The ant colony optimization (ACO) metaheuristic is a population-based approach to the solution of combinatorial optimization problems. The basic ACO idea is that a large number of simple artificial agents are able to build good solutions to hard combinatorial optimization problems via low-level based communications. Real ants cooperate in their search for food by depositing chemical traces (pheromones) on the ground. An artificial ant colony simulates this behavior. Artificial ants cooperate by using a common memory that corresponds to the pheromone deposited by real ants. The artificial pheromone is accumulated at run-time through a learning mechanism. Artificial ants are implemented as parallel processes whose role is to build problem solutions using a constructive procedure driven by a combination of artificial pheromone, problem data and a heuristic function used to evaluate successive constructive steps. Recently, several ACO based algorithms that can be applied to a wide class of combinatorial optimization problems have been studied and developed. In some of these domains, such as the quadratic assignment problem, the sequential ordering problem, the vehicle routing with time window, and telecommunication routing, the developed algorithms are among the best currently worldwide available, and for many benchmark instances new best known solutions have been computed.
Bayesian networks are one of the most popular tools for reasoning in uncertain domains. By structuring the domain by means of cause-effect relationships, the model allows the power of probability theory to be fully exploited for applications. Bayesian networks enable predictive and diagnostic reasoning to be realized by a probability propagation in the graphical structure that represents the cause-effect relationships. Bayesian networks can be automatically inferred from the data alone and be used for the specific purpose of discovering knowledge in databases. Credal networks extend Bayesian networks in the direction of robustness. A credal network can be induced from a small or an incomplete database, and still guarantee that inferences are robust.
Genetic algorithms were developed by John Holland in 1975. They emulate the principle of the survival of the fittest we observe in the process of natural evolution. Genetic algorithms encode problems into a string data structure called chromosome, and apply genetic operators such as selection, crossover, and mutation to form a search algorithm. They require no domain knowledge - only a performance evaluation function and they use probabilistic transition rules to direct the search. This feature makes genetic algorithms very well suited to solve problems which lack a precise description of the search domain.
Local search employs the idea that a given solution S may be improved by a series of small changes. Those solutions obtained by modifying solution S are called neighbors of S. The local search algorithm starts with some initial solution and moves from neighbor to neighbor as long as it is possible to decrease the objective function value. The main problem with this strategy is how to escape from local minima that is, those points in which the search cannot find any further neighborhood solution that decreases the objective function value. Different strategies have been proposed to solve this problem. One of the most efficient of these strategies is tabu search. Tabu search allows the search to explore solutions that do not decrease the objective function value only in those cases where these solutions are not forbidden. This is usually obtained by keeping track of the last solutions in term of the action used to transform one solution to the next. When an action is performed it is considered tabu for the next T iterations, where T is the tabu status length. A solution is forbidden if it is obtained by applying a tabu action to the current solution.
Optimisation and simulation go hand-in-hand. A simulation model of a process can allow to test its behaviour under different working conditions (or scenarios) at a small cost, quickly, and in a risk-free virtual environment. A simulation model can be used to identify bottlenecks in a process and thus select candidates for optimisation. A simulation model can also provide a safe test bench to evaluate the performance and the side-effects of new optimised management policies, before transferring them to the real world.
The logistic and industrial processes request the use of optimization procedures with high performance. They need swift and flexible algorithms. The traditional technologies are in crisis because of very high number of solutions. New tools & methods, studied in research centers worldwide and based on genetic algorithms of new generation, are necessary.
The utilization of these sophisticated optimization tools allow both to optimize the logistic and industrial processes and to reduce the incidence of the costs, and above all to valuate risk, benefits and costs associated to alternative scenarios. These intelligent systems are revolutionizing the world of transports, production and data mining optimization, securing more competitive performance.
AntOptima algorithms can provide an answer using optimization methods based on Ant Colony Optimization or other stat-of the-art techniques.
We offer a variety of advanced scheduling tools to remove bottlenecks and streamline your production schedule.
The process of knowledge discovery in databases allows valuable patterns and regularities to be discovered from the data alone.
AntOptima is able to offer consulting, recognized at an international level, in distribution and logistics, in intermodal and urban transportation.
The smart hotel, managed completely in an electronic way. From the reservation process, to your check-out, everything is carried out automatically 24 hours a day, 7 days a week.
Thanks to the features and to the efficiency of our solutions, important logistic and industrial companies are shift to a logic of optimization and cutting costs.
Mimicking the behaviour of ants, bees and birds started as a poor man’s version of artificial intelligence. It may, though, be the key to the real thing
Metaheuristics like ant colony optimization (ACO) can be used to solve combinatorial optimization problems. In this paper we refer on its successful application to the vehicle routing problem (VRP). At the beginning, we introduce the VRP and some of its variants. The variants of VRP were designed to reproduce the kind of situations faced in the real-world. Further, we introduce the fundamentals of ant colony optimization, and we present in few words its application to the solution of the VRP. At the end, we discuss the applications of ACO to a number of real-world problems: a VRP with time windows for a major supermarket chain in Switzerland; a VRP with pickup and delivery for a leading distribution company in Italy and an on-line VRP in the city of Lugano, Switzerland, where clients’ orders arrive during the delivery process.
EURONEWS (http://www.euronews.net), the multilingual European TV channel, reports that scientists in Lugano, Switzerland have been developing a way to optimise the movement of a fleet of trucks to make delivery services more efficient. Their unlikely source of inspiration has been the master of logistics in the natural world- the ant. Watch the video