A Transportation Problem with Minimum Quantity Commitment
We study a transportation problem with the minimum quantity commitment (MQC), which is faced by a famous international company. The company has a large number of cargos for carriers to ship to the United States. However, the U.S. Marine Federal Commission stipulates that when shipping cargos to the United States, shippers must engage their carriers with an MQC. With such a constraint of MQC, the transportation problem becomes intractable. To solve it practically, we provide a mixed-integer programming model defined by a number of strong facets. Based on this model, a branch-and-cut search scheme is applied to solve small-size instances and a linear programming rounding heuristic for large ones. We also devise a greedy approximation method, whose solution quality depends on the scale of the minimum quantity if the transportation cost forms a distance metric. Extensive experiments have been conducted to measure the performance of the formulations and the algorithms and have shown that the linear rounding heuristic behaves best.

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 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: 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: 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: A Bidirectional Adaptive Sequential Gaussian Approximation for MIMO Systems
abstract: A bidirectional adaptive detection method was proposed to prevent error propagation based on sequential Gaussian approximation algorithm for MIMO systems. Conventional sequential Gaussian approximation detection executes a tree search at the receiver, when the number of searching paths is large, error propagation is well prevented at the cost of high complexity, when few paths are searched, serious error propagation may occur. In the new method, transmitted symbols were estimated parallel both in sequential order and inverse order, and the number of searching paths was determined by comparing the results of bidirectional detection, which can effectively prevent error propagation at reduced complexity. The novel algorithm can be executed at low delay because of parallel computing, and shows good performance at low complexity.

title: Fault accommodation for nonlinear systems using fuzzy adaptive sliding control
abstract: An active fault accommodation control law is developed for a class of nonlinear systems to guarantee the closed-loop stability in the presence of a fault, based on a fuzzy logic system representation of the dynamics due to faults. It uses fuzzy logic system to approximate the dynamic caused by the fault. Through the adaptive process of the parameters, the dynamics caused by the fault is counteracted. The fuzzy sliding mode control is introduced to attenuate the fuzzy approximation error. Simultaneity, the closed-loop system is stable in Lyapunov sense and the tracking error converges to a neighbourhood of zero. The example of the proposed design indicates that the fault accommodation control law is effective for a nonlinear system.

title: Inequivalent manifold ranking for content-based image retrieval
abstract: We propose to improve the effectiveness and scalability of graph based manifold ranking methods in image retrieval applications by emphasizing reliable images while damping the effect of noisy or irregular ones. Label information is firstly passed between most reliable data points, then propagated to less reliable ones on manifold structure. By treating these images inequivalently, undesirable effect of noisy samples is greatly reduced, thus effectiveness of manifold ranking algorithms is enhanced. Also, graph size in terms of number of nodes and edges is dramatically reduced, resulting in a great speed-up of the algorithm. Our experiment on real world image data set demonstrates the effectiveness of the proposed approach.

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: Optimal array pattern synthesis using semidefinite programming
abstract: We present a new technique to solve array pattern synthesis problems by using semidefinite programming. We first formulate (or reformulate) the array design problems into semidefinite programming problems, and then use the recently developed efficient numerical algorithms and software to compute the numerical solution of antenna array weights. Using this approach, we can directly solve not only the standard synthesis problems for nonuniform arrays, but also the synthesis problems for arrays having power restrictions and uncertainties. Numerical examples are presented to illustrate our approach.