A multi-objective programming model for scheduling emergency medicine residents

Scheduling emergency medicine residents (EMRs) is a complex task, which considers a large number of rules (often conflicting) related to various aspects such as limits on the number of consecutive work hours, number of day and night shifts that should be worked by each resident, resident staffing requirements according to seniority levels for the day and night shifts, restrictions on the number of consecutive day and night shifts assigned, vacation periods, weekend off requests, and fair distribution of responsibilities among the residents. Emergency rooms (ERs) are stressful workplaces, and in addition shift work is well-known to be more demanding than regular daytime work. For this reason, preparing schedules that suit the working rules for EMRs is especially important for reducing the negative impact on shift workers physiologically, psychologically, and socially. In this paper, we present a goal programming (GP) model that accommodates both hard and soft constraints for a monthly planning horizon. The hard constraints should be adhered to strictly, whereas the soft constraints can be violated when necessary. The relative importance values of the soft constraints have been computed by the analytical hierarchy process (AHP), which are used as coefficients of the deviations from the soft constraints in the objective function. The model has been tested in the ER of a major local university hospital. The main conclusions of the study are that problems of realistic size can be solved quickly and the generated schedules have very high qualities compared to the manually prepared schedules, which require a lot of effort and time from the chief resident who is responsible for this duty.     

A load dispatch method using fractional programming and semidefinite programming

This paper describes a load dispatch method which minimizes power cost—[fuel cost]/[electric output]—for a power system with thermal plants and energy storage facilities. The proposed method employs fractional programming to convert a minimization problem with fractional objective function to a series of quadratic minimization problems, and semidefinite programming to solve converted problems. The method provides the optimum time‐dependent power output/input and storage level of energy storage facilities as well as time‐dependent power output of thermal plants. The method has been applied to a power system with five thermal plants, two energy storage facilities of various performances, and five load demands. The optimum load scheme of four time mesh points is obtained for the thermal plants and energy storage facilities. The fractional programming successfully converges the optimal scheme through a few iterations. The semidefinite programming deals with a variable matrix of 164 dimensions, and 185 inequality constraints. © 2001 Scripta Technica, Electr Eng Jpn, 138(2): 49–58, 2002     

Modeling the effect of pigment morphology on the dynamic compression of coating layers using DEM

In the present paper, discrete element method (DEM) was employed to investigate the effect of pigment morphology on packing dynamics and compressive behavior of paper coating layers in calendering process. Spherical, platy, and needle-like particles, representing GCC, delaminated clay, and aragonite PCC pigments, were considered in this study. For each particle shape, the compression of coating structures formed by mono-sized and poly-dispersed pigments were modeled. Stress–strain behavior of the coating layers and in-plane and out-of-plane movements of the pigment particles during the compression were computed under the same maximum compressive stress. Simulation results revealed that the in-plane movements of the pigment particles during compression in the calender nip were small in magnitude (<0.35 μm). These findings help to better understand the smoothening phenomena of coating structures during the calendering process.     

On modeling the design of cellular manufacturing systems

A new optimization model is discussed for the design of cellular manufacturing systems. It is based on an integer programming formulation that updates some other models by eliminating redundant machine assignment and cost coefficients dependent on cell configuration. To reduce computational burdens, a simplified integer programming model and a decomposition algorithm are proposed. Several computer solutions were performed to evaluate the performance of the new model. The computational results are discussed.     

Dynamic Current Sharing in Paralleling Power MOSFET

By combining the theory with the practice,the dynamic current shar-ing in paralleling power MOSFET is analysed.The principles of choosing parameters in-fluencing dynamic current sharing are described,and some experiment results are given on the basis of these steps.     

基于VC＋＋的仿真训练系统编程

给出了以PCI-I64IOT型I/O卡为核心的硬件接口电路和软件设计思想,详细分析了编程方法。实践表明,利用该系统能完全实现侦察专业的全部训练科目,满足部队和院校训练的需要。     

Simultaneous solving of balancing and sequencing problems with station-dependent assembly times for mixed-model assembly lines

In spite of many studies, investigating balancing and sequencing problems in Mixed-Model Assembly Line (MMAL) individually, this paper solves them simultaneously aiming to minimize total utility work. A new Mixed-Integer Linear Programming (MILP) model is developed to provide the exact solution of the problem with station-dependent assembly times. Because of NP-hardness, a Simulated Annealing (SA) is applied and compared to the Co-evolutionary Genetic Algorithm (Co-GA) from the literature. To strengthen the search process, two main hypotheses, namely simultaneous search and feasible search, are developed contrasting Co-GA. Various parameters of SA are reviewed to calibrate the algorithm by means of Taguchi design of experiments. Numerical results statistically show the efficiency and effectiveness of the proposed SA in terms of both the quality of solution and the time of achieving the best solution. Finally, the contribution of each hypothesis in this superiority is analyzed.     

Consistent hierarchical economic NMPC for a class of hybrid systems using neighboring-extremal updates

A hierarchical two-layer control algorithm is developed for a class of hybrid (discrete-continuous dynamic) systems to support economically optimal operation of batch or continuous processes with a predefined production schedule. For this class of hybrid systems, the optimal control moves as well as the controlled switching times between two adjacent modes are determined online. In contrast to closely related schemes for integrated scheduling and control, the sequence of modes is not optimized. On the upper layer, the economic optimal control problem is solved rigorously by a slow hybrid economic model predictive controller at a low sampling rate. On the lower layer, a fast hybrid neighboring-extremal controller is based on the same economic optimal control problem as the slow controller to ensure consistency between both layers. The fast neighboring-extremal controller updates rather than tracks the optimal trajectories from the upper layer to account for disturbances. Consequently, the fast controller steers the process to its operational bounds under disturbances and the economic potential of the process is exploited anytime. The suggested two-layer control algorithm provides fully consistent control action on the fast and slow time-scale and thus avoids performance degradation and even infeasibilities which are commonly encountered if inconsistent optimal control problems are formulated and solved.     

Multi-label core vector machine with a zero label

Multi-label core vector machine (Rank-CVM) is an efficient and effective algorithm for multi-label classification. But there still exist two aspects to be improved: reducing training and testing computational costs further, and detecting relevant labels effectively. In this paper, we extend Rank-CVM via adding a zero label to construct its variant with a zero label, i.e., Rank-CVMz, which is formulated as the same quadratic programming form with a unit simplex constraint and non-negative ones as Rank-CVM, and then is solved by Frank–Wolfe method efficiently. Attractively, our Rank-CVMz has fewer variables to be solved than Rank-CVM, which speeds up training procedure dramatically. Further, the relevant labels are effectively detected by the zero label. Experimental results on 12 benchmark data sets demonstrate that our method achieves a competitive performance, compared with six existing multi-label algorithms according to six indicative instance-based measures. Moreover, on the average, our Rank-CVMz runs 83 times faster and has slightly fewer support vectors than its origin Rank-CVM.     

Dynamic response of Ti3SiC2 under shock compression up to 112 GPa

Ti₃SiC₂ is of interest due to its unique dual nature reminiscent of both brittle ceramics and ductile metals at ambient conditions. In this work, plate-impact experiments have been performed to study the dynamic behavior of Ti₃SiC₂ under shock compression up to 112 GPa by using laser velocity interferometer and electric pin techniques. Hugoniot elastic limits (HEL), spall strength, and Hugoniot equations of state have been obtained based on measured particle velocity profiles and shock wave velocities. The ratio of spall strength to HEL for Ti₃SiC₂ is larger than brittle ceramics but smaller than metals. This result indicates that the dual nature of Ti₃SiC₂ remains at least up to 10 GPa. On the other hand, the linearity of the Hugoniot equation of state, $D=6.9\text{0}1(22)+1.153(53)u_{\text{p}}$, suggests that the initial structure of Ti₃SiC₂ should be stable up to 112 GPa, in contrast to the result reported by Jordan et al. [J. Appl. Phys., 93 (2003) 9639].