34027055
Improvement heuristic for the flow-shop scheduling problem: An adaptive-learning approach
Abstract We propose an improvement-heuristic approach for the general flow-shop problem (n/m/Cmax) based on the idea of adaptive learning. The approach employs a one-pass heuristic to give a good starting solution in the search space and uses a weight parameter to perturb the data of the original problem to obtain improved solutions. The weights are then adjusted employing a learning strategy which involves reinforcement and backtracking. The learning is similar to that in neural networks. The random perturbation allows a non-deterministic local search. We apply the improvement-heuristic approach in conjunction with three well-known heuristics in the literature, namely, Palmer’s Slope Index, CDS and NEH. We test our approach on several benchmark problem sets including Taillard’s, Carlier’s, Heller’s and Reeves’. We compare our results to the best-known upper-bound solutions and find that for many problems we match the best-known upper bound. For one problem we discover a new upper bound.
21945943
Heuristics and augmented neural networks for task scheduling with non-identical machines
Abstract We propose new heuristics along with an augmented-neural-network (AugNN) formulation for solving the makespan minimization task-scheduling problem for the non-identical machine environment. We explore four task and three machine-priority rules, resulting in 12 combinations of single-pass heuristics. The task-priority rules are Highest-Level-First (HLF), Highest-Total-Remaining-Processing-Time-First (HTRPTF), Smallest-Latest-Finish-Time-First (SLFTF) and Minimum-Slack-First (MSF). For machine priority, we propose a greedy rule called Fastest-Available-Machine-First (FAMF), and two non-greedy rules: (1) Fastest-Available-Machine-First-With-Conditional-Wait-1 (FAMF-CW-1), and (2) Fastest-Available-Machine-First-With-Conditional-Wait-2 (FMF-CW-2). The AugNN approach integrates neural-network learning with domain and problem specific knowledge through heuristics, to produce good results. A single-pass heuristic is embedded in a neural network designed specifically for each problem. We give the AugNN formulation for each of the 12 heuristics and show computational results on 100 randomly generated problems of sizes ranging from 20 to 70 tasks and 2 to 5 machines. Results demonstrate that AugNN provides significant improvement over single-pass heuristics. The reduction in the gap between the obtained solution and the lower-bound due to AugNN over single-pass heuristics ranged from 24.4% to 50%. As far as heuristics, the non-greedy machine-priority rules performed significantly better than the greedy rule. The average gaps for the non-greedy rules ranged from 16.1% to 23.5% compared to 33.7% to 40.4% for greedy. For AugNN, for non-greedy rules, the gap ranged from 11.5% to 15.5% compared to 18.4% to 25.0% for greedy. The HLF and HTRPTF task priority rules performed better than the other two. The HTRPTF/FAMF-CW-1 combination gave the best results, closely followed by HLF/FAMF-CW-2 and HTRPTF/FAMF-CW-2 combinations.
14456979
Supporting Quality of Service in IP multicast networks
Developers and providers of data, voice and video applications have to satisfy numerous Quality of Service (QoS) requirements and cope with different architectures emerging to provide these QoS in the Internet. IntServ, DiffServ or MPLS are technologies currently used in different administrative domains in the Internet to provide QoS. With multicast applications emerging, such as video conferencing, distance learning and tele surgery, it has become almost impossible to provide end-to-end QoS over multiple administrative business domains without interworking these technologies. This paper analyses general issues on supporting QoS for multicast applications and reviews different ways to implement IP multicasting in IntServ, DiffServ or MPLS based networks. Subsequently, network configurations based on these different architectures and their interworking to provide end-to-end QoS are also discussed.
5921169
Learning to rank networked entities
Several algorithms have been proposed to learn to rank entities modeled as feature vectors, based on relevance feedback. However, these algorithms do not model network connections or relations between entities. Meanwhile, Pagerank and variants find the stationary distribution of a reasonable but arbitrary Markov walk over a network, but do not learn from relevance feedback. We present a framework for ranking networked entities based on Markov walks with parameterized conductance values associated with the network edges. We propose two flavors of conductance learning problems in our framework. In the first setting, relevance feedback comparing node-pairs hints that the user has one or more hidden preferred communities with large edge conductance, and the algorithm must discover these communities. We present a constrained maximum entropy network flow formulation whose dual can be solved efficiently using a cutting-plane approach and a quasi-Newton optimizer. In the second setting, edges have types, and relevance feedback hints that each edge type has a potentially different conductance, but this is fixed across the whole network. Our algorithm learns the conductances using an approximate Newton method.
8220547
A tree division approach to support local failure recovery for multicasting in MPLS networks
This paper introduces a novel approach to support local failure recovery in multicast trees. Although, the presented approach can be implemented in any large backbone network that employs multicast communication mode, we concentrate our discussion on MPLS networks. In the proposed approach, a multicast tree is divided into several domains where each domain represents a sub-tree of the original one. Backup paths are built between the root of the domain and each leaf router which is called a border router. Simulation results conducted on two network topologies show the performance of the proposed approach in terms of the total capacity required for reserving the backup paths and in terms of the maximum delay needed to notify about link/node failure. The results have shown that tree division approach compared with global recovery approach reduces the time needed to notify the router that is responsible for switching the traffic to the backup path.
2887541
Recovering 3D human pose from monocular images
We describe a learning-based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labeling of body parts in the image. Instead, it recovers pose by direct nonlinear regression against shape descriptor vectors extracted automatically from image silhouettes. For robustness against local silhouette segmentation errors, silhouette shape is encoded by histogram-of-shape-contexts descriptors. We evaluate several different regression methods: ridge regression, relevance vector machine (RVM) regression, and support vector machine (SVM) regression over both linear and kernel bases. The RVMs provide much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. The loss of depth and limb labeling information often makes the recovery of 3D pose from single silhouettes ambiguous. To handle this, the method is embedded in a novel regressive tracking framework, using dynamics from the previous state estimate together with a learned regression value to disambiguate the pose. We show that the resulting system tracks long sequences stably. For realism and good generalization over a wide range of viewpoints, we train the regressors on images resynthesized from real human motion capture data. The method is demonstrated for several representations of full body pose, both quantitatively on independent but similar test data and qualitatively on real image sequences. Mean angular errors of 4-6/spl deg/ are obtained for a variety of walking motions.
13564962
Modelling and verification of interworking between SIP and H.323
There are currently two standards for signaling and control of Internet telephone calls, namely ITU-T Recommendation H.323 and the IETF session initiation protocol (SIP). Both protocols provide similar functionality using different mechanisms. Both H.323 and SIP provide interoperability, flexibility and scalability in different aspects and environments. Interworking between SIP and H.323 is essential to ensure full end-to-end connectivity through the network, although the current number of deployed commercial SIP endpoints is relatively small compared to widely deployed H.323 endpoints.To formally specify the SIP-H.323 Interworking, this paper gives a new system model for interworking between SIP and H.323. Five main components of this system are modeled using system description language and message sequence chart: H.323 endpoint, H.323 gatekeeper, SIP-H.323 Interworking facility, SIP server, SIP endpoint. We design and define the internal structure and behavior for each component, which is established for simulating and verifying interworking between SIP and H.323. Under two different configurations that both protocols work within the same administrative domain and that both protocols are operating in separate administrative domains, we use a series of scenarios to show that our model meets the functional specifications outlined in the SIP-H323-Interworking specification documents.