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 Scheduling -- 1.2.1. 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 -- 4.5.2. Long-Horizon Instances with CPT -- 4.6. General Solution Strategy and Other Examples -- 4.6.1. Solution Framework -- 4.6.2. Master/Slave Task Sequences and Bottleneck Units -- 4.6.3. Natural Periodicity and Pattern Scheduling -- 4.6.4. General Heuristics for Task Assignment -- 4.6.5. Other Examples -- CONCLUSION -- APPENDIX A. PROBLEM DATA OF EXAMPLES 3-1, 3-2 AND 3-3 (MMSP) -- REFERENCES -- POWER GENERATION AND DEMAND SCHEDULING BASED ON STOCHASTIC PROGRAMMING -- ABSTRACT -- 1. NOTATIONS -- Indices and Sets -- Parameters -- Variables and Functions -- 2. INTRODUCTION -- 3. DEREGULATION AND POWER MARKETS IN ELECTRIC INDUSTRY -- 3.1. Day-Ahead Electricity Market and Real-Time Market -- 4. GENERATION SCHEDULING -- 4.1. Unit Commitment Optimization -- A. Priority Lists (PL).

B. Dynamic Programming (DP) -- C. Lagrangian Relaxation (LR) -- D. Mixed Integer Programming (MIP) -- E. Other Approaches as Genetic Algorithm, Neural Network, Etc. -- 4.2. Generation Scheduling: A Deterministic Approach -- A. Case 1: Power Balance Constraint -- B. Case 2: Case 1 + Start-up Cost -- C. Case 3: Case 2 + Spinning Reserve -- D. Case 4: Case 3 + Minimum up and down Time Constraint -- E. Case 5: Case 4 + Ramping Constraints -- F. Case 6: Case 5 + Interruptible Loads -- G. Case 7: Case 6 + Dispatchable Load -- H. Case 8: Case 7 + Shiftable Load -- I. Case 9: Case 8 + Transmission Line Constraint -- 5. FUNDAMENTAL OF STOCHASTIC PROGRAMMING -- 5.1. Random Variables -- 5.2. Stochastic Programming -- 5.2.1. Distribution Problems -- 5.2.2. Recourse Problems -- 5.3. Scenario Tree -- 5.3.1. Scenario Generation -- Monte Carlo Sampling -- A. Markov Chain Monte Carlo Sampling -- 5.3.2. Scenario Reduction -- 6. STOCHASTIC UNIT COMMITMENT WITH DEMAND SCHEDULING -- 6.1. Generation Scheduling: A Stochastic Approach -- 6.2. Case Study -- 6.3. Simulation Results -- A. Case 1: Stochastic Unit Commitment with Interruptible Load Consideration -- B. Case 2: Case 1 + Dispatchable Loads -- C. Case 3: Case 2 + Shiftable Load -- CONCLUSION -- REFERENCES -- ECONOMIC LOAD SCHEDULING OF THERMAL POWER GENERATING UNITS -- ABSTRACT -- 1. INTRODUCTION -- 2. DIFFERENT SOLUTION TECHNIQUES -- 3. OPERATING COST OF THERMAL GENERATING UNITS -- 4. ECONOMIC LOAD SCHEDULING OF THERMAL GENERATING UNITS NEGLECTING LOSSES AND NO GENERATOR LIMITS -- Example 1.1. -- 5. ECONOMIC LOAD SCHEDULING OF THERMAL GENERATING UNITS NEGLECTING LOSSES AND CONSIDERING GENERATOR LIMITS -- Example 1.2. -- Solution: -- 6. ECONOMIC LOAD SCHEDULING OF THERMAL GENERATING UNITS INCLUDING LOSSES AND CONSIDERING GENERATOR LIMITS -- Example 1.3. -- Solution.

7. NON-CONVEX ECONOMIC LOAD SCHEDULING OF THERMAL GENERATING UNITS -- 1.7.1. Effects pf Valve Point Loading -- 1.7.2. Formulation of Economic Load Scheduling (ELS) Problem with Valve Point Loading -- 1.7.3. Formulation of Economic Emisssion Scheduling (EES) Problem -- 1.7.4. Formulation of Combined Economic Emisssion Scheduling (CEES) Problem -- 1.7.5. Particle Swarm Optimization (PSO) -- 1.7.6. Differential Evolution -- 1.7.6.1. Initialization -- 1.7.6.2. Mutation Operation -- 1.7.6.3. Crossover Operation -- 1.7.6.4. Selection Operation -- Systems and Results -- Example 1.4. -- Solution: -- Example 1.5 -- Solution: -- Example 1.6 -- Solution: -- Example 1.7 -- Solution -- CONCLUSION -- REFERENCES -- CONCEPTS AND METHODS FOR SCHEDULING FIELD MACHINERY OPERATIONS -- ABSTRACT -- INTRODUCTION -- SCHEDULING PROBLEMS IN BIO-PRODUCTION SYSTEMS -- SCHEDULING OF SEQUENTIAL FIELD OPERATIONS -- PROBLEM FORMULATION -- CASE STUDY -- CONCLUSION -- REFERENCES -- SINGLE MACHINE SCHEDULING PROBLEMS UNDER LEARNING EFFECT AND DETERIORATING JOBS -- ABSTRACT -- 1. INTRODUCTION -- 2. PROBLEM FORMULATION -- 3. SOME SINGLE MACHINE SCHEDULING PROBLEMS UNDER THE LEARNING EFFECT AND DETERIORATING JOBS -- 3.1. The Problem njCCj,...,2,1maxmax -- Theorem 1 -- Proof -- Theorem 2 -- Proof -- 3.2. The Problem jC -- Theorem 3 -- Proof -- Theorem 4 -- Proof -- 3.3. The Problem 2jC -- Theorem 5 -- Proof -- Theorem 6 -- Proof -- 3.4. The Problem njdCLjj,...,2,1maxmax -- Theorem 7 -- Proof -- Theorem 8 -- Proof -- CONCLUSION -- REFERENCES -- SCHEDULING PROBLEMS AND SOLUTIONS IN WIMAX NETWORKS -- ABSTRACT -- 1. INTRODUCTION -- 2. WIMAX NETWORKS -- 2.1. Network Architecture -- 2.2. Accessing Techniques in the Physical Layer -- 2.3. Frame Structures -- 2.4. QoS Service Classes -- 3. SCHEDULING SOLUTIONS UNDER THE PMP ARCHITECTURE -- 3.1. Connection-Based Scheduling Solution.

3.2. MSS-Based Scheduling Solution -- 3.3. Subchannel-Based Scheduling Solution -- 4. SCHEDULING SOLUTIONS UNDER THE RELAY ARCHITECTURE -- 4.1. Scheduling Solution Using RSs to Improve Network Throughput -- 4.2. Scheduling Solution Using RSs to Conserve MSSs' Energy -- 5. SCHEDULING SOLUTIONS UNDER THE MESH ARCHITECTURE -- 5.1. Scheduling Solution to Enhance Concurrent Transmissions -- 5.2. Scheduling Solution to Reduce Scheduling Length -- CONCLUSION -- REFERENCES -- SCHEDULING AT A CROSS DOCK FACILITY WITH STOCHASTIC TRUCK ARRIVAL TIMES -- ABSTRACT -- INTRODUCTION -- MODEL FORMULATION -- Sets -- Parameters -- Decision Variables -- SOLUTION ALGORITHM -- Post Pareto Simulation -- Monte Carlo Procedure (MCP) -- For n = 1:N, j = 1:

A. Analysis of Serial Algorithm for ECG Computer Simulation.