UCAV空面多目标攻击三维轨迹规划技术

研究了单架无人作战飞机(UCAV)攻击多个地面目标的三维轨迹规划问题。首先,将问题形式化为一类特殊的旅行商问题(TSP),即带动力学约束的邻域访问TSP问题(DCTSPN)。其次,针对规划空间维度过高、搜索代价过大的问题,提出了一种基于概率路标图(PRM)的方法。该方法借鉴了基于采样的运动规划方法的思想,并结合多种组合优化技术,将原本连续状态空间中的轨迹规划问题转化为离散拓扑图上的路由问题。求解过程分为离线预处理和在线查询两个阶段。离线阶段采用Halton拟随机采样算法及Noon-Bean转换方法,将原问题转化为经典的非对称旅行商问题(ATSP);在线阶段根据战场态势的实时变化,快速更新路标图,然后采用LKH算法在线求解问题的近似最优解。为了保证生成的飞行轨迹满足平台的运动学/动力学约束,算法基于Gauss伪谱法构建了局部轨迹规划器。最后,以攻击时间最短为优化指标对算法进行了仿真实验。结果表明,本文提出的方法能够以较高的精度和在线收敛速度生成真实可行的、较优的多目标攻击轨迹。     

The Serbian defence institution: the need for clear policy guidance to improve the Serbian defence planning,management, and command processes

ABSTRACT

The uncritical layering of western liberal defence governance norms and concepts on top of existing legacy concepts has impeded achieving coherent military capabilities and capacities when Serbia’s political and military leadership tried to reform the defence system using Western benchmarking principles and Western countries’ best practices. The process of this change has been more valuable than its actual output, as defined by increased capabilities. Such outcomes should be reflective of policy guidance, and can be thought of as closing the trinity loop of a defence planning system: plans, money and execution. This article addresses two key functional areas of the Serbian defence institution. First, it assesses the current state of defence planning to discern its strengths and weaknesses to ascertain if plans are tied to financial decision-making. Second, a full examination of current Serbian defence management is conducted to discern whether weaknesses exist that distract from producing operational capabilities. Both areas are analyzed thoroughly and some solutions for change are proposed. Also, the article analyzes the appearance of two negative phenomena in the planning process – economization and managerialism.     

Scheduling maintenance and semiresumable jobs on a single machine

The majority of scheduling literature assumes that the machines are available at all times. In this paper, we study single machine scheduling problems where the machine maintenance must be performed within certain intervals and hence the machine is not available during the maintenance periods. We also assume that if a job is not processed to completion before the machine is stopped for maintenance, an additional setup is necessary when the processing is resumed. Our purpose is to schedule the maintenance and jobs to minimize some performance measures. The objective functions that we consider are minimizing the total weighted job completion times and minimizing the maximum lateness. In both cases, maintenance must be performed within a fixed period T, and the time for the maintenance is a decision variable. In this paper, we study two scenarios concerning the planning horizon. First, we show that, when the planning horizon is long in relation to T, the problem with either objective function is NP-complete, and we present pseudopolynomial time dynamic programming algorithms for both objective functions. In the second scenario, the planning horizon is short in relation to T. However, part of the period T may have elapsed before we schedule any jobs in this planning horizon, and the remaining time before the maintenance is shorter than the current planning horizon. Hence we must schedule one maintenance in this planning horizon. We show that the problem of minimizing the total weighted completion times in this scenario is NP-complete, while the shortest processing time (SPT) rule and the earliest due date (EDD) rule are optimal for the total completion time problem and the maximum lateness problem respectively. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 845–863, 1999     

Multiperiod resource allocation with a smoothing objective

We consider a multiperiod resource allocation problem, where a single resource is allocated over a finite planning horizon of T periods. Resource allocated to one period can be used to satisfy demand of that period or of future periods, but backordering of demand is not allowed. The objective is to allocate the resource as smoothly as possible throughout the planning horizon. We present two models: the first assumes that the allocation decision variables are continuous, whereas the second considers only integer allocations. Applications for such models are found, for example, in subassembly production planning for complex products in a multistage production environment. Efficient algorithms are presented to find optimal allocations for these models at an effort of O(T²). Among all optimal policies for each model, these algorithms find the one that carries the least excess resources throughout the planning horizon. © 1995 John Wiley & Sons, Inc.     

Planning horizon procedures for machine replacement models with several possible replacement alternatives

This paper develops a forward algorithm and planning horizon procedures for an important machine replacement model where it is assumed that the technological environment is improving over time and that the machine-in-use can be replaced by any of the several different kinds of machines available at that time. The set of replacement alternatives may include (i) new machines with different types of technologies such as labor- and capital- intensive, (ii) used machines, (iii) repairs and/or improvements which affect the performance characteristics of the existing machine, and so forth. The forward dynamic programming algorithm in the paper can be used to solve a finite horizon problem. The planning horizon results give a procedure to identify the forecast horizon T such that the optimal replacement decision for the first machine based on the forecast of machine technology until period T remains optimal for any problem with horizon longer than T and, for that matter, for the infinite horizon problem. A flow chart and a numerical example have been included to illustrate the algorithm.     

Disruption management in production planning

We study the problem of recovering a production plan after a disruption, where the disruption may be caused by incidents such as power failure, market change, machine breakdown, supply shortage, worker no‐show, and others. The new recovery plan we seek after has to not only suit the changed environment brought about by the disruption, but also be close to the initial plan so as not to cause too much customer unsatisfaction or inconvenience for current‐stage and downstream operations. For the general‐cost case, we propose a dynamic programming method for the problem. For the convex‐cost case, a general problem which involves both cost and demand disruptions can be solved by considering the cost disruption first and then the demand disruption. We find that a pure demand disruption is easy to handle; and for a pure cost disruption, we propose a greedy method which is provably efficient. Our computational studies also reveal insights that will be helpful to managing disruptions in production planning. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

基于几何算法的水下航行器路径规划

在分析水下航行器路径规划影响因素及主要障碍物特点的基础上,提出了一种基于几何算法的水下航行器路径规划算法,并采用该算法对障碍物进行建模和路径规划研究,解决了水下航行器航经多障碍物海区的路径规划问题.最后,通过仿真试验验证了该算法的准确性与可行性.     

Computational study of the multiechelon production planning problem

In this article we try to identify appropriate solution procedures for different types of multiechelon production planning problems. We conduct an extensive computational study on uncapacitated multiechelon production planning problems with serial and assembly types of bill-of-material structures. Problems are formulated as both single-source fixed charge network problems and as multicommodity flow problems with fixed charges. Solution procedures considered are branch and cut, Lagrangean relaxation (for the network formulation), and branch and bound (for the multicommodity formulation). Three hundred problems with various problem structures are tested. Our conclusions suggest the best approach for each type of problem structure. © 1997 John Wiley & Sons, Inc.     

A nested benders decomposition approach for telecommunication network planning

Despite its ability to result in more effective network plans, the telecommunication network planning problem with signal‐to‐interference ratio constraints gained less attention than the power‐based one because of its complexity. In this article, we provide an exact solution method for this class of problems that combines combinatorial Benders decomposition, classical Benders decomposition, and valid cuts in a nested way. Combinatorial Benders decomposition is first applied, leading to a binary master problem and a mixed integer subproblem. The subproblem is then decomposed using classical Benders decomposition. The algorithm is enhanced using valid cuts that are generated at the classical Benders subproblem and are added to the combinatorial Benders master problem. The valid cuts proved efficient in reducing the number of times the combinatorial Benders master problem is solved and in reducing the overall computational time. More than 120 instances of the W‐CDMA network planning problem ranging from 20 demand points and 10 base stations to 140 demand points and 30 base stations are solved to optimality. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

Exact algorithms for integrated facility location and production planning problems

We consider a class of facility location problems with a time dimension, which requires assigning every customer to a supply facility in each of a finite number of periods. Each facility must meet all assigned customer demand in every period at a minimum cost via its production and inventory decisions. We provide exact branch‐and‐price algorithms for this class of problems and several important variants. The corresponding pricing problem takes the form of an interesting class of production planning and order selection problems. This problem class requires selecting a set of orders that maximizes profit, defined as the revenue from selected orders minus production‐planning‐related costs incurred in fulfilling the selected orders. We provide polynomial‐time dynamic programming algorithms for this class of pricing problems, as well as for generalizations thereof. Computational testing indicates the advantage of our branch‐and‐price algorithm over various approaches that use commercial software packages. These tests also highlight the significant cost savings possible from integrating location with production and inventory decisions and demonstrate that the problem is rather insensitive to forecast errors associated with the demand streams. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Level workforce planning for multistage transfer lines

In this article, we define two different workforce leveling objectives for serial transfer lines. Each job is to be processed on each transfer station for c time periods (e.g., hours). We assume that the number of workers needed to complete each operation of a job in precisely c periods is given. Jobs transfer forward synchronously after every production cycle (i.e., c periods). We study two leveling objectives: maximin workforce size () and min range (R). Leveling objectives produce schedules where the cumulative number of workers needed in all stations of a transfer line does not experience dramatic changes from one production cycle to the next. For and a two‐station system, we develop a fast polynomial algorithm. The range problem is known to be NP‐complete. For the two‐station system, we develop a very fast optimal algorithm that uses a tight lower bound and an efficient procedure for finding complementary Hamiltonian cycles in bipartite graphs. Via a computational experiment, we demonstrate that range schedules are superior because not only do they limit the workforce fluctuations from one production cycle to the next, but they also do so with a minor increase in the total workforce size. We extend our results to the m‐station system and develop heuristic algorithms. We find that these heuristics work poorly for min range (R), which indicates that special structural properties of the m‐station problem need to be identified before we can develop efficient algorithms. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 577–590, 2016     

Operations planning revisited: theoretical and practical implications of methodology

Parts of NATO’s contemporary planning framework called the comprehensive operations planning directive (COPD), and parts of the operation-level planning process should be revised since they suffer from methodological inconsistency. This claim is defended by discussing contradicting methodological properties and heuristics applied when framing and managing a military problem in accordance with the COPD. The methodological inconsistency within the COPD; in other words, simultaneously applying contradictory methodological properties, implies one theoretical and three practical implications. The theoretical implication is summarised in a meta-theoretical framework and explained by discussing five methodological properties: non-linearity, emergence, independently changeable generalisations, invariance and boundaries. The three practical implications of methodology imply that methodology is guiding: the problem-frame, conceptual development and action. To improve military planners’ understanding and management of these four identified implications, NATO is recommended to develop a “handbook of methodology.” The purpose of such a handbook should be to emphasise the utility of methodology when planning military operations.     

A concave‐cost production planning problem with remanufacturing options

We focus on the concave‐cost version of a production planning problem where a manufacturer can meet demand by either producing new items or by remanufacturing used items. Unprocessed used items are disposed. We show the NP‐hardness of the problem even when all the costs are stationary. Utilizing the special structure of the extreme‐point optimal solutions for the minimum concave‐cost problem with a network flow type feasible region, we develop a polynomial‐time heuristic for the problem. Our computational study indicates that the heuristic is a very efficient way to solve the problem as far as solution speed and quality are concerned. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

An FPTAS for scheduling a two‐machine flowshop with one unavailability interval

We study a deterministic two‐machine flowshop scheduling problem with an assumption that one of the two machines is not available in a specified time period. This period can be due to a breakdown, preventive maintenance, or processing unfinished jobs from a previous planning horizon. The problem is known to be NP‐hard. Pseudopolynomial dynamic programming algorithms and heuristics with worst case error bounds are given in the literature to solve the problem. They are different for the cases when the unavailability interval is for the first or second machine. The existence of a fully polynomial time approximation scheme (FPTAS) was formulated as an open conjecture in the literature. In this paper, we show that the two cases of the problem under study are equivalent to similar partition type problems. Then we derive a generic FPTAS for the latter problems with O(n⁵/ε⁴) time complexity. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

Multiperiod capacity expansion and shipment planning with linear costs

In this paper we consider a multiperiod deterministic capacity expansion and shipment planning problem for a single product. The product can be manufactured in several producing regions and is required in a number of markets. The demands for each of the markets are non-decreasing over time and must be met exactly during each time period (i.e., no backlogging or inventorying for future periods is permitted). Each region is assumed to have an initial production capacity, which may be increased at a given cost in any period. The demand in a market can be satisfied by production and shipment from any of the regions. The problem is to find a schedule of capacity expansions for the regions and a schedule of shipments from the regions to the markets so as to minimize the discounted capacity expansion and shipment costs. The problem is formulated as a linear programming model, and solved by an efficient algorithm using the operator theory of parametric programming for the transporation problem. Extensions to the infinite horizon case are also provided.     

Single‐warehouse multi‐retailer inventory systems with full truckload shipments

We consider a multi‐stage inventory system composed of a single warehouse that receives a single product from a single supplier and replenishes the inventory of n retailers through direct shipments. Fixed costs are incurred for each truck dispatched and all trucks have the same capacity limit. Costs are stationary, or more generally monotone as in Lippman (Management Sci 16, 1969, 118–138). Demands for the n retailers over a planning horizon of T periods are given. The objective is to find the shipment quantities over the planning horizon to satisfy all demands at minimum system‐wide inventory and transportation costs without backlogging. Using the structural properties of optimal solutions, we develop (1) an O(T²) algorithm for the single‐stage dynamic lot sizing problem; (2) an O(T³) algorithm for the case of a single‐warehouse single‐retailer system; and (3) a nested shortest‐path algorithm for the single‐warehouse multi‐retailer problem that runs in polynomial time for a given number of retailers. To overcome the computational burden when the number of retailers is large, we propose aggregated and disaggregated Lagrangian decomposition methods that make use of the structural properties and the efficient single‐stage algorithm. Computational experiments show the effectiveness of these algorithms and the gains associated with coordinated versus decentralized systems. Finally, we show that the decentralized solution is asymptotically optimal. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009     

Rescheduling to minimize makespan on a changing number of identical processors

We consider the problem of rescheduling n jobs to minimize the makespan on m parallel identical processors when m changes value. We show this problem to be NP-hard in general. Call a list schedule totally optimal if it is optimal for all m = 1, …,n. When n is less than 6, there always exists a totally optimal schedule, but for n ≥ 6 this can fail. We show that an exact solution is less robust than the largest processing time first (LPT) heuristic and discuss implications for polynomial approximation schemes and hierarchical planning models.     

敏捷成像卫星自主规划模型与算法

针对敏捷成像卫星自主规划问题,将规划、决策、执行和信息反馈相结合,提出星上自主规划框架,并介绍框架结构和模块功能。在分析主要约束条件的基础上,建立基于时间线约束网络的问题模型。通过将各种卫星动作前后衔接,组合成能够完成不同任务的动作序列,提出一种面向卫星动作序列的启发式算法。该算法分为规划和决策两个部分,并在卫星执行每一个动作序列的同时基于多种启发式规则进行规划,在动作序列执行结束时进行决策。实验结果表明了自主规划框架和模型的合理性以及算法的有效性。     

Short-term retrenchment planning in hierarchical manpower systems

We address the issue of short-term retrenchment planning required of organizations that are phasing down their manpower levels at rates faster than are allowed by natural attrition. Specifically, the problem we study is as follows: given the initial and target grade populations in a hierarchical manpower system at the end of a finite time horizon and the per-period rate of natural attrition for each grade, find a stationary manpower policy that minimizes the maximum per-period rate of retrenchment across all the grades over all stationary policies that yield the target grade populations at the end of the horizon. Because the problem is a nonconvex, nonseparable, nonlinear program, we develop a heuristic in which the promotion proportions of all the grades are successively fixed, starting from the lowest grade. We prove optimality of the heuristic policy in three nontrivial situations. In a computational experiment, in 135 out of 150 randomly generated instances (i.e., in 90% of the cases), the heuristic yielded a solution that was as good or better than that yielded by a benchmark computer program that solves the present problem as a nonlinear program. Further, the average computational time under the heuristic was an order of magnitude less than that under the program. © 1995 John Wiley & Sons, Inc.     

Dynamic order acceptance and capacity planning on a single bottleneck resource

We present a tactical decision model for order acceptance and capacity planning that maximizes the expected profits from accepted orders, allowing for aggregate regular as well as nonregular capacity. The stream of incoming order arrivals is the main source of uncertainty in dynamic order acceptance and the company only has forecasts of the main properties of the future incoming projects. Project proposals arrive sequentially with deterministic interarrival times and a decision on order acceptance and capacity planning needs to be made each time a proposal arrives and its project characteristics are revealed. We apply stochastic dynamic programming to determine a profit threshold for the accept/reject decision as well as to deterministically allocate a single bottleneck resource to the accepted projects, both with an eye on maximizing the expected revenues within the problem horizon. We derive a number of managerial insights based on an analysis of the influence of project and environmental characteristics on optimal project selection and aggregate capacity usage. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007