Gianpaolo GHIANI

Gianpaolo GHIANI

Professore I Fascia (Ordinario/Straordinario)

Settore Scientifico Disciplinare MAT/09: RICERCA OPERATIVA.

Dipartimento di Ingegneria dell'Innovazione

Centro Ecotekne Pal. O - S.P. 6, Lecce - Monteroni - LECCE (LE)

Ufficio, Piano terra

Telefono +39 0832 29 7791 +39 0832 29 7819

Professore Ordinario

Area di competenza:

Decision Support Systems, Business Intelligence, Optimization, Logistics

Orario di ricevimento

Giovedì, 11:00 (corpo O, studio O-202) o in altro giorno/fascia oraria (da concordare)

Visualizza QR Code Scarica la Visit Card

Curriculum Vitae

Gianpaolo Ghiani è Professore Ordinario di Ricerca Operativa (SSD MAT/09) presso la Facoltà di Ingegneria dell’Università del Salento, dove insegna “Business Intelligence”, “Metodi di Supporto alle Decisioni” e “Metodi e Modelli per la Logistica”.

Conseguita la laurea in Ingegneria Elettronica, ha ottenuto il titolo di dottore di ricerca in Ingegneria Elettronica ed Informatica presso l'Università degli Studi di Napoli "Federico II".

Nel 1998 ha ricevuto il Transportation Science Dissertation Award dall'Institute for Operations Resarch and Management Science (INFORMS).

Nel 1998-99 è stato postdoctoral al GERAD (Groupe d'Etudes et de Recherche en Analyse des Decisions) di Montreal.

La sua attività di ricerca è incentrata sulla risoluzione di problemi di ottimizzazione discreta e sulla pianificazione e controllo dei sistemi logistici. I suoi articoli scientifici sono stati pubblicati su riviste internazionali comprendenti: Mathematical Programming, Operations Research, Operations Research Letters, Networks, Transportation Science, Transportation Research, Optimization Methods and Software, Computational Optimization and Applications, Computers and Operations Research, International Transactions in Operational Research, European Journal of Operational Research, Journal of the Operational Research Society, Parallel Computing, Journal of Intelligent Manufacturing Systems.

Ha tenuto corsi ufficiali ed integrativi presso l'Università degli Studi di Napoli "Federico II", l'Università degli Studi di Lecce, l'Università della Calabria, l’Università degli Studi di Brescia e l'Università di Verona.

È autore, con G. Laporte e R. Musmanno del volume “Introduction to Logistics Systems Planning and Control” (Wiley, New York, 2003).

E’ inoltre co-editor del volume "Modelli e metodi per le decisioni in condizioni di incertezza e rischio" (Mc-Graw Hill Italia, 2009).

 

Didattica

A.A. 2018/2019

BUSINESS INTELLIGENCE

Corso di laurea MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE

Tipo corso di studio Laurea Magistrale

Lingua INGLESE

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0

Anno accademico di erogazione 2018/2019

Per immatricolati nel 2018/2019

Anno di corso 1

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso Percorso comune

Sede Lecce

DECISION SUPPORT SYSTEMS

Corso di laurea COMPUTER ENGINEERING

Tipo corso di studio Laurea Magistrale

Lingua INGLESE

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0

Anno accademico di erogazione 2018/2019

Per immatricolati nel 2018/2019

Anno di corso 1

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce

A.A. 2017/2018

BUSINESS INTELLIGENCE

Corso di laurea MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE

Tipo corso di studio Laurea Magistrale

Lingua INGLESE

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0

Anno accademico di erogazione 2017/2018

Per immatricolati nel 2017/2018

Anno di corso 1

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce

DECISION SUPPORT SYSTEMS

Corso di laurea COMPUTER ENGINEERING

Tipo corso di studio Laurea Magistrale

Lingua INGLESE

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0

Anno accademico di erogazione 2017/2018

Per immatricolati nel 2017/2018

Anno di corso 1

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce

A.A. 2016/2017

BUSINESS INTELLIGENCE

Corso di laurea MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0 Ore Studio individuale: 144.0

Anno accademico di erogazione 2016/2017

Per immatricolati nel 2016/2017

Anno di corso 1

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce - Università degli Studi

DECISION SUPPORT SYSTEMS

Corso di laurea COMPUTER ENGINEERING

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0 Ore Studio individuale: 144.0

Anno accademico di erogazione 2016/2017

Per immatricolati nel 2016/2017

Anno di corso 1

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce - Università degli Studi

A.A. 2015/2016

BUSINESS INTELLIGENCE

Corso di laurea MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0 Ore Studio individuale: 144.0

Anno accademico di erogazione 2015/2016

Per immatricolati nel 2015/2016

Anno di corso 1

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce - Università degli Studi

DECISION SUPPORT SYSTEMS

Corso di laurea COMPUTER ENGINEERING

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0 Ore Studio individuale: 144.0

Anno accademico di erogazione 2015/2016

Per immatricolati nel 2015/2016

Anno di corso 1

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce - Università degli Studi

A.A. 2014/2015

BUSINESS INTELLIGENCE

Corso di laurea MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 78.0 Ore Studio individuale: 147.0

Anno accademico di erogazione 2014/2015

Per immatricolati nel 2014/2015

Anno di corso 1

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce - Università degli Studi

DECISION SUPPORT SYSTEMS

Corso di laurea COMPUTER ENGINEERING

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 78.0 Ore Studio individuale: 147.0

Anno accademico di erogazione 2014/2015

Per immatricolati nel 2014/2015

Anno di corso 1

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce - Università degli Studi

A.A. 2013/2014

BUSINESS INTELLIGENCE

Corso di laurea MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 78.0 Ore Studio individuale: 147.0

Anno accademico di erogazione 2013/2014

Per immatricolati nel 2013/2014

Anno di corso 1

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce - Università degli Studi

DECISION SUPPORT SYSTEMS

Corso di laurea COMPUTER ENGINEERING

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 78.0 Ore Studio individuale: 147.0

Anno accademico di erogazione 2013/2014

Per immatricolati nel 2013/2014

Anno di corso 1

Struttura DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Percorso PERCORSO COMUNE

Sede Lecce - Università degli Studi

Torna all'elenco
BUSINESS INTELLIGENCE

Corso di laurea MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE

Settore Scientifico Disciplinare MAT/09

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0

Per immatricolati nel 2018/2019

Anno accademico di erogazione 2018/2019

Anno di corso 1

Semestre Secondo Semestre (dal 04/03/2019 al 04/06/2019)

Lingua INGLESE

Percorso Percorso comune (999)

Sede Lecce

Calculus. Probability and Statistics. Linear Algebra.

This course addresses the principles and practice of Business Intelligence (BI), with an emphasis on applictions to logistics, transportation and supply chain management.

Knowledge and understanding. The course describes methods and models to design decision support/automation systems.

  • Students will acquire the basic cognitive tools to think analytically, creatively, critically and in an inquiring way, and have the abstraction and problem-solving skills needed to cope with complex systems.
  • They will have solid knowledge of BI methodologies.
  • They will be able to design and develop complex systems to improve decision-making processes.

Applying knowledge and understanding. After the course the student should be able to:

  • describe and use the main BI techniques;
  • understand the differences among several algorithms solving the same problem and recognize which one is better under different conditions;
  • explain experimental results to business people.

Making judgements. Students must have the ability to assess a BI system and must arrive at original and autonomous ideas and judgments.. The course promotes the development of independent judgment in the appropriate choice of techniques/models and the critical ability to interpret the goodness of the results of the chosen models/methods.

Communication. It is essential that students are able to communicate with a varied and composite audience, not culturally homogeneous, in a clear, logical and effective way, using the methodological tools acquired and their scientific knowledge and, in particular, the specialty vocabulary. Students should be able to organize effective dissemination and study material through the most common presentation tools, including computer-based ones, to communicate the results of data analysis processes, for example by using visualization and reporting tools aimed at different types of audiences.

Learning skills. Students must acquire the critical ability to relate, with originality and autonomy, to the typical problems of data mining and, in general, cultural issues related to other similar areas. They should be able to develop and apply independently the knowledge and methods learnt with a view to possible continuation of studies at higher (doctoral) level or in the broader perspective of cultural and professional self-improvement of lifelong learning. Therefore, students should be able to switch to exhibition forms other than the source texts in order to memorize, summarize for themselves and for others, and disseminate scientific knowledge.

The course consists of lectures, classroom exercises and home assignments. Lectures aim at providing the methodological foundations. They are given using slides and/or a blackboard. Students are invited to participate by asking questions and presenting examples. The exercises and home assignments are about the solution of practical problems with software tools.

The exam consists of two parts:

  • a written test made up of 10 questions [10 marks];
  • an oral exam in which students must:
  1. discuss a presentation of their own on an advanced course topic [10 marks];
  2. show their ability to use the software tools presented in the course (Python libraries for machine learning, AMPL, ...) [10 marks].

Office Hours

By appointment. As a rule, on Thursdays at 11:00. Please contact the instructor by email or at the end of the lectures.

PART I – INTRODUCTION (5 hours)

1.1 Introducing BI (5 hours)

 

PART II – PROGRAMMING SKILLS (8 hours)

2.1 Getting started in Python (8 hours)

 

PART III – DESCRIPTIVE ANALYTICS (10 hours)

3.1 Making sense of data, visualizing and exploring data (1 hour)

3.2 Descriptive stastical measure (9 hours)

 

PART IV – PREDICTIVE ANALYTICS (32 hours)

4.1 Forecasting: basics (2 hours)

4.2 Extrapolating time-series (8 hours)

4.3 Regression models (4 hours)

4.4 Basics of classification models (2 hours)

4.5 Performance evaluation with analytical methods: queueing models (6 hours)

4.6 Performance evaluation with discrete event simulation: basics, random number generation, output analysis, SIMIO tutorial (10 hours)

 

PART V – PRESCRIPTIVE ANALYTICS (26 hour)

5.1. Optimization model review, AMPL (8 hours)

5.2 Applications to logistics, manufacturing and transportation (18 hours)

Handouts (available on the intranet at https://intranet.unisalento.it/web/businessintelligence/).

For consultation:

  • Ghiani, Gianpaolo, Gilbert Laporte, and Roberto Musmanno. Introduction to logistics systems management. John Wiley & Sons, 2013.
  • Evans, James Robert. Business analytics: Methods, models, and decisions. Vol. 3. Upper Saddle River, NJ: Pearson, 2013.
BUSINESS INTELLIGENCE (MAT/09)
DECISION SUPPORT SYSTEMS

Corso di laurea COMPUTER ENGINEERING

Settore Scientifico Disciplinare MAT/09

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0

Per immatricolati nel 2018/2019

Anno accademico di erogazione 2018/2019

Anno di corso 1

Semestre Primo Semestre (dal 24/09/2018 al 21/12/2018)

Lingua INGLESE

Percorso PERCORSO COMUNE (999)

Sede Lecce

Calculus. Probability and Statistics. Linear Algebra. Programming skills.

The course provides the theoretical foundations, the practical skills and the development tools to design and deploy intelligent systems that support or automate complex decisions. Applications include motion planning in robotics, designing non-player characters in video games, machine scheduling in the manufacturing sector, portfolio optimization in the financial industry, timetabling and crew rostering in transportation, … Methodologies and algorithms taken from Operations Research, Statistics and Artificial Intelligence are analyzed and compared.

Knowledge and understanding. The course describes methods and models to design decision support/automation systems.

  • Students will acquire the basic cognitive tools to think analytically, creatively, critically and in an inquiring way, and have the abstraction and problem-solving skills needed to cope with complex systems.
  • They will have solid knowledge of decision support/automation systems.
  • They will be able to design and develop complex systems to improve decision-making processes.

Applying knowledge and understanding. After the course the student should be able to:

 

  • describe and use the main decision support/automation techniques;
  • understand the differences among several algorithms solving the same problem and recognize which one is better under different conditions;
  • tackle decision support/automation problems by selecting the appropriate methods and justifying his/her choices;
  • tackle new decision support/automation problems by designing suitable algorithms and evaluating the results;
  • explain experimental results to people without a computer science background.

Making judgements. Students must have the ability to assess a decision support/automation system and must arrive at original and autonomous ideas and judgments.. The course promotes the development of independent judgment in the appropriate choice of techniques/models and the critical ability to interpret the goodness of the results of the chosen models/methods.

Communication. It is essential that students are able to communicate with a varied and composite audience, not culturally homogeneous, in a clear, logical and effective way, using the methodological tools acquired and their scientific knowledge and, in particular, the specialty vocabulary. Students should be able to organize effective dissemination and study material through the most common presentation tools, including computer-based ones, to communicate the results of data analysis processes, for example by using visualization and reporting tools aimed at different types of audiences.

Learning skills. Students must acquire the critical ability to relate, with originality and autonomy, to the typical problems of data mining and, in general, cultural issues related to other similar areas. They should be able to develop and apply independently the knowledge and methods learnt with a view to possible continuation of studies at higher (doctoral) level or in the broader perspective of cultural and professional self-improvement of lifelong learning. Therefore, students should be able to switch to exhibition forms other than the source texts in order to memorize, summarize for themselves and for others, and disseminate scientific knowledge.

The course consists of lectures, classroom exercises and home assignments. Lectures aim at providing the methodological foundations. They are given using slides and/or a blackboard. Students are invited to participate by asking questions and presenting examples. The exercises and home assignments are about the solution of practical problems with software tools.

The exam consists of two parts:

  • a written test made up of 10 questions [10 marks];
  • an oral exam in which students must:
  1. discuss a presentation of their own on an advanced course topic [10 marks];
  2. show their ability to use the software tools presented in the course (Python libraries for machine learning, STRIPS, AMPL, ...) [10 marks].

Office Hours

By appointment. As a rule, on Thursdays at 11:00. Please contact the instructor by email or at the end of the lectures.

PART I – DECISION-MAKING PROCESSES (4 hours)

1.1 Introduction. Data, information, knowledge, decisions. Taxonomy of decisions. Decision support methodologies. (2hours)

1.2 Intelligent agents. (2 hours)

 

PART II – SIMULATION (10 hours)

2.1 Evaluation: experimentation, simulation and analitical methods (1 hour)

2.2 Pseudo-random number generation. (3 hours)

2.3 Monte Carlo simulation. Discrete-event simulation. Variance reduction techniques. (6 hours)

 

PART III -  KNOWLEDGE, REASONING AND PLANNING (28 hours)

3.1 Search. Uninformed and informed search. A* algorithm.  (3 hours)

3.2 Basics of optimization. Optimization model review. Convex Optimization. Linear Optimization. (10 hours)

3.3 Local search. Simulated Annealing. Genetic Algorithms. (4 hours)

3.4 Adversarial search. Basics of Game Theory. (4 hours)

3.5 Propositional and first-order logic (recap) (4 hours)

3.5 Planning. The STRIPS language (3 hours)

 

PART IV -  PLANNING IN UNCERTAIN ENVIRONMENTS (13 hours)

3.1 Decision making under uncertainty (1 hour)

3.2 Decision making under risk (2 hours)

3.3 Sequential decision processes (4 hours)

3.4 Dynamic Programming (6 hours)

 

PART IV – LEARNING (16 hours)

4.1 Introduction (1 hour)

4.2 Supervised learning: linear and polynomial regression, naive Bayes classifier, classification and regression trees, linear classification with hard threshold, linear classification with logistic regression, basics of neural networks (8 hours); non parametric classification; model selection (8 hours)

4.3 Unsupervised learning: clustering: k-means algorithm, determination of the number of clusters; rule mining: the a-priori algorithm (4 hours)

4.4 Reinforcement learning (3 hours)

Handouts (available on the intranet at https://intranet.unisalento.it/web/decisionsupportsystems/).

For consultation:

  • Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited, 2016.
DECISION SUPPORT SYSTEMS (MAT/09)
BUSINESS INTELLIGENCE

Corso di laurea MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE

Settore Scientifico Disciplinare MAT/09

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0

Per immatricolati nel 2017/2018

Anno accademico di erogazione 2017/2018

Anno di corso 1

Semestre Secondo Semestre (dal 01/03/2018 al 01/06/2018)

Lingua INGLESE

Percorso PERCORSO COMUNE (999)

Sede Lecce

BUSINESS INTELLIGENCE (MAT/09)
DECISION SUPPORT SYSTEMS

Corso di laurea COMPUTER ENGINEERING

Settore Scientifico Disciplinare MAT/09

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0

Per immatricolati nel 2017/2018

Anno accademico di erogazione 2017/2018

Anno di corso 1

Semestre Primo Semestre (dal 25/09/2017 al 22/12/2017)

Lingua INGLESE

Percorso PERCORSO COMUNE (999)

Sede Lecce

DECISION SUPPORT SYSTEMS (MAT/09)
BUSINESS INTELLIGENCE

Corso di laurea MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE

Settore Scientifico Disciplinare MAT/09

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0 Ore Studio individuale: 144.0

Per immatricolati nel 2016/2017

Anno accademico di erogazione 2016/2017

Anno di corso 1

Semestre Secondo Semestre (dal 01/03/2017 al 02/06/2017)

Lingua

Percorso PERCORSO COMUNE (999)

Sede Lecce - Università degli Studi

BUSINESS INTELLIGENCE (MAT/09)
DECISION SUPPORT SYSTEMS

Corso di laurea COMPUTER ENGINEERING

Settore Scientifico Disciplinare MAT/09

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0 Ore Studio individuale: 144.0

Per immatricolati nel 2016/2017

Anno accademico di erogazione 2016/2017

Anno di corso 1

Semestre Primo Semestre (dal 26/09/2016 al 22/12/2016)

Lingua

Percorso PERCORSO COMUNE (999)

Sede Lecce - Università degli Studi

DECISION SUPPORT SYSTEMS (MAT/09)
BUSINESS INTELLIGENCE

Corso di laurea MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE

Settore Scientifico Disciplinare MAT/09

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0 Ore Studio individuale: 144.0

Per immatricolati nel 2015/2016

Anno accademico di erogazione 2015/2016

Anno di corso 1

Semestre Secondo Semestre (dal 29/02/2016 al 03/06/2016)

Lingua

Percorso PERCORSO COMUNE (999)

Sede Lecce - Università degli Studi

BUSINESS INTELLIGENCE (MAT/09)
DECISION SUPPORT SYSTEMS

Corso di laurea COMPUTER ENGINEERING

Settore Scientifico Disciplinare MAT/09

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 81.0 Ore Studio individuale: 144.0

Per immatricolati nel 2015/2016

Anno accademico di erogazione 2015/2016

Anno di corso 1

Semestre Primo Semestre (dal 21/09/2015 al 18/12/2015)

Lingua

Percorso PERCORSO COMUNE (999)

Sede Lecce - Università degli Studi

DECISION SUPPORT SYSTEMS (MAT/09)
BUSINESS INTELLIGENCE

Corso di laurea MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE

Settore Scientifico Disciplinare MAT/09

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 78.0 Ore Studio individuale: 147.0

Per immatricolati nel 2014/2015

Anno accademico di erogazione 2014/2015

Anno di corso 1

Semestre Secondo Semestre (dal 02/03/2015 al 06/06/2015)

Lingua

Percorso PERCORSO COMUNE (999)

Sede Lecce - Università degli Studi

BUSINESS INTELLIGENCE (MAT/09)
DECISION SUPPORT SYSTEMS

Corso di laurea COMPUTER ENGINEERING

Settore Scientifico Disciplinare MAT/09

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 78.0 Ore Studio individuale: 147.0

Per immatricolati nel 2014/2015

Anno accademico di erogazione 2014/2015

Anno di corso 1

Semestre Primo Semestre (dal 29/09/2014 al 13/01/2015)

Lingua

Percorso PERCORSO COMUNE (999)

Sede Lecce - Università degli Studi

DECISION SUPPORT SYSTEMS (MAT/09)
BUSINESS INTELLIGENCE

Corso di laurea MANAGEMENT ENGINEERING - INGEGNERIA GESTIONALE

Settore Scientifico Disciplinare MAT/09

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 78.0 Ore Studio individuale: 147.0

Per immatricolati nel 2013/2014

Anno accademico di erogazione 2013/2014

Anno di corso 1

Semestre Secondo Semestre (dal 03/03/2014 al 31/05/2014)

Lingua

Percorso PERCORSO COMUNE (999)

Sede Lecce - Università degli Studi

BUSINESS INTELLIGENCE (MAT/09)
DECISION SUPPORT SYSTEMS

Corso di laurea COMPUTER ENGINEERING

Settore Scientifico Disciplinare MAT/09

Tipo corso di studio Laurea Magistrale

Crediti 9.0

Ripartizione oraria Ore Attività frontale: 78.0 Ore Studio individuale: 147.0

Per immatricolati nel 2013/2014

Anno accademico di erogazione 2013/2014

Anno di corso 1

Semestre Primo Semestre (dal 30/09/2013 al 21/12/2013)

Lingua

Percorso PERCORSO COMUNE (999)

Sede Lecce - Università degli Studi

DECISION SUPPORT SYSTEMS (MAT/09)

Pubblicazioni

AWARDS

My doctoral thesis was awarded the "Transportation Science Dissertation Award" from the "Institute for Operations Research and the Management Sciences" (INFORMS) in 1998.

BOOKS

[B.3] Introduction to Logistics Systems Management (Wiley, 2013), with G. Laporte and R. Musmanno. Available on Amazon.com.

[B.2] Introduction to Logistics Systems Planning and Control (Wiley, 2003), with G. Laporte and R. Musmanno. Available on Amazon.com. 

[B.1] Decision Support under Risk and Uncertainty (Mc-Graw Hill Italia, 2008), with R. Musmanno (eds). Available on the Mc-Graw Hill Italy website

INDUSTRY

I have always been interested in research projects that make a difference in the real-world. Here are a couple of such projects I worked on.

[P.1] Large scale real-time fleet management at e-Courier Ltd (London, UK). Accounts of this project appeared in the Economist and Financial Times.

[P.2] Automated guided vehicle coordination, product allocation and layout design for innovative automated storage and retrieval systems for Smoov.

(SELECTED) PAPERS IN INTERNATIONAL JOURNALS

[I.68] T. Calogiuri, G. Ghiani, E. Guerriero, R. Mansini (2018) A Branch-and-Bound Algorithm for the Time-Dependent Rural Postman Problem. Forthcoming in Computers and Operations Research

[I.67] A. Arigiano, T. Calogiuri, G. Ghiani, E. Guerriero, (2018) A branch‐and‐bound algorithm for the time‐dependent travelling salesman problem, Networks, Published on line 19 June 2018, https://doi.org/10.1002/net.21830

[I.66] A. Arigliano, G. Ghiani, A. Grieco, E. Guerriero, I. Plana, (2018) Time-Dependent Asymmetric Traveling Salesman Problem with Time Windows: Properties and an Exact Algorithm. Forthcoming in Discrete Applied Mathematics

[I.65] T. Adamo, G. Ghiani, E. Guerriero, E. Manni (2018). “A learn-and-construct framework for general mixed-integer programming problems”. International Transactions in Operational Research. To appear.

[I.64] T. Adamo, T. Bektas, G. Ghiani, E. Guerriero, E. Manni (2018). “Path and speed optimization for conflict-free pickup and delivery under time windows”. Transportation Science. Published on line. May 17th, 2018. DOI: http://doi.org/10.1287/trsc.2017.0816

[I.63] A. Arigliano, G. Ghiani, A. Grieco, E. Guerriero (2017). Single-machine time-dependent scheduling problems with fixed rate-modifying activities and resumable jobs. 4OR, vol. 14, p. 1-15

[I.62] T. Adamo, G. Ghiani, E. Guerriero, E. Manni (2017). “Automatic Instantiation of a Variable Neighborhood Descent from a Mixed-Integer Programming model”. Operations Research perspectives. 4:123-135. DOI: http://doi.org/10.1016/j.orp.2017.09.001

[I.61] G. Ghiani, E. Manni, A. Romano (2017). “Training offer selection and course timetabling for remedial education”. Computers & Industrial Engineering. 111:282-288. DOI: http://doi.org/10.1016/j.cie.2017.07.034

[I.60] T. Adamo, G. Ghiani, A. Grieco, E. Guerriero, E. Manni (2017). “MIP neighborhood synthesis through semantic feature extraction and automatic algorithm configuration”. Computers & Operations Research. 83:106-119. DOI: http://dx.doi.org/10.1016/j.cor.2017.01.021

[I.59] C. Triki, A. Alalawin, G. Ghiani, E. Manni (2017). “Approximated neighborhood evaluation for the design of the logistics support of complex engineering systems”. RAIRO – Operations Research. 51(1):1-16. DOI: http://dx.doi.org/10.1051/ro/2016002

[I.58] G. Ghiani, A. Grieco, A. Guerrieri, A. Manni, E. Manni (2015). “Large-scale assembly job shop scheduling problems with bill of materials: models and algorithms”. WSEAS Transactions on Business and Economics, 12:161-172.

[I.57] T. Calogiuri, G. Ghiani, E. Guerriero (2015), “The Time-dependent Quickest Path Problem: Properties and Bounds”, Networks, 66/2, 112-117.

[I.56] G. Ghiani, A. Guerrieri, A. Manni, E. Manni (2015). “Estimating travel and service times for automated route planning and service certification in municipal waste management”. Waste Management. 46:40-46.

[I.55] M. Gendreau, G. Ghiani, E. Guerriero (2015), “Time-Dependent Routing Problems: a Review”, Computers and Operations Research, 64, 189-197.

[I.54] G. Ghiani, G. Laporte, E. Manni (2015). “Model-based Automatic Neighborhood Design by Unsupervised Learning”. Computers and Operations Research 54, 108–116.

[I.53] G. Ghiani and E. Guerriero (2014), “A Lower Bound for the Quickest Path Problem”, Computers and Operations Research, 50, 154–160.

[I.52] G. Ghiani, E. Guerriero (2014) “A Note on the Ichoua, Gendreau and Potvin (2003) Travel Time Model”, Transportation Science, 48/3, 458–462.

[I.51] G. Ghiani, A. Manni, E. Manni, M. Toraldo (2014), “The impact of an efficient collection sites location on the zoning phase in municipal solid waste management”. Waste Management. 34(11):1949-1956.

[I.50] J.-F. Cordeau, G. Ghiani, E. Guerriero (2014) “Analysis and Branch-and-Cut Algorithm for the Time-Dependent Travelling Salesman Problem”, Transportation Science, 48/1, 46-58.

[I.49] G. Ghiani, D. Laganà, E. Manni, R. Musmanno, D. Vigo (2014) “Operations Research in Solid Waste Management: a survey on strategic and tactical issues”, Computers and Operations Research 24, 22-32.

[I.48] G. Ghiani, E. Guerriero, A. Manni, E. Manni, A. Potenza (2013). “Simultaneous Personnel and Vehicle Shift Scheduling in the Waste Management Sector”. Waste Management. 33/7, 1589-1594.

[I.47] G. Ghiani, D. Laganà, E. Manni, C. Triki (2012), “Capacitated location of collection sites in an urban waste management system”, Waste Management, 32/7, 1291-1296.

[I.46] G. Ghiani, E. Manni, B.W. Thomas (2011) “A Comparison of Anticipatory Algorithms for the Dynamic and Stochastic Traveling Salesman Problem”, Transportation Science, 46/3, 374-387.

[I.45] G. Ghiani, R. Musmanno, C. Triki (2011) “Probabilistic Model and Solution Algorithm for the Electricity Retailers in the Italian Market”, Algorithmic Operations Research (ISSN: 1718-3235) 6/2 (2011).

[I.44] G. Ghiani, E. Manni, A. Quaranta (2010), “Shift Scheduling Problem in the Same-Day Courier Industry”, Transportation Science, 44/1, 116-124.

[I.43] G. Ghiani, A. Grieco, E. Guerriero (2010), Solving the job sequencing and tool switching problem as a nonlinear least cost Hamiltonian cycle problem, Networks 55/4, 379–385.

[I.42] G. Ghiani, E. Manni, A. Quaranta, C. Triki (2009), “Anticipatory Algorithms for Same-Day Courier Dispatching”, Transportation Research – Part E, 45, 95-105.

[I.41] P. Beraldi, G. Ghiani, R. Musmanno, F. Vocaturo (2010), “Efficient neighborhood search for the probabilistic multi-vehicle pickup and delivery problem”, Asian-Pacific Journal of Operational Research 27/3, 301-314.

[I.40] G. Ghiani, G. Laporte, E. Manni, R. Musmanno. “Waiting Strategies for the Dynamic and Stochastic Traveling Salesman Problem”. International Journal of Operations Research. 5(4):233-241, 2008.

[I.39] G. Ghiani, D. Laganà, G. Laporte, F. Mari (2010), “Ant Colony Optimization for the Arc Routing Problem with Intermediate Facilities under Capacity and Length Restrictions”, Journal of Heuristics, 16/2, 211-233.

[I.38] G. Ghiani, E. Manni e C. Triki (2008) “The Lane Covering Problem with Time Windows”, Journal of Discrete Mathematical Sciences and Cryptography 11/1, 67-81.

[I.37] P. Beraldi, G. Ghiani, A.Grieco, E. Guerriero (2008), “Rolling-Horizon and Fix-and-Relax Heuristics for the Parallel Machine Lot-sizing and Scheduling Problem with Sequence-Dependent Set-up Costs”, Computers and Operations Research, 35/11, 3644-3656.

[I.36] G. Ghiani, A. Quaranta, C. Triki (2007), “New Policies for the Dynamic Travelling Salesman Problem”, Optimization Methods and Software, 22/6, 971 – 983.

[I.35] G. Ghiani, A. Grieco, E. Guerriero (2007), “An exact solution to the TLP problem in a NC Machine”, Robotics and Computer-Integrated Manufacturing 23/6, 645-649.

[I.34] A. Attanasio, A. Fuduli, G. Ghiani, C. Triki (2007), “Integrated Shipment Dispatching and Packing Problems: a Case Study”, Journal of Mathematical Modelling and Algorithms 6, 77-85.

[I.33] G. Ghiani, G. Laporte, F. Semet (2006), “The Black and White Traveling Salesman Problem”, Operations Research 54, 366-378.

[I.32] P. Beraldi, G. Ghiani, A, Grieco, E. Guerriero (2006), “Scenario-Based Planning for Lot-Sizing and Scheduling with Uncertain Processing Times”, International Journal of Production Economics, 101/1, 140-149.

[I.31] P. Beraldi, G. Ghiani, A. Grieco, E. Guerriero (2006), “A Fix and Relax Heuristic for a Stochastic Lot-Sizing Problem”, Computational Optimization and Applications 33, 303-318.

[I.30] G. Ghiani, P. Legato, R. Musmanno, F. Vocaturo (2007), “A Combined Procedure for Discrete Simulation-Optimization Problems based on the Simulated Annealing Framework”, Computational Optimization and Applications 38, 133-145.

[I.29] P. Beraldi, G. Ghiani, G. Laporte, R. Musmanno (2005), “Efficient Neighborhood Search for the Probabilistic Pickup and Delivery Travelling Salesman Problem”, Networks, 45/4, 195-198.

[I.28] A. Attanasio, G. Ghiani, L. Grandinetti, F. Guerriero (2006), “Auction Algorithms for Decentralized Parallel Machine Scheduling”, Parallel Computing 32/9, 701-709.

[I.27] G. Ghiani, P. Legato, R. Musmanno, F. Vocaturo (2004), “Optimization via Simulation: Solution Concepts, Algorithms, Parallel Computing Strategies and Commercial Software”, Journal of Computing, 3/3, 7-12.

[I.26] G. Ghiani, F. Guerriero, G. Laporte, R. Musmanno (2004), “The Arc Routing Problem with Intermediate Facilities under Capacity and Distance Restrictions”, Journal of Mathematical Modelling and Algorithms, 3, 209-223.

[I.25] G. Ghiani, D. Laganà, R. Musmanno (2006), A New Constructive Heuristic for the Undirected Rural Postman Problem, Computers and Operations Research 33/12, 3450-3457.

[I.24] G. Ghiani, R. Musmanno, Recent algorithmic advances for Arc Routing Problems (2006), Computers and Operations Research 33/12, 3361-3362.

[I.23] P. Caricato, G. Ghiani, A. Grieco, R. Musmanno (2005), Batch Scheduling in a Two-Stage Flow Shop with Parallel and Bottleneck Machines, Journal of Statistics and Management Sciences 8/1, 121-130.

[I.22] P. Caricato, G. Ghiani, A. Grieco, R. Musmanno (2007), Improved Formulation, Branch-and-Cut and Tabu Search Heuristic for Single Loop Material Flow System Design, European Journal of Operational Research, 178\1, 85-91.

[I.21] A. Attanasio, J.F. Cordeau, G. Ghiani, G. Laporte (2004), “Parallel Tabu Search Heuristics for the Dynamic Multi-Vehicle Dial-a-Ride Problem”, Parallel Computing 30/3, 377-387.

[I.20] E. Cabral, G. Ghiani, M. Gendreau, G. Laporte (2004), “Solving the Hierarchical Chinese Postman Problem as a Rural Postman Problem”, European Journal of Operational Research, 155, 44-50.

[I.19] G. Ghiani, R. Musmanno, G. Paletta, C. Triki (2005), “A Heuristic for the Periodic Rural Postman Problem”, Computers and Operations Research 32/2, 219-228.

[I.18] G. Ghiani, F. Guerriero, G. Laporte, R. Musmanno (2003), “Real-Time Vehicle Routing: Solution Concepts, Algorithms and Parallel Computing Strategies”, “invited review”, European Journal of Operational Research 151, 1-11.

[I.17] G. Ghiani, A. Grieco, E. Guerriero, R. Musmanno (2003), “Allocating Production Batches to Subcontractors by Fuzzy Goal Programming”, International Transactions in Operational Research 10/3, 295-306.

[I.16] P. Caricato, G. Ghiani, A. Grieco, E. Guerriero (2003), “Parallel Tabu Search for a Pickup and Delivery Problem under Track Contention “, Parallel Computing 29, 631-639.

[I.15] G. Ghiani, F. Guerriero, G. Improta, R. Musmanno (2005), “Solving a Complex Public Waste Collection Problem in Southern Italy”, International Transactions in Operational Research, 12, 135-144.

[I.14] G. Bruno, G. Ghiani, G. Improta, E. Manni (2005), “A Tabu Search Heuristic for the Optimization of a Multi-Stage Component Placement System”, Journal of Discrete Mathematical Sciences and Criptography, 8/2, 271-285.

[I.13] G. Ghiani, L. Grandinetti, F. Guerriero, R. Musmanno (2002), “A Lagrangean Heuristic for the Plant Location Problem with Multiple Facilities in the Same Site”, Optimization Methods and Software, 17/6, 1059-1076.

[I.12] G. Ghiani, F. Guerriero, R. Musmanno (2002), “The Capacitated Plant Location Problem with Multiple Facilities in the Same Site”, Computers and Operations Research, 29/13, 1903-1912.

[I.11] B. De Rosa, G. Ghiani, G. Improta, R. Musmanno (2001), “The Arc Routing and Scheduling Problem with Transshipment”, Transportation Science, 36/3, 301-313.

[I.10] G. Ghiani, G. Laporte (2001), “Location-Arc Routing Problems”, OPSEARCH, 38/2, 151-159.

[I.9] G. Ghiani, G. Improta (2001), “The Laser-Plotter Beam Routing Problem”, Journal of the Operational Research Society 52, 945-951.

[I.8] G. Ghiani, G. Improta, G. Laporte (2001), “The Capacitated Arc Routing Problem with Intermediate Facilities”, Networks 37/3, 134-143.

[I.7] G. Ghiani, G. Laporte (2000), “A Branch-and-Cut Algorithm for the Undirected Rural Postman Problem”, Mathematical Programming, 87/3, 467-481.

[I.6] G. Ghiani, G. Laporte (1999), “Eulerian Location Problems”, Networks, 34\4, 291-302.

[I.5] G. Ghiani, G. Improta (2000), “An Efficient Transformation of the Generalized Vehicle Routing Problem”, European Journal of Operational Research, 122, 11-17.

[I.4] G. Bruno, G. Ghiani, G. Improta (2000), “Dynamic Positioning of Idle Automated Guided Vehicles”, Journal of Intelligent Manufacturing, 11, 209-215.

[I.3] G. Ghiani, G. Improta (2000), “An Algorithm for the Hierarchical Chinese Postman Problem”, Operations Research Letters, 26/1, 27-32.

[I.2] G. Ghiani et al. (1998), “Some personal views on the current state and the future of Locational Analysis”, European Journal of Operational Research, 104, 269-287.

[I.1] G. Bruno, G. Ghiani, G. Improta (1998), “A Multi-modal Approach to the Location of a Rapid Transit Line”, European Journal of Operational Research, 104, 321-332.

(SELECTED) BOOK CHAPTERS

[BC.1] A. Attanasio, G. Ghiani, L. Grandinetti, E. Guerriero, F. Guerriero, "Operations Research Methods for Resource Management and Scheduling in a Computational Grid: a Survey", in: L. Grandinetti (Eds), "Grid Computing: The New Frontier Of High Performance Computing", Collana "Advances in Computing", Elsevier, Amsterdam, 2005, ISBN: 0-444-51999-8.

[BC.2] A. Attanasio, J. Bregman, G. Ghiani & E. Manni. “Real-time Fleet Management at eCourier Ltd”. In: Dynamic Fleet Management - Concepts, Systems, Algorithms & Case Studies, Springer Verlag, Series: Operations Research/Computer Science Interfaces Series , Vol. 38, Zeimpekis, V.; Tarantilis, C.D.; Giaglis, G.M.; Minis, I. (Eds.) 2007, ISBN: 978-0-387-71721-0.

Temi di ricerca

Integration of Optimization and Machine Learning.

Mathematical modelling and optimisation of a variety of problems in distribution logistics and manufacturing.

Question Answering Systems and Chatbots