Optimization and Measurement in Humanitarian Operations: Addressing Practical Needs
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OPTIMIZATION AND MEASUREMENT IN HUMANITARIAN OPERATIONS: ADDRESSING PRACTICAL NEEDS A Thesis Presented to The Academic Faculty by Mallory Soldner In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Industrial and Systems Engineering Georgia Institute of Technology August 2014 Copyright c 2014 by Mallory Soldner OPTIMIZATION AND MEASUREMENT IN HUMANITARIAN OPERATIONS: ADDRESSING PRACTICAL NEEDS Approved by: Dr. Özlem Ergun, Advisor Dr. Jarrod Goentzel School of Industrial and Systems Center for Transportation and Logistics, Engineering School of Engineering Georgia Institute of Technology Massachusetts Institute of Technology Dr. Julie Swann, Advisor Dr. Joel Sokol School of Industrial and Systems School of Industrial and Systems Engineering Engineering Georgia Institute of Technology Georgia Institute of Technology Dr. Alan Erera Date Approved: 25 June 2014 School of Industrial and Systems Engineering Georgia Institute of Technology To my grandparents, Marjorie & John and Harriet & Fred iii ACKNOWLEDGEMENTS Simply put, I would never have made it to this point without the support of so many people in my life. I am grateful to everyone who has helped me along the way, more than can be expressed here. First and foremost, I would like to thank my advisors, Ozlem and Julie, who guided me at every step of the PhD. They steered me in interesting and productive research directions, challenged me to think independently and rigorously, encouraged me when things seemed bleak, and gave me the space and opportunity to actively collaborate with practitioners in my field of study. I am especially grateful for their support in these last months of the thesis. I would also like to thank the other members of my thesis advisory committee, Jarrod, Alan, and Joel, for their time and feedback relating to this work, especially Jarrod for his guidance on our transportation delay research. This thesis would not have been possible without the support of the Logistics Develop- ment Unit at the UN World Food Programme. I am thankful to Bernard for his vision in establishing the Georgia Tech - WFP collaboration and to Martin, Wolfgang, and Mirjana for their support. Working with LDU’s supply chain team was a privilege and a pleasure, and I especially thank Sergio, Rui, Zmarak, and Luis. Rome became a second home to me during these years, and I am grateful for the nice times and travels shared together with Madeline, Cameron, Amin, Claudia, Edita, and other friends in Italy and now around the world. At Georgia Tech, I am grateful to the world-class faculty that I had the privilege to learn from and to the staff of ISyE for their support, especially to Pam and Yvonne for their encouragement and help with so many of the details. My fellow graduate students were one of the biggest highlights of my time at ISyE. I thank Tugce for being the best office-mate on the planet and being there through thick and thin, Daniel and Tim for so many nights grilling on the porch, and Veronique, Ilke, Monica, iv Soheil, Bahar, Isil, Andres, Stefania, Murat, Ezgi, Diego, Rodrigue, Vinod, Norbert, and many others for their support and friendship throughout these years. Finally, I would like to thank my parents and family. I am thankful to my Mom for always supporting me, to Ron for always being a phone call away, and to my Dad for his encouragement. I also recognize Brigid, Gretchen, and Rachel for being lifelong friends, and my Godson, Nicholas, and niece, Elizabeth, for always making me smile. v Contents DEDICATION ....................................... iii ACKNOWLEDGEMENTS ............................... iv LIST OF TABLES .................................... ix LIST OF FIGURES ................................... x SUMMARY ......................................... xii I INTRODUCTION ................................. 1 1.1 Chapter 2: Stable Assignment Problems for Staffing: Negotiated Complete Stable Matchings . 1 1.2 Chapter 3: Managing Bottlenecks in Port and Overland Transport Networks for Humanitarian Aid . 3 1.3 Chapter 4: A Case Study on Implementation of Supply Chain Key Perfor- mance Indicators at a Large Humanitarian Organization . 4 1.4 Chapter 5: Conclusion . 5 II STABLE ASSIGNMENT PROBLEMS FOR STAFFING: NEGOTIATED COMPLETE STABLE MATCHINGS ..................... 6 2.1 Introduction . 6 2.2 Literature Review . 7 2.2.1 Stable Matching Background and Concepts . 7 2.2.2 Related Literature and Contributions . 8 2.3 Approach and Results . 11 2.3.1 Negotiation Mechanisms . 11 2.3.2 Minimizing the Number of Negotiations . 13 2.3.3 Minimizing the Cost of Negotiations . 19 2.3.4 Extension of the Negotiation Paradigm: Altering the Sequencing of Preferences . 29 2.4 Conclusion . 33 2.4.1 Future Directions . 35 III MANAGING BOTTLENECKS IN PORT AND OVERLAND TRANS- PORT NETWORKS FOR HUMANITARIAN AID ............ 37 3.1 Introduction . 37 vi 3.2 Literature Review and Contributions . 39 3.3 Modeling Port and Corridor Delays . 41 3.3.1 Port and Corridor Queuing Delay Model . 41 3.3.2 Structure of the Wait Function with Respect to Arrival Rate . 45 3.3.3 Computational Insights and Examples . 47 3.4 Incorporating Delay Modeling into Delivery Routing . 49 3.4.1 Convex Cost Flow Model . 49 3.4.2 Optimality Conditions for Minimizing Port and Corridor Delays . 52 3.4.3 Computational Experiments . 54 3.5 Network Improvements: Investments for Decreased Congestion Delays . 57 3.5.1 Impact of Parametric Changes . 57 3.5.2 Simultaneously Optimizing for Network Improvements and Routing . 62 3.5.3 Computational Experiments . 67 3.6 Conclusion and Future Directions . 74 IV A CASE STUDY ON THE IMPLEMENTATION OF SUPPLY CHAIN KEY PERFORMANCE INDICATORS AT A LARGE HUMANITAR- IAN ORGANIZATION .............................. 77 4.1 Introduction . 77 4.2 Research Approach . 79 4.3 Literature Review and Contributions . 79 4.4 Case Study . 81 4.4.1 Background on the Supply Chain KPI Project . 81 4.4.2 Supply Chain KPI Implementation Phases . 82 4.4.3 Addressing Strategy, Culture, and Organizational Structure . 83 4.4.4 Adapting Existing Supply Chain KPI Approaches . 88 4.5 Conclusion . 96 4.5.1 Limitations and Future Directions . 97 V CONCLUSION .................................... 99 Appendix A — PROOF APPENDIX FOR STABLE ASSIGNMENT PROB- LEMS FOR STAFFING (CHAPTER 2) ....................102 vii Appendix B — CONVEX COST ROUTING MODEL WITH INLAND DELIVERIES .....................................112 REFERENCES ......................................113 viii List of Tables 1 Summary of Notation . 9 2 Notation Reference for the Port-Corridor Delay Model . 42 3 Estimated Port-Corridor Parameters for Syria (µji processing rates are in vessels/month; other units are as indicated) . 55 4 Investment Modeling Additional Notation . 64 5 Estimated Investment Costs in thousands of USD . 71 6 Upper and Lower Bounds (units correspond to those in Table 3, “-” denotes no upper bound is defined, so one can be set arbitrarily high in the formulation) 71 ix List of Figures 1 Matching Instance: Unstable Matching (Left) and Stable Matching (Right) . 8 2 Negotiation can be used to achieve complete stable matchings. 12 3 Fractional optimal solution to minNegotiations under Append-to-End. 15 4 Example of the solution structure where Algorithm minAgentNegotiations has a worst case optimality gap of 100%. 22 5 Average solution time comparison: Algorithm minAgentNegotiations vs. In- teger Programming (*only five trials were run for N = 1000) . 24 6 Box-and-whisker plots of the optimality gap of Algorithm minAgentNegotia- tions – Actual on the left and Upper Bound (according to Expression 17) on the right (*only five trials were run for N = 1000) . 25 7 Optimality Gap Percentage as Preference List Length Increases forN = 100 . 26 8 Optimal Solution Value as Preference List Length Increases forN = 100 . 27 9 Box-and-whisker plots of the optimality gap of modified Algorithm minA- gentNegotiations for general costs – Actual on the left and Upper Bound (according to Expression 17) on the right. 28 10 Fractional optimal solution to minNegotiations under Move-to-Beginning . 31 11 Two station, tandem queuing model for port and corridor delays . 42 12 Preemptive breakdown model illustration. Three parameters, fi; ri, and vi, characterize breakdowns at Corridor i. ..................... 44 13 Total delay for two networks with the same availability but different break- down frequency and mean time to repair as µci increases. 48 14 Main ports for Syrian humanitarian operations . 50 15 Main ports in West Africa for humanitarian operations . 51 16 Port and Corridor Routing and Delay Network . 52 17 Performance of the Proportional to Path Effective Processing Rate routing policy . 56 18 Proportion of flow per path: Proportional to Path Effective Processing Rate vs. Optimal for λ = 31, the estimated 2013 monthly total flow . 57 19 Total wait for different values of fi as µci increases. Increasing fi improves Ai and decreases total delay. 60 20 Total wait for different values of ri as µci increases. Decreasing ri improves Ai and decreases total delay. 60 x 21 Wpi − Wci as µpi increases. The port is the time bottleneck for values of µpi left of the marking where the function crosses the x − axis, and the corridor is the bottleneck to the right. 61 22 Total Wait as the budget increases under different policies (λ = 69 and no fixed costs or bounds are used in this experiment) . 72 23 Total Wait as the budget increases under different policies (λ = 69 and with fixed costs and bounds in this experiment) . 72 24 Optimality Gap Percentage