Optimization of Military Convoy Routing

Motoriseeritud rännakukolonnide optimeerimine on matemaatilise optimeerimise probleem, milles püütakse leida optimaalset marsruutimislahendust ja vastavat ajakava samaaegsetelt liikuvatele rännakukolonnidele. Käesolevas töös luuakse valik erinevatel optimeerimistehnikatel põhinevaid meetodeid, mida testides püütakse leida parimat Eesti oludele vastavat rännakukolonnide marsruutimise optimeerimismeetodit. Häid tulemusi saavutati kasutades osalise täisarvulise planeerimise mudelit koos heuristiliste täiendustega, rakendades jaga-ja-piira tehnikal põhinevat täpset algoritmi, kui ka kasutades fikseeritud järjestusega marsruutimislahendust. Lisaks töötati bakalaureusetöö koostamise käigus välja optimeerimismeetodeid kasutav rakendus, mille abil on võimalik võrrelda erinevate meetodite käitumist ja omadusi, esitada arvutuste tulemusena leitud teekondi ja ajagraafikuid ning animeerida Eesti kaardil rännakukolonnide liikumist. Töö tulemusena võib väita, et matemaatilise optimeerimise meetodid on sobivad päriseluliste rännakukolonnide optimeerimisprobleemide kiireks ja kvaliteetseks lahendamiseks ja et neid meetodeid kasutades on võimalik parandada rännakukolonnide kavandamisel tehtavate planeerimisotsuste kvaliteeti.Convoy movement problem is a mathematical optimization problem which tries to find optimal routing and scheduling solution for concurrent military convoy movements. In this thesis several optimization methodologies are designed and tested to find best suited algorithm for solving practical convoy routing instances in Estonia. Encouraging results are obtained by using a mixed integer programming model together with simple heuristics, by creating an exact branch-and-bound methodology and by developing fixed-order based routing approach. Bachelor’s thesis also provides a complementary application to compare qualities of designed methods, to present calculated routes and schedules and to display convoy movement animations on the map of Estonia. Thesis illustrates that methods of mathematical optimization can be used to solve realworld instances of convoy movement problem fast and with quality results and hence improve decisionmaking in operational convoy planning practice

Robust rolling stock in rapid transit network

This paper focuses on the railway rolling stock circulation problem in rapid transit networks, in which frequencies are high and distances are relatively short. Although the distances are not very large, service times are high due to the large number of intermediate stops required to allow proper passenger flow. The main complicating issue is the fact that the available capacity at depot stations is very low, and both capacity and rolling stock are shared between different train lines. This forces the introduction of empty train movements and rotation maneuvers, to ensure sufficient station capacity and rolling stock availability. However, these shunting operations may sometimes be difficult to perform and can easily malfunction, causing localized incidents that could propagate throughout the entire network due to cascading effects. This type of operation will be penalized with the goal of selectively avoiding them and ameliorating their high malfunction probabilities. Critic trains, defined as train services that come through stations that have a large number of passengers arriving at the platform during rush hours, are also introduced. We illustrate our model using computational experiments drawn from RENFE (the main Spanish operator of suburban passenger trains) in Madrid, Spain. The results of the model, achieved in approximately 1 min, have been received positively by RENFE planner

Robust rolling stock under uncertain demand in rapid transit networks

This paper focuses on the railway rolling stock circulation problem in rapid transit networks where the known demand and train schedule must be met by a given fleet. In rapid transit networks the frequencies are high and distances are relatively short. Although the distances are not very large, service times are high due to the large number of intermediate stops required to allow proper passenger flow. The previous circumstances and the reduced capacity of the depot stations and that the rolling stock is shared between the different lines, force the introduction of empty trains and a careful control on shunting operation. In practice the future demand is generally unknown and the decisions must be based on uncertain forecast. We have developed a stochastic rolling stock formulation of the problem. The computational experiments were developed using a commercial line of the Madrid suburban rail network operated by RENFE (The main Spanish operator of suburban trains of passengers). Comparing the results obtained by deterministic scenarios and stochastic approach some useful conclusions may be obtained

Optimal Supply Delivery Under Military Specific Constraints

Through-out military history, the need to safely and effectively allocate resources to various military operations was a task of extreme importance. Satisfying the needs of multiple consumers by optimally pairing with appropriate suppliers falls into the category of vehicle routing problems (VRP), which has been intensively studied over the years. In general, finding the optimal solution to VRP is known to be NP-hard. The proposed solutions rely on mathematical programming and the size of the problems that can be optimally solved is typically limited. In military settings, balancing the needs of multiple consumers with the current operational environment has always been a challenge. This balancing is equally crucial to the survivability of transporters and consumers. The main goal is finding an optimal way of ensuring required delivery while minimizing Soldiers risks. We show that under certain assumptions we can formulate this problem as a linear programming problem with specific constraints

Network Topology and Time Criticality Effects in the Modularised Fleet Mix Problem

In this paper, we explore the interplay between network topology and time criticality in a military logistics system. A general goal of this work (and previous work) is to evaluate land transportation requirements or, more specifically, how to design appropriate fleets of military general service vehicles that are tasked with the supply and re-supply of military units dispersed in an area of operation. The particular focus of this paper is to gain a better understanding of how the logistics environment changes when current Army vehicles with fixed transport characteristics are replaced by a new generation of modularised vehicles that can be configured task-specifically. The experimental work is conducted within a well developed strategic planning simulation environment which includes a scenario generation engine for automatically sampling supply and re-supply missions and a multi-objective meta-heuristic search algorithm (i.e. Evolutionary Algorithm) for solving the particular scheduling and routing problems. The results presented in this paper allow for a better understanding of how (and under what conditions) a modularised vehicle fleet can provide advantages over the currently implemented system

Integration of Timetable Planning and Rolling Stock in Rapid Transit Networks

The aim of this paper is to propose an integrated planning model to adequate the offered capacity and system frequencies to attend the increased passenger demand and traffic congestion around urban and suburban areas. The railway capacity is studied in line planning, however, these planned frequencies were obtained without accounting for rolling stock flows through the rapid transit network. In order to provide the problem more freedom to decide rolling stock flows and therefore better adjusting these flows to passenger demand, a new integrated model is proposed, where frequencies are readjusted. Then, the railway timetable and rolling stock assignment are also calculated, where shunting operations are taken into account. These operations may sometimes malfunction, causing localized incidents that could propagate throughout the entire network due to cascading effects. This type of operations will be penalized with the goal of selectively avoiding them and ameliorating their high malfunction probabilities. Swapping operations will also be ensured using homogeneous rolling stock material and ensuring parkings in strategic stations. We illustrate our model using computational experiments drawn from RENFE (the main Spanish operator of suburban passenger trains) in Madrid, Spain. The results show that through this integrated approach a greater robustness degree can be obtaine

Cost Adaptation for Robust Decentralized Swarm Behaviour

Decentralized receding horizon control (D-RHC) provides a mechanism for coordination in multi-agent settings without a centralized command center. However, combining a set of different goals, costs, and constraints to form an efficient optimization objective for D-RHC can be difficult. To allay this problem, we use a meta-learning process -- cost adaptation -- which generates the optimization objective for D-RHC to solve based on a set of human-generated priors (cost and constraint functions) and an auxiliary heuristic. We use this adaptive D-RHC method for control of mesh-networked swarm agents. This formulation allows a wide range of tasks to be encoded and can account for network delays, heterogeneous capabilities, and increasingly large swarms through the adaptation mechanism. We leverage the Unity3D game engine to build a simulator capable of introducing artificial networking failures and delays in the swarm. Using the simulator we validate our method on an example coordinated exploration task. We demonstrate that cost adaptation allows for more efficient and safer task completion under varying environment conditions and increasingly large swarm sizes. We release our simulator and code to the community for future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201