server. Programs may also be recorded on a local media server. The program guide provides users with VCR-like control over programs that are played back from the media servers and over real-time cached copies of the programs. The program guide also provides users with an opportunity to designate gift recipients for whom programs may be recorded.

Client-server based interactive television program guide system with remote server recording

For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor Aj ≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values Aj are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association.

User Association for Load Balancing in Heterogeneous Cellular Networks

For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rates for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor A_j>=1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide optimal association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values A_j are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a max received power association.

When selecting a television program for recording, a user may configure the delivery of the selected television program and associated data and interactive applications to different user equipment devices in a home network, which may have different capabilities. Because the user equipment devices in the home network may have different capabilities, the user may wish to deliver different types and amount of content, different amounts of data, and different versions of interactive applications to the user equipment devices in the home network.

Interactive media guidance system having multiple devices

The popularity of wireless communication systems can be seen almost everywhere in the form of cellular networks, WLANs, and WPANs. In addition, small portable devices have been increasingly equipped with multiple communication interfaces building a heterogeneous environment in terms of access technologies. The desired ubiquitous computing environment of the future has to exploit this multitude of connectivity alternatives resulting from diverse wireless communication systems and different access technologies to provide useful services with guaranteed quality to users. Many new applications require a ubiquitous computing environment capable of accessing information from different portable devices at any time and everywhere. This has motivated researchers to integrate various wireless platforms such as cellular networks, WLANs, and MANETs. Integration of different technologies with different capabilities and functionalities is an extremely complex task and involves issues at all layers of the protocol stack. This article envisions an architecture for state-of-the-art heterogeneous multihop networks, and identifies research issues that need to be addressed for successful integration of heterogeneous technologies for the next generation of wireless and mobile networks.

Issues in integrating cellular networks WLANs, AND MANETs: a futuristic heterogeneous wireless network

SUMMARY In this research, a genetic algorithm based solution approach is proposed to address the machine cell-part grouping problem. Three different objective functions considered are (1) minimize total moves (intercell as well as intracell moves), (2) minimize cell load variation, and (3) minimize both the above objective functions simultaneously. The total moves are determined as the weighted sum of both intercell and intracell moves. In the second objective function, cell load variation is minimized to aid the smooth flow of materials inside each cell and is obtained by computing the difference between the workload on the machine and the average load on the cell. The utilization of the workstations in a cell is evaluated and used in determining the best machine cell-part grouping. Furthermore, the sequence of operations and the impact of the layout of cells are also considered. We show that the results of the genetic algorithm based approach are comparatively better than the known results. The developm...

A genetic algorithm-based approach to cell composition and layout design problems

This paper explains the development and implementation of a new methodology for expanding existing computer networks. Expansion is achieved by adding new communication links and computer nodes such that reliability measures of the network are optimized within specified constraints. A genetic algorithm-based computer network expansion methodology (GANE) is developed to optimize a specified objective function (reliability measure) under a given set of network constraints. This technique is very powerful because the same approach can be extended to solve different types of problems; the only modification required is the objective function evaluation module. The versatility of the genetic algorithm is illustrated by applying it to solve various network expansion problems (optimize diameter, average distance and computer network reliability for network expansion). The results are compared with the optimal solutions computed using an exhaustive search of complete solution space. The results demonstrate that GANE is very effective (in both accuracy and computation time) and applies to a wide range of problems, but it does not guarantee the optimal results for every problem. >

Anup Kumar

Papers

Issues in integrating cellular networks WLANs, AND MANETs: a futuristic heterogeneous wireless network

A genetic algorithm-based approach to cell composition and layout design problems

Genetic-algorithm-based reliability optimization for computer network expansion

Minimizing flow time variance in a single machine system using genetic algorithms

Recent network design techniques using evolutionary algorithms