# Vittorio ManiezzoUniversity of Bologna | UNIBO · Department of Computer Science and Engineering DISI

Vittorio Maniezzo

ph.d.

## About

169

Publications

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29,529

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Citations since 2016

## Publications

Publications (169)

Matheuristics are heuristic algorithms based on mathematical tools such as the ones provided by mathematical programming, that are structurally general enough to be applied to different problems with little adaptations to their abstract structure. The result can be metaheuristic hybrids having components derived from the mathematical model of the p...

The development of technologies that exploit the Internet of Things (IoT) paradigm has led to the increasingly widespread use of networks formed by different devices scattered throughout the territory. The Publish/Subscribe paradigm is one of the most used communication paradigms for applications of this type. However, adopting these systems due to...

Diving heuristics are methods that progressively enlarge a partial solution up to its possible completion, thus ˇjump˘into a solution with no way back. While this is common to all constructive heuristics, diving ones are usually characterized by working on the mathematical formulation of the problem to solve. Some contributions of this type showed...

Very Large-Scale Neighborhood Search is not an algorithm or a class of algorithms, but rather a conceptual framework which can be used for solving combinatorial optimization problems. The approach “concentrates on neighborhood search algorithms where the size of the neighborhood is ‘very large’ with respect to the size of the input data.” Typically...

Kernel search is a purely matheuristic method, which leverages MIP solvers to obtain heuristic, or possibly optimal, solutions of instances encoded as (mixed) integer linear programming problems. It was first presented as a method to solve mixed-integer linear problems defined on binary variables modeling items selection, together with other intege...

The corridor method is a general method originally proposed as a way to gain efficiency in dynamic programming search, possibly losing optimality. Later, it has been extended beyond DP to other exact optimization methods. The basic idea is that of using the exact method over successive restricted portions of the solution space of the given problem....

Decompositions are methods derived from the “divide et impera” principle, dictating to break up a difficult problem into smaller ones, and to solve each of the smaller ones separately, ultimately recomposing the individual solutions to get the overall one. Decompositions have longly been applied to solve optimization problems, and they come in many...

A specialized thread of metaheuristic research, bordering and often overlapping with Artificial Intelligence, studied heuristics that evolved whole sets of candidate solutions, often named “populations” of solutions. Genetic algorithms were among the first results, and following their success it became common to get inspiration from some natural ph...

Matheuristics have become widespread and effective methods for tackling the generalized assignment problem (GAP) and many other NP-hard problems. In fact, in this book we have many such methods, ranging from metaheuristics and mathematical programming techniques but mainly to real matheuristics. In these methods we will see no parameter settings, b...

The generalized assignment problem (GAP) asks to assign nclients to mservers in such a way that the assignment cost is minimized, provided that all clients are assigned to a server and that the capacity of each server is not exceeded. It is a problem that appears, by itself or as a subproblem, in a very high number of practical applications and has...

Fore-and-Back, previously also denoted as Forward&Backward or simply as F&B (though the algorithm presented in this chapter differs in some details from the previously published ones), is an extension of beam search that can improve its effectiveness and that, when run with no limits on computational resources, becomes an exact solution method. How...

Metaheuristic approaches can be classified according to different criteria, one being the number of solutions that are evolved at each stage of the algorithm: one single solution or more than one. This chapter deals with metaheuristic algorithms that evolve one single solution; they are all enhancements of a basic local search procedure. Many diffe...

This book is the first comprehensive tutorial on matheuristics. Matheuristics are based on mathematical extensions of previously known heuristics, mainly metaheuristics, and on original, area-specific approaches. This tutorial provides a detailed discussion of both contributions, presenting the pseudocodes of over 40 algorithms, abundant literature...

Warehouse premarshalling (also pre-marshalling or remarshalling) is the activity of reordering items in a storage location so that subsequent retrieval orders can be serviced with little or no need for further relocations. It has deep impact on warehouse efficiency. We are interested in a stochastic case, where pickup orders become known only at th...

This special issue of the International Transactions in Operational Research focuses on Matheuristics and Metaheuristics and is the largest published to date, highlighting the importance of the field and the broad scope of these methods and the reach of their applications. Academicians and practitioners responded with enthusiasm to three parallel c...

Centralized overlay network management, such as for Service Overlay Networks, is an important topicfor Internet based services. The computational efficiency of the central controller node is of paramount importance to guarantee the quality of the service. The paper considers the problem where the network can be also asymmetric and each node require...

Mobile platforms have matured to a point where they can provide the infrastructure required to support sophisticated optimization codes. This opens the possibility to envisage new interest for distributed application codes and the opportunity to intensify research on optimization algorithms requiring limited computational resources, as provided by...

This paper is a survey of the research contributions made by Walter J. Gutjahr during his career so far, and provides a classification of his areas of research, along with a discussion of the results presented in his most significant publications. Although works are divided into theoretical and application-oriented contributions, linkages among the...

State-Space Relaxation (SSR) is an approach often used to compute by dynamic programming (DP) effective bounds for many combinatorial optimization problems. Currently, the most effective exact approaches for solving many Vehicle Routing Problems (VRPs) are DP algorithms making use of SSR for computing their bounding components. In particular, most...

Matheuristics are methods that exploit mathematical programming techniques in heuristic and metaheuristic frameworks, granting to mathematical programming approaches the problem robust-ness and time effectiveness that characterize heuristics, or exploiting the mathematical programming model formulation in the customization of a heuristic for specif...

In recent years, GPU computing has become an increasingly important tool to develop efficient applications in several areas, including optimization. One of the optimization approaches that seems to take most advantage from GPU computing is dynamic programming. In this paper, we investigate the application of GPU computing to the two-dimensional gui...

This paper describes an application of a matheuristic algorithm to a real-world city logistics problem for a mid-sized town, whose core could be modeled as a multitrip vehicle routing problem with time windows, pickup and deliveries, and heterogeneous fleet. The proposed matheuristic is based on a dual ascent procedure applied to an extended set co...

The nesting problem is an irregular two-dimensional cutting problem where the shapes of the pieces to cut and the master surfaces are irregular in shape and different in size. In particular, we consider nesting problems where the master surface could contain defects. Some of them can be accepted (i.e., incorporated) in certain types of pieces, whil...

Matheuristic algorithms have begun to demonstrate that they can be the state of the art for some optimization problems. This paper puts forth that they can represent a viable option also in an applicative context. The possibility to get a solution quality vali-dation or a model grounded construction may become a significant competitive advantage ag...

The combination of exact and heuristic methods is as old as mathematical programming (MP) itself, because usually exact methods cannot work properly without a good bound on the optimal cost, which helps pruning the search space. However, it is only in very recent years that the combination has also gone the other way round, permitting the use of me...

Peer-to-peer (P2P) computing already accounts for a large part of the traffic on the Internet, and it is likely to become as ubiquitous as current client/server architectures in next generation information systems. This paper addresses a central problem of P2P systems: the design of an optimal overlay communication network for a set of processes on...

Soft variable fixing has emerged as one of the main techniques that the area of matheuristics can contribute to general metaheuristics. Recent years have in fact witnessed a fruitful interplay of methods that were originally proposed as general metaheuristcs with methods rooted in mathematic programming, which can be applied alone or as hybrids for...

Soft variable fixing has emerged as one of the main techniques that the area of matheuristics can contribute to general metaheuristics. Recent years have in fact witnessed a fruitful interplay of methods that were originally proposed as general metaheuristcs with methods rooted in mathematic programming, which can be applied alone or as hybrids for...

Finite capacity planning is a central problem in manufacturing industries. At the heart of it lies a scheduling optimization problem, which has been so far studied in the optimization literature mainly in abstract forms, like job shop scheduling. There is a huge gap between the job shop instances used as benchmark in the literature and the scheduli...

Matheuristics are heuristic algorithms made by the interoperation of metaheuristics and mathematic programming (MP) techniques. An essential feature is the exploitation in some part of the algorithms of features derived from the mathematical model of the problems of interest, thus the definition “model-based metaheuristics” appearing in the title o...

Decomposition techniques are well-known as a means for obtaining tight lower bounds for combinatorial optimization problems, and thus as a component for solution methods. Moreover a long-established research literature uses them for defining problem-specific heuristics. More recently it has been observed that they can be the basis also for designin...

Several industrial problems involve placing objects into a container without overlap, with the goal of minimizing a certain objective function. These problems arise in many industrial fields such as apparel manufacturing, sheet metal layout, shoe manufacturing, ...

Large part of combinatorial optimization research has been devoted to the study of exact methods leading to a number of very
diversified solution approaches. Some of those older frameworks can now be revisited in a metaheuristic perspective, as they
are quite general frameworks for dealing with optimization problems. In this work, we propose to inv...

The capacitated arc routing problem (CARP) focuses on servicing edges of an undirected network graph. A wide spectrum of applications like mail delivery, waste collection or street maintenance outlines the relevance of this problem. A realistic variant of the CARP arises from the need of intermediate facilities (IFs) to load up or unload the servic...

An established research line in ACO systems supports the intuition that ant algorithms are particularly fit for dynamic optimization
problems because of their ability to construct an internal representation of the essential elements of the problem to solve,
a representation which needs to be updated and not reconstructed when the instance changes.

Solid waste collection in urban areas is a central topic for local environmental agencies. The operational problem, the definition
of collection routes given the vehicle fleet, can greatly benefit of computerized support already for medium sized town. While
the operational constraints can vary, the core problem can be identified as a capacitated ar...

Ant Colony Optimization (ACO) is a class of metaheuristic algorithms sharing the common approach of constructing a solution on the basis of information provided both by a standard constructive heuristic and by previously constructed solutions. This paper is composed of three parts. The first one frames ACO in current trends of research on metaheuri...

We propose an iterative memory-based algorithm for solving a class of combinatorial optimization problems. The algorithm generates
a sequence of gradually improving solutions by exploiting at each iteration the knowledge gained in previous iterations. At
each iteration, the algorithm builds an enumerative tree and stores at each tree level a set of...

This paper describes a system of shallow and deep knowledge acquisition and representation for diagnostic expert systems. The acquisition system is integrated into a diagnostic expert system shell. Shallow knowledge is represented in a failure model as a set of cause-effect relations among the possible faults, while deep knowledge is represented in...

This work presents an original algorithmic model of some essential features of psychogenetic theory, as was proposed by J.Piaget. Specifically, we modeled some elements of cognitive structure learning in children from 0 to 4 months of life. We are in fact convinced that the study of well-established cognitive models of human learning can suggest ne...

Simulation and forecast of traffic flows raise significant computational issues, to be faced by means of advanced optimization techniques. The solution obtained depends moreover on a number of operational parameters, whose setting heavily affects the proposed results. We present an application implementing an original traffic flow simulation and fo...

Industrial companies routinely face computationally intensive problems and deploy heuristic solutions, chosen only on the basis that no more computing power is available to search for possibly better solutions. Recent advances on heuristic and metaheuristic solution approaches have led to the development of very effective methodologies, several of...

This article proposes a new transformation of undirected arc-routing problems into equivalent node-routing problems, with emphasis on the transformation of Capacitated Arc Routing Problems (CARP) into Capacitated Vehicle Routing Problems (CVRP). For this last case, an analogue transformation has already been proposed in Pearn et al., where each req...

The increased human mobility, combined with high use of private cars, increases the load on the environment and raises issues about the quality of life. The use of private cars lends to high levels of air pollution in cities, parking problems, noise pollution, congestion, and the resulting low transfer velocity (and, thus, inefficiency in the use o...

In peer-to-peer (P2P) networks it is a central problem to maintain a so called overlay network with certain desired properties. An overlay network is defined by logical connections (i. e., the "who knows whom" relation) between peers over an underlying physical network. If node i is connected to node j in an overlay network, it means that node i kn...

The Covering Tour Problem (CTP) is a generalization of the Traveling Salesman Problem (TSP) which has several practical applications
in the area of distribution network design. Given an undirected graph, the problem asks to identify a minimum cost cycle passing
through a subset of vertices such that every vertex not in the cycle lies within a given...

Following the difficulty of public transport to adequately cover all passenger transportation needs, different innovative mobility services are emerging. Among those are car pooling services, which are based on the idea that sets of car owners having the same travel destination share their vehicles. Until now these systems have had a limited use du...

A new software package, named Archirota, for simulating traffic in roundabouts is introduced. Its simulation module is entirely
based on cellular automata and is automatically configured for real-world geocoded data. Archirota can be used both as a support
for designing new roundabout and for modeling and simulating existing ones. Tests on actual u...

The Capacitated Arc Routing Problem (CARP) is a prototypical optimization problem asking a fleet of vehicles to serve a set
of customer demands located on the arcs of a network. The problem is closely related to Vehicle Routing Problem (VRP), and
in fact every CARP instance can be transformed into an equivalent VRP instance using a graph which has...

Designing an optimal overlay communication network for a set of processes on the Internet is a central problem of peer-to-peer (P2P) computing. Such a network defines membership and allows for members to disseminate information within the group. The network has to be robust and the available bandwidth has to be utilized in an optimal manner to allo...

Car pooling is a transportation service organized by a large company which encourages its employees to pick up colleagues while driving to/from work to minimize the number of private cars travelling to/from the company site. The car pooling problem consists of defining the subsets of employees that will share each car and the paths the drivers shou...

The most effective technique to enhance performances of multidimensional databases consists in materializing redundant aggregates called views. In the classical approach to materialization, each view includes all and only the measures of the cube it aggregates. In this paper we investigate the benefits of materializing views in vertical fragments,...

Introduction Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The first algorithm which can be classified within this framework was presented in 1991 [21 , 13] and, since then, many diverse variants of the basic principle have been reported in the literature. The essential t...

or via WWW at URLhttp://www.cs.unibo.it/. Plain-text abstracts organized,by year are available in the directory ABSTRACTS. Recent Titles from the UBLCS Technical Report Series 2003-10 A Scalable Architecture for Responsive Auction Services Over the Internet, Amoroso, A., Fanzieri

Solid waste collection in urban areas is a central topic for local environmental agencies. The operational problem, the definition of collection routes given the vehicle fleet, can greatly benefit of computerized support already for medium sized town. While the operational constraints can greatly vary, the core problem can be identified as a capaci...

The rising auto usage deriving from growth in jobs and residential population is making traffic congestion less tolerable in urban and suburban areas. This results in air pollution, energy waste and unproductive and unpleasant consumption of people’s time. Public transport can not be the only answer to this increasing transport demand. Car pooling...

Designing an optimal overlay communication network for a set of processes on the Internet is a central problem of peer-to-peer (P2P) computing. Such a network defines membership and allows for members to disseminate information within the group. The network has to be robust and the available bandwidth has to be utilized in an optimal manner to allo...

This paper describes the application of the ANTS approach, a variant of the Ant Colony Optimization (ACO) paradigm, to the VFP

Ant Colony Optimization is a paradigm for designing combinatorial optimization metaheuristic algorithms, which construct a solution on the basis of information provided both by some standard constructive heuristic and by previously obtained solutions. In this chapter, we present current results obtained by ACO algorithms on several hard combinatori...

Metaheuristics in general and ant-based systems in particular have shown remarkable success in solving combinatorial optimization
problems. However, a few problems exist for which the best performing heuristic algorithm is not a metaheuristic. These few
are often characterized by a very highly constrained search space. This is a situation in which...

We consider the capacitated p-median problem (CPMP) in which a set of n customers must be partitioned into p disjoint clusters so that the total dissimilarity within each cluster is minimized and constraints on maximum cluster capacities are met. The dissimilarity of a cluster is computed as the sum of the dissimilarities existing between each enti...

The rising car usage deriving from growth in jobs and residential population results in air pollution, energy waste and unproductive and unpleasant consumption of people's time. Public transport cannot be the only answer to this increasing transport demand. Car pooling has emerged to be a viable possibility for reducing private car usage in congest...

The materialization of fragmented views in data warehouses has the objective of improving the system response time for a given
workload. It represents a combinatorial optimization problem arising in the logical design of data warehouses which has so
far received little attention from the optimization community. This paper describes the application...

The problem considered in this paper consists in assigning frequencies to radio links between base stations and mobile transmitters in order to minimize the global interference over a given region. This problem is NP-hard and few results have been reported on techniques for solving it to optimality. We have applied to this problem an ANTS metaheuri...

The rising auto usage deriving from growth in jobs and residential population is making traffic congestion less tolerable in urban and suburban areas. This results in air pollution, energy waste and unproductive and unpleasant consumption of people's time. Public transport cannot be the only answer to this increasing transport demand. Car pooling h...

In this paper we consider the Frequency Assignment Problem, where the objective is to minimize the cost due to interference arising in a solution. We use a quadratic 0-1 integer programming formulation of the problem as a basis to derive new lower bounds and problem reduction rules. A tree search algorithm, that uses the lower bounds and dominance...

We consider the problem of scheduling a set of activities satisfying precedence constraints in order to minimize the sum of the costs associated with the starting times of the activities. We consider the case in which the activity starting time cost functions are irregular functions. This problem can be solved in polynomial time either when the pre...

This article introduces two new techniques far solving the Quadratic Assignment Problem. The first is a heuristic technique, defined in accordance with the Ant System metaphor, and includes as a distinctive feature the use of a new lower bound at: each constructive step. The second is a branch-and-hound exact: approach, containing some elements int...

In recent years, there has been growing interest in algorithms
inspired by the observation of natural phenomena to define computational
procedures that can solve complex problems. We describe a distributed
heuristic algorithm that was inspired by the observation of the behavior
of ant colonies, and we propose its use for the quadratic assignment
pr...

. Ant Colony Optimization (ACO) is a class of constructive metaheuristic algorithms sharing the common approach of constructing a solution on the basis of information provided both by a standard constructive heuristic and by previously constructed solutions. This tutorial is composed of three parts. The rst one frames the ACO approach in current tr...

The problem considered in this paper consists in defining an assignment of frequencies to radio links, to be established between base stations and mobile transmitters, which minimizes the global interference over a given region. This problem is NP-hard and few results have been reported on techniques for solving it to optimality. We have applied to...

: A combination of distributed computation, positive feedback and constructive greedy heuristic is proposed as a new approach to stochastic optimization and problem solving. Positive feedback accounts for rapid discovery of very good solutions, distributed computation avoids premature convergence, and greedy heuristic helps the procedure to find ac...

this paper we present the results of a research relative to the ascertainment of limits and potentialities of genetic algorithms ([Dorigo, 1989], [DeJong-Spears, 1989], [Goldberg, 1989]) in addressing highly constrained problems, that is optimization problems, where a minimal change to a feasible solution is very likely to generate an unfeasible on...

In recent years there has been growing interest in algorithms inspired by the observation of natural phenomena to define computational procedures which can solve complex problems. In this article we introduce a distributed heuristic algorithm which was inspired by the observation of the behavior of ant colonies and we propose its use for the Quadra...