Scheduling problems and solutions /:
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York :
NOVA/Nova Science Publishers, Inc.,
[2012]
|
| Schriftenreihe: | Computer science, technology and applications.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | 1 online resource : illustrations. |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781614707691 1614707693 |
Internformat
MARC
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| 245 | 0 | 0 | |a Scheduling problems and solutions / |c Hussein M. Khodr, editor. |
| 264 | 1 | |a New York : |b NOVA/Nova Science Publishers, Inc., |c [2012] | |
| 300 | |a 1 online resource : |b illustrations. | ||
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| 504 | |a Includes bibliographical references and index. | ||
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| 546 | |a English. | ||
| 505 | 0 | |a SCHEDULING PROBLEMS AND SOLUTIONS ; SCHEDULING PROBLEMS AND SOLUTIONS ; CONTENTS ; PREFACE ; INTEGRATION OF OPERATION PLANNING AND SCHEDULING IN SUPPLY CHAIN SYSTEMS: A REVIEW ; ABSTRACT ; 1. INTRODUCTION ; 2. INTEGRATION IN SUPPLY CHAIN DECISION-MAKING ; 2.1. Classification of Modeling Approaches ; 2.2. Agent-Based Models for SCM ; 2.3. Challenges in SCM ; CONCLUSION ; ACKNOWLEDGMENTS ; REFERENCES ; APPLY HEURISTICS AND META-HEURISTICS TO LARGE-SCALE PROCESS BATCH SCHEDULING ; ABSTRACT ; 1. INTRODUCTION ; 1.1. General Review on Process Scheduling. | |
| 505 | 8 | |a 1.2. Complexity of Process Scheduling1.2.1. Processing Sequences ; 1.2.2. Intermediate Storage Policies ; 1.2.3. Changeovers ; 1.2.4. Operation Modes of Processing Tasks; 1.2.5. Demand Patterns ; 1.2.6. Resource Considerations ; 1.2.7. Scheduling Objectives ; 1.3. Solution Methods for Process Scheduling ; 1.4. Strategies for Large-Scale Process Scheduling ; 1.5. Summary of the Research Background ; 1.6. Problems to be investigated ; 2. RULE-EVOLUTIONARY APPROACHES FOR SMSP ; 2.1. Problem Description ; 2.2. MILP Model for SMSP ; 2.2.1. Notations ; (A) Indices ; (B) Sets ; (C) Parameters. | |
| 505 | 8 | |a (D) Variables Positive Variables: ; Binary Variables: ; 2.2.2. Milp Model ; (A) Problem Constraints ; (B) Objective Functions ; 2.2.3. Solutions for Example 2-1 ; 2.3. Heuristic Rules and Random Search ; 2.3.1. Seven Rules for the Minimization of Makespan Related Objectives ; 2.3.2. Performance of Different Rules ; 2.3.3. Procedure of the Genetic Algorithm ; 2.3.4. Simulation Experiments of GA Combined with Different Rules ; 2.4. Rule-Evolutionary Approaches ; 2.4.1. Mixed Chromosome and Evaluation Procedure in ARS ; 2.4.2. Observation of ARS in Solving Problems. | |
| 505 | 8 | |a 2.5. Effectiveness of the Rule-Evolutionary Approaches for Large-Scale Examples 3. HEURISTICS AND META-HEURISTICS FOR MMSP ; 3.1. Problem Description ; 3.2. Solution by MILP ; 3.3. Genetic Algorithms ; 3.3.1. Position Selection Rules ; 3.3.2. Two Sample Schedules of Example 3-1 ; 3.3.3. A Penalty Method to the Infeasible Schedules ; 3.3.4. Comparison of GA and MILP ; 3.4. Global Search Framework ; 4. PATTERN MATCHING METHOD FOR MPSP ; 4.1. Problem Description ; 4.2. A Motivating Example ; 4.3. Pattern Scheduling for the Motivating Example. | |
| 505 | 8 | |a 4.3.1. State Consumption and Replenishment Equations 4.3.2. Natural Periodicity Analysis ; Master/Slave Task Sequences and Crucial Units ; Natural Periodicity Analysis; 4.3.3. Two Pattern Schedules ; Heuristics for Task Assignment in Example 4-1 ; Pattern Schedule I ; Pattern Schedule II ; 4.4. Heuristic Method for Small-Size Instances in Example 4-1 ; 4.4.1. Task Sequences Based on Heuristics and Search Trees ; 4.4.2. Solution of Small-Size Instances by a Solver ; 4.5. Decomposition of Long-Horizon Instances in Example 4-1 ; 4.5.1. Long-Horizon Instances with VPT. | |
| 650 | 0 | |a Production scheduling. |0 http://id.loc.gov/authorities/subjects/sh85118098 | |
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| 700 | 1 | |a Khodr, Hussein M., |e editor. | |
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Datensatz im Suchindex
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| contents | SCHEDULING PROBLEMS AND SOLUTIONS ; SCHEDULING PROBLEMS AND SOLUTIONS ; CONTENTS ; PREFACE ; INTEGRATION OF OPERATION PLANNING AND SCHEDULING IN SUPPLY CHAIN SYSTEMS: A REVIEW ; ABSTRACT ; 1. INTRODUCTION ; 2. INTEGRATION IN SUPPLY CHAIN DECISION-MAKING ; 2.1. Classification of Modeling Approaches ; 2.2. Agent-Based Models for SCM ; 2.3. Challenges in SCM ; CONCLUSION ; ACKNOWLEDGMENTS ; REFERENCES ; APPLY HEURISTICS AND META-HEURISTICS TO LARGE-SCALE PROCESS BATCH SCHEDULING ; ABSTRACT ; 1. INTRODUCTION ; 1.1. General Review on Process Scheduling. 1.2. Complexity of Process Scheduling1.2.1. Processing Sequences ; 1.2.2. Intermediate Storage Policies ; 1.2.3. Changeovers ; 1.2.4. Operation Modes of Processing Tasks; 1.2.5. Demand Patterns ; 1.2.6. Resource Considerations ; 1.2.7. Scheduling Objectives ; 1.3. Solution Methods for Process Scheduling ; 1.4. Strategies for Large-Scale Process Scheduling ; 1.5. Summary of the Research Background ; 1.6. Problems to be investigated ; 2. RULE-EVOLUTIONARY APPROACHES FOR SMSP ; 2.1. Problem Description ; 2.2. MILP Model for SMSP ; 2.2.1. Notations ; (A) Indices ; (B) Sets ; (C) Parameters. (D) Variables Positive Variables: ; Binary Variables: ; 2.2.2. Milp Model ; (A) Problem Constraints ; (B) Objective Functions ; 2.2.3. Solutions for Example 2-1 ; 2.3. Heuristic Rules and Random Search ; 2.3.1. Seven Rules for the Minimization of Makespan Related Objectives ; 2.3.2. Performance of Different Rules ; 2.3.3. Procedure of the Genetic Algorithm ; 2.3.4. Simulation Experiments of GA Combined with Different Rules ; 2.4. Rule-Evolutionary Approaches ; 2.4.1. Mixed Chromosome and Evaluation Procedure in ARS ; 2.4.2. Observation of ARS in Solving Problems. 2.5. Effectiveness of the Rule-Evolutionary Approaches for Large-Scale Examples 3. HEURISTICS AND META-HEURISTICS FOR MMSP ; 3.1. Problem Description ; 3.2. Solution by MILP ; 3.3. Genetic Algorithms ; 3.3.1. Position Selection Rules ; 3.3.2. Two Sample Schedules of Example 3-1 ; 3.3.3. A Penalty Method to the Infeasible Schedules ; 3.3.4. Comparison of GA and MILP ; 3.4. Global Search Framework ; 4. PATTERN MATCHING METHOD FOR MPSP ; 4.1. Problem Description ; 4.2. A Motivating Example ; 4.3. Pattern Scheduling for the Motivating Example. 4.3.1. State Consumption and Replenishment Equations 4.3.2. Natural Periodicity Analysis ; Master/Slave Task Sequences and Crucial Units ; Natural Periodicity Analysis; 4.3.3. Two Pattern Schedules ; Heuristics for Task Assignment in Example 4-1 ; Pattern Schedule I ; Pattern Schedule II ; 4.4. Heuristic Method for Small-Size Instances in Example 4-1 ; 4.4.1. Task Sequences Based on Heuristics and Search Trees ; 4.4.2. Solution of Small-Size Instances by a Solver ; 4.5. Decomposition of Long-Horizon Instances in Example 4-1 ; 4.5.1. Long-Horizon Instances with VPT. |
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| discipline | Wirtschaftswissenschaften |
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| id | ZDB-4-EBA-on1162318317 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:29:57Z |
| institution | BVB |
| isbn | 9781614707691 1614707693 |
| language | English |
| lccn | 2020687321 |
| oclc_num | 1162318317 |
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| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource : illustrations. |
| psigel | ZDB-4-EBA |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | NOVA/Nova Science Publishers, Inc., |
| record_format | marc |
| series | Computer science, technology and applications. |
| series2 | Computer science, technology and application |
| spelling | Scheduling problems and solutions / Hussein M. Khodr, editor. New York : NOVA/Nova Science Publishers, Inc., [2012] 1 online resource : illustrations. text txt rdacontent computer c rdamedia online resource cr rdacarrier data file rda Computer science, technology and application Includes bibliographical references and index. Description based on print version record. English. SCHEDULING PROBLEMS AND SOLUTIONS ; SCHEDULING PROBLEMS AND SOLUTIONS ; CONTENTS ; PREFACE ; INTEGRATION OF OPERATION PLANNING AND SCHEDULING IN SUPPLY CHAIN SYSTEMS: A REVIEW ; ABSTRACT ; 1. INTRODUCTION ; 2. INTEGRATION IN SUPPLY CHAIN DECISION-MAKING ; 2.1. Classification of Modeling Approaches ; 2.2. Agent-Based Models for SCM ; 2.3. Challenges in SCM ; CONCLUSION ; ACKNOWLEDGMENTS ; REFERENCES ; APPLY HEURISTICS AND META-HEURISTICS TO LARGE-SCALE PROCESS BATCH SCHEDULING ; ABSTRACT ; 1. INTRODUCTION ; 1.1. General Review on Process Scheduling. 1.2. Complexity of Process Scheduling1.2.1. Processing Sequences ; 1.2.2. Intermediate Storage Policies ; 1.2.3. Changeovers ; 1.2.4. Operation Modes of Processing Tasks; 1.2.5. Demand Patterns ; 1.2.6. Resource Considerations ; 1.2.7. Scheduling Objectives ; 1.3. Solution Methods for Process Scheduling ; 1.4. Strategies for Large-Scale Process Scheduling ; 1.5. Summary of the Research Background ; 1.6. Problems to be investigated ; 2. RULE-EVOLUTIONARY APPROACHES FOR SMSP ; 2.1. Problem Description ; 2.2. MILP Model for SMSP ; 2.2.1. Notations ; (A) Indices ; (B) Sets ; (C) Parameters. (D) Variables Positive Variables: ; Binary Variables: ; 2.2.2. Milp Model ; (A) Problem Constraints ; (B) Objective Functions ; 2.2.3. Solutions for Example 2-1 ; 2.3. Heuristic Rules and Random Search ; 2.3.1. Seven Rules for the Minimization of Makespan Related Objectives ; 2.3.2. Performance of Different Rules ; 2.3.3. Procedure of the Genetic Algorithm ; 2.3.4. Simulation Experiments of GA Combined with Different Rules ; 2.4. Rule-Evolutionary Approaches ; 2.4.1. Mixed Chromosome and Evaluation Procedure in ARS ; 2.4.2. Observation of ARS in Solving Problems. 2.5. Effectiveness of the Rule-Evolutionary Approaches for Large-Scale Examples 3. HEURISTICS AND META-HEURISTICS FOR MMSP ; 3.1. Problem Description ; 3.2. Solution by MILP ; 3.3. Genetic Algorithms ; 3.3.1. Position Selection Rules ; 3.3.2. Two Sample Schedules of Example 3-1 ; 3.3.3. A Penalty Method to the Infeasible Schedules ; 3.3.4. Comparison of GA and MILP ; 3.4. Global Search Framework ; 4. PATTERN MATCHING METHOD FOR MPSP ; 4.1. Problem Description ; 4.2. A Motivating Example ; 4.3. Pattern Scheduling for the Motivating Example. 4.3.1. State Consumption and Replenishment Equations 4.3.2. Natural Periodicity Analysis ; Master/Slave Task Sequences and Crucial Units ; Natural Periodicity Analysis; 4.3.3. Two Pattern Schedules ; Heuristics for Task Assignment in Example 4-1 ; Pattern Schedule I ; Pattern Schedule II ; 4.4. Heuristic Method for Small-Size Instances in Example 4-1 ; 4.4.1. Task Sequences Based on Heuristics and Search Trees ; 4.4.2. Solution of Small-Size Instances by a Solver ; 4.5. Decomposition of Long-Horizon Instances in Example 4-1 ; 4.5.1. Long-Horizon Instances with VPT. Production scheduling. http://id.loc.gov/authorities/subjects/sh85118098 Ordonnancement (Gestion) BUSINESS & ECONOMICS Production & Operations Management. bisacsh TECHNOLOGY & ENGINEERING Industrial Engineering. bisacsh TECHNOLOGY & ENGINEERING Industrial Technology. bisacsh Production scheduling fast Khodr, Hussein M., editor. Print version: Scheduling problems and solutions New York : Nova Science Publishers, 2012. 9781614706892 (hardcover) (DLC) 2011025573 Computer science, technology and applications. http://id.loc.gov/authorities/names/no2010162081 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=540233 Volltext |
| spellingShingle | Scheduling problems and solutions / Computer science, technology and applications. SCHEDULING PROBLEMS AND SOLUTIONS ; SCHEDULING PROBLEMS AND SOLUTIONS ; CONTENTS ; PREFACE ; INTEGRATION OF OPERATION PLANNING AND SCHEDULING IN SUPPLY CHAIN SYSTEMS: A REVIEW ; ABSTRACT ; 1. INTRODUCTION ; 2. INTEGRATION IN SUPPLY CHAIN DECISION-MAKING ; 2.1. Classification of Modeling Approaches ; 2.2. Agent-Based Models for SCM ; 2.3. Challenges in SCM ; CONCLUSION ; ACKNOWLEDGMENTS ; REFERENCES ; APPLY HEURISTICS AND META-HEURISTICS TO LARGE-SCALE PROCESS BATCH SCHEDULING ; ABSTRACT ; 1. INTRODUCTION ; 1.1. General Review on Process Scheduling. 1.2. Complexity of Process Scheduling1.2.1. Processing Sequences ; 1.2.2. Intermediate Storage Policies ; 1.2.3. Changeovers ; 1.2.4. Operation Modes of Processing Tasks; 1.2.5. Demand Patterns ; 1.2.6. Resource Considerations ; 1.2.7. Scheduling Objectives ; 1.3. Solution Methods for Process Scheduling ; 1.4. Strategies for Large-Scale Process Scheduling ; 1.5. Summary of the Research Background ; 1.6. Problems to be investigated ; 2. RULE-EVOLUTIONARY APPROACHES FOR SMSP ; 2.1. Problem Description ; 2.2. MILP Model for SMSP ; 2.2.1. Notations ; (A) Indices ; (B) Sets ; (C) Parameters. (D) Variables Positive Variables: ; Binary Variables: ; 2.2.2. Milp Model ; (A) Problem Constraints ; (B) Objective Functions ; 2.2.3. Solutions for Example 2-1 ; 2.3. Heuristic Rules and Random Search ; 2.3.1. Seven Rules for the Minimization of Makespan Related Objectives ; 2.3.2. Performance of Different Rules ; 2.3.3. Procedure of the Genetic Algorithm ; 2.3.4. Simulation Experiments of GA Combined with Different Rules ; 2.4. Rule-Evolutionary Approaches ; 2.4.1. Mixed Chromosome and Evaluation Procedure in ARS ; 2.4.2. Observation of ARS in Solving Problems. 2.5. Effectiveness of the Rule-Evolutionary Approaches for Large-Scale Examples 3. HEURISTICS AND META-HEURISTICS FOR MMSP ; 3.1. Problem Description ; 3.2. Solution by MILP ; 3.3. Genetic Algorithms ; 3.3.1. Position Selection Rules ; 3.3.2. Two Sample Schedules of Example 3-1 ; 3.3.3. A Penalty Method to the Infeasible Schedules ; 3.3.4. Comparison of GA and MILP ; 3.4. Global Search Framework ; 4. PATTERN MATCHING METHOD FOR MPSP ; 4.1. Problem Description ; 4.2. A Motivating Example ; 4.3. Pattern Scheduling for the Motivating Example. 4.3.1. State Consumption and Replenishment Equations 4.3.2. Natural Periodicity Analysis ; Master/Slave Task Sequences and Crucial Units ; Natural Periodicity Analysis; 4.3.3. Two Pattern Schedules ; Heuristics for Task Assignment in Example 4-1 ; Pattern Schedule I ; Pattern Schedule II ; 4.4. Heuristic Method for Small-Size Instances in Example 4-1 ; 4.4.1. Task Sequences Based on Heuristics and Search Trees ; 4.4.2. Solution of Small-Size Instances by a Solver ; 4.5. Decomposition of Long-Horizon Instances in Example 4-1 ; 4.5.1. Long-Horizon Instances with VPT. Production scheduling. http://id.loc.gov/authorities/subjects/sh85118098 Ordonnancement (Gestion) BUSINESS & ECONOMICS Production & Operations Management. bisacsh TECHNOLOGY & ENGINEERING Industrial Engineering. bisacsh TECHNOLOGY & ENGINEERING Industrial Technology. bisacsh Production scheduling fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85118098 |
| title | Scheduling problems and solutions / |
| title_auth | Scheduling problems and solutions / |
| title_exact_search | Scheduling problems and solutions / |
| title_full | Scheduling problems and solutions / Hussein M. Khodr, editor. |
| title_fullStr | Scheduling problems and solutions / Hussein M. Khodr, editor. |
| title_full_unstemmed | Scheduling problems and solutions / Hussein M. Khodr, editor. |
| title_short | Scheduling problems and solutions / |
| title_sort | scheduling problems and solutions |
| topic | Production scheduling. http://id.loc.gov/authorities/subjects/sh85118098 Ordonnancement (Gestion) BUSINESS & ECONOMICS Production & Operations Management. bisacsh TECHNOLOGY & ENGINEERING Industrial Engineering. bisacsh TECHNOLOGY & ENGINEERING Industrial Technology. bisacsh Production scheduling fast |
| topic_facet | Production scheduling. Ordonnancement (Gestion) BUSINESS & ECONOMICS Production & Operations Management. TECHNOLOGY & ENGINEERING Industrial Engineering. TECHNOLOGY & ENGINEERING Industrial Technology. Production scheduling |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=540233 |
| work_keys_str_mv | AT khodrhusseinm schedulingproblemsandsolutions |