k-Center problems with minimum coverage
In this work, we study an extension of the k-center facility location problem, where centers are required to service a minimum of clients. This problem is motivated by requirements to balance the workload of centers while allowing each center to cater to a spread of clients. We study three variants of this problem, all of which are shown to be N P-hard. In-approximation hardness and approximation algorithms with factors equal or close to the best lower bounds are provided. Generalizations, including vertex costs and vertex weights, are also studied.

title: A stochastic beam search for the berth allocation problem
abstract: In this paper, the optimization of the Berth Allocation Problem (BAP) is transformed into a multiple stage decision making procedure and a new multiple stage search method, namely stochastic beam search algorithm, is proposed to solve it. New techniques such as an improved beam search scheme, a two-phase node goodness estimation, and a stochastic node selection criteria are proposed. Real-life information provided by Singapore Port was collected as our test data. Experimental results show that the proposed stochastic beam search is more accurate and efficient than both the state-of-the-art meta-heuristic and the traditional determinist beam search.

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: A high-resolution electron momentum spectroscopy and density functional theory study into the complete valence electronic structure of allene
abstract: A study of the electronic structure of the complete valence shell of allene (1,2-propadiene) is reported. New high-resolution binding-energy spectra were measured in the energy regime 6–34.5 eV over a range of different target electron momenta, so that momentum distributions (MDs) could be determined for each molecular orbital. These data supersede the low-resolution work of Braidwood et al. [ (1994) 2075–2087], and also clarify some ambiguities with the earlier results. Theoretical MDs were calculated using a plane wave impulse approximation (PWIA) model for the reaction mechanism and density functional theory (DFT) for the wave function. Three basis sets, at the local spin density (LSD) approximation level and, additionally, incorporating nonlocal corrections such as the generalized gradient approximation (GGA), were studied. A critical comparison between the experimental and theoretical MDs was made, and it allowed us to determine the “optimum” wave function for allene from the basis sets we considered. This wave function is then used to derive allene's chemically interesting molecular properties. A summary of some of these results and a comparison of them with those of other workers is also presented with the level of agreement typically being good.

title: Integrating expert vision systems and spatial databases by unifying knowledge representation schemes: development in remote sensing image analysis expert systems and geographical information systems
abstract: Integration of remote sensing and geographical information system (GIS) technologies can significantly strengthen our capability to handle geographical information. However, so far little practical integration has been achieved. The major barriers to the integration include the problems with data format, data accuracy and system communication. Previous efforts made on these problems could not provide satisfied solutions. 
This research explores a new path for achieving the integration. The three problems, which at first may seem to be independent of each other, are considered as different aspects of a problem with knowledge representation and are tackled by unifying the systems to be integrated on a common knowledge representation framework. Three new techniques--the communication subschema, the relational-linear quadtree, and the fuzzy representation method--have been developed to provide such a framework. A unified data model has been defined to implement the techniques. 
The three techniques have been successfully applied to the integration of GIS and remote sensing systems. Encouraging results have been achieved in the practice of designing, implementing, and integrating a GIS and two remote sensing image analysis systems.

title: An Adaptive MIMO System Based on Unified Belief Propagation Detection
abstract: An adaptive multiple input and multiple output (MIMO) system based on the unified belief propagation (BP) detection is proposed. A BP detector composed of a serial of component detectors is first presented. With a unified design of the BP detector, a multi-rate MIMO system adaptive to channel variations is then developed, which aims at achieving the maximal throughput performance. The usage of the unified BP detector eliminates the multiple detector modules at the receiver in conventional multi-mode adaptive MIMO systems. Further, the linear complexity to space time block length and parallel operation of the unified BP detection provide the advantages of low complexity, low detection delay and smooth switching between different data rates. With the average receiving signal-to-noise ratio (SNR) based switching mechanism, the proposed adaptive MIMO framework adaptively chooses the appropriate rate for the transmission. Simulation results are provided to show the BER and complexity performance of a specific BP detector as well as the throughput performance of the adaptive scheme.

title: On the competitiveness of on-line real-time task scheduling
abstract: With respect to on-line scheduling algorithms that must direct the service of sporadic task requests we quantify the benefit of clairvoyancy, i.e., the power of possessing knowledge of various task parameters of future events. Specifically, we consider the problem of preemptively sheduling sporadic task requests in both uni- and multi-processor environments. If a task request is successfuly scheduled to completion, a value equal to the task's execution time is obtained; otherwise no value is obtained. We prove that no on-line scheduling algorithm can guarantee a cumulative value greater than 1/4th the value obtainable by a clairvoyant scheduler; i.e., we prove a 1/4th upper bound on the competitive factor of on-line real-time schedulers. We present an online uniprocessor scheduling algorithm TD1 that actually has a competitive factor of 1/4; this bound is thus shown to be tight. We further consider the effect of restricting the amount of overloading permitted (the loading factor), and quantify the relationship between the loading factor and the upper bound on the competitive factor. Other results of a similar nature deal with the effect of value densities (measuring the importance of type of a task). Generalizations to dual-processor on-line scheduling are also considered. For the dual-processor case, we prove an upper bound of 1/2 on the competitive factor. This bound is shown to be tight in the special case when all the tasks have the same density and zero laxity.

title: Throughput-oriented MAC for mobile ad hoc networks: A game-theoretic approach
abstract: The conservative nature of the 802.11 channel access scheme has instigated extensive research whose goal is to improve the spatial reuse and/or energy consumption of a mobile ad hoc network. 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., require multiple transceivers). Furthermore, they do not fully exploit the potential of power control due to the heuristic nature of power allocation. In this paper, we propose a distributed, single-channel MAC protocol (GMAC) that is inspired by game theory. In GMAC, each transmitter computes a utility function that maximizes the link's achievable throughput. The utility function includes a pricing factor that accounts for energy consumption. GMAC allows multiple potential transmitters to contend through an admission phase that enables them to determine the transmission powers that achieve the Nash equilibrium (NE). Simulation results indicate that GMAC significantly improves the network throughput over the 802.11 scheme and over another single-channel power-controlled MAC protocol (POWMAC). These gains are achieved at no extra energy cost. Our results also indicate that GMAC performs best under high node densities and large data packet sizes.