Heuristics, Metaheuristics and Approximate Methods in Planning and Scheduling [electronic resource] /
Rabadi, Ghaith.editor.
The scope of this book is limited to heuristics, metaheuristics, and approximate methods and algorithms as applied to planning and scheduling problems. While it is not possible to give a comprehensive treatment of this topic in one book, the aim of this work is to provide the reader with a diverse set of planning and scheduling problems and different heuristic approaches to solve them. The problems range from traditional single stage and parallel machine problems to more modern settings such as robotic cells and flexible job shop networks. Furthermore, some chapters deal with deterministic problems while some others treat stochastic versions of the problems. Unlike most of the literature that deals with planning and scheduling problems in the manufacturing and production environments, in this book the environments were extended to nontraditional applications such as spatial scheduling (optimizing space over time), runway scheduling, and surgical scheduling. The solution methods used in the different chapters of the book also spread from well-established heuristics and metaheuristics such as Genetic Algorithms and Ant Colony Optimization to more recent ones such as Meta-RaPS.
Description based upon print version of record.
Introduction; Contents; Contributors; 1 Approximation Algorithms for Spatial Scheduling; 1.1 Introduction to Spatial Scheduling; 1.2 General Problem Formulation; 1.3 Feasible Schedule Construction: The Bottom-Left Time-Incrementing Heuristic; 1.4 The Packing Factor Heuristic; 1.4.1 Area Assignment Heuristic; 1.4.2 Job Input Orderings; 1.5 Local Search; 1.6 Metaheuristic for Randomized Priority Search (Meta-RaPS); 1.6.1 Construction Stage; 1.6.2 Improvement Stage; 1.6.3 Complete Meta-RaPS Algorithm; 1.7 Comparison of Methods: Computational Results on Benchmark Problems
1.7.1 Problem Generation1.7.2 Parameter Settings and Algorithm Implementations; 1.7.3 Results and Discussion; 1.8 Conclusion; References; 2 Estimating the Costs of Planned Changes Implied by Freezing Production Plans; 2.1 Introduction; 2.2 Literature Review; 2.2.1 Rolling Horizon Approach ; 2.2.2 Algorithm RH ; 2.2.3 Freezing the Schedule Within the Planning Horizon ; 2.2.4 Introducing Change Costs ; 2.3 Release Change Cost Model with Fixed Capacity; 2.3.1 Single-Product Model; 2.3.2 Mathematical Model; 2.3.3 Primal Model; 2.3.4 Dual Model; 2.3.5 Examples of Freezing the Schedules
2.4 Behavior of Positive and Negative Release Changes2.4.1 Excess Demand; 2.4.2 Excess Pre-release; 2.5 Release Change Costs for a Single Product; 2.5.1 Negative Release Change Costs; 2.5.2 Positive Release Change Costs; 2.5.3 Freezing Costs Inside an Epoch or Across Epochs; 2.6 Numerical Examples; 2.6.1 Settings and Assumptions; 2.6.2 Examples of Setting Release Change Costs; 2.7 Conclusion; References; 3 Stochastic Scheduling for a Network of Flexible Job Shops; 3.1 Introduction: Problem Statement and Related Literature; 3.2 Stochastic Model for a Network of Flexible Job Shops
3.2.1 Model Formulation for the NFJS Problem3.2.2 The L-Shaped Method for the NFJS Problem; 3.2.3 Optimality Cuts; 3.2.4 Alternative Valid Inequalities for Inducing Stage-II Feasibility That Also Provide a Stage-I Lower Bound; 3.3 Valid Inequalities for Further Tightening the Model Formulation; 3.3.1 Flow-Balance Constraints; 3.3.2 Re-Entrant Flow-Based Constraints; 3.3.3 Deadlock Prevention Constraints; 3.4 Computational Results; 3.4.1 Design of Test Problems; 3.4.2 Experimental Results; 3.5 Heuristic Methods for the Solution of the NFJS Problem; 3.6 Concluding Remarks; References
4 A Free-Slack-Based Genetic Algorithm for the Robotic Cell Problem with Controllable Processing Times4.1 Introduction; 4.2 Problem Description; 4.2.1 Mathematical Formulation; 4.2.2 Literature Review; 4.3 Free-Slack-Based Genetic Algorithm; 4.3.1 Initial Processing Times Generation; 4.3.1.1 Deterministic Generation Method; 4.3.1.2 Random Generation; 4.3.1.3 One by One Generation Method; 4.3.2 Approximate Solution of the RCP (with Fixed Processing Times); 4.3.3 Update of the Processing Times; 4.3.3.1 Elongate the Processing Times; 4.3.3.2 Update of the Critical Operations
4.3.4 Find the Optimal Robot Moves
Includes bibliographical references at the end of each chapters and index.
The scope of this book is limited to heuristics, metaheuristics, and approximate methods and algorithms as applied to planning and scheduling problems. While it is not possible to give a comprehensive treatment of this topic in one book, the aim of this work is to provide the reader with a diverse set of planning and scheduling problems and different heuristic approaches to solve them. The problems range from traditional single stage and parallel machine problems to more modern settings such as robotic cells and flexible job shop networks. Furthermore, some chapters deal with deterministic problems while some others treat stochastic versions of the problems. Unlike most of the literature that deals with planning and scheduling problems in the manufacturing and production environments, in this book the environments were extended to nontraditional applications such as spatial scheduling (optimizing space over time), runway scheduling, and surgical scheduling. The solution methods used in the different chapters of the book also spread from well-established heuristics and metaheuristics such as Genetic Algorithms and Ant Colony Optimization to more recent ones such as Meta-RaPS.
Operations research.
Decision making.
Business logistics.
Management science.
Operations Research/Decision Theory.
Supply Chain Management.
Logistics.
Operations Research, Management Science.