Claudio Arbib | Università Degli Studi Dell'Aquila (original) (raw)

Papers by Claudio Arbib

Lecture Notes in Computer Science, 2016

The Closest String Problem (CSP) calls for finding an n-string that minimizes its maximum distanc... more The Closest String Problem (CSP) calls for finding an n-string that minimizes its maximum distance from m given n-strings. Integer linear programming (ILP) proved to be able to solve large CSPs under the Hamming distance, whereas for the Levenshtein distance, preferred in computational biology, no ILP formulation has so far be investigated. Recent research has however demonstrated that another metric, rank distance, can provide interesting results with genomic sequences. Moreover, CSP under rank distance can easily be modeled via ILP: optimal solutions can then be certified, or information on approximation obtained via dual gap. In this work we test this ILP formulation on random and biological data. Our experiments, conducted on strings with up to 600 nucleotides, show that the approach outperforms literature heuristics. We also enforce the formulation by cover inequalities. Interestingly, due to the special structure of the rank distance between two strings, cover separation can be done in polynomial time.

ESA Special Publication, Jul 1, 2003

European Journal of Operational Research, May 1, 2021

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Cutting operations in manufacturing are characterized by practical requirements and utility crite... more Cutting operations in manufacturing are characterized by practical requirements and utility criteria that usually increase the complexity of formulations or, even worse, are difficult to be modeled in terms of mathematical programming. However, disregarding or just simplifying those requirements often leads to solutions considered not attractive or even useless by the manufacturer. In this paper we consider a rich two-dimensional cutting stock problem that covers the whole specification of a family of wood cutting machines produced by a worldwide leader in industrial machinery manufacturing. A sequential value correction heuristic is implemented to minimize the employed stock area while reducing additional objective functions.

AIRO Springer series, 2019

This chapter presents a real-time emergency evacuation handling system based on internet of thing... more This chapter presents a real-time emergency evacuation handling system based on internet of things (IoT) technologies. The IoT infrastructure has a core computational component that is in charge of minimizing the time necessary to evacuate people from a building. The space and time dimension are discretized according to metrics and models in literature, as well as original methods. The component formulates and solves a linearized, time-indexed flow problem on a network that represents feasible movements of people at a suitable frequency. Accurate parameter setting makes the computational time to solve the model compliant with real-time use. An application of the proposed IoT system and its core algorithm to handle safe evacuation test in Palazzo Camponeschi—a building in L’Aquila (Italy) now and then used for exhibitions—is described, and diverse uses of the methodology are presented.

Cologne Twente Workshop on Graphs and Combinatorial Optimization, 2011

The Cologne-Twente Workshop (CTW) on Graphs and Combinatorial Optimization started off as a serie... more The Cologne-Twente Workshop (CTW) on Graphs and Combinatorial Optimization started off as a series of workshops on theory and applications of discrete algorithms, graphs and combinatorial structures in the wide sense, organized by either

We present a computational component that allows to evaluate the minimum time necessary to evacua... more We present a computational component that allows to evaluate the minimum time necessary to evacuate people from a place (e.g., a public building). The space and time dimension are discretized according to metrics and models in literature. The component formulates and solves a linearized, time-indexed flow problem [3] on a network that represents feasible movements of people monitored at a suitable frequency. This computational component is the core part of an IoT infrastructure aimed at monitoring crowds in public spaces for planning evacuation paths. The CPU time to solve the model is compliant with real-time use. An application of the algorithm to a real location with real data is described, and diverse uses of the methodology are presented.

Computers & Operations Research

Computers & Operations Research

Social Science Research Network, 2022

Proceedings of the 11th International Conference on Operations Research and Enterprise Systems

In this paper we deal with the problem of deciding the best assortment and cut of defective bidim... more In this paper we deal with the problem of deciding the best assortment and cut of defective bidimensional stocks. The problem, originating in a glass manufacturing process, can arise in various industrial contexts. We propose a novel bilevel programming approach describing a competition between two decision makers with contrasting objectives: one aims at fulfilling production requirements, the other at generating defects that, damaging the products, reduce yield as much as possible. By exploiting nice properties of adversarial optimal solutions, the bilevel program is rewritten as a one-level 0-1 linear program. Computational results achieved on random instances with realistic features are discussed, showing the quality and the benefits of the proposed approach in reducing the yield loss from defective material in a worst-case perspective.

ESA Special Publication, Jul 1, 2003

Theory and Practice of Control and Systems, 1999

Communications in computer and information science, 2022

Proceedings of the 10th International Conference on Operations Research and Enterprise Systems

We consider the problem of moving production lots within the clean room of LFoundry, an important... more We consider the problem of moving production lots within the clean room of LFoundry, an important Italian manufacturer of micro-electronic devices. The problem is modeled as dial-a-ride in a dynamic environment with the objective of makespan minimization. This general objective is achieved by minimizing, at each optimization cycle, the largest completion time among the vehicles, so as to locally balance their workloads. A cluster-first route-second heuristic is devised for on-line use and compared to the actual practice through a computational experience based on real plant data.

Lecture Notes in Economics and Mathematical Systems, 1999

Distributing material flows among the workstations of a plant is a crucial problem in order to re... more Distributing material flows among the workstations of a plant is a crucial problem in order to reduce both production and logistics costs, especially when product mix and volume production are very large. Optimal solutions should meet due dates requirements while assigning operations in accordance to the production capacity available at the moment. Pursuing this objective is however complicated in case of a large product mix, due to the possibly large number of machine set-ups required. This paper deals with a real production process consisting in the assembly of micropumps and dispensers carried out by a major international manufacturer in its plants in Centre Italy. Two articulated methods based on column generation are devised for tackling situations of different size and complexity, and a sample of their potential effectiveness is exhibited.

Production and Operations Management

IEEE Access, 2021

Natural disasters can cause widespread damage to buildings and infrastructures and kill thousands... more Natural disasters can cause widespread damage to buildings and infrastructures and kill thousands of living beings. These events are difficult to be overcome both by the populations and by government authorities. Two challenging issues require in particular to be addressed: find an effective way to evacuate people first, and later to rebuild houses and other infrastructures. An adequate recovery strategy to evacuate people and start reconstructing damaged areas on a priority basis can then be a game changer allowing to overcome effectively those terrible circumstances. In this perspective, we here present DiReCT, an approach based on i) a dynamic optimization model designed to timely formulate an evacuation plan of an area struck by an earthquake, and ii) a decision support system, based on double deep Q Network, able to guide efficiently the reconstruction the affected areas. The latter works by considering both the resources available and the needs of the various stakeholders involved (e.g., residents social benefits and political priorities). The ground on which both the above solutions stand was a dedicated geographical data extraction algorithm, called "GisToGraph", especially developed for this purpose. To check applicability of the whole approach, we dovetailed it on the real use-case of the historical city center of L'Aquila (Italy) using detailed GIS data and information on urban land structure and buildings vulnerability. Several simulations were run on the underlining network generated. First, we ran experiments to safely evacuate in the shortest possible time as many people as possible from an endangered area towards a set of safe places. Then, using DDQN, we generated different reconstruction plans and selected the best ones considering both social benefits and political priorities of the building units. The described approaches are comprised in a more general data science framework delved to produce an effective response to natural disasters.

Lecture Notes in Computer Science, 2016

The Closest String Problem (CSP) calls for finding an n-string that minimizes its maximum distanc... more The Closest String Problem (CSP) calls for finding an n-string that minimizes its maximum distance from m given n-strings. Integer linear programming (ILP) proved to be able to solve large CSPs under the Hamming distance, whereas for the Levenshtein distance, preferred in computational biology, no ILP formulation has so far be investigated. Recent research has however demonstrated that another metric, rank distance, can provide interesting results with genomic sequences. Moreover, CSP under rank distance can easily be modeled via ILP: optimal solutions can then be certified, or information on approximation obtained via dual gap. In this work we test this ILP formulation on random and biological data. Our experiments, conducted on strings with up to 600 nucleotides, show that the approach outperforms literature heuristics. We also enforce the formulation by cover inequalities. Interestingly, due to the special structure of the rank distance between two strings, cover separation can be done in polynomial time.

ESA Special Publication, Jul 1, 2003

European Journal of Operational Research, May 1, 2021

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Cutting operations in manufacturing are characterized by practical requirements and utility crite... more Cutting operations in manufacturing are characterized by practical requirements and utility criteria that usually increase the complexity of formulations or, even worse, are difficult to be modeled in terms of mathematical programming. However, disregarding or just simplifying those requirements often leads to solutions considered not attractive or even useless by the manufacturer. In this paper we consider a rich two-dimensional cutting stock problem that covers the whole specification of a family of wood cutting machines produced by a worldwide leader in industrial machinery manufacturing. A sequential value correction heuristic is implemented to minimize the employed stock area while reducing additional objective functions.

AIRO Springer series, 2019

This chapter presents a real-time emergency evacuation handling system based on internet of thing... more This chapter presents a real-time emergency evacuation handling system based on internet of things (IoT) technologies. The IoT infrastructure has a core computational component that is in charge of minimizing the time necessary to evacuate people from a building. The space and time dimension are discretized according to metrics and models in literature, as well as original methods. The component formulates and solves a linearized, time-indexed flow problem on a network that represents feasible movements of people at a suitable frequency. Accurate parameter setting makes the computational time to solve the model compliant with real-time use. An application of the proposed IoT system and its core algorithm to handle safe evacuation test in Palazzo Camponeschi—a building in L’Aquila (Italy) now and then used for exhibitions—is described, and diverse uses of the methodology are presented.

Cologne Twente Workshop on Graphs and Combinatorial Optimization, 2011

The Cologne-Twente Workshop (CTW) on Graphs and Combinatorial Optimization started off as a serie... more The Cologne-Twente Workshop (CTW) on Graphs and Combinatorial Optimization started off as a series of workshops on theory and applications of discrete algorithms, graphs and combinatorial structures in the wide sense, organized by either

We present a computational component that allows to evaluate the minimum time necessary to evacua... more We present a computational component that allows to evaluate the minimum time necessary to evacuate people from a place (e.g., a public building). The space and time dimension are discretized according to metrics and models in literature. The component formulates and solves a linearized, time-indexed flow problem [3] on a network that represents feasible movements of people monitored at a suitable frequency. This computational component is the core part of an IoT infrastructure aimed at monitoring crowds in public spaces for planning evacuation paths. The CPU time to solve the model is compliant with real-time use. An application of the algorithm to a real location with real data is described, and diverse uses of the methodology are presented.

Computers & Operations Research

Computers & Operations Research

Social Science Research Network, 2022

Proceedings of the 11th International Conference on Operations Research and Enterprise Systems

In this paper we deal with the problem of deciding the best assortment and cut of defective bidim... more In this paper we deal with the problem of deciding the best assortment and cut of defective bidimensional stocks. The problem, originating in a glass manufacturing process, can arise in various industrial contexts. We propose a novel bilevel programming approach describing a competition between two decision makers with contrasting objectives: one aims at fulfilling production requirements, the other at generating defects that, damaging the products, reduce yield as much as possible. By exploiting nice properties of adversarial optimal solutions, the bilevel program is rewritten as a one-level 0-1 linear program. Computational results achieved on random instances with realistic features are discussed, showing the quality and the benefits of the proposed approach in reducing the yield loss from defective material in a worst-case perspective.

ESA Special Publication, Jul 1, 2003

Theory and Practice of Control and Systems, 1999

Communications in computer and information science, 2022

Proceedings of the 10th International Conference on Operations Research and Enterprise Systems

We consider the problem of moving production lots within the clean room of LFoundry, an important... more We consider the problem of moving production lots within the clean room of LFoundry, an important Italian manufacturer of micro-electronic devices. The problem is modeled as dial-a-ride in a dynamic environment with the objective of makespan minimization. This general objective is achieved by minimizing, at each optimization cycle, the largest completion time among the vehicles, so as to locally balance their workloads. A cluster-first route-second heuristic is devised for on-line use and compared to the actual practice through a computational experience based on real plant data.

Lecture Notes in Economics and Mathematical Systems, 1999

Distributing material flows among the workstations of a plant is a crucial problem in order to re... more Distributing material flows among the workstations of a plant is a crucial problem in order to reduce both production and logistics costs, especially when product mix and volume production are very large. Optimal solutions should meet due dates requirements while assigning operations in accordance to the production capacity available at the moment. Pursuing this objective is however complicated in case of a large product mix, due to the possibly large number of machine set-ups required. This paper deals with a real production process consisting in the assembly of micropumps and dispensers carried out by a major international manufacturer in its plants in Centre Italy. Two articulated methods based on column generation are devised for tackling situations of different size and complexity, and a sample of their potential effectiveness is exhibited.

Production and Operations Management

IEEE Access, 2021

Natural disasters can cause widespread damage to buildings and infrastructures and kill thousands... more Natural disasters can cause widespread damage to buildings and infrastructures and kill thousands of living beings. These events are difficult to be overcome both by the populations and by government authorities. Two challenging issues require in particular to be addressed: find an effective way to evacuate people first, and later to rebuild houses and other infrastructures. An adequate recovery strategy to evacuate people and start reconstructing damaged areas on a priority basis can then be a game changer allowing to overcome effectively those terrible circumstances. In this perspective, we here present DiReCT, an approach based on i) a dynamic optimization model designed to timely formulate an evacuation plan of an area struck by an earthquake, and ii) a decision support system, based on double deep Q Network, able to guide efficiently the reconstruction the affected areas. The latter works by considering both the resources available and the needs of the various stakeholders involved (e.g., residents social benefits and political priorities). The ground on which both the above solutions stand was a dedicated geographical data extraction algorithm, called "GisToGraph", especially developed for this purpose. To check applicability of the whole approach, we dovetailed it on the real use-case of the historical city center of L'Aquila (Italy) using detailed GIS data and information on urban land structure and buildings vulnerability. Several simulations were run on the underlining network generated. First, we ran experiments to safely evacuate in the shortest possible time as many people as possible from an endangered area towards a set of safe places. Then, using DDQN, we generated different reconstruction plans and selected the best ones considering both social benefits and political priorities of the building units. The described approaches are comprised in a more general data science framework delved to produce an effective response to natural disasters.