The shortest route cut and fill problem in linear topological structure
In this paper, we study the Shortest Route Cut and Fill (SRCF) problem where a fleet of vehicles with fixed capacities travel between cut and fill points to finish all cut and fill requirements so that the travel time of the fleet is minimized. We state and classify the SRCF problem and present a complete view of its computational complexities with NP-hardness proof for the basic linear topological structure. Moreover, a simple cycle-based optimization method is proposed to solve the SRCF in linear time complexity, for the version of single vehicle in linear structure without time window constraint.

title: A compromised large-scale neighborhood search heuristic for capacitated air cargo loading planning
abstract: Cost effectiveness is central to the air freight forwarders. In this work, we study how an air freight forwarder should plan its cargo loading in order to minimize the total freight cost given a limited number of rented containers. To solve the problem efficiently for practical implementation, we propose a new large-scale neighborhood search heuristic. The proposed large-scale neighborhood relaxes the subset-disjoint restriction made in the existing literature; the relaxation risks a possibility of infeasible exchanges while at the same time it avoids the potentially large amount of checking effort required to enforce the subset-disjoint restriction. An efficient procedure is then used to search for improvement in the neighborhood. We have also proposed a subproblem to address the difficulties caused by the fixed charges. The compromised large-scale neighborhood (CLSN) search heuristic has shown stably superior performance when compared with the traditional large-scale neighborhood search and the mixed integer programming model.

title: k-Center Problems with Minimum Coverage
abstract: The k-center problem is a well-known facility location problem and can be described as follows: Given a complete undirected graph G=(V,E), a metric d:V×V→ℝ + and a positive integer k, we seek a subset U ⊆ V of at most k centers which minimizes the maximum distances from points in V to U. Formally, the objective function is given by: 
 
$$ \textstyle\min_{U\subseteq V,|U|\leq k}\max_{v\in V}\min_{r\in U}d(v,r).$$ 
 
As a typical example, we may want to set up k service centers (e.g., police stations, fire stations, hospitals, polling centers) and minimize the maximum distances between each client and these centers. The problem is known to be \(\mathcal{NP}\)-hard .

title: Meta-heuristic Algorithm for the Transshipment Problem with Fixed Transportation Schedules
abstract: In this work, we develop an efficient meta-heuristic genetic algorithm for a different type of transshipment problem with supplier and customer time windows, where flow through the crossdock (a kind of transshipment center) is constrained by fixed transportation schedules and crossdocks capacities, and which was proved to be NP -complete in the strong sense. And computational experiments are conducted to show the proposed heuristic outperforms CPLEX.

title: A Compromised Large-Scale Neighborhood Search Heuristic for Cargo Loading Planning
abstract: In this work, we propose a compromised large-scale neighborhood, which is embedded in simulated annealing to solve a cargo loading planning problem arising in logistics industry. It is "compromised" because it makes a tradeoff between the extensive backward checking work incurred in traditional subset-disjoint restriction and the possible infeasibility resulting from the relaxing the restriction. Extensive experiments have shown the competitive advantages of the heuristic approach. The proposed neighborhood search method is generally applicable.

title: Flexible Demand Assignment Problem
abstract: This paper proposed a state-of-the-art local search for solving Flexible Demand Assignment problem (FDA) which considers the balance between revenue and cost in demand assignment. Different than the published studies, our research splits the FDA problem into three core subproblems as operators for neighborhood construction. The three specified subproblems One Bin Repack, Two Bins Repack and Unpack are proposed completely based on mathematical modelling, computational complexity, executive conditions and greedy solving methods. Benchmark experimental results have shown that the proposed local search improved to the best published heuristics by 2.34%.

title: Simulation of the defect removal process with queuing theory

title: Single Event Upset: An Embedded Tutorial
abstract: With the continuous downscaling of CMOS technologies, the reliability has become a major bottleneck in the evolution of the next generation systems. Technology trends such as transistor down-sizing, use of new materials, and system on chip architectures continue to increase the sensitivity of systems to soft errors. These errors are random and not related to permanent hardware faults. Their causes may be internal (e.g., interconnect coupling) or external (e.g., cosmic radiation). To meet the system reliability requirements it is necessary for both the circuit designers and test engineers to get the basic knowledge of the soft errors. We present a tutorial study of the radiation-induced single event upset phenomenon caused by external radiation, which is a major source of soft errors. We summarize basic radiation mechanisms and the resulting soft errors in silicon. Soft error mitigation techniques with time and space redundancy are illustrated. An industrial design example, the IBM z990 system, shows how the industry is dealing with soft errors these days.

title: GMAC: A Game-theoretic MAC Protocol for Mobile Ad Hoc Networks
abstract: The conservative nature of the 802.11 ad hoc scheme has instigated extensive research whose goal is to improve the spatial reuse and/or energy consumption of this scheme. Transmission power control (TPC) was shown to be effective in achieving this goal. Despite their demonstrated performance gains, previously proposed power-controlled channel access protocols often incur extra hardware cost (e.g., multiple transceivers). Furthermore, they do not fully exploit the potential of power control due to the heuristic nature of power allocation and “interference margin” computations. In this paper, we propose a distributed, single-channel MAC protocol (GMAC) that is inspired by game theory. In GMAC, multiple potential transmitters are first involved in an admission phase, which enables terminals to compute the transmission powers that achieve a Nash equilibrium (NE) for the given utility function. Subsequently, successful contenders can simultaneously proceed with their transmissions. Simulation results indicate that GMAC improves the network throughput over the 802.11 scheme by about 80%, and over another single-channel power-controlled MAC protocol (POWMAC) by about 40%. These gains are achieved at no extra energy cost.

title: Query Planning for Searching Inter-dependent Deep-Web Databases
abstract: Increasingly, many data sources appear as online databases, hidden behind query forms, thus forming what is referred to as the deep web. It is desirable to have systems that can provide a high-level and simple interface for users to query such data sources, and can automate data retrieval from the deep web. However, such systems need to address the following challenges. First, in most cases, no single database can provide all desired data, and therefore, multiple different databases need to be queried for a given user query. Second, due to the dependencies present between the deep-web databases, certain databases must be queried before others. Third, some database may not be available at certain times because of network or hardware problems, and therefore, the query planning should be capable of dealing with unavailable databases and generating alternative plans when the optimal one is not feasible. 
 
This paper considers query planning in the context of a deep-web integration system. We have developed a dynamic query planner to generate an efficient query order based on the database dependencies. Our query planner is able to select the topKquery plans. We also develop cost models suitable for query planning for deep web mining. Our implementation and evaluation has been made in the context of a bioinformatics system, SNPMiner. We have compared our algorithm with a naive algorithm and the optimal algorithm. We show that for the 30 queries we used, our algorithm outperformed the naive algorithm and obtained very similar results as the optimal algorithm. Our experiments also show the scalability of our system with respect to the number of data sources involved and the number of query terms.

title: On scheduling concurrent multicast sessions over resource reserved network
abstract: Resource reserved network can provide guaranteed quality of service to end users, especially those services requiring high bandwidth and short delay, such as video conferencing, video on demand, etc. In this paper, we consider the multicast packing problem, where multiple multicast sessions request network services simultaneously. To maximize the profit, network service provider need to carefully schedule these multicast sessions under the capacity constraint. We first formulate the problem as an integer programming problem and propose an optimization algorithm using branch-and-bound technique. A valid upper bound and a lower bound are then derived by using a statistical method and a greedy heuristic, respectively. The simulation results illustrate that our optimization algorithm performs effectively within the two boundaries.