Below, we briefly summarize contributions of each chapter. Apart from minor adap-tations, the chapters are exact copies of papers submitted to or published in academic journals. The majority of the work in Chapters 2, 3, 5, and 6 has been done inde-pendently under close supervision of co-authors. The main contributions of the work in Chapter 4 were to generate the idea, develop the case study, and write the pa-per. The first author’s main contributions were to generate the idea, implement the methods, and run the experiments.
Chapter 2: H. de Vries, L.N. van Wassenhove. Evidence-Based Vehicle Planning for Humanitarian Field Operations. Submitted to Manufacturing & Service Opera-tions Management.
This chapter discusses the applicability and cost-effectiveness of advanced approaches to optimize planning and routing of humanitarian field operations. Combining in-sights from expert interviews, literature, and extensive numerical analyses, we show that “optimal” planning system characteristics depend strongly on organizational, demand-related, and operational context factors.
Chapter 3: H. de Vries, J.J. van de Klundert, A.P.M. Wagelmans. The Roadside Healthcare Facility Location Problem. Submitted to Production and Operations Man-agement. This paper won the INFORMS Healthcare 2015 best student paper award.
In this chapter, we consider the problem of selecting locations for clinics along African highways so as to maximize their impact in terms of truck driver patient volume and health service effectiveness. We develop models for this problem, present numerical experiments for the network of major transport corridors in Southern and Eastern Africa, and discuss policy implications.
Chapter 4: J. Nunez Ares, H. de Vries, D. Huisman. A Column Generation Approach for Locating Roadside Clinics in Africa based on Effectiveness and Equity.
Published in European Journal of Operations Research.
This chapter analyzes the trade-off between effectiveness and equity of healthcare delivery among African truck drivers. We show that networks that are close to optimal with respect to both objectives can be designed efficiently through column generation techniques.
Chapter 5: H. de Vries, A.P.M. Wagelmans, E. Hasker, C. Lumbala, P. Lutumba, S.J. de Vlas, J.J. van de Klundert. Forecasting Human African Trypanosomiasis Prevalences from Population Screening Data using Continuous Time Models. Pub-lished in PLoS Computational Biology.
In this chapter, we propose several models for the relationship between the timing of screening operations for Human African Trypanosomiasis (HAT, also called sleeping sickness) and disease prevalence. The models are then fitted and tested by means of a dataset with data on prevalence levels and screening operations in the Democratic Republic of Congo (DRC) and used to analyze screening requirements for elimination and eradication.
Chapter 6: H. de Vries, A.P.M. Wagelmans, J.J. van de Klundert. Optimizing Population Screening for Infectious Diseases. In preparation for journal submission.
The final chapter considers the problem of planning population screening operations so as to minimize the burden of infectious disease. For a broad class of diseases, we propose general solution methods and simple planning policies, as well as general methods to analyze the relationship between screening capacity and long term dis-ease burden. Using the models developed for HAT in Chapter 5, we show that the planning policies recommended by the WHO could likely be improved upon and that simple policies are hardly inferior to approaches employing advanced optimization techniques.
Finally, Chapter 7 summarizes the main findings presented in this thesis.
Evidence-Based Vehicle
Planning for Humanitarian Field Operations 1
2.1 Introduction
Decision support software has substantially transformed private sector logistics and enabled companies to significantly decrease transportation costs and/or improve ser-vice levels. This has not happened yet in humanitarian logistics. Given the substan-tial funds spent on humanitarian operations, this is rather surprising, especially since there has been a significant increase in humanitarian case load. Logistics efforts play a key role in delivering disaster relief and development services and transportation is, after salaries, the largest cost category for international humanitarian organizations (IHOs) (Pedraza-Martinez et al., 2011). The cost of 4x4 vehicle fleets of IHOs is es-timated to exceed $1 billion per year and the number of vehicles is expected to triple
1Apart from several minor adaptations, this chapter is a direct copy of the article: H. de Vries, L.N. van Wassenhove (2017). Evidence-Based Vehicle Planning for Humanitarian Field Operations.
Submitted to Manufacturing & Service Operations Management.
by 2050 (Pedraza-Martinez et al., 2011). One would expect that advanced planning and routing software could have a substantial positive impact.
Gustavsson (2003) suggests three internal hurdles that have kept IHOs from re-alizing these apparent gains: (1) lack of logistics expertise, (2) undervaluation of IT systems, and (3) difficulties in getting the necessary funding. Literature and semi-structured interviews with fleet managers and logistics experts from IHOs reveal three additional hypotheses:
1. The effectiveness increase realized by advanced planning and routing does not outweigh the cost of implementation, operation, and maintenance.
2. Available planning solutions do not fit the complex context of humanitarian organizations. E.g., they may be too data-intensive or pursue objectives not aligned with humanitarian missions.
3. Implementing advanced planning and routing competes with innovations and responsibilities that have a higher priority.
This chapter aims to shed a light on the cost-effectiveness of advanced planning and routing approaches in humanitarian contexts. In Section 2.2, we develop a framework for cost-effectiveness based on our literature review and a set of interviews with humanitarian logistics experts. Next, Section 2.3 proposes a simple model that relates operational effectiveness to the specific context of IHOs and to their planning systems. This model then serves as a basis for extensive numerical analyses in Section 2.4.
In general, our results urge researchers and decision makers to adopt an evidence-based approach when evaluating planning and routing software by considering external validity of results, internal validity of underlying models, and by extending evaluation criteria beyond effectiveness alone. More specifically, we make seven contributions.
First, we fill the gap in literature on the cost-effectiveness of advanced planning and routing systems in IHOs. Second, we add nuance to the literature on humanitar-ian last-mile logistics by highlighting the types of costs and counter-productivities that may come with advanced planning approaches. Third, we propose a simple and
intuitive model for the effectiveness of a planning system, which could serve as a ba-sis for further modeling studies on fleet management in humanitarian organizations.
Fourth, we provide evidence stressing the importance of a careful analysis of the op-erational context when choosing what planning system to implement. Fifth, we show how (in)effective standard planning and routing solutions can be in the humanitarian context. Sixth, we hypothesize general relationships between contextual factors and the optimal planning system. Finally, we assist decision makers in prioritizing adap-tations of their planning system (e.g., stimulating car pooling and increasing mission lengths) by showing their respective potential impact.