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Advances in bio-inspired computing for combinatorial optimization problems / / Camelia-Mihaela Pintea
Advances in bio-inspired computing for combinatorial optimization problems / / Camelia-Mihaela Pintea
Autore Pintea Camelia-Mihaela
Edizione [1st ed. 2014.]
Pubbl/distr/stampa Heidelberg, Germany : , : Springer, , 2014
Descrizione fisica 1 online resource (x, 188 pages) : illustrations (some color)
Disciplina 006.3
Collana Intelligent Systems Reference Library
Soggetto topico Biologically-inspired computing
Combinatorial optimization
ISBN 3-642-40179-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Part I Biological Computing and Optimization -- Part II Ant Algorithms -- Part III Bio-inspired Multi-Agent Systems -- Part IV Applications with Bio-inspired Algorithms -- Part V Conclusions and Remarks.
Record Nr. UNINA-9910299735003321
Pintea Camelia-Mihaela  
Heidelberg, Germany : , : Springer, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Algoritmi di computazione per matching perfetti di costo minimo. Tesi di laurea in ottimizzazione combinatoria / laureanda Aiello Lorena Giorgia ; relatore Paolo Nobili
Algoritmi di computazione per matching perfetti di costo minimo. Tesi di laurea in ottimizzazione combinatoria / laureanda Aiello Lorena Giorgia ; relatore Paolo Nobili
Autore Aiello, Lorena Giorgia
Pubbl/distr/stampa Lecce : Università del Salento. Facoltà di Scienze MM. FF. NN. Corso di Laurea Specialistica in Matematica, a.a. 2011-12
Descrizione fisica 99 p. ; 30 cm
Disciplina 519
Altri autori (Persone) Nobili, Paolo
Soggetto topico Combinatorial optimization
Classificazione AMS 90C27
AMS 90C35
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Record Nr. UNISALENTO-991001803029707536
Aiello, Lorena Giorgia  
Lecce : Università del Salento. Facoltà di Scienze MM. FF. NN. Corso di Laurea Specialistica in Matematica, a.a. 2011-12
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Algoritmi efficienti per il problema del matching pesato. Tesi di laurea / laureando Alessandro Melissano ; relatore Paolo Nobili
Algoritmi efficienti per il problema del matching pesato. Tesi di laurea / laureando Alessandro Melissano ; relatore Paolo Nobili
Autore Melissano, Alessandro
Pubbl/distr/stampa Lecce : Università del Salento. Facoltà di Scienze MM. FF. NN. Corso di Laurea Magistrale in Matematica, a.a. 2012-13
Descrizione fisica 80 p. ; 30 cm
Altri autori (Persone) Siciliano, Salvatore
Soggetto topico Mathematical programming
Combinatorial optimization
Classificazione AMS 90C27
AMS 90C35
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Record Nr. UNISALENTO-991002537339707536
Melissano, Alessandro  
Lecce : Università del Salento. Facoltà di Scienze MM. FF. NN. Corso di Laurea Magistrale in Matematica, a.a. 2012-13
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Algoritmo di ricerca locale per il problema di Set Covering. Tesi di laurea / laureando Giuseppe Reho ; relat. Paolo Nobili
Algoritmo di ricerca locale per il problema di Set Covering. Tesi di laurea / laureando Giuseppe Reho ; relat. Paolo Nobili
Autore Reho, Giuseppe
Pubbl/distr/stampa Lecce : Università degli Studi. Facoltà di Scienze. Corso di laurea in Matematica, a.a. 2001-02
Descrizione fisica 86 p. ; 30 cm
Altri autori (Persone) Nobili, Paolo
Soggetto topico Combinatorial optimization
Approximation methods and heuristics
Classificazione AMS 90C27
AMS 90C59
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Record Nr. UNISALENTO-991001721439707536
Reho, Giuseppe  
Lecce : Università degli Studi. Facoltà di Scienze. Corso di laurea in Matematica, a.a. 2001-02
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Algoritmo per il massimo stabile pesato in un grafo claw-free. Tesi di laurea / laureanda Ilaria De Pascalis ; relat. Paolo Nobili
Algoritmo per il massimo stabile pesato in un grafo claw-free. Tesi di laurea / laureanda Ilaria De Pascalis ; relat. Paolo Nobili
Autore De Pascalis, Ilaria
Pubbl/distr/stampa Lecce : Università del Salento. Dipartimento di Matematica e Fisica "Ennio De Giorgi". Corso di laurea magistrale in Matematica, a.a. 2016-17
Descrizione fisica 56 p. : ill. (some col.) ; 30 cm
Disciplina 510
Altri autori (Persone) Nobili, Paolo
Soggetto topico Mathematical programming
Combinatorial optimization
Graph theory
Classificazione AMS 90C27
AMS 90C35
AMS 05C22
AMS 05C85
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Record Nr. UNISALENTO-991003567419707536
De Pascalis, Ilaria  
Lecce : Università del Salento. Dipartimento di Matematica e Fisica "Ennio De Giorgi". Corso di laurea magistrale in Matematica, a.a. 2016-17
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Analisi logica dei dati: classificazione con giustifiazione. Tesi di laurea / laureanda Erika Limosani ; relat. Paolo Nobili
Analisi logica dei dati: classificazione con giustifiazione. Tesi di laurea / laureanda Erika Limosani ; relat. Paolo Nobili
Autore Limosani, Erika
Pubbl/distr/stampa Lecce : Università del Salento. Dipartimento di Matematica e Fisica "E. De Giorgi". Corso di laurea in Matematica, a.a. 2017-18
Descrizione fisica 56 p. ; 30 cm
Disciplina 519
Altri autori (Persone) Nobili, Paolo
Soggetto topico Combinatorial optimization
Classificazione AMS 90C27
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Record Nr. UNISALENTO-991003638959707536
Limosani, Erika  
Lecce : Università del Salento. Dipartimento di Matematica e Fisica "E. De Giorgi". Corso di laurea in Matematica, a.a. 2017-18
Materiale a stampa
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Applications of combinatorial optimization / / edited by Vangelis Th. Paschos
Applications of combinatorial optimization / / edited by Vangelis Th. Paschos
Edizione [Revised and updated second edition.]
Pubbl/distr/stampa London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014
Descrizione fisica 1 online resource (449 p.)
Disciplina 519.64
Collana Mathematics and Statistics Series
Soggetto topico Combinatorial optimization
ISBN 1-119-00538-8
1-119-01522-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright; Contents; Preface; Chapter 1: Airline Crew Pairing Optimization; 1.1. Introduction; 1.2. Definition of the problem; 1.2.1. Constructing subnetworks; 1.2.2. Pairing costs; 1.2.3. Model; 1.2.4. Case without resource constraints; 1.3. Solution approaches; 1.3.1. Decomposition principles; 1.3.2. Column generation, master problem and subproblem; 1.3.3. Branching methods for finding integer solutions; 1.4. Solving the subproblem for column generation; 1.4.1. Mathematical formulation; 1.4.2. General principle of effective label generation
1.4.3. Case of one single resource: the bucket method1.4.4. Case of many resources: reduction of the resource space; 1.4.4.1. Reduction principle; 1.4.4.2. Approach based on the Lagrangian relaxation; 1.4.4.3. Approach based on the surrogate relaxation; 1.5. Conclusion; 1.6. Bibliography; Chapter 2: The Task Allocation Problem; 2.1. Presentation; 2.2. Definitions and modeling; 2.2.1. Definitions; 2.2.2. The processors; 2.2.3. Communications; 2.2.4. Tasks; 2.2.5. Allocation types; 2.2.5.1. Static allocation; 2.2.5.2. Dynamic allocation; 2.2.5.3. With or without pre-emption
2.2.5.4. Task duplication2.2.6. Allocation/scheduling; 2.2.7. Modeling; 2.2.7.1. Modeling costs; 2.2.7.2. Constraints; 2.2.7.3. Objectives of the allocation; 2.2.7.3.1. Minimizing the execution duration; 2.2.7.3.2. Minimizing the global execution and communication cost; 2.2.7.3.3. Load balancing; 2.3. Review of the main works; 2.3.1. Polynomial cases; 2.3.1.1. Two-processor cases; 2.3.1.2. Tree case; 2.3.1.3. Other structures; 2.3.1.4. Restrictions on the processors or the tasks; 2.3.1.5. Minmax objective; 2.3.2. Approximability; 2.3.3. Approximate solution; 2.3.3.1. Heterogenous processors
2.3.3.2. Homogenous processors2.3.4. Exact solution; 2.3.5. Independent tasks case; 2.4. A little-studied model; 2.4.1. Model; 2.4.2. A heuristic based on graphs; 2.4.2.1. Transformation of the problem; 2.4.2.2. Modeling; 2.4.2.3. Description of the heuristic; 2.5. Conclusion; 2.6. Bibliography; Chapter 3: A Comparison of Some Valid Inequality Generation Methods for General 0-1 Problems; 3.1. Introduction; 3.2. Presentation of the various techniques tested; 3.2.1. Exact separation with respect to a mixed relaxation; 3.2.2. Approximate separation using a heuristic
3.2.3. Restriction + separation + relaxed lifting (RSRL)3.2.4. Disjunctive programming and the lift and project procedure; 3.2.5. Reformulation-linearization technique (RLT); 3.3. Computational results; 3.3.1. Presentation of test problems; 3.3.2. Presentation of the results; 3.3.3. Discussion of the computational results; 3.4. Bibliography; Chapter 4: Production Planning; 4.1. Introduction; 4.2. Hierarchical planning; 4.3. Strategic planning and productive system design; 4.3.1. Group technology; 4.3.2. Locating equipment; 4.4. Tactical planning and inventory management
4.4.1. A linear programming model for medium-term planning
Record Nr. UNINA-9910132155703321
London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applications of combinatorial optimization / / edited by Vangelis Th. Paschos
Applications of combinatorial optimization / / edited by Vangelis Th. Paschos
Edizione [Revised and updated second edition.]
Pubbl/distr/stampa London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014
Descrizione fisica 1 online resource (449 p.)
Disciplina 519.64
Collana Mathematics and Statistics Series
Soggetto topico Combinatorial optimization
ISBN 1-119-00538-8
1-119-01522-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright; Contents; Preface; Chapter 1: Airline Crew Pairing Optimization; 1.1. Introduction; 1.2. Definition of the problem; 1.2.1. Constructing subnetworks; 1.2.2. Pairing costs; 1.2.3. Model; 1.2.4. Case without resource constraints; 1.3. Solution approaches; 1.3.1. Decomposition principles; 1.3.2. Column generation, master problem and subproblem; 1.3.3. Branching methods for finding integer solutions; 1.4. Solving the subproblem for column generation; 1.4.1. Mathematical formulation; 1.4.2. General principle of effective label generation
1.4.3. Case of one single resource: the bucket method1.4.4. Case of many resources: reduction of the resource space; 1.4.4.1. Reduction principle; 1.4.4.2. Approach based on the Lagrangian relaxation; 1.4.4.3. Approach based on the surrogate relaxation; 1.5. Conclusion; 1.6. Bibliography; Chapter 2: The Task Allocation Problem; 2.1. Presentation; 2.2. Definitions and modeling; 2.2.1. Definitions; 2.2.2. The processors; 2.2.3. Communications; 2.2.4. Tasks; 2.2.5. Allocation types; 2.2.5.1. Static allocation; 2.2.5.2. Dynamic allocation; 2.2.5.3. With or without pre-emption
2.2.5.4. Task duplication2.2.6. Allocation/scheduling; 2.2.7. Modeling; 2.2.7.1. Modeling costs; 2.2.7.2. Constraints; 2.2.7.3. Objectives of the allocation; 2.2.7.3.1. Minimizing the execution duration; 2.2.7.3.2. Minimizing the global execution and communication cost; 2.2.7.3.3. Load balancing; 2.3. Review of the main works; 2.3.1. Polynomial cases; 2.3.1.1. Two-processor cases; 2.3.1.2. Tree case; 2.3.1.3. Other structures; 2.3.1.4. Restrictions on the processors or the tasks; 2.3.1.5. Minmax objective; 2.3.2. Approximability; 2.3.3. Approximate solution; 2.3.3.1. Heterogenous processors
2.3.3.2. Homogenous processors2.3.4. Exact solution; 2.3.5. Independent tasks case; 2.4. A little-studied model; 2.4.1. Model; 2.4.2. A heuristic based on graphs; 2.4.2.1. Transformation of the problem; 2.4.2.2. Modeling; 2.4.2.3. Description of the heuristic; 2.5. Conclusion; 2.6. Bibliography; Chapter 3: A Comparison of Some Valid Inequality Generation Methods for General 0-1 Problems; 3.1. Introduction; 3.2. Presentation of the various techniques tested; 3.2.1. Exact separation with respect to a mixed relaxation; 3.2.2. Approximate separation using a heuristic
3.2.3. Restriction + separation + relaxed lifting (RSRL)3.2.4. Disjunctive programming and the lift and project procedure; 3.2.5. Reformulation-linearization technique (RLT); 3.3. Computational results; 3.3.1. Presentation of test problems; 3.3.2. Presentation of the results; 3.3.3. Discussion of the computational results; 3.4. Bibliography; Chapter 4: Production Planning; 4.1. Introduction; 4.2. Hierarchical planning; 4.3. Strategic planning and productive system design; 4.3.1. Group technology; 4.3.2. Locating equipment; 4.4. Tactical planning and inventory management
4.4.1. A linear programming model for medium-term planning
Record Nr. UNINA-9910808645303321
London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applications of combinatorial optimization [[electronic resource] /] / edited by Vangelis Th. Paschos
Applications of combinatorial optimization [[electronic resource] /] / edited by Vangelis Th. Paschos
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (409 p.)
Disciplina 519.64
Altri autori (Persone) PaschosVangelis Th
Collana Combinatorial optimization
Soggetto topico Combinatorial optimization
ISBN 1-118-60028-2
1-118-60034-7
1-118-60011-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Applications of Combinatorial Optimization; Title Page; Copyright Page; Table of Contents; Preface; Chapter 1. Airline Crew Pairing Optimization; 1.1. Introduction; 1.2. Definition of the problem; 1.2.1. Constructing subnetworks; 1.2.2. Pairing costs; 1.2.3. Model; 1.2.4. Case without resource constraints; 1.3. Solution approaches; 1.3.1. Decomposition principles; 1.3.2. Column generation, master problem and subproblem; 1.3.3. Branching methods for finding integer solutions; 1.4. Solving the subproblem for column generation; 1.4.1. Mathematical formulation
1.4.2. General principle of effective label generation1.4.3. Case of one single resource: the bucket method; 1.4.4. Case of many resources: reduction of the resource space; 1.5. Conclusion; 1.6. Bibliography; Chapter 2. The Task Allocation Problem; 2.1. Presentation; 2.2. Definitions and modeling; 2.2.1. Definitions; 2.2.2. The processors; 2.2.3. Communications; 2.2.4. Tasks; 2.2.5. Allocation types; 2.2.6. Allocation/scheduling; 2.2.7. Modeling; 2.3. Review of the main works; 2.3.1. Polynomial cases; 2.3.2. Approximability; 2.3.3. Approximate solution; 2.3.4. Exact solution
2.3.5. Independent tasks case2.4. A little-studied model; 2.4.1. Model; 2.4.2. A heuristic based on graphs; 2.5. Conclusion; 2.6. Bibliography; Chapter 3. A Comparison of Some Valid Inequality Generation Methods for General 0-1 Problems; 3.1. Introduction; 3.2. Presentation of the various techniques tested; 3.2.1. Exact separation with respect to a mixed relaxation; 3.2.2. Approximate separation using a heuristic; 3.2.3. Restriction + separation + relaxed lifting (RSRL); 3.2.4. Disjunctive programming and the lift and project procedure; 3.2.5. Reformulation-linearization technique (RLT)
3.3. Computational results3.3.1. Presentation of test problems; 3.3.2. Presentation of the results; 3.3.3. Discussion of the computational results; 3.4. Bibliography; Chapter 4. Production Planning; 4.1. Introduction; 4.2. Hierarchical planning; 4.3. Strategic planning and productive system design; 4.3.1. Group technology; 4.3.2. Locating equipment; 4.4. Tactical planning and inventory management; 4.4.1. A linear programming model for medium-term planning; 4.4.2. Inventory management; 4.4.3. Wagner and Whitin model; 4.4.4. The economic order quantity model (EOQ)
4.4.5. The EOQ model with joint replenishments4.5. Operations planning and scheduling; 4.5.1. Tooling; 4.5.2. Robotic cells; 4.6. Conclusion and perspectives; 4.7. Bibliography; Chapter 5. Operations Research and Goods Transportation; 5.1. Introduction; 5.2. Goods transport systems; 5.3. Systems design; 5.3.1. Location with balancing requirements; 5.3.2. Multiproduct production-distribution; 5.3.3. Hub location; 5.4. Long-distance transport; 5.4.1. Service network design; 5.4.2. Static formulations; 5.4.3. Dynamic formulations; 5.4.4. Fleet management; 5.5. Vehicle routing problems
5.5.1. Definitions and complexity
Record Nr. UNINA-9910141601303321
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applications of combinatorial optimization [[electronic resource] /] / edited by Vangelis Th. Paschos
Applications of combinatorial optimization [[electronic resource] /] / edited by Vangelis Th. Paschos
Edizione [1st ed.]
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (409 p.)
Disciplina 519.64
Altri autori (Persone) PaschosVangelis Th
Collana Combinatorial optimization
Soggetto topico Combinatorial optimization
ISBN 1-118-60028-2
1-118-60034-7
1-118-60011-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Applications of Combinatorial Optimization; Title Page; Copyright Page; Table of Contents; Preface; Chapter 1. Airline Crew Pairing Optimization; 1.1. Introduction; 1.2. Definition of the problem; 1.2.1. Constructing subnetworks; 1.2.2. Pairing costs; 1.2.3. Model; 1.2.4. Case without resource constraints; 1.3. Solution approaches; 1.3.1. Decomposition principles; 1.3.2. Column generation, master problem and subproblem; 1.3.3. Branching methods for finding integer solutions; 1.4. Solving the subproblem for column generation; 1.4.1. Mathematical formulation
1.4.2. General principle of effective label generation1.4.3. Case of one single resource: the bucket method; 1.4.4. Case of many resources: reduction of the resource space; 1.5. Conclusion; 1.6. Bibliography; Chapter 2. The Task Allocation Problem; 2.1. Presentation; 2.2. Definitions and modeling; 2.2.1. Definitions; 2.2.2. The processors; 2.2.3. Communications; 2.2.4. Tasks; 2.2.5. Allocation types; 2.2.6. Allocation/scheduling; 2.2.7. Modeling; 2.3. Review of the main works; 2.3.1. Polynomial cases; 2.3.2. Approximability; 2.3.3. Approximate solution; 2.3.4. Exact solution
2.3.5. Independent tasks case2.4. A little-studied model; 2.4.1. Model; 2.4.2. A heuristic based on graphs; 2.5. Conclusion; 2.6. Bibliography; Chapter 3. A Comparison of Some Valid Inequality Generation Methods for General 0-1 Problems; 3.1. Introduction; 3.2. Presentation of the various techniques tested; 3.2.1. Exact separation with respect to a mixed relaxation; 3.2.2. Approximate separation using a heuristic; 3.2.3. Restriction + separation + relaxed lifting (RSRL); 3.2.4. Disjunctive programming and the lift and project procedure; 3.2.5. Reformulation-linearization technique (RLT)
3.3. Computational results3.3.1. Presentation of test problems; 3.3.2. Presentation of the results; 3.3.3. Discussion of the computational results; 3.4. Bibliography; Chapter 4. Production Planning; 4.1. Introduction; 4.2. Hierarchical planning; 4.3. Strategic planning and productive system design; 4.3.1. Group technology; 4.3.2. Locating equipment; 4.4. Tactical planning and inventory management; 4.4.1. A linear programming model for medium-term planning; 4.4.2. Inventory management; 4.4.3. Wagner and Whitin model; 4.4.4. The economic order quantity model (EOQ)
4.4.5. The EOQ model with joint replenishments4.5. Operations planning and scheduling; 4.5.1. Tooling; 4.5.2. Robotic cells; 4.6. Conclusion and perspectives; 4.7. Bibliography; Chapter 5. Operations Research and Goods Transportation; 5.1. Introduction; 5.2. Goods transport systems; 5.3. Systems design; 5.3.1. Location with balancing requirements; 5.3.2. Multiproduct production-distribution; 5.3.3. Hub location; 5.4. Long-distance transport; 5.4.1. Service network design; 5.4.2. Static formulations; 5.4.3. Dynamic formulations; 5.4.4. Fleet management; 5.5. Vehicle routing problems
5.5.1. Definitions and complexity
Record Nr. UNINA-9910809344503321
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui