Agent-based model of maritime search operations:a validation using test-driven simulation modeling

Maritime search operations (and search operations in general) are one of the classic applications of Operational Research (OR). This paper presents a generic agent-based model for maritime search operations which can be used to analyse operations such as search and rescue and patrol. Agent-based simulation (ABS) is a relatively new addition to existing OR techniques. The key elements of an ABS model are agents, their behaviours and their interactions with other agents and the environment. A search operation involves at least two types of agent: a searcher and a target. The unique characteristic of ABS is that we model agents’ behaviours and their interactions at the individual level. Hence, ABS offers an alternative modelling approach to analyse search operations. The second objective of our work is to show how test-driven simulation modelling (TDSM) can be used to validate the agent-based maritime search-operation model

Validating an integer non-linear program optimization model of a wireless sensor network using agent-based simulation

Deploying wireless sensor networks (WSN) along a barrier line to provide surveillance against illegal intruders is a fundamental sensor-allocation problem. To maximize the detection probability of intruders with a limited number of sensors, we propose an integer non-linear program optimization model which considers multiple types of sensors and targets, probabilistic detection functions and sensor-reliability issues. An agent-based simulation (ABS) model is used to validate the analytic results and evaluate the performance of the WSN under more realistic conditions, such as intruders moving along random paths. Our experiment shows that the results from the optimization model are consistent with the results from the ABS model. This increases our confidence in the ABS model and allows us to conduct a further experiment using moving intruders, which is more realistic, but it is challenging to find an analytic solution. This experiment shows the complementary benefits of using optimization and ABS models

A two-level facility location and sizing problem for maximal coverage

This paper presents a two-stage hierarchical location problem for systems where the lower level facilities act as the first points contact for the customers while the upper level facilities act as suppliers of the lower level facilities that either serve them or provide advanced services to customers. Furthermore, more recent and realistic coverage constructs such as gradual and cooperative covering are included in our setting. Although our problem can be applicable in various settings, the most fitting application is in wireless telecommunication networks to determine the location of base stations and mobile switching centers. We have developed two competing formulations for the problem, each of which involve nonlinear components that are difficult to deal with. We then develop their respective linearizations and tested their performances. These formulations are solved by commercial optimizers for a set of reasonably large problem instances and it is found that majority of the problems can be solved within a maximum of 10% optimality gap within a short time

Optimizing source and receiver placement in multistatic sonar

17 USC 105 interim-entered record; under review.Multistatic sonar networks consisting of non-collocated sources and receivers are a promising development in sonar systems, but they present distinct mathematical challenges compared to the monostatic case in which each source is collocated with a receiver. This paper is the first to consider the optimal placement of both sources and receivers to monitor a given set of target locations. Prior publications have only considered optimal placement of one type of sensor, given a fixed placement of the other type. We first develop two integer linear programs capable of optimally placing both sources and receivers within a discrete set of locations. Although these models are capable of placing both sources and receivers to any degree of optimality desired by the user, their computation times may be unacceptably long for some applications. To address this issue, we then develop a two-step heuristic process, Adapt-LOC, that quickly selects positions for both sources and receivers, but with no guarantee of optimality. Based on this, we also create an iterative approach, Iter-LOC, which leads to a locally optimal placement of both sources and receivers, at the cost of larger computation times relative to Adapt-LOC. Finally, we perform computational experiments demonstrating that the newly developed algorithms constitute a powerful portfolio of tools, enabling the user to slect an appropriate level of solution quality, given the available time to perform computations. Our experiments include three real-world case studies.Dr. Craparo is funded by the Office of Naval Research

Optimizing source and receiver placement in multistatic sonar networks to monitor fixed targets

17 USC 105 interim-entered record; under review.The article of record as published may be found at https://doi.org/10.1016/j.ejor.2018.02.006Multistatic sonar networks consisting of non-collocated sources and receivers are a promising develop ment in sonar systems, but they present distinct mathematical challenges compared to the monostatic case in which each source is collocated with a receiver. This paper is the first to consider the optimal placement of both sources and receivers to monitor a given set of target locations. Prior publications have only considered optimal placement of one type of sensor, given a fixed placement of the other type. We first develop two integer linear programs capable of optimally placing both sources and receivers within a discrete set of locations. Although these models are capable of placing both sources and receivers to any degree of optimality desired by the user, their computation times may be unacceptably long for some applications. To address this issue, we then develop a two-step heuristic process, Adapt-LOC, that quickly selects positions for both sources and receivers, but with no guarantee of optimality. Based on this, we also create an iterative approach, Iter-LOC, which leads to a locally optimal placement of both sources and receivers, at the cost of larger computation times relative to Adapt-LOC. Finally, we perform compu tational experiments demonstrating that the newly developed algorithms constitute a powerful portfolio of tools, enabling the user to slect an appropriate level of solution quality, given the available time to perform computations. Our experiments include three real-world case studies.Office of Naval Research

Test-driven simulation modelling:a case study using agent-based maritime search-operation simulation

Model verification and validation (V&V) is one of the most important activities in simulation modelling. Model validation is especially challenging for agent-based simulation (ABS). Techniques that can help to improve V&V in simulation modelling are needed. This paper proposes a V&V technique called Test-Driven Simulation Modelling (TDSM) which applies techniques from Test-Driven Development in software engineering to simulation modelling. The main principle in TDSM is that a unit test for a simulation model has to be specified before the simulation model is implemented. Hence, TDSM explicitly embeds V&V in simulation modelling. We use a case study in maritime search operations to demonstrate how TDSM can be used in practice. Maritime search operations (and search operations in general) are one of the classic applications of Operational Research (OR). Hence, we can use analytical models from the vast search theory literature for unit tests in TDSM. The results show that TDSM is a useful technique in the verification and validation of simulation models, especially ABS models. This paper also shows that ABS can offer an alternative modelling approach in the analysis of maritime search operations

A collaborative decision support framework for sustainable cargo composition in container shipping services

This paper proposes a decision support system (DSS) for optimizing cargo composition, and resulting stowage plan, in a containership of a shipping company in collaboration with en-route ports in the service. Due to considerable growth in transportation over years, an increasing number of containers are being handled by containerships, and ports consequently. Trade imbalances between regions and recent disruptions, such as LA/LB/Shanghai port congestion, blocking of Suez canal, drought in Panama canal, typhoons at ports, COVID-19 restrictions and the lack- and then over-supply of empty containers, have resulted in an accumulation of containers in exporting ports around the world. These factors have underscored the urgency of sustainability and circular economy within the shipping industry. The demand for container transportation is higher than the ship capacities in the recent times. In this regard, it is essential for shipping companies to generate a cargo composition plan for each service by selecting and transporting containers with relatively high financial returns, while offering a realistic stowage plan considering ship stability, capacity limitations and port operations. Ultimately, the selected containers should enable a ship stowage plan which keeps the ship seaworthy obeying complex stability considerations and minimizes the vessel stay at the ports, and port carbon emissions consequently, through efficient collaboration with en-route ports. This study provides a bi-level programming based DSS that selects the set of containers to be loaded at each port of service and generates a detailed stowage plan considering revenue, stowage efficiency and quay crane operational considerations. Numerical experiments indicate that the proposed DSS is capable of returning high-quality solutions within reasonable solution times for all ship sizes, cargo contents and shipping routes, supporting the principles of the circular economy in the maritime domain.</jats:p

Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms

Due to their advantages in flexibility, scalability, survivability, and cost-effectiveness, drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields. This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms. Given a set of available air defense systems, the problem determines the location of each air defense system in a predetermined region, such that the cost for enemy drones to pass through the region would be maximized. The cost is calculated based on a counterpart drone path planning problem. To solve this adversarial problem, we first propose an exact iterative search algorithm for small-size problem instances, and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances. We implement the evolutionary framework with six popular evolutionary algorithms. Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms