Scheduling problems and solutions / Hussein M. Khodr, editor.

Format:

Book

Contributor:

Khodr, Hussein M.

Series:

Computer science, technology and applications.

Computer science, technology and application

Language:

English

Subjects (All):

Production scheduling.

Physical Description:

1 online resource (342 p.)

Edition:

1st ed.

Place of Publication:

New York : Nova Science Publishers, 2012.

Language Note:

English

Summary:

In recent years more attention has been devoted to the computer aided scheduling problems in industries process. Computer programs are now available in order for production engineers to develop an understanding for how a decision can be taken while making the scheduling process easier to determine when, where and how to produce a set of products. Due to the discrete decisions involved, these scheduling problems have a combinatorial nature and are therefore challenging from a computational complexity standpoint. With the tools, scheduling studies can be run to simulate present industry conditions and to help with the long-range planning of new facilities for a given production plan. The tools also provide an opportunity for the production engineers to do such things as optimise the production process and apply diverse mathematical optimisation techniques.

Contents:

Intro

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

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

APPENDIX A. PROBLEM DATA OF EXAMPLES 3-1, 3-2 AND 3-3 (MMSP)

POWER GENERATION AND DEMAND SCHEDULING BASED ON STOCHASTIC PROGRAMMING

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

ECONOMIC LOAD SCHEDULING OF THERMAL POWER GENERATING UNITS

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.

Example 1.5

Example 1.6

Example 1.7

Solution

CONCEPTS AND METHODS FOR SCHEDULING FIELD MACHINERY OPERATIONS

INTRODUCTION

SCHEDULING PROBLEMS IN BIO-PRODUCTION SYSTEMS

SCHEDULING OF SEQUENTIAL FIELD OPERATIONS

PROBLEM FORMULATION

CASE STUDY

SINGLE MACHINE SCHEDULING PROBLEMS UNDER LEARNING EFFECT AND DETERIORATING JOBS

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

3.2. The Problem jC

Theorem 3

Theorem 4

3.3. The Problem 2jC

Theorem 5

Theorem 6

3.4. The Problem njdCLjj,...,2,1maxmax

Theorem 7

Theorem 8

SCHEDULING PROBLEMS AND SOLUTIONS IN WIMAX NETWORKS

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

SCHEDULING AT A CROSS DOCK FACILITY WITH STOCHASTIC TRUCK ARRIVAL TIMES

MODEL FORMULATION

Sets

Decision Variables

SOLUTION ALGORITHM

Post Pareto Simulation

Monte Carlo Procedure (MCP)

For n = 1:N, j = 1:|J|

End

NUMERICAL EXAMPLES

TRAFFIC FLOW SCHEDULING BASED ON LOCAL REGULARITY PREDICTION

2. CHARACTERIZATION OF LOCAL REGULARITY

2.1. The Wavelet Transform

2.2. Local Regularity Estimation

3. POINTWISE HÖLDER EXPONENT PREDICTION

3.1. Adaptive Prediction Algorithm with Updating of Noise Process Characteristics

Algorithm 1: Kalman Filter Based Adaptive Prediction Algorithm

3.2. Evaluation of the Proposed Prediction Algorithm

4. POINTWISE HÖLDER EXPONENT BASED SCHEDULING DISCIPLINE

5. PERFORMANCE OF THE PROPOSED SCHEDULING SCHEME

LOAD-PREDICTION DYNAMIC SCHEDULING AND ITS APPLICATION TO BIOMEDICAL COMPUTER SIMULATIONS

1. GENERAL-PURPOSE GPU, CUDA, MULTI-CORE CPU AND OPENMP

A. General-Purpose Gpu and Cuda

B. Multi-Core CPU and OpenMP

2. LOAD-PREDICTION DYNAMIC SCHEDULING SCHEME

A. The Main Ideas and Characteristics of LPDS

B. The Scheduling Algorithm of LPDS

3. ECG SIMULATION USING PSS AND LPDS.

A. Analysis of Serial Algorithm for ECG Computer Simulation.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

ISBN:

1-61470-769-3

OCLC:

830323673