Spaghetti: systems thinking and the US Army

In the mid-2000s, the United States Army was embroiled in counterinsurgency missions in Iraq and Afghanistan that required deeper understanding of local social systems. The Army turned to systems thinking and design thinking to model and understand the world, define problems, and develop approaches to strategic and operational challenges. However, the Army’s approach as expressed in publications and doctrine encourages the development of complicated, unsupported, and unfalsifiable hypotheses. The risk is that the Army will act on incorrect assumptions and develop plans that are fragile.     

Implementing defence policy: a benchmark-“lite”

Most countries put significant amounts of time and effort in writing and issuing high-level policy documents. These are supposed to guide subsequent national defence efforts. But do they? And how do countries even try to ensure that they do? This paper reports on a benchmarking effort of how a few “best of breed” small- to medium-sized defence organisations (Australia, Canada, and New Zealand) deal with these issues. We find that most countries fail to link goals to resources and pay limited attention to specific and rigorous ex-ante or post-hoc evaluation, even when compared to their own national government-wide provisions. We do, however, observe a (modest) trend towards putting more specific goals and metrics in these documents that can be – and in a few rare cases were – tracked. The paper identifies 42 concrete policy “nuggets” – both “do’s and don’ts” – that should be of interest to most defence policy planning/analysis communities. It ends with two recommendations that are in line with recent broader (non-defence) scholarship on the policy formulation-policy implementation gap: to put more rigorous emphasis on implementation (especially on achieving desired policy effects), but to do so increasingly in more experiential (“design”) ways, rather than in industrial-age bureaucratic ones (“PPBS”-systems).     

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     

Capacity expansion and cost efficiency improvement in the warehouse problem

The warehouse problem with deterministic production cost, selling prices, and demand was introduced in the 1950s and there is a renewed interest recently due to its applications in energy storage and arbitrage. In this paper, we consider two extensions of the warehouse problem and develop efficient computational algorithms for finding their optimal solutions. First, we consider a model where the firm can invest in capacity expansion projects for the warehouse while simultaneously making production and sales decisions in each period. We show that this problem can be solved with a computational complexity that is linear in the product of the length of the planning horizon and the number of capacity expansion projects. We then consider a problem in which the firm can invest to improve production cost efficiency while simultaneously making production and sales decisions in each period. The resulting optimization problem is non‐convex with integer decision variables. We show that, under some mild conditions on the cost data, the problem can be solved in linear computational time. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 367–373, 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.     

基于 ANP 的作战筹划能力模糊评价方法

基于网络层次分析（ANP）的模糊评价方法,通过对作战筹划能力进行分析评价,为提升作战筹划能力提供参考。首先运用 ANP 法,提出包含4个方面18个指标的评估指标体系,构建作战筹划能力评估模型,然后通过模糊综合评价法对作战筹划能力进行评价。该评估模型探索了筹划能力评价的方法,为作战筹划能力评价工作提供新思路。     

基于Dijkstra算法的水平航迹规划

战术飞行任务的航迹规划是依靠地形信息和敌情信息,在某些约束条件下,找出作战飞机从起飞点到目标突防生存概率最大、航程最短的飞行轨迹的过程。采用简易方法建立了威胁模型,用Dijkstra算法实现了作战飞机整体最优水平航迹解算,并给出了具体算例,验证了算法的有效性和实用性。     

浅谈在营区规划中对营区文化的塑造

当前在军队工程建设中,对营区规划的主导思想已形成基本一致的认识,但多从物质技术层面去理解,就营区规划单位而言,尚没有广泛认识到营区文化也是规划中的重要内容以及在营区现代化建设中起着不可替代的作用。本文针对此情况重点论述了营区文化的主要内容和重要作用,并提倡通过对营区空间形态的创造来塑造有特色的营区文化,最终创造出一个充满凝聚力的,有利于长期发展的营区环境。     

Balancing and optimizing a portfolio of R&D projects

A mathematical formulation of an optimization model designed to select projects for inclusion in an R&D portfolio, subject to a wide variety of constraints (e.g., capital, headcount, strategic intent, etc.), is presented. The model is similar to others that have previously appeared in the literature and is in the form of a mixed integer programming (MIP) problem known as the multidimensional knapsack problem. Exact solution of such problems is generally difficult, but can be accomplished in reasonable time using specialized algorithms. The main contribution of this paper is an examination of two important issues related to formulation of project selection models such as the one presented here. If partial funding and implementation of projects is allowed, the resulting formulation is a linear programming (LP) problem which can be solved quite easily. Several plausible assumptions about how partial funding impacts project value are presented. In general, our examples suggest that the problem might best be formulated as a nonlinear programming (NLP) problem, but that there is a need for further research to determine an appropriate expression for the value of a partially funded project. In light of that gap in the current body of knowledge and for practical reasons, the LP relaxation of this model is preferred. The LP relaxation can be implemented in a spreadsheet (even for relatively large problems) and gives reasonable results when applied to a test problem based on GM's R&D project selection process. There has been much discussion in the literature on the topic of assigning a quantitative measure of value to each project. Although many alternatives are suggested, no one way is universally accepted as the preferred way. There does seem to be general agreement that all of the proposed methods are subject to considerable uncertainty. A systematic way to examine the sensitivity of project selection decisions to variations in the measure of value is developed. It is shown that the solution for the illustrative problem is reasonably robust to rather large variations in the measure of value. We cannot, however, conclude that this would be the case in general. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 18–40, 2001     

The selection allocation problem

The Selection Allocation Problem (SAP) is a single period decision problem which involves selecting profit‐maximizing (or cost‐minimizing) activities from various distinct groups, and determining the volume of those activities. The activities in each group are selected subject to the availability of that group's resource, which is provided by either pooling or blending raw inputs from several potential sources. Imbedded in the decision process is the additional task of determining how much raw input is to be allocated to each group to form the resource for that group. Instances of this problem can be found in many different areas, such as in tool selection for flexible manufacturing systems, facility location, and funding for social services. Our goal in this paper is to identify and exploit special structures in the (SAP) and use those structures to develop an efficient solution procedure. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 707–725, 1999