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|a Scheduling problems and solutions /
|c Hussein M. Khodr, editor.
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|a New York :
|b NOVA/Nova Science Publishers, Inc.,
|c [2012]
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|b illustrations.
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1 |
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|a Computer science, technology and application
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|a Includes bibliographical references and index.
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|a Description based on print version record.
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| 546 |
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|a English.
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0 |
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|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.
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|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.
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| 505 |
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|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.
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|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.
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|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.
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|a Production scheduling.
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