Roberto Montemanni

Design patterns from biology for distributed computing

Recent developments in information technology have brought about important changes in distributed computing. New environments such as massively large-scale, wide-area computer networks and mobile ad hoc networks have emerged. Common characteristics of these environments include extreme dynamicity, unreliability, and large scale. Traditional approaches to designing distributed applications in these environments based on central control, small scale, or strong reliability assumptions are not suitable for exploiting their enormous potential. Based on the observation that living organisms can effectively organize large numbers of unreliable and dynamically-changing components (cells, molecules, individuals, etc.) into robust and adaptive structures, it has long been a research challenge to characterize the key ideas and mechanisms that make biological systems work and to apply them to distributed systems engineering. In this article we propose a conceptual framework that captures several basic biological processes in the form of a family of design patterns. Examples include plain diffusion, replication, chemotaxis, and stigmergy. We show through examples how to implement important functions for distributed computing based on these patterns. Using a common evaluation methodology, we show that our bio-inspired solutions have performance comparable to traditional, state-of-the-art solutions while they inherit desirable properties of biological systems including adaptivity and robustness.

Time dependent vehicle routing problem with a multi ant colony system

The Time Dependent Vehicle Routing Problem (TDVRP) consists in optimally routing a fleet of vehicles of fixed capacity when travel times are time dependent, in the sense that the time employed to traverse each given arc, depends on the time of the day the travel starts from its originating node. The optimization method consists in finding solutions that minimize two hierarchical objectives: the number of tours and the total travel time. Optimization of total travel time is a continuous optimization problem that in our approach is solved by discretizing the time space in a suitable number of subspaces. New time dependent local search procedures are also introduced, as well as conditions that guarantee that feasible moves are sought for in constant time. This variant of the classic Vehicle Routing Problem is motivated by the fact that in urban contexts variable traffic conditions play an essential role and can not be ignored in order to perform a realistic optimization. In this paper it is shown that when dealing with time constraints, like hard delivery time windows for customers, the known solutions for the classic case become unfeasible and the degree of unfeasibility increases with the variability of traffic conditions, while if no hard time constraints are present, the classic solutions become suboptimal. Finally an application of the model to a real case is presented. The model is integrated with a robust shortest path algorithm to compute time dependent paths between each customer pairs of the time dependent model.

Ant Colony System for a Dynamic Vehicle Routing Problem

An aboundant literature on vehicle routing problems is available. However, most of the work deals with static problems, where all data are known in advance, i.e. before the optimization has started.The technological advances of the last few years give rise to a new class of problems, namely the dynamic vehicle routing problems, where new orders are received as time progresses and must be dynamically incorporated into an evolving schedule.In this paper a dynamic vehicle routing problem is examined and a solving strategy, based on the Ant Colony System paradigm, is proposed.Some new public domain benchmark problems are defined, and the algorithm we propose is tested on them.Finally, the method we present is applied to a realistic case study, set up in the city of Lugano (Switzerland).

Ant colony optimization for real-world vehicle routing problems

Abstract Ant colony optimization (ACO) is a metaheuristic for combinatorial optimization problems. In this paper we report on its successful application to the vehicle routing problem (VRP). First, we introduce the VRP and some of its variants, such as the VRP with time windows, the time dependent VRP, the VRP with pickup and delivery, and the dynamic VRP. These variants have been formulated in order to bring the VRP closer to the kind of situations encountered in the real-world. Then, we introduce the basic principles of ant colony optimization, and we briefly present its application to the solution of the VRP and of its variants. Last, 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; a time dependent VRP for freight distribution in the city of Padua, Italy, where the travel times depend on the time of the day; and an on-line VRP in the city of Lugano, Switzerland, where customers’ orders arrive during the delivery process.

An exact algorithm for the robust shortest path problem with interval data

The robust deviation shortest path problem with interval data is studied in this paper.After the formulation of the problem in mathematical terms, an exact algorithm, based on a very simple concept, is described. Some practical improvements to the basic idea, which speed up the method, are also presented.Computational results corroborate the correctness of the conjecture on which the algorithnt is based and the potential of the approach. In particular, our method is proven to be able to retrieve high-quality solutions very quickly on some families of networks. For this reason, it could alternatively be used as a fast heuristic method.

A branch and bound algorithm for the robust shortest path problem with interval data

Many real problems can be modelled as robust shortest path problems on interval digraphs, where intervals represent uncertainty about real costs and a robust path is not too far from the shortest path for each possible configuration of the arc costs. A branch and bound algorithm for this problem is presented.

A Benders decomposition approach for the robust spanning tree problem with interval data

Abstract The robust spanning tree problem is a variation, motivated by telecommunications applications, of the classic minimum spanning tree problem. In the robust spanning tree problem edge costs lie in an interval instead of having a fixed value. Interval numbers model uncertainty about the exact cost values. A robust spanning tree is a spanning tree whose total cost minimizes the maximum deviation from the optimal spanning tree over all realizations of the edge costs. This robustness concept is formalized in mathematical terms and is used to drive optimization. This paper describes a new exact method, based on Benders decomposition, for the robust spanning tree problem with interval data. Computational results highlight the efficiency of the new method, which is shown to be very fast on all the benchmarks considered, and in particular on those that were harder to solve for the methods previously known.

The robust shortest path problem with interval data via Benders decomposition

Abstract.Many real problems can be modelled as robust shortest path problems on digraphs with interval costs, where intervals represent uncertainty about real costs and a robust path is not too far from the shortest path for each possible configuration of the arc costs.In this paper we discuss the application of a Benders decomposition approach to this problem.Computational results confirm the efficiency of the new algorithm. It is able to clearly outperform state-of-the-art algorithms on many classes of networks. For the remaining classes we identify the most promising algorithm among the others, depending of the characteristics of the networks.

Exact algorithms for the minimum power symmetric connectivity problem in wireless networks

In this paper we consider the problem of assigning transmission powers to the nodes of a wireless network in such a way that all the nodes are connected by bidirectional links and the total power consumption is minimized.Two mixed integer programming formulations are presented together with some new valid inequalities for the polytopes associated. A preprocessing technique and two exact algorithms based on the formulations previously introduced are also proposed.Comprehensive computational results, which show the effectiveness of the new valid inequalities and of the preprocessing technique are presented. The experiments also show that the exact approaches we propose outperform more complex methods recently appeared in the literature.

An improved tabu search algorithm for the fixed-spectrum frequency-assignment problem

A tabu search algorithm with a dynamic tabu list for the fixed-spectrum frequency-assignment problem is presented. For cellular problems, the algorithm can be combined with an efficient cell reoptimization step. The algorithm is tested on several sets of test problems and compared with existing algorithms of established performance. In particular, it is used to improve some of the best existing assignments for COST 259 benchmarks. These results add support to the claim that the algorithm is the most effective available, at least when solution quality is a more important criterion than solution speed. The algorithm is robust and easy to tune.