拦截弹推进及弹道参数的模糊优化研究

在拦截弹推进系统和弹道参数的设计中 ,引入多目标多约束的模糊优化方法 ,在约束条件一定的情况下 ,合理选择和分配几个重要的设计变量 ,使得拦截弹的总质量最小、拦截时间最短。     

Manpower allocation with time windows and job‐teaming constraints

In this work, we study manpower allocation with time windows and job‐teaming constraints. A set of jobs at dispersed locations requires teams of different types of workers where each job must be carried out in a preestablished time window and requires a specific length of time for completion. A job is satisfied if the required composite team can be brought together at the job's location for the required duration within the job's time window. The objective is to minimize a weighted sum of the total number of workers and the total traveling time. We show that construction heuristics used with simulated annealing is a good approach to solving this NP‐hard problem. In experiments, this approach is compared with solutions found using CPLEX and with lower bounds obtained from a network flow model. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

再入快速抵达轨迹优化设计方法

针对再入初始速度大、飞行时间约束苛刻的轨迹设计问题,提出一种基于遗传算法和攻角+倾侧角联合优化的再入快速抵达轨迹优化设计方法。该方法在再入初段利用较大攻角迅速减小弹道倾角和拉平弹道,在再入后段/滑翔段联合设计和优化攻角+倾侧角变化规律以显著降低终端飞行速度,同时满足终端高度、终端航向角、最大动压、最大热流等约束条件。该方法能够提升传统升力体飞行器再入快速到达能力并拓展其应用范围。仿真结果表明,在典型飞行器参数和较大初始再入速度条件下,全程飞行时间小于12 min,终端速度能够小于7Ma,横向机动距离超过800 km。     

基于Gauss伪谱法和直接打靶法结合的月球定点着陆轨道优化

将一种求解最优控制问题的新方法—高斯伪谱法( Gauss Pseudospectral Method-GPM)和传统的直接打靶法有效结合,对月球着陆器定点软着陆轨道快速优化问题做出了研究.推导了高精度模型下着陆动力学方程.针对优化方法各自的特点和多约束条件下最优月球软着陆轨道设计的难点,提出了问题求解的串行优化策略:将控制变量和终端时间一同作为优化变量,同时离散控制变量与状态变量,取较少的Gauss节点,利用GPM求解初值,初值的求解采用从可行解到最优解的串行优化策略;在Gauss节点上离散控制变量,利用直接打靶法求解精确最优解.仿真结果表明,本文提出的轨道优化方法具有较强的鲁棒性和快速收敛性.     

Due‐window assignment with unit processing‐time jobs

In due‐window assignment problems, jobs completed within a designated time interval are regarded as being on time, whereas early and tardy jobs are penalized. The objective is to determine the location and size of the due‐window, as well as the job schedule. We address a common due‐window assignment problem on parallel identical machines with unit processing time jobs. We show that the number of candidate values for the optimal due‐window starting time and for the optimal due‐window completion time are bounded by 2. We also prove that the starting time of the first job on each of the machines is either 0 or 1, thus introducing a fairly simple, constant‐time solution for the problem. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

初始优化轨道实时计算的拟牛顿法--防空导弹轨道优化的实时计算方法之一

提出防空导弹轨道优化的一些实时计算问题,并且应用拟牛顿法计算初始优化轨道.     

基于气动-弹道一体化模型的飞行器外形优化设计

为提升飞行器气动外形优化设计效果,研究了新型飞行器在大空域、宽速域范围的气动适应性问题,提出一种基于气动-弹道一体化模型的外形优化设计方法.通过考虑气动和弹道的耦合作用,综合利用类型函数/形状函数转换技术、气动工程估算方法和Radau伪谱法,建立飞行器气动-弹道一体化模型.基于该模型,通过明确优化目标和约束条件,给出基...     

Minimizing total completion time in two-processor task systems with prespecified processor allocations

We consider the problem of scheduling multiprocessor tasks with prespecified processor allocations to minimize the total completion time. The complexity of both preemptive and nonpreemptive cases of the two-processor problem are studied. We show that the preemptive case is solvable in O(n log n) time. In the nonpreemptive case, we prove that the problem is NP-hard in the strong sense, which answers an open question mentioned in Hoogeveen, van de Velde, and Veltman (1994). An efficient heuristic is also developed for this case. The relative error of this heuristic is at most 100%. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 231–242, 1998     

Detection of a Markovian target with optimization of the search efforts under generalized linear constraints

This paper deals with search for a target following a Markovian movement or a conditionally deterministic motion. The problem is to allocate the search efforts, when search resources renew with generalized linear constraints. The model obtained is extended to resource mixing management. New optimality equations of de Guenin's style are obtained. Practically, the problem is solved by using an algorithm derived from the FAB method. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 117–142, 2002; DOI 10.1002/nav.10009     

星际探测太阳帆行星和太阳借力轨道全局优化

以太阳帆在20年内飞行至距离太阳200 AU以远进行星际探测为目标,研究太阳帆通过行星借力和太阳借力的轨道全局优化问题。建立太阳帆时间最优转移轨道数学模型,分析行星借力和太阳借力的约束条件,并用这些约束条件构造目标函数,从而将轨道优化的四点边值问题转化为求解无约束条件下的多变量优化问题。通过选取合理的约束权重,采用遗传算法获得大范围的粗略解,代入到序列二次规划算法中获得高精度解。仿真结果表明,虽然太阳帆通过太阳借力已获得相当大的加速度,但加上木星借力仍然可以节省相当多的飞行时间。提出的轨道优化思路,可以为太阳系逃逸任务轨道初步设计提供参考。     

A technique which combines modified pattern search methods with composite designs and polynomial constraints to solve constrained optimization problems

This paper presents a method of selecting design parameters which optimizes a specific measure (aircraft design example: minimum weight, maximum mission effectiveness) and guarantees designated levels of response in specified areas (such as combal ceiling, acceleration time). The method employs direct search optimization applied to a nonlinear functional constrained by nonlinear surfaces. The composite design technique is combined with regression methods to determine adequate surface representations with a minimum of required data points. A sensitivity analysis is conducted at the optimum set of design parameters to test for uniqueness.     

Polynomial‐time approximation schemes for two‐machine open shop scheduling with nonavailability constraints

This paper addresses a two‐machine open shop scheduling problem, in which the machines are not continuously available for processing. The processing of an operation affected by a non‐availability interval can be interrupted and resumed later. The objective is to minimize the makespan. We present two polynomial‐time approximation schemes, one of which handles the problem with one non‐availability interval on each machine and the other for the problem with several non‐availability intervals on one of the machines. Problems with a more general structure of the non‐availability intervals are not approximable in polynomial time within a constant factor, unless . © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

The multicommodity network flow model revised to include vehicle per time period and node constraints

The minimum-cost formulation of the problem of determining multicommodity flows over a capacitated network subject to resource constraints has been treated in previobs papers. In those treatments only capacitated arcs were assumed and a uniform unit of measure like short tons was used for all commodities. This paper treats the effect of constraints on the nodes of the network, allows the commodities to be measured in their “natural” units and allows the network capacities to be expressed in vehicles per time period-in some cases giving a more accurate representation of the capacities of the network. This paper describes the solution procedure which uses the column generation technique; it also discusses computational experience.     

Scheduling with earliest start and due date constraints

We consider the scheduling of n tasks on a single resource. Each task becomes available for processing at time a_i, must be completed by time b_i, and requires d_i time units for processing. The aim is to find a schedule that minimizes the elapsed time to complete all jobs. We present solution algorithms for this problem when job splitting is permitted and when job splitting is not permitted. Then we consider several scheduling situations which arise in practice where these models may apply.     

Solving quadratic assignment problems with rectangular distances and integer programming

The problem considered involves the assignment of n facilities to n specified locations. Each facility has a given nonnegative flow from each of the other facilities. The objective is to minimize the sum of transportation costs. Assume these n locations are given as points on a two-dimensional plane and transportation costs are proportional to weighted rectangular distances. Then the problem is formulated as a binary mixed integer program. The number of integer variables (all binary) involved equals the number of facilities squared. Without increasing the number of integer variables, the formulation is extended to include “site costs” Computational results of the formulation are presented.     

Managerial inventory formulations with stockout objectives and fiscal constraints

Most inventory formulations seek to minimize the sum of ordering costs, holding costs, and stockout costs: however, management often directs inventory policy by specifying a maximum investment level and/or a purchasing budget constraint. Within these limitations, they expect lower level managers to optimize some level of customer satisfaction, such as minimum stockouts or minimum shortages. The author has developed several cases of these “managerial” inventory formulations and has presented some computational results.     

Cost-based due-date assignment with the use of classical and neural-network approaches

Traditional methods of due-date assignment presented in the literature and used in practice generally assume cost-of-earliness and cost-of-tardiness functions that may bear little resemblance to true costs. For example, practitioners using ordinary least-squares (OLS) regression implicitly minimize a quadratic cost function symmetric about the due date, thereby assigning equal second-order costs to early completion and tardy behavior. In this article the consequences of such assumptions are pointed out, and a cost-based assignment scheme is suggested whereby the cost of early completion may differ in form and/or degree from the cost of tardiness. Two classical approaches (OLS regression and mathematical programming) as well as a neural-network methodology for solving this problem are developed and compared on three hypothetical shops using simulation techniques. It is found for the cases considered that: (a) implicitly ignoring cost-based assignments can be very costly; (b) simpler regression-based rules cited in the literature are very poor cost performers; (c) if the earliness and tardiness cost functions are both linear, linear programming and neural networks are the methodologies of choice; and (d) if the form of the earliness cost function differs from that of the tardiness cost function, neural networks are statistically superior performers. Finally, it is noted that neural networks can be used for a wide range of cost functions, whereas the other methodologies are significantly more restricted. © 1997 John Wiley & Sons, Inc.     

The parallel replacement problem with demand and capital budgeting constraints

A generalized parallel replacement problem is considered with both fixed and variable replacement costs, capital budgeting, and demand constraints. The demand constraints specify that a number of assets, which may vary over time, are required each period over a finite horizon. A deterministic, integer programming formulation is presented as replacement decisions must be integer. However, the linear programming relaxation is shown to have integer extreme points if the economies of scale binary variables are fixed. This allows for the efficient computation of large parallel replacement problems as only a limited number of 0–1 variables are required. Examples are presented to provide insight into replacement rules, such as the “no‐splitting‐rule” from previous research, under various demand scenarios. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 40–56, 2000     

Proportionate flow shop: New complexity results and models with due date assignment

It is known that the proportionate flow shop minimum makespan $Fm/prpt/C_{\max }$ problem is solved optimally by any permutation job sequence. We show that the $Fm/prpt/C_{\max }$ problem is at least ordinary NP‐hard when missing operations are allowed and present some solvable cases. We then consider the standard proportionate flow shop problem (with no missing operations) and show that the solution algorithms for a class of single‐machine due date assignment problems can be extended/generalized to the corresponding proportionate flow shop problems. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 98–106, 2015