Identifying sufficient deception in military logistics
Introduction
Supporting military missions with the informed decisions gathered via intelligent expert systems provides competitive advantages to the nations since such systems can prepare the personnel and valuable resources against the threats demonstrated by the adversaries in anti-access/area-denial (A2/AD) fields. Gordon, Matsumura et al. (2013) define anti-access (A2) operations as geographic, military, or diplomatic challenges that restrict the ability to enter an operational area of interest. Area-denial (AD) is stated as the potential and realized threats within that operational area. The decision makers must excel in making challenging decisions in sea, rail, truck and air transportation operations. Both the joint logistics over-the-shore and inter-modal transportation are challenging problems due to the highly variable and uncertain feature of natural processes, disruptions, limited resources, time restrictions and economic conditions. Thus, a holistic expert system is necessary to tackle efficiently and effectively the problem of transporting military commodities and personnel from their origin to final destination (throughout the full length of the logistics trail). Such intelligent framework should also account for minimizing the overall transportation cost and projecting potential threats. As suggested by Louvieris, Gregoriades, and Garn (2010) and Butler, Boggess, and Bridges (1999), it is highly critical for any automated system to capture and process dynamically changing information regarding the domain, status, and objective of any military task based on hypothesis of the enemy’s intent as adversaries may infer and anticipate a military’s potential objectives.
As an illustrative example with five origin and destination points on a road logistics network, Fig. 1 demonstrates how the decisions of both the adversary (i.e., interdicting network elements via observing the activities on a specific route) and the military decision maker (i.e., hiding and transportation operations along a network element) can be dynamically changing. Scheduled shipments (represented via solid black straight lines) between origins and destinations are subject to the changes in the road logistics network. Ensuring that the logistics operations are successfully carried out under hostile environment, this study seeks to answer the following research questions while accounting for limited available resources (time, budget, personnel, equipment, etc.):
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What is the best route/path to follow to deliver the required amount of commodities from supply nodes to demand nodes in a road logistics network? What is the process to evaluate potential trail projections in terms of pre-determined performance measures?
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Given both the limited resources and the dynamic A2/AD environment, how can deception strategies (falsifying and hiding) be embedded in the logistical operations?
This study contributes to the current military logistics literature in many ways. First, to mimic the actions of the adversary (attacker) and formulate the corresponding courses of actions of the military decision makers (defenders), we propose a two-stage mathematical formulation where the attacker would like to create the maximum cost of transportation by observing/interdicting some shipments along routes (denoted as arcs), and the defender would still like to be able to transport the commodities at minimum cost from supply to demand nodes without revealing too much information. Second, to the best of our knowledge, this is the first study that aims to model deception strategies along with other operational challenges that military logistics and transportation activities possess, such as capacities at supply points and routes, time window restrictions on shipments, demand constraints at the destinations, limited deception capability and attacker’s interdiction choices on the network. The proposed expert system incorporates two deception strategies into the military’s movements: (i) Falsifying signifies including empty convoys on routes on which commodities are being hauled, and (ii) hiding refers to concealing routes in the logistics network. Finally, the two-stage framework suffers from several computational challenges, such as non-convexity and non-linearity. To be able to overcome them and provide practical insights, we performed several methodological reformulations. However, the resulting single-stage convex formulation cannot be solved in reasonable computational time. Two greedy algorithms with rounding techniques are developed to tackle realistic problem instances. The real-world experimental results demonstrate that the greedy algorithms can find good-quality solutions within a reasonable computational time.
The rest of this paper is organized as follows. Some of the related studies are reviewed in Section 2. Section 3 illustrates the efforts in formulating the two-stage nonconvex mathematical model and the further changes required to convert it into a convex model. It also describes the necessary steps of the greedy methods that can handle large problem instances of this problem in short computational time. Section 4 explains the sensitivity analyses that were conducted to test the reliability of the proposed greedy solution technique compared to the commercial solver. It then presents the results of the rounding technique implemented at the end of the greedy solution techniques. Finally, Section 5 reports potential future directions.
Section snippets
Literature review
Military logistics operations are often carried out in dynamically changing and unpredictable environments (McGinnis, 1992). When looking at the literature, A2/AD has been subject to many assessments studying strategies to overcome the threats involved in these environments. In fact, Krepinevich, Watts, and Work (2003) explain the conditions that have contributed to the emergence of the A2/AD environment and its challenges. They define the A2/AD framework as follows: “If anti-access (A2)
Methodology
This section provides a two-stage model formulation where there are two players: the defender and the attacker. The defender can respond to the attacker’s strategy when choosing their own. On the other hand, the defender can anticipate the attacker’s reaction. The defender chooses a course of action concerning the hauling of commodities (destination, time of day, tonnage, etc.). Containing some false cues, that information can be given to the attacker. The attacker knows that some information
Computational results
The results section is split into two subsections. Section 4.1 presents results that are obtained by running the greedy heuristics without the rounding technique. Section 4.2 shows results from running the greedy heuristics with the rounding technique.
All formulations in Section 3, as well as the two algorithms, were modeled in IBM ILOG CPLEX Optimization Studio 12.6.3 by using IBM OPL. All test problems were run on a Core i5-6200U 2.30 GHz, 8 GB RAM computer.
Three different sets of network
Conclusions
The military logistics planning under A2/AD environments provides unique challenges in addition to those studied in the classical transportation and routing problems. Of particular interest are the two deception strategies, and embedding them in a representative environment that captures both adversary’s and defender’s roles. In addition to the traditional transportation problem restrictions (i.e., capacity, demand constraints), the unique challenges are first formulated in the context of a
CRediT authorship contribution statement
Rosemonde Ausseil: Conceptualization, Data curation, Formal analysis, Writing - original draft. Ridvan Gedik: Conceptualization, Data curation, Formal analysis, Writing - original draft. Amy Bednar: Conceptualization, Data curation, Formal analysis, Writing - original draft. Mark Cowan: Conceptualization, Data curation, Formal analysis, Writing - original draft.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The experiments described and the resulting data presented herein, unless otherwise noted, were funded under PE 62784, Project T40 “Military Engineering Applied Research”, Task 24 supported by Battelle Memorial Institute under Contract Awards number US001-0000552238, managed by the U.S. Army Research Laboratory through the U.S. Army Corps of Engineers, Engineer Research and Development Center. The work described in this document/presentation was conducted at the University of New Haven, West
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