Dynamic rescheduling heuristics for military village search environments (original) (raw)
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Robust static planning tool for military village search missions: model and heuristics
The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 2012
In the contemporary military environment, making decisions on how to best utilize resources to accomplish a mission with a set of specified constraints is difficult. A Cordon and Search of a village (a.k.a. village search) is an example of such a mission. Leaders must plan the mission, assigning assets (e.g. soldiers, robots, unmanned aerial vehicles, military working dogs) to accomplish the given task in accordance with orders from higher headquarters. Computer tools can assist these leaders in making decisions, and do so in a manner that will ensure the chosen solution is within mission constraints and is robust against uncertainty in environmental parameters. Currently, no such tools exist at the tactical or operational level to assist decision makers in their planning process and, as a result, individual experience and simplistic data tables are the only tools available. Using robustness concepts, this paper proposes a methodology, a mathematical model, and resource allocation h...
A demonstration of a simulation tool for planning robust military village searches
2010
In the current military environment, village searches are conducted daily by a variety of search team types. Staff officers planning these resource allocation problems currently rely on experience and simple data tables to develop the plans. The Robust People, Animals, and Robots Search (RoPARS) planning tool for village searches developed at Colorado State University can assist military planners with this tedious process. The tool consists of a graphical user interface and a resource allocation engine. Its output is a mission plan that is robust against uncertainty in the battlefield environment (e.g., unit speed, temperature, enemy contact). The contributions of this paper include the RoPARS tool and its robustness concepts, mathematical models, and resource allocation heuristics.
A mathematical model of robust military village searches for decision making purposes
2009
In the modern, fast-paced, high technology military, making decisions on how to best utilize resources to accomplish a mission with a set of specified constraints is difficult. A Cordon and Search of a village (i.e., village search) is an example of such a mission. Leaders must plan the mission, assigning assets (e.g., soldiers, robots, unmanned aerial vehicles, military working dogs) to accomplish the given task in accordance with orders from higher headquarters. Computer tools can assist these leaders in making decisions, and do so in a manner that will ensure the chosen solution is within mission constraints and is robust against uncertainty in environmental parameters. Currently, no such tools exist at the tactical or operational level to assist decision makers in their planning process and, as a result, individual experience is the only tool available. This paper proposes a methodology and a mathematical model for village searches that applies robustness concepts resulting in a decision-making tool for military leaders to use in mission planning. 1
Tactical planning using heuristics
Proceedings of the 14th Belgium-Netherlands Artificial Intelligence Conference (BNAIC'02)}, 2014
Modern transportation problems are highly dynamic and time critical. A planning system for transportation problems must therefore include efficient and flexible planning and replanning strategies. In this paper we introduce a general agent-based framework for highly dynamic order-based transportation planning problems where a tactical planner and several operational planners (one for each transport agent) are distinguished. In particular we discuss the role of the tactical planner responsible for dynamic task allocation of orders ...
Adaptation in Dynamic Environments: A Case Study in Mission Planning
IEEE Transactions on Evolutionary Computation, 2012
Many random events usually are associated with executions of operational plans at various companies and organizations. For example, some tasks might be delayed and/or executed earlier. Some operational constraints can be introduced due to new regulations or business rules. In some cases, there might be a shift in the relative importance of objectives associated with these plans. All these potential modifications create a huge pressure on planning staff for generating plans that can adapt quickly to changes in environment during execution. In this paper we address adaptation in dynamic environments. Many researchers in evolutionary community addressed the problem of optimization in dynamic environments. Through an overview on applying evolutionary algorithms for solving dynamic optimization problems, we classify the work into two main categories: (1) finding/tracking optima and (2) adaptation and we discuss their relevance for solving planning problems. Based on this discussion, we propose a computational approach to adaptation within the context of planning. This approach models the dynamic planning problem as a multi-objective optimization problem and an evolutionary mechanism is incorporated, this adapts the current solution to new situations when a change occurs. As the multi-objective model is used, the proposed approach produces a set of non-dominated solutions after each planning cycle. This set of solutions can be perceived as an informationrich data set which can be used to support the adaptation process against the effect of changes. The main question is how to exploit this set efficiently? In this paper we propose a method based on the concept of centroids over a number of changing-time steps, at each step we obtain a set of non-dominated solutions. We carried out a case study on this proposed approach. Mission planning was used for our experiments and experimental analysis. We selected mission planning as our test environment because battlefields are always highly dynamic and uncertain and can be conveniently used to demonstrate different types of changes, especially time-varying constraints. The obtained results support the significance of our centroid-based approach.
Decision-theoretic military operations planning
Proc. ICAPS, 2004
Military operations planning involves concurrent actions, resource assignment, and conflicting costs. Individual tasks sometimes fail with a known probability, promoting a decision-theoretic approach. The planner must choose between multiple tasks that achieve similar outcomes but have different costs. The military domain is particularly suited to automated methods because hundreds of tasks, specified by many planning staff, need to be quickly and robustly coordinated. The authors are not aware of any previous planners that handle all characteristics of the operations planning domain in a single package. This paper shows that problems with such features can be successfully approached by realtime heuristic search algorithms, operating on a formulation of the problem as a Markov decision process. Novel automatically generated heuristics, and classic caching methods, allow problems of interesting sizes to be handled. Results are presented on data provided by the Australian Defence Science and Technology Organisation.
A Strategic/Tactical Architecture for Planning in Dynamic Environments
We present a novel planning architecture that is composed of strategic and tactical layers. The complementary roles of these two layers are discussed, along with their relationship to one another, and a mathematical model is presented. The planning architecture is motivated by the requirements of dynamic systems, and it provides a clearly-defined optimality measure for an agent operating in a stochastic environment. Although the architecture can be applied in any domain, it is thus particularly well-suited to highly dynamic environments. Preliminary experimental results are presented.
A survey of military planning systems
2004
Future military operations rely on increasingly complex joint and multinational environments. This calls for innovative concepts, doctrine, and technologies to support the emergence of new planning and execution systems that are more flexible, adaptive, interoperable, and responsive to a time-varying uncertain environment. The ability to conduct joint operations imposes shared information and interoperability requirements to operate among coalition members as growing complexity and rapid pace of military operations transit from a rigid vertical organizational structure to a more integrated, modular and tailored one. In that regard, Network Centric Operations (NCO) offers a unique setting to take on emerging challenges. Even though deliberate planning tools focus on providing "on the fly" precise tailoring and time phasing of force deployment in crisis situations, suitable coordinated responses are subject to a variety of real-time constraints, local views reflecting incomplete time-varying uncertain information from multiple sources, bounded computational resources and communication bandwidth. The combination of artificial intelligence, operations research and data mining techniques to mention a few, and webbased and information technologies, offer a great opportunity to address new planning system design and integration requirements. In this paper pertinent mission planning and scheduling systems designed to support relevant and specific Air Force and in a certain extent Joint and Navy Forces needs were reviewed. The survey addresses various issues associated with mission planning functions and provides a brief description of methods, tools, and procedures used to plan and schedule complex military operations. Emerging techniques used to build advanced mission planning systems are also examined.
Transactions on computational science and computational intelligence, 2021
In this study, our focus is on the design of mission scheduling techniques capable of working in dynamic environments with unmanned aerial vehicles, to determine effective mission schedules in real-time. The effectiveness of mission schedules for unmanned aerial vehicles is measured using a surveillance value metric, which incorporates information about the amount and usefulness of information obtained from surveilling targets. We design a set of dynamic heuristic techniques, which are compared and evaluated based on their ability to maximize surveillance value in a wide range of scenarios generated by a randomized model. We consider two comparison heuristics, three value-based heuristics, and a metaheuristic that intelligently switches between the best value-based heuristics. The novel metaheuristic is shown to find effective solutions that are the best on average as all other techniques that we evaluate in all scenarios that we consider.
A Graphical User Interface for Simulating Robust Military Village Searches
-In the current military environment, village searches are conducted daily. To accomplish a village search task in accordance with orders provided by higher headquarters, the mission leaders must plan and allocate resources (e.g., soldiers, robots, military working dogs, unmanned aerial vehicles) efficiently. The plans these leaders create are based on personal experience and planning data found in military field manuals. The Robust People, Animals, and Robots Search (RoPARS) planning tool for village search developed at Colorado State University can assist military leaders in the planning process. The tool consists of a graphical user interface and a resource allocation engine. This tool allows a user to create a simulation for a given village. These simulations allow military leaders to visualize how a given plan would be executed and to develop plans for the mission that are robust against uncertainty in the environment.