Jan Mazal

Applying the ant colony optimisation algorithm to the capacitated multi-depot vehicle routing problem

The multi-depot vehicle routing problem MDVRP is an extension of a classic vehicle routing problem VRP. There are many heuristic and metaheuristic algorithms e.g., tabu search, simulated annealing, genetic algorithms as this is an NP-hard problem and, therefore, exact methods are not feasible for more complex problems. Another possibility is to adapt the ant colony optimisation ACO algorithm to this problem. This article presents an original solution of authors to the MDVRP problem via ACO algorithm. The first part deals with the algorithm including its principles and parameters. Then several examples and experiments are shown.

Model of optimal maneuver used in tactical decision support system

This paper deals with the model of optimal maneuver of units and its implementation in the Tactical Decision Support System (TDSS). The model is designed to be used for planning the optimal movement of units (soldiers, vehicles, unmanned robots) on the battlefield. The paper is separated into three main parts. Firstly, the model of maneuver is discussed; it is divided into five independent layers: topographic layer, elevation data layer, weather layer, hostile units layer, and friendly units layer. Impact of each layer on the model is then analyzed. Secondly, experiments used for verification of the model are presented. Finally, the implementation of the model in the TDSS is shown. TDSS is used to support commanders in their decisionmaking processes.

Using the Ant Colony Optimization algorithm for the Capacitated Vehicle Routing Problem

This paper deals with the application of the Ant Colony Optimization (ACO) algorithm to solve the Capacitated Vehicle Routing Problem (CVRP). The first part presents the basic approach and concept which has been inspired by nature. Next, the basic features and parameters of the algorithm are discussed. Then, a number of experiments are introduced which served to verify the algorithm. We chose Christofides, Mingozzi and Toths CVRP instances as benchmark problems. The results we obtained are compared with other state-of-the-art algorithms. Next, the improvement of the algorithm is presented. The last part of the paper presents the application of the problem in practice; the primary objective is to plan the distribution of supplies and logistics in the real environment. Finally, the paper summarizes some perspectives of our future work.

Tactical Decision Support System to Aid Commanders in Their Decision-Making

This paper deals with the Tactical Decision Support System TDSS being developed at University of Defence, Brno, since 2006. TDSS is a command and control system designed for commanders of the Czech Army to support them in their decision-making processes on the tactical level. In the first part of the article, the basic characteristics and functions of the system are introduced. Next, advanced models of military tactics are presented including the model of optimal logistics on the battlefield, model of optimal cooperative reconnaissance by a fleet of unmanned aerial vehicles, and model of optimal cooperative reconnaissance by a fleet of ground elements. The last part of the article discusses the basic metaheuristic methods and approached used in our models. Finally, the paper summarizes some perspectives of our future work.

Tactical and operational software library

This paper deals with the concept of the Tactical and Operational Software Library (TOSL). The goal of this library is to provide functions and services to be used for combat and tactical modelling. First, the paper presents the main purpose of the library. Next, the key functions and services are introduced. Finally, the paper demonstrates the utilization of the library in our Tactical Decision Support System (TDSS). This system is introduced very briefly in the paper; only functions using the library are emphasized. At the end of the paper, perspectives of our future work are discussed.

Two-Stage Stochastic Programming for Transportation Network Design Problem

The transportation network design problem is a well-known optimization problem with many practical applications. This paper deals with demand-based applications, where the operational as well as many other decisions are often made under uncertainty. Capturing the uncertain demand by using scenario-based approach, we formulate the two-stage stochastic mixed-integer linear problem, where the decision, which is made under uncertainty, of the first-stage program, is followed by the second-stage decision that reacts to the observed demand. Such a program may reach solvability limitations of algorithms for large scale real world data, so we refer to the so-called hybrid algorithm that combines a traditional optimization algorithm and a suitable genetic algorithm. The obtained results are presented in an explanatory form with the use of a sequence of figures.

UGV development with supervised autonomy

The article is focused on problematic of the middle size wheeled 6×6 UGV development (approx. 1100kg), lessons learned, achieved results and its system integration in the digitized battlefield. Based on previous experiences with UGV systems, there were identified fundamental factors of tactical and technological requirements, which converge to a set of priorities, including some (routine) processes automation, mainly, certain level of general autonomy and automatic navigation. Advantage of the full navigation autonomy is obvious, but still, the robotic system suffers by limited level of reliability, so there is a need for some level of operator supervision. But step by step also our robotic systems gets further and even with limited budget and sensors are able to perform advanced tasks.

Improving the Ant Colony Optimization Algorithm for the Multi-Depot Vehicle Routing Problem and Its Application

This paper addresses our solution to the Multi-Depot Vehicle Routing Problem (MDVRP) based on the Ant Colony Optimization (ACO) algorithm. The first part introduces the basic concepts and principles of the algorithm along with its key parameters. The primary part of the article deals with the improvement of the original algorithm. The improvement consists in the distribution of algorithm’s key processes, which can be executed simultaneously, to the individual cores of a multi-core processor. This part also includes several experiments (based on the Cordeau’s test instances) we conducted to verify the value of improvement. The last part of the article presents the real application of the problem and our solution. Finally, the paper summarizes some perspectives of our future work.

Geographical Data and Algorithms Usable for Decision-Making Process

The traffic-ability of military vehicles in the terrain outside of communication is not trivial matter. The article deals with the application of different types of geographic data sources within the decision-making process. The Commanders of the military or rescue units are participated in this decision-making process especially in the field. The key role is offered for the use of geographic information systems and application of geographical-tactical analyses, the methods of mathematical modelling, simulation and optimization for the purpose of scheduling the appropriate routes of movements of military vehicles in the operating environment. The special algorithms for searching of optimal path have to satisfy the criteria that are set of for solution of tactical tasks in autonomous systems. It is possible the time and speed limits and safety of peoples to determine. The geographical conditions, tactical conditions and types of vehicles are the most important factors for estimation of time of movement in the different types of terrain. Lots of experiments in military area in the Czech Republic have been performed and unmanned ground vehicle TAROS for gathering of the data was used and method of laser-scanning was tested. The measurement has been evaluated by mathematical statistics and computer sciences. Solution of these tests it is possible to use also in crisis management for emergency systems in case of natural disasters such as floods, fires etc.