Constant thrust programming for a multistage rocket flown in vacuum with a given initial/final thrust-to-weight ratio in an arbitrary stage

The burn time and burnout velocity of a multistage rocket flown vertically in vacuum with constant thrust tangential to the flight path and a prescribed initial/final thrust-to-weight ratio in an arbitrary stage have been determined. The present paper also deals with optimal staging under given conditions of flight.     

A novel MINLP-based representation of the original complex model for predicting gasoline emissions

The Environmental Protection Agency (EPA) introduced Reformulated Gasoline (RFG) requirements as a measure to reduce emissions from gasoline-powered vehicles in certain geographic areas. As part of this effort, the EPA developed empirical models for predicting emissions as a function of gasoline properties and established statutory baseline emissions from a representative set of gasolines. All reformulated gasoline requires certification via this model, known as the Complex Model, and all refiners and importers calculate emissions performance reductions from the statutory baseline gasoline. The current representation of the Complex Model is extremely difficult to implement within refinery operations models or to use in combination with models for designer gasoline. RFG and boutique fuels are key driving forces in the North American refining industry.The RFG models introduce increasingly complex constraints with the major limitation that they are implicitly defined through a series of complicated disjunctions assembled by the EPA in the form of spreadsheets. This implicit and cumbersome representation of the emissions predictive models renders rigorous optimization and sensitivity analysis very difficult to address directly. In this paper, we discuss how the federal government requirements for reformulated gasoline can be restated as a set of mixed-integer nonlinear programming (MINLP) constraints with the aid of disjunctive programming techniques. We illustrate the use of this model with two simple example fuel blending problems.     

Practical method for generation expansion planning based on dynamic programming

This paper presents an efficient method which provides the optimal generation mix and the optimal generation construction process. The approximation method in which the dynamic programming technique and gradient method are combined is applied to determine the optimal generation mix with hydropower generation technologies. The successive approximations dynamic programming (SADP) technique, which is very suitable for high-dimensional multistage decision process problems, is used for obtaining the optimal generation construction process. The effectiveness and feasibility of the developed technique are demonstrated on a practical power system model which has five types of generation technologies including a hydropower generation technology.     

A dynamic linear programming approach to market allocation of renewable resources in the italian energy system: The case of solar thermal and biogas technologies

In Italy solar thermal energy and energy from biogas are two possible means of reducing dependence on energy imports. Using a multiperiod LP model (MARKAL) the authors assessed the likely potential of both technologies under various circumstances. The study covered the period 1980–2005, in five segments of five years. It focused only on the subsystem of the energy end-uses which can be substituted for by solar thermal and biogas technologies. The overall non-renewable sources which can be saved in 20 years by these technologies total 450 PJ (1 PJ = 10^{1 5} J) if the fuel prices rise at 0 per cent average annual, 1450 PJ if the fuel prices rise at 4.2 per cent average annual, 1860 PJ if the fuel prices rise at 7.2 per cent average annual and 3780 PJ if the fuel prices rise at 15 per cent average annual. However the most competitive technologies appear to be solar water heaters used mainly in the private and commercial sectors and biogas systems used mainly in the agricultural sector. The study was carried out by APRE under ENEA (formerly CNEN) contract and was intended to serve as an analytical basis for establishing an overall development and demonstration strategy for end-use renewable technologies in Italy.     

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     

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.     

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.     

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.