wrap. ...

Click here to load reader

  • date post

    13-Aug-2020
  • Category

    Documents

  • view

    5
  • download

    0

Embed Size (px)

Transcript of wrap. ...

  • warwick.ac.uk/lib-publications

    A Thesis Submitted for the Degree of PhD at the University of Warwick

    Permanent WRAP URL:

    http://wrap.warwick.ac.uk/135016

    Copyright and reuse:

    This thesis is made available online and is protected by original copyright.

    Please scroll down to view the document itself.

    Please refer to the repository record for this item for information to help you to cite it.

    Our policy information is available from the repository home page.

    For more information, please contact the WRAP Team at: [email protected]

    http://go.warwick.ac.uk/lib-publications http://wrap.warwick.ac.uk/135016 mailto:[email protected]

  • Meta-Heuristic & Hyper-Heuristic Scheduling Tools for

    Biopharmaceutical Production

    by

    Folarin Bolude Oyebolu

    Thesis

    Submitted to the University of Warwick

    for the degree of

    Doctor of Philosophy

    Warwick Business School

    July 2019

  • Contents

    List of Tables v

    List of Figures vii

    Acknowledgments ix

    Declarations x

    Abstract xi

    Abbreviations xii

    Chapter 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

    1.2 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . 2

    1.3 Structure of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 3

    Chapter 2 Background on the Biopharmaceutical Industry 5 2.1 Drug and Process Development . . . . . . . . . . . . . . . . . . . 7

    2.2 Biopharmaceutical Manufacturing . . . . . . . . . . . . . . . . . 10

    2.3 Emerging Trends and Challenges . . . . . . . . . . . . . . . . . . 12

    Chapter 3 Literature Review 14 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

    3.2 Lot Sizing and Scheduling . . . . . . . . . . . . . . . . . . . . . 15

    3.2.1 Deterministic Models . . . . . . . . . . . . . . . . . . . . 17

    3.2.2 Stochastic Models . . . . . . . . . . . . . . . . . . . . . 17

    3.3 Solution Approaches . . . . . . . . . . . . . . . . . . . . . . . . 19

    3.3.1 Exact Methods . . . . . . . . . . . . . . . . . . . . . . . 19

    i

  • 3.3.2 Heuristics, Meta-Heuristics, and Hyper-Heuristics . . . . 19

    3.3.3 Simulation Optimisation . . . . . . . . . . . . . . . . . . 25

    3.4 The Bioprocessing Context . . . . . . . . . . . . . . . . . . . . . 26

    Chapter 4 Lot-Sizing & Scheduling for Biopharmaceuticals 28 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

    4.2 Mathematical Formulation . . . . . . . . . . . . . . . . . . . . . 29

    4.2.1 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . 29

    4.2.2 Constraints . . . . . . . . . . . . . . . . . . . . . . . . . 31

    4.2.3 Objective Functions . . . . . . . . . . . . . . . . . . . . 33

    4.2.4 Formulation Assumptions . . . . . . . . . . . . . . . . . 34

    4.3 Industrial Case Study . . . . . . . . . . . . . . . . . . . . . . . . 34

    4.4 The Proposed Genetic Algorithm with Construction Heuristic . . 38

    4.4.1 Construction Heuristic . . . . . . . . . . . . . . . . . . . 40

    4.4.2 Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . 44

    4.5 Empirical Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 48

    4.5.1 Comparison with Mathematical Programming . . . . . . . 48

    4.5.2 Algorithm Components . . . . . . . . . . . . . . . . . . 54

    4.5.3 Multi-Objective Optimisation . . . . . . . . . . . . . . . 58

    4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

    Chapter 5 Scheduling Strategies for Continuous Bioprocesses 62 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

    5.2 Model Framework . . . . . . . . . . . . . . . . . . . . . . . . . 65

    5.2.1 Bioprocess Model . . . . . . . . . . . . . . . . . . . . . 67

    5.2.2 Discrete-Event Simulation Framework . . . . . . . . . . 72

    5.2.3 Scheduling Strategies . . . . . . . . . . . . . . . . . . . 73

    5.2.4 Optimisation Algorithms . . . . . . . . . . . . . . . . . 74

    5.3 Evaluating the Bioprocess and Simulation Model . . . . . . . . . 74

    5.3.1 Optimal Processing Run Time . . . . . . . . . . . . . . . 75

    5.3.2 Process Configurations and Multiple Bioreactors . . . . . 83

    5.4 Single-Product Scheduling Strategies . . . . . . . . . . . . . . . 85

    5.4.1 The (s,B) Inventory Policy . . . . . . . . . . . . . . . . . 85

    5.4.2 The (s1,s2,B) Inventory Policy . . . . . . . . . . . . . . . 88

    5.4.3 Evaluation of the Scheduling Strategies . . . . . . . . . . 88

    5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

    ii

  • Chapter 6 Dynamic Scheduling for Continuous Bioprocesses in a Multi- Product Facility 98 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

    6.2 Problem Domain . . . . . . . . . . . . . . . . . . . . . . . . . . 100

    6.2.1 Problem Description . . . . . . . . . . . . . . . . . . . . 100

    6.2.2 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . 101

    6.3 Scheduling Policies . . . . . . . . . . . . . . . . . . . . . . . . . 102

    6.3.1 Fixed Cycle Policies . . . . . . . . . . . . . . . . . . . . 102

    6.3.2 Base-Stock Policies . . . . . . . . . . . . . . . . . . . . . 104

    6.3.3 Artificial Neural Network Policy . . . . . . . . . . . . . . 109

    6.4 Optimisation Algorithms . . . . . . . . . . . . . . . . . . . . . . 111

    6.4.1 Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . 112

    6.4.2 Covariance Matrix Adaptation Evolution Strategy . . . . . 113

    6.5 Case Study Description . . . . . . . . . . . . . . . . . . . . . . . 115

    6.6 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . 117

    6.6.1 Description of the Standard Policy . . . . . . . . . . . . . 117

    6.6.2 Evaluation of Optimised Policies . . . . . . . . . . . . . 118

    6.6.3 Policy with Flexible Process Duration . . . . . . . . . . . 123

    6.6.4 Tuned Policy Parameters . . . . . . . . . . . . . . . . . . 124

    6.6.5 Production Schedule(s) for the Facility . . . . . . . . . . 128

    6.6.6 EA Performance and Statistical Analysis . . . . . . . . . 129

    6.6.7 Evaluating the Sensitivity of Optimised Policy Solutions . 135

    6.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

    Chapter 7 Conclusion and Future Work 139 7.1 Summary and Contribution of Thesis . . . . . . . . . . . . . . . . 139

    7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

    7.2.1 Chapter 4 . . . . . . . . . . . . . . . . . . . . . . . . . . 140

    7.2.2 Chapter 5 . . . . . . . . . . . . . . . . . . . . . . . . . . 141

    7.2.3 Chapter 6 . . . . . . . . . . . . . . . . . . . . . . . . . . 142

    Bibliography 143

    Appendix A Chapter 4 Appendix 155 A.1 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

    iii

  • Appendix B Chapter 5 Appendix 158 B.1 Bioprocess Model Parameters . . . . . . . . . . . . . . . . . . . 158

    B.2 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160

    B.3 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

    Appendix C Chapter 6 Appendix 163 C.1 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

    C.2 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

    Appendix D Publication(s) by the Author 171

    iv

  • List of Tables

    2.1 Top selling pharmaceuticals of 2017. . . . . . . . . . . . . . . . . 6

    4.1 Product demand forecast. . . . . . . . . . . . . . . . . . . . . . . 35

    4.2 Production rates of facilities. . . . . . . . . . . . . . . . . . . . . 37

    4.3 Manufacturing yields of facilities. . . . . . . . . . . . . . . . . . 37

    4.4 Manufacturing costs of facilities. . . . . . . . . . . . . . . . . . . 37

    4.5 Case study parameters. . . . . . . . . . . . . . . . . . . . . . . . 38

    4.6 Factorial tests of GA parameters. . . . . . . . . . . . . . . . . . . 48

    4.7 Cost breakdowns of GA and MILP at increasing demand scale. . . 49

    4.8 Computational statistics and scaling behaviour of the optimisation

    algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

    4.9 Analysis of construction heuristic components. . . . . . . . . . . 54

    4.10 Cost breakdown of optimisations with reneging. . . . . . . . . . . 57

    5.1 Process scheduling parameters. . . . . . . . . . . . . . . . . . . . 69

    5.2 Process failure events, consequences, and the associated risk. . . . 70

    5.3 Single-product process economics parameters. . . . . . . . . . . . 76

    5.4 Sensitivity analysis of optimal process duration to process eco-

    nomics parameters. . . . . . . . . . . . . . . . . . . . . . . . . . 77

    5.5 Optimal process durations at various yearly demand targets. . . . 82

    5.6 Process economics parameters for multiple parallel bioreactor sce-

    narios. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

    5.7 Domain of gene values. . . . . . . . . . . . . . . . . . . . . . . . 91

    5.8 Performance of single-product scheduling strategies at varying de-

    mand scales. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91