Military Operations Research Research Articles

A hybrid multi-objective evolutionary algorithm with high solving efficiency for UAV defense programming

Emerging Unmanned Aerial Vehicle (UAV) application patterns, including the Loyal Wingman and Unmanned Swarms, have significantly challenged the administration and defense against illegal or trespassing UAVs. The weapon-target assignment (WTA) problem, a famous combinatorial optimization problem in military operations research, is decisive for the success of UAV defense programming. Related investigations face two main challenges: 1) Constructing a multi-objective optimization model reflecting the complexities of new UAV application patterns, and 2) solving the time-consuming WTA problem with high-dimension decision variables as it is NP-complete. This paper introduces a modified differential evolution based on a new mapping method called Pareto-Optimal-Matching that solves multi-objective binary optimization problems and constructs a 3 objective Sensor-Weapon-Target Assignment model. The proposed algorithm overcomes the inherent limitation of traditional differential evolution, which only performs well in continuous problems. Promising results were obtained based on different simulation metrics under various problems (proposed model, MOKP, and ZDT5), validating the superior performance of the proposed algorithm.

Military Operations Research Society (MORS) Oral History Project Interview of Dr. Mark T. Lewellyn

Military Operations Research Society (MORS) Oral History Project Interview of Dr. Mark T. Lewellyn

Military Operations Research Society (MORS) Oral History Project Interview of Dr. Willie J. McFadden II, FS

Military Operations Research Society (MORS) Oral History Project Interview of Dr. Willie J. McFadden II, FS

PROBABILISTIC RISK ASSESSMENT FOR INTEGRATING SURVIVABILITY AND SAFETY MEASURES ON NAVAL SHIPS

Conflicts of today are characterized by both traditional and irregular tactics and by non-state actors making innovative use of modern technologies. These conditions set new demands on naval ships. The aim of this investigation is to describe how, based on probabilistic risk assessment, the concept of operation for a naval ship can be turned into safety scenarios to be used in the evaluation of risk. In this investigation, civilian state-of-the-art methods for probabilistic risk assessment are merged with the specific demands of naval ships. Relevant aspects of safety culture, codes, regulations and rules are analysed with respect to requirements on safety scenarios, and military operational research with respect to modelling military systems. The results show that the scenarios must have calculable probability and must be adapted to the vessel in question. Results from simulations show that modelling operational tasks is one way to support experts in the definition of safety scenarios.

Analyzing Problem Solving Difficulties on Applied Military Operation Research

Operations Research is a scientific discipline that can be used to help make decisions in all fields. In the military field, the application of operations research is very much so that every officer must be able to apply it in carrying out his duties. Indonesian Navy officers received education about operations research, especially the analytic hierarchy process when they attended the Staff and Command College. A total of 20 student officers were examined to find out the difficulties they faced in solving problem solving for the analytic hierarchy process material through written answers to questions and in-depth interviews using Polya's problem-solving steps. The results obtained are that all officers did not experience problems at the time of problem identification. However, almost half of them experienced problems when planning solutions, carrying out calculations and conducting reviews. The recommendation submitted is to use a software application so that the calculations are correct and fast.

Military Operations Research Society (MORS) Oral History Project: Interview of Mr. Edward A. “Ted” Smyth, FS

Military Operations Research Society (MORS) Oral History Project: Interview of Mr. Edward A. “Ted” Smyth, FS

Automated Support for Battle Operational–Strategic Decision-Making

Battle casualties are the subject of study in military operations research, which applies mathematical models to quantify the probability of victory vs. loss. In particular, different approaches have been proposed to model the course of battles. However, none of them provide adequate decision-making support for high-level command. To overcome this situation, this paper presents an innovative high-level decision-making model, which uses an adaptive and predictive control architecture. The paper reports empirical evidence supporting our model by considering one of the greatest battles of World War II: the Battle of Crete.

Military Operations Research Society (MORS) Oral History Project Interview of Vice Admiral Ronald A. Route

Military Operations Research Society (MORS) Oral History Project Interview of Vice Admiral Ronald A. Route

Military Operations Research: Origins, Recent Past, and Present

Operations Research (OR) (also called Operational Research and Operations Analysis) has its origins in tackling military problems and requirements using scientific methods and common sense. This paper aims to provide an overview of its origins, a summary on recent past, and current practices. While many ingenious persons have come up with creative ways to ensure the military means and tools were more effective and efficient over the centuries, not all of these individual efforts can qualify as an organized scientific discipline. Thus, it is important to note that the author refrains from the premise of “the older the better” and focus of the paper is on “practical” emergence of OR as an “applied science” in the years just preceding and during World War II (WW2). Military applications of OR, where it stemmed from, are provided throughout the paper. After WW2, unemployed OR analysts started using their skills on making things better, more effective, and more efficient in the civilian industry and this tradition continues today with great success. It would be over ambitious to attempt to cover all of OR’s applications and contributions in a single paper. Therefore, the attempt here is to provide a glimpse of Military OR’s origins, recent past, current application areas.

Military Operations Research Society (MORS) Oral History Project: Interview of Dr. Eileen A. Bjorkman

Military Operations Research Society (MORS) Oral History Project: Interview of Dr. Eileen A. Bjorkman

A modified crow search algorithm for the weapon-target assignment problem

The Weapon-Target Assignment (WTA) problem is one of the most important optimization problems in military operation research. In the WTA problem, assets of defense aim the best assignment of each weapon to target for decreasing expected damage directed by the offense. In this paper, Modified Crow Search Algorithm (MCSA) is proposed to solve the WTA problem. In MCSA, a trial mechanism is used to improve the quality of solutions using parameter LIMIT. If the solution is not improved after a predetermined number of iterations, then MCSA starts with a new position in the search space. Experimental results on the different sizes of the WTA problem instances show that MCSA outperforms CSA in all problem instances. Also, MCSA achieved better results for 11 out of 12 problem instances compared with four state-of-the-art algorithms. The source codes of MCSA for the WTA are publicly available at http://www.3mrullah.com/MCSA.html.

Swarm Intelligence Algorithms for Weapon-Target Assignment in a Multilayer Defense Scenario: A Comparative Study

Weapon-target assignment (WTA) is a kind of NP-complete problem in military operations research. To solve the multilayer defense WTA problems when the information about enemy’s attacking plan is symmetric to the defender, we propose four heuristic algorithms based on swarm intelligence with customizations and improvements, including ant colony optimization (ACO), binary particle swarm optimization (BPSO), integer particle swarm optimization (IPSO) and sine cosine algorithm (SCA). Our objective is to assess and compare the performance of different algorithms to determine the best algorithm for practical large-scale WTA problems. The effectiveness and performance of various algorithms are evaluated and compared by means of a benchmark problem with a small scale, the theoretical optimal solution of which is known. The four algorithms can obtain satisfactory solutions to the benchmark problem with high quality and high robustness, while IPSO is superior to BPSO, ACO and SCA with respect to the solution quality, algorithmic robustness and computational efficiency. Then, IPSO is applied to a large-scale WTA problem, and its effectiveness and performance are further assessed. We demonstrate that IPSO is capable of solving large-scale WTA problems with high efficiency, high quality and high robustness, thus meeting the critical requirements of real-time decision-making in modern warfare.

Lanchester Models for Irregular Warfare

Military operations research and combat modeling apply mathematical models to analyze a variety of military conflicts and obtain insights about these phenomena. One of the earliest and most important set of models used for combat modeling is the Lanchester equations. Legacy Lanchester equations model the mutual attritional dynamics of two opposing military forces and provide some insights regarding the fate of such engagements. In this paper, we review recent developments in Lanchester modeling, focusing on contemporary conflicts in the world. Specifically, we present models that capture irregular warfare, such as insurgencies, highlight the role of target information in such conflicts, and capture multilateral situations where several players are involved in the conflict (such as the current war in Syria).

Iterated Local Search in Combinatorial Optimization Problem

Iterated local search (ILS) is a very powerful optimization method for continuous-valued numerical optimization. However, ILS has seldom been used to solve combinatorial integer-valued optimization problems. In this paper, the iterated local search (ILS) with random restarts algorithm is applied to solve combinatorial optimization problems, e.g., the classical weapon-target allocation (WTA) problem which arises from the military operations research. The mathematical model of the WTA problem is explained in detail. Then the idea of ILS with random restarts is explained. A comparison of the algorithm with several existing search approaches shows that the ILS outperforms its competitors on the tested WTA problem.

Simplified swarm optimization with initialization scheme for dynamic weapon–target​ assignment problem

The dynamic weapon–target assignment (DWTA) problem is a critical issue in the field of military operations research. The problem is highly constrained; thus, the use of an evolutionary method to solve the DWTA problem often encounters a population of infeasible solutions resulting in prohibitive computational burden. Aiming at accelerating the solution process and improving the solution quality, this work proposes an improved simplified swarm optimization called SSODT with two novel schemes: the deterministic initialization scheme, and the target exchange scheme. The deterministic initialization scheme is used in population initialization and utilizes problem-specific knowledge of DWTA to speed up the convergence of SSODT by generating a promising feasible solution which has a greater potential for evolving globally. The target exchange scheme is a local search updating feasible solutions in a manner that exchanges their variables without violating the engagement feasibility to enhance the exploitation capability of SSODT. The proposed method is empirically verified on thirty-six artificial problems and compared with widely popular evolutionary methods. The results demonstrate that the proposed SSODT is better than its competitors in terms of both solution quality and efficiency.

Dancing links for optimal timetabling

Military Operations Research Society. All rights reserved. Algorithms for timetabling solutions typically involve sequential allocation of students to courses and resources as the algorithm unfolds. Most current solutions, to this end, commonly use some form of stochastic optimization. In this paper, we propose a novel paradigm for optimal timetabling that comprises two distinct phases. First, we enumerate all feasible course schedules, along with their costs, using a modified implementation of Knuth’s Dancing Links technique for the exact cover problem. To our knowledge, the only prior use of this implementation has been to solve games such as Sudoku and N-Queens. Once the list of all solutions that satisfy prerequisite and time-clash constraints is generated, the second phase applies a standard deterministic optimization to allocate students to these feasible schedules. This method has been applied to a real complex timetabling problem in the Royal Australian Navy helicopter aircrew training program. The results are compared, in terms of computational time, to an exhaustive best practice backtracking algorithm for generating a complete set of feasible schedules, as well as to a pure integer linear programming solution for generating schedules and allocating students to schedules.

Solving the moving target search problem using indistinguishable searchers

Searching for a single target in a discrete space and time is a well-known problem in military operational research that also finds applications in other areas such as search and rescue. Solving this problem is difficult, as search routes depend on the knowledge of where the target may be at a given time, which itself changes as the search proceeds. It is even more so for multiple searchers, as the size of the state space now depends on the number of searchers. This contribution deals with this problem variant for a single moving target by assuming that searchers are not only identical, but also indistinguishable. In the standard branch and bound approach to this problem, this assumption permits the calculation of bounds by solving minimum-cost flow problems, which are independent of the number of searchers and where there is no need to relax the integrality of the search effort. Both of these outcomes are novel in comparison to previous efforts with multiple searchers. The author illustrates the proposed approach in the context of a counter-piracy scenario where warships aim to deter and interdict pirates and where the pirate motion model derives from an environmental forecast of the likelihood of piracy and the Markov assumption. Computational experiments are performed to compare the proposal to an alternative found in the literature to solve the problem to optimality.

Bistatic sonobuoy deployment strategies for detecting stationary and mobile underwater targets

The problem of determining effective allocation schemes of underwater sensors for surveillance, search, detection, and tracking purposes is a fundamental research area in military operations research. Among the various sensor types, multistatic sonobuoy systems are a promising development in submerged target detection systems. These systems consist of sources (active sensors) and receivers (passive sensors), which need not be collocated. A multistatic sonobuoy system consisting of a single source and receiver is called a bistatic system. The sensing zone of this fundamental system is defined by Cassini ovals. The unique properties and unusual geometrical profile of these ovals distinguish the bistatic sensor allocation problem from conventional sonar placement problems. This study is aimed at supporting decision makers in making the best use of bistatic sonobuoys to detect stationary and mobile targets transiting through an area of interest. We use integral geometry and geometric probability concepts to derive analytic expressions for the optimal source and receiver separation distances to maximize the detection probability of a submerged target. We corroborate our analytic results using Monte Carlo simulation. Our approach constitutes a valuable “back of the envelope” method for the important and difficult problem of analyzing bistatic sonar performance.

The Study on Application of MOOC in Postgraduate Military Operation Research Courses

The role of Massive Open Online Course (MOOC), which has unique advantages and characteristics, is not established in postgraduate military operation research courses. This paper investigated the status quo of traditional postgraduate military operation research courses and analyzed the characteristics of MOOC. Our results suggest that the military academy should be exposed to the forefront of educational reform and get rid of the drawbacks and shackles of traditional military operation research courses, so that they can focus on postgraduate learning needs and promote the construction of military operations research courses to a new height.

Enterprise-level business component identification in business architecture integration

The component-based business architecture integration of military information systems is a popular research topic in the field of military operational research. Identifying enterprise-level business components is an important issue in business architecture integration. Currently used methodologies for business component identification tend to focus on software-level business components, and ignore such enterprise concerns in business architectures as organizations and resources. Moreover, approaches to enterprise-level business component identification have proven laborious. In this study, we propose a novel approach to enterprise-level business component identification by considering overall cohesion, coupling, granularity, maintainability, and reusability. We first define and formulate enterprise-level business components based on the component business model and the Department of Defense Architecture Framework (DoDAF) models. To quantify the indices of business components, we formulate a create, read, update, and delete (CRUD) matrix and use six metrics as criteria. We then formulate business component identification as a multi-objective optimization problem and solve it by a novel meta-heuristic optimization algorithm called the ‘simulated annealing hybrid genetic algorithm (SHGA)’. Case studies showed that our approach is more practical and efficient for enterprise-level business component identification than prevalent approaches.