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Bandit algorithms / Tor Lattimore, Csaba Szepesvári
| Bandit algorithms / Tor Lattimore, Csaba Szepesvári |
| Autore | Lattimore, Tor |
| Pubbl/distr/stampa | Cambridge ; New York, NY : Cambridge University Press, 2020 |
| Descrizione fisica | xviii, 518 p. : ill. ; 26 cm |
| Disciplina | 518.1 |
| Altri autori (Persone) | Szepesvári, Csabaauthor |
| Soggetto topico |
Resource allocation - Mathematical models
Decision making - Mathematical models Algorithms Probabilities Mathematical optimization |
| ISBN | 9781108486828 |
| Classificazione |
AMS 68Q-xx
LC QA402 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991004402927907536 |
Lattimore, Tor
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| Cambridge ; New York, NY : Cambridge University Press, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
| ||
Linear programming and resource allocation modeling / / Michael J. Panik
| Linear programming and resource allocation modeling / / Michael J. Panik |
| Autore | Panik Michael J. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2019 |
| Descrizione fisica | 1 online resource (451 pages) |
| Disciplina | 519.72 |
| Soggetto topico |
Linear programming
Resource allocation - Mathematical models |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-50947-5
1-119-50945-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Mathematical Foundations -- Introduction to Linear Programming -- Computational Aspects of Linear Programming -- Variations of the Standard Simplex Routine -- Duality Theory -- Linear Programming and the Theory of the Firm -- Sensitivity Analysis -- Analyzing Structural Changes -- Parametric Programming -- Parametric Programming and the Theory of the Firm -- Duality Revisited -- Simplex-Based Methods of Optimization -- Data Envelopment Analysis (DEA). |
| Record Nr. | UNINA-9910468021203321 |
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Panik Michael J.
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| Hoboken, New Jersey : , : Wiley, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Linear programming and resource allocation modeling / / Michael J. Panik
| Linear programming and resource allocation modeling / / Michael J. Panik |
| Autore | Panik Michael J. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2019 |
| Descrizione fisica | 1 online resource (451 pages) |
| Disciplina | 519.72 |
| Collana | THEi Wiley ebooks |
| Soggetto topico |
Linear programming
Resource allocation - Mathematical models |
| ISBN |
1-119-50946-7
1-119-50947-5 1-119-50945-9 |
| Classificazione |
417
519.7/2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Mathematical Foundations -- Introduction to Linear Programming -- Computational Aspects of Linear Programming -- Variations of the Standard Simplex Routine -- Duality Theory -- Linear Programming and the Theory of the Firm -- Sensitivity Analysis -- Analyzing Structural Changes -- Parametric Programming -- Parametric Programming and the Theory of the Firm -- Duality Revisited -- Simplex-Based Methods of Optimization -- Data Envelopment Analysis (DEA). |
| Record Nr. | UNINA-9910539336403321 |
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Panik Michael J.
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| Hoboken, New Jersey : , : Wiley, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multi-armed bandit allocation indices [[electronic resource] /] / John Gittins, Kevin Glazebrook, Richard Weber
| Multi-armed bandit allocation indices [[electronic resource] /] / John Gittins, Kevin Glazebrook, Richard Weber |
| Autore | Gittins John C. <1938-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Chichester, : Wiley, 2011 |
| Descrizione fisica | 1 online resource (311 p.) |
| Disciplina |
519.5
519.8 |
| Altri autori (Persone) |
GlazebrookKevin D. <1950->
WeberRichard <1953-> |
| Soggetto topico |
Resource allocation - Mathematical models
Mathematical optimization Programming (Mathematics) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-37409-9
9786613374097 0-470-98004-4 0-470-98003-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Multi-armed Bandit Allocation Indices; Contents; Foreword; Foreword to the first edition; Preface; Preface to the first edition; 1 Introduction or exploration; Exercises; 2 Main ideas: Gittins index; 2.1 Introduction; 2.2 Decision processes; 2.3 Simple families of alternative bandit processes; 2.4 Dynamic programming; 2.5 Gittins index theorem; 2.6 Gittins index; 2.6.1 Gittins index and the multi-armed bandit; 2.6.2 Coins problem; 2.6.3 Characterization of the optimal stopping time; 2.6.4 The restart-in-state formulation; 2.6.5 Dependence on discount factor
2.6.6 Myopic and forwards induction policies2.7 Proof of the index theorem by interchanging bandit portions; 2.8 Continuous-time bandit processes; 2.9 Proof of the index theorem by induction and interchange argument; 2.10 Calculation of Gittins indices; 2.11 Monotonicity conditions; 2.11.1 Monotone indices; 2.11.2 Monotone jobs; 2.12 History of the index theorem; 2.13 Some decision process theory; Exercises; 3 Necessary assumptions for indices; 3.1 Introduction; 3.2 Jobs; 3.3 Continuous-time jobs; 3.3.1 Definition; 3.3.2 Policies for continuous-time jobs 3.3.3 The continuous-time index theorem for a SFABP of jobs3.4 Necessary assumptions; 3.4.1 Necessity of an infinite time horizon; 3.4.2 Necessity of constant exponential discounting; 3.4.3 Necessity of a single processor; 3.5 Beyond the necessary assumptions; 3.5.1 Bandit-dependent discount factors; 3.5.2 Stochastic discounting; 3.5.3 Undiscounted rewards; 3.5.4 A discrete search problem; 3.5.5 Multiple processors; Exercises; 4 Superprocesses, precedence constraints and arrivals; 4.1 Introduction; 4.2 Bandit superprocesses; 4.3 The index theorem for superprocesses 4.4 Stoppable bandit processes4.5 Proof of the index theorem by freezing and promotion rules; 4.5.1 Freezing rules; 4.5.2 Promotion rules; 4.6 The index theorem for jobs with precedence constraints; 4.7 Precedence constraints forming an out-forest; 4.8 Bandit processes with arrivals; 4.9 Tax problems; 4.9.1 Ongoing bandits and tax problems; 4.9.2 Klimov's model; 4.9.3 Minimum EWFT for the M/G/1 queue; 4.10 Near optimality of nearly index policies; Exercises; 5 The achievable region methodology; 5.1 Introduction; 5.2 A simple example; 5.3 Proof of the index theorem by greedy algorithm 5.4 Generalized conservation laws and indexable systems5.5 Performance bounds for policies for branching bandits; 5.6 Job selection and scheduling problems; 5.7 Multi-armed bandits on parallel machines; Exercises; 6 Restless bandits and Lagrangian relaxation; 6.1 Introduction; 6.2 Restless bandits; 6.3 Whittle indices for restless bandits; 6.4 Asymptotic optimality; 6.5 Monotone policies and simple proofs of indexability; 6.6 Applications to multi-class queueing systems; 6.7 Performance bounds for the Whittle index policy; 6.8 Indices for more general resource configurations; Exercises 7 Multi-population random sampling (theory) |
| Record Nr. | UNINA-9910133454203321 |
|
Gittins John C. <1938->
|
||
| Chichester, : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multi-armed bandit allocation indices [[electronic resource] /] / John Gittins, Kevin Glazebrook, Richard Weber
| Multi-armed bandit allocation indices [[electronic resource] /] / John Gittins, Kevin Glazebrook, Richard Weber |
| Autore | Gittins John C. <1938-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Chichester, : Wiley, 2011 |
| Descrizione fisica | 1 online resource (311 p.) |
| Disciplina |
519.5
519.8 |
| Altri autori (Persone) |
GlazebrookKevin D. <1950->
WeberRichard <1953-> |
| Soggetto topico |
Resource allocation - Mathematical models
Mathematical optimization Programming (Mathematics) |
| ISBN |
1-283-37409-9
9786613374097 0-470-98004-4 0-470-98003-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Multi-armed Bandit Allocation Indices; Contents; Foreword; Foreword to the first edition; Preface; Preface to the first edition; 1 Introduction or exploration; Exercises; 2 Main ideas: Gittins index; 2.1 Introduction; 2.2 Decision processes; 2.3 Simple families of alternative bandit processes; 2.4 Dynamic programming; 2.5 Gittins index theorem; 2.6 Gittins index; 2.6.1 Gittins index and the multi-armed bandit; 2.6.2 Coins problem; 2.6.3 Characterization of the optimal stopping time; 2.6.4 The restart-in-state formulation; 2.6.5 Dependence on discount factor
2.6.6 Myopic and forwards induction policies2.7 Proof of the index theorem by interchanging bandit portions; 2.8 Continuous-time bandit processes; 2.9 Proof of the index theorem by induction and interchange argument; 2.10 Calculation of Gittins indices; 2.11 Monotonicity conditions; 2.11.1 Monotone indices; 2.11.2 Monotone jobs; 2.12 History of the index theorem; 2.13 Some decision process theory; Exercises; 3 Necessary assumptions for indices; 3.1 Introduction; 3.2 Jobs; 3.3 Continuous-time jobs; 3.3.1 Definition; 3.3.2 Policies for continuous-time jobs 3.3.3 The continuous-time index theorem for a SFABP of jobs3.4 Necessary assumptions; 3.4.1 Necessity of an infinite time horizon; 3.4.2 Necessity of constant exponential discounting; 3.4.3 Necessity of a single processor; 3.5 Beyond the necessary assumptions; 3.5.1 Bandit-dependent discount factors; 3.5.2 Stochastic discounting; 3.5.3 Undiscounted rewards; 3.5.4 A discrete search problem; 3.5.5 Multiple processors; Exercises; 4 Superprocesses, precedence constraints and arrivals; 4.1 Introduction; 4.2 Bandit superprocesses; 4.3 The index theorem for superprocesses 4.4 Stoppable bandit processes4.5 Proof of the index theorem by freezing and promotion rules; 4.5.1 Freezing rules; 4.5.2 Promotion rules; 4.6 The index theorem for jobs with precedence constraints; 4.7 Precedence constraints forming an out-forest; 4.8 Bandit processes with arrivals; 4.9 Tax problems; 4.9.1 Ongoing bandits and tax problems; 4.9.2 Klimov's model; 4.9.3 Minimum EWFT for the M/G/1 queue; 4.10 Near optimality of nearly index policies; Exercises; 5 The achievable region methodology; 5.1 Introduction; 5.2 A simple example; 5.3 Proof of the index theorem by greedy algorithm 5.4 Generalized conservation laws and indexable systems5.5 Performance bounds for policies for branching bandits; 5.6 Job selection and scheduling problems; 5.7 Multi-armed bandits on parallel machines; Exercises; 6 Restless bandits and Lagrangian relaxation; 6.1 Introduction; 6.2 Restless bandits; 6.3 Whittle indices for restless bandits; 6.4 Asymptotic optimality; 6.5 Monotone policies and simple proofs of indexability; 6.6 Applications to multi-class queueing systems; 6.7 Performance bounds for the Whittle index policy; 6.8 Indices for more general resource configurations; Exercises 7 Multi-population random sampling (theory) |
| Record Nr. | UNINA-9910830276503321 |
|
Gittins John C. <1938->
|
||
| Chichester, : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Multi-armed bandit allocation indices / / John Gittins, Kevin Glazebrook, Richard Weber
| Multi-armed bandit allocation indices / / John Gittins, Kevin Glazebrook, Richard Weber |
| Autore | Gittins John C. <1938-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Chichester, : Wiley, 2011 |
| Descrizione fisica | 1 online resource (311 p.) |
| Disciplina | 519.5 |
| Altri autori (Persone) |
GlazebrookKevin D. <1950->
WeberRichard <1953-> |
| Soggetto topico |
Resource allocation - Mathematical models
Mathematical optimization Programming (Mathematics) |
| ISBN |
1-283-37409-9
9786613374097 0-470-98004-4 0-470-98003-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Multi-armed Bandit Allocation Indices; Contents; Foreword; Foreword to the first edition; Preface; Preface to the first edition; 1 Introduction or exploration; Exercises; 2 Main ideas: Gittins index; 2.1 Introduction; 2.2 Decision processes; 2.3 Simple families of alternative bandit processes; 2.4 Dynamic programming; 2.5 Gittins index theorem; 2.6 Gittins index; 2.6.1 Gittins index and the multi-armed bandit; 2.6.2 Coins problem; 2.6.3 Characterization of the optimal stopping time; 2.6.4 The restart-in-state formulation; 2.6.5 Dependence on discount factor
2.6.6 Myopic and forwards induction policies2.7 Proof of the index theorem by interchanging bandit portions; 2.8 Continuous-time bandit processes; 2.9 Proof of the index theorem by induction and interchange argument; 2.10 Calculation of Gittins indices; 2.11 Monotonicity conditions; 2.11.1 Monotone indices; 2.11.2 Monotone jobs; 2.12 History of the index theorem; 2.13 Some decision process theory; Exercises; 3 Necessary assumptions for indices; 3.1 Introduction; 3.2 Jobs; 3.3 Continuous-time jobs; 3.3.1 Definition; 3.3.2 Policies for continuous-time jobs 3.3.3 The continuous-time index theorem for a SFABP of jobs3.4 Necessary assumptions; 3.4.1 Necessity of an infinite time horizon; 3.4.2 Necessity of constant exponential discounting; 3.4.3 Necessity of a single processor; 3.5 Beyond the necessary assumptions; 3.5.1 Bandit-dependent discount factors; 3.5.2 Stochastic discounting; 3.5.3 Undiscounted rewards; 3.5.4 A discrete search problem; 3.5.5 Multiple processors; Exercises; 4 Superprocesses, precedence constraints and arrivals; 4.1 Introduction; 4.2 Bandit superprocesses; 4.3 The index theorem for superprocesses 4.4 Stoppable bandit processes4.5 Proof of the index theorem by freezing and promotion rules; 4.5.1 Freezing rules; 4.5.2 Promotion rules; 4.6 The index theorem for jobs with precedence constraints; 4.7 Precedence constraints forming an out-forest; 4.8 Bandit processes with arrivals; 4.9 Tax problems; 4.9.1 Ongoing bandits and tax problems; 4.9.2 Klimov's model; 4.9.3 Minimum EWFT for the M/G/1 queue; 4.10 Near optimality of nearly index policies; Exercises; 5 The achievable region methodology; 5.1 Introduction; 5.2 A simple example; 5.3 Proof of the index theorem by greedy algorithm 5.4 Generalized conservation laws and indexable systems5.5 Performance bounds for policies for branching bandits; 5.6 Job selection and scheduling problems; 5.7 Multi-armed bandits on parallel machines; Exercises; 6 Restless bandits and Lagrangian relaxation; 6.1 Introduction; 6.2 Restless bandits; 6.3 Whittle indices for restless bandits; 6.4 Asymptotic optimality; 6.5 Monotone policies and simple proofs of indexability; 6.6 Applications to multi-class queueing systems; 6.7 Performance bounds for the Whittle index policy; 6.8 Indices for more general resource configurations; Exercises 7 Multi-population random sampling (theory) |
| Record Nr. | UNINA-9911019259203321 |
|
Gittins John C. <1938->
|
||
| Chichester, : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Resource allocation problems in supply chains / / by K. Ganesh, McKinsey & Company, Inc., Chennai, India [and three others]
| Resource allocation problems in supply chains / / by K. Ganesh, McKinsey & Company, Inc., Chennai, India [and three others] |
| Autore | Ganesh K. |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Bingley : , : Emerald Insight, , 2015 |
| Descrizione fisica | 1 online resource (197 p.) |
| Disciplina | 658.7 |
| Soggetto topico |
Resource allocation - Mathematical models
Mathematical optimization Programming (Mathematics) |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-78560-398-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Resource Allocation Problems in Supply Chains; Copyright page; Abstract; Contents; List of Tables; List of Figures; List of Symbols and Abbreviations; About the Authors; Section 1 Introduction; 1.1. Supply Chain Management; 1.2. Resource Allocation Problems in Supply Chain; 1.3. Motivation of Resource Allocation Problems; 1.3.1. Resource Allocation Variant in Bi-Objective Capacitated Supply Chain Network; 1.3.2. Resource Allocation Variant in Bi-Objective Bound Driven Capacitated Supply Chain Network
1.3.3. Resource Allocation Variant in Multiple Measures Driven Capacitated Multi-Echelon Supply Chain Network1.3.4. Resource Allocation Variant in Integrated Decision and Upper Bound Driven Capacitated Multi-Echelon Supply Chain Network; 1.3.5. Resource Allocation Variant in Integrated Decision and Time Driven Capacitated Multi-Echelon Supply Chain Network; 1.3.6. Resource Allocation Variant in Integrated Decision, Bound and Time Driven Capacitated Multi-Echelon Supply Chain Network; 1.4. Scope of the Present Study; Section 2 Literature Review; 2.1. Resource Allocation Problem 2.2. Review of the RA Variants Addressed in Current Research2.2.1. Bi-Objective Generalized Assignment Problem; 2.2.2. Multi-Commodity Network Flow Problem; 2.2.3. Multiple Measures Resource Allocation Problem; 2.2.4. Mixed Capacitated Arc Routing Problem; 2.2.5. Employee Routing Problem; 2.2.6. Vehicle Routing Problem with Backhauls with Time Windows; 2.3. Observations and Research Gap; 2.4. Summary; Section 3 Bi-Objective Capacitated Supply Chain Network; 3.1. Bi-Objective Resource Allocation Problem with Varying Capacity; 3.2. Solution Methodology to Solve BORAPVC 3.2.1. Mathematical Programming Model for BORAPVC3.2.2. Simulated Annealing with Population Size Initialization through Neighborhood Generation for GAP and BORAPVC; 3.2.2.1. Parameter settings for SAPING; 3.3. Computational Experiments and Results; 3.4. Conclusion; Section 4 Bi-Objective Bound Driven Capacitated Supply Chain Network; 4.1. Bi-Objective Resource Allocation Problem with Bound and Varying Capacity; 4.2. Solution Methodology to Solve IRARPUB; 4.2.1. Recursive Function Inherent Genetic Algorithm (REFING) for MCNF and BORAPBVC; 4.3. Computational Experiments and Results 4.3.1. Performance of Solution Methodology4.4. Case Study Demonstration; 4.4.1. Problem Identification and Discussion; 4.4.1.1. Patient Distribution System (PDS); 4.4.1.2. Input to the Central Body; 4.4.1.3. Flow chart for the allocation of patients; 4.4.1.4. Problem identification; 4.4.1.5. Assumptions; 4.4.2. Formulation of the Problem; 4.4.3. Model Testing; 4.4.4. Analysis of Results and Discussion; 4.4.5. Managerial Implications; 4.4.6. Summary for Case Study; 4.5. Conclusion; Section 5 Multiple Measures Driven Capacitated Multi-Echelon Supply Chain Network 5.1. Multiple Measures Resource Allocation Problem for Multi-Echelon Supply |
| Record Nr. | UNINA-9910461392603321 |
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Ganesh K.
|
||
| Bingley : , : Emerald Insight, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Resource allocation problems in supply chains / / by K. Ganesh, McKinsey & Company, Inc., Chennai, India [and three others]
| Resource allocation problems in supply chains / / by K. Ganesh, McKinsey & Company, Inc., Chennai, India [and three others] |
| Autore | Ganesh K. |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Bingley : , : Emerald Insight, , 2015 |
| Descrizione fisica | 1 online resource (197 p.) |
| Disciplina | 658.7 |
| Soggetto topico |
Resource allocation - Mathematical models
Mathematical optimization Programming (Mathematics) |
| ISBN | 1-78560-398-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Resource Allocation Problems in Supply Chains; Copyright page; Abstract; Contents; List of Tables; List of Figures; List of Symbols and Abbreviations; About the Authors; Section 1 Introduction; 1.1. Supply Chain Management; 1.2. Resource Allocation Problems in Supply Chain; 1.3. Motivation of Resource Allocation Problems; 1.3.1. Resource Allocation Variant in Bi-Objective Capacitated Supply Chain Network; 1.3.2. Resource Allocation Variant in Bi-Objective Bound Driven Capacitated Supply Chain Network
1.3.3. Resource Allocation Variant in Multiple Measures Driven Capacitated Multi-Echelon Supply Chain Network1.3.4. Resource Allocation Variant in Integrated Decision and Upper Bound Driven Capacitated Multi-Echelon Supply Chain Network; 1.3.5. Resource Allocation Variant in Integrated Decision and Time Driven Capacitated Multi-Echelon Supply Chain Network; 1.3.6. Resource Allocation Variant in Integrated Decision, Bound and Time Driven Capacitated Multi-Echelon Supply Chain Network; 1.4. Scope of the Present Study; Section 2 Literature Review; 2.1. Resource Allocation Problem 2.2. Review of the RA Variants Addressed in Current Research2.2.1. Bi-Objective Generalized Assignment Problem; 2.2.2. Multi-Commodity Network Flow Problem; 2.2.3. Multiple Measures Resource Allocation Problem; 2.2.4. Mixed Capacitated Arc Routing Problem; 2.2.5. Employee Routing Problem; 2.2.6. Vehicle Routing Problem with Backhauls with Time Windows; 2.3. Observations and Research Gap; 2.4. Summary; Section 3 Bi-Objective Capacitated Supply Chain Network; 3.1. Bi-Objective Resource Allocation Problem with Varying Capacity; 3.2. Solution Methodology to Solve BORAPVC 3.2.1. Mathematical Programming Model for BORAPVC3.2.2. Simulated Annealing with Population Size Initialization through Neighborhood Generation for GAP and BORAPVC; 3.2.2.1. Parameter settings for SAPING; 3.3. Computational Experiments and Results; 3.4. Conclusion; Section 4 Bi-Objective Bound Driven Capacitated Supply Chain Network; 4.1. Bi-Objective Resource Allocation Problem with Bound and Varying Capacity; 4.2. Solution Methodology to Solve IRARPUB; 4.2.1. Recursive Function Inherent Genetic Algorithm (REFING) for MCNF and BORAPBVC; 4.3. Computational Experiments and Results 4.3.1. Performance of Solution Methodology4.4. Case Study Demonstration; 4.4.1. Problem Identification and Discussion; 4.4.1.1. Patient Distribution System (PDS); 4.4.1.2. Input to the Central Body; 4.4.1.3. Flow chart for the allocation of patients; 4.4.1.4. Problem identification; 4.4.1.5. Assumptions; 4.4.2. Formulation of the Problem; 4.4.3. Model Testing; 4.4.4. Analysis of Results and Discussion; 4.4.5. Managerial Implications; 4.4.6. Summary for Case Study; 4.5. Conclusion; Section 5 Multiple Measures Driven Capacitated Multi-Echelon Supply Chain Network 5.1. Multiple Measures Resource Allocation Problem for Multi-Echelon Supply |
| Record Nr. | UNINA-9910797572503321 |
|
Ganesh K.
|
||
| Bingley : , : Emerald Insight, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Resource allocation problems in supply chains / / by K. Ganesh, McKinsey & Company, Inc., Chennai, India [and three others]
| Resource allocation problems in supply chains / / by K. Ganesh, McKinsey & Company, Inc., Chennai, India [and three others] |
| Autore | Ganesh K. |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Bingley : , : Emerald Insight, , 2015 |
| Descrizione fisica | 1 online resource (197 p.) |
| Disciplina | 658.7 |
| Soggetto topico |
Resource allocation - Mathematical models
Mathematical optimization Programming (Mathematics) |
| ISBN | 1-78560-398-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Resource Allocation Problems in Supply Chains; Copyright page; Abstract; Contents; List of Tables; List of Figures; List of Symbols and Abbreviations; About the Authors; Section 1 Introduction; 1.1. Supply Chain Management; 1.2. Resource Allocation Problems in Supply Chain; 1.3. Motivation of Resource Allocation Problems; 1.3.1. Resource Allocation Variant in Bi-Objective Capacitated Supply Chain Network; 1.3.2. Resource Allocation Variant in Bi-Objective Bound Driven Capacitated Supply Chain Network
1.3.3. Resource Allocation Variant in Multiple Measures Driven Capacitated Multi-Echelon Supply Chain Network1.3.4. Resource Allocation Variant in Integrated Decision and Upper Bound Driven Capacitated Multi-Echelon Supply Chain Network; 1.3.5. Resource Allocation Variant in Integrated Decision and Time Driven Capacitated Multi-Echelon Supply Chain Network; 1.3.6. Resource Allocation Variant in Integrated Decision, Bound and Time Driven Capacitated Multi-Echelon Supply Chain Network; 1.4. Scope of the Present Study; Section 2 Literature Review; 2.1. Resource Allocation Problem 2.2. Review of the RA Variants Addressed in Current Research2.2.1. Bi-Objective Generalized Assignment Problem; 2.2.2. Multi-Commodity Network Flow Problem; 2.2.3. Multiple Measures Resource Allocation Problem; 2.2.4. Mixed Capacitated Arc Routing Problem; 2.2.5. Employee Routing Problem; 2.2.6. Vehicle Routing Problem with Backhauls with Time Windows; 2.3. Observations and Research Gap; 2.4. Summary; Section 3 Bi-Objective Capacitated Supply Chain Network; 3.1. Bi-Objective Resource Allocation Problem with Varying Capacity; 3.2. Solution Methodology to Solve BORAPVC 3.2.1. Mathematical Programming Model for BORAPVC3.2.2. Simulated Annealing with Population Size Initialization through Neighborhood Generation for GAP and BORAPVC; 3.2.2.1. Parameter settings for SAPING; 3.3. Computational Experiments and Results; 3.4. Conclusion; Section 4 Bi-Objective Bound Driven Capacitated Supply Chain Network; 4.1. Bi-Objective Resource Allocation Problem with Bound and Varying Capacity; 4.2. Solution Methodology to Solve IRARPUB; 4.2.1. Recursive Function Inherent Genetic Algorithm (REFING) for MCNF and BORAPBVC; 4.3. Computational Experiments and Results 4.3.1. Performance of Solution Methodology4.4. Case Study Demonstration; 4.4.1. Problem Identification and Discussion; 4.4.1.1. Patient Distribution System (PDS); 4.4.1.2. Input to the Central Body; 4.4.1.3. Flow chart for the allocation of patients; 4.4.1.4. Problem identification; 4.4.1.5. Assumptions; 4.4.2. Formulation of the Problem; 4.4.3. Model Testing; 4.4.4. Analysis of Results and Discussion; 4.4.5. Managerial Implications; 4.4.6. Summary for Case Study; 4.5. Conclusion; Section 5 Multiple Measures Driven Capacitated Multi-Echelon Supply Chain Network 5.1. Multiple Measures Resource Allocation Problem for Multi-Echelon Supply |
| Record Nr. | UNINA-9910823776003321 |
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Ganesh K.
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||
| Bingley : , : Emerald Insight, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Resource economics / Jon M. Conrad
| Resource economics / Jon M. Conrad |
| Autore | Conrad, Jon M. |
| Pubbl/distr/stampa | Cambrdige : Cambridge University Press, 1999 |
| Descrizione fisica | x, 213 p. : ill. ; 24 cm. |
| Disciplina | 333 |
| Soggetto topico |
Microsoft Excel (Computer file)
Natural resources - Management - Mathematical models Resource allocation - Mathematical models |
| ISBN | 0521640121 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | en |
| Record Nr. | UNISALENTO-991001478719707536 |
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Conrad, Jon M.
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| Cambrdige : Cambridge University Press, 1999 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
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