Evidence-Based Optimization in Humanitarian Logistics

Humanitarian crises like the Syrian war, Ebola, the earthquake in Haiti, the Indian Ocean tsunami, and the ongoing HIV epidemic prompt substantial demands for humanitarian aid. Logistics plays a key role in aid delivery and represents a major cost category for humanitarian organizations. Optimizing logistics has long been at the core of operations research: the discipline that explores the use of advanced analytical methods to improve decision making. The commercial sector has substantially benefited from such methods. This thesis discusses whether and how such methods can also guide policy and decision making in the humanitarian sector. This is done through in-depth analyses of three case studies. The first investigates suitability of advanced planning and routing tools. Next, we investigate decision support methods for designing networks of roadside HIV clinics. The third case study concerns the deployment of mobile teams that screen for infectious disease outbreaks. Optimization tools come with assumptions about objectives to be reached and about their link with the decisions to be optimized. In humanitarian logistics, safeguarding adequacy of these assumptions is challenging but crucial. Throughout our case-studies, we explore how “best available evidence” can be used to link decisions to objectives, so as to enable evidence-based optimization in humanitarian logistics

Incorporating Driving Range Variability in Network Design for Refueling Facilities

To stimulate and facilitate the use of alternative-fuel vehicles, it is crucial to have a network of refueling or recharging stations in place that guarantees that vehicles can reach (most of) their destinations without running out of fuel. Because initial investments in these stations are restricted, it is important to choose their locations deliberately. A fast growing stream of literature therefore analyzes the problem of locating refueling or recharging stations. The models proposed in these studies assume that the driving range is fixed, although reality shows that the driving range is highly stochastic. These models thereby misrepresent the actual coverage a network of refueling stations provides to drivers. This paper introduces two problems that do take the stochastic nature of the driving range into account. We first introduce the Expected Flow Refueling Location Problem, which is to maximize the expected number of drivers who can complete their trip without running out of fuel. The Chance Constrained Flow Refueling Location Problem is to maximize the number of drivers for which the probability of running out of fuel is below a certain threshold. We prove the problems to be strongly NP-hard, propose novel mixed-integer programming formulations for these problems, and show how these models can be extended to the case that the driving range varies during a trip. Furthermore, we extensively analyze and compare our models using randomly generated problem instances and a real life case study about the Florida state highway network. Our results show that taking the stochastic nature of the driving range into account can substantially improve the network coverage, that optimal solutions are highly robust with respect to data impreciseness, and that the potential gains of stochastic models heavily depend on the driving range distribution. Based on the results, we discuss policy implications

The Roadside Healthcare Facility Location Problem

__Abstract__ Providing African truck drivers with adequate access to healthcare is an effective way to reduce the burden and the spread of HIV and other infectious diseases. Therefore, NGO North Star Alliance builds a network of healthcare facilities along major African trucking routes. Choosing the locations of new facilities presents novel and complex optimization problems. This paper considers a general design problem: the Roadside Health Care Facility location Problem (RHFLP). RFHLP entails to select locations for new facilities and to choose for each of these facilities whether or not to add healthcare services for HIV, STIs, Tuberculosis, and/or Malaria to the standard healt

A Column Generation Approach for Locating Roadside Clinics in Africa based upon Effectiveness and Equity

Long distance truck drivers in Sub-Saharan Africa are extremely vulnerable to HIV and other infectious diseases. The NGO North Star Alliance aims to alleviate this situation by placing so-called Roadside Wellness Centers (RWCs) at busy truck stops along major truck routes. Currently, locations for new RWCs are chosen so as to maximize the expected patient volume and to ensure continuity of access along the routes. As North Star's network grows larger, the objective to provide equal access to healthcare along the different truck routes gains importance. This paper considers the problem to locate a fixed number of RWCs based on these effectiveness and equity objectives. We come up with a novel, set-partitioning type of formulation for the problem and propose a column generation algorithm to solve it. Additionally, we propose and analyze several state-of-the-art acceleration techniques, including dual stabilization, column pool management, and accelerated pricing, which solves the pricing problem as a sequence of shortest path problems. Though the facility location problem is strongly NP-hard, our algorithm yields near-optimal solutions to large randomly generated problem instances within an acceptable amount of time. Our analysis of the trade-off between the equity criterion and North Star's current criteria shows that solutions that are close to optimal with respect to each of the effectiveness and equity objectives are likely to be attainable

The Roadside Healthcare Facility Location Problem: A Managerial Network Design Challenge

The population of truck drivers plays a key role in the spread of HIV and other infectious diseases in sub-Saharan Africa. Truck drivers thereby affect the health and lives of many, but also suffer from poor health and significantly reduced life expectancy themselves. Due to professional circumstances, their health service needs are generally not well addressed. Therefore, the non-governmental organization North Star Alliance builds a network of healthcare facilities along the largest trucking routes in subSaharan Africa. This paper studies the problem where to place additional facilities, and which health service packages to offer at each facility. The objective combines the maximization of the patient volume at these facilities and the maximization of the effectiveness of the health service delivery to the population served. The latter criterion is modeled through three novel access measures which capture the needs for effective service provisioning. The resulting optimization problem is essentially different from previously studied healthcare facility location problems because of the specific mobile nature of health service demand of truck drivers. Applying our model to the network of major transport corridors in South-East Africa, we investigate several prominent questions managers and decision makers face. We show that the present network expansion strategy, which primarily focuses on patient volumes, may need to be reconsidered: substantial gains in effectiveness can be made when allowing a small reduction in patient volumes. We furthermore show that solutions are rather robust to data impreciseness and that long term network planning can bring substantial benefits, particularly in greenfield situations

Do Optimization Models for Humanitarian Operations Need a Paradigm Shift?

Optimization approaches for planning and routing of humanitarian field operations have been studied intensively. Yet, their adoption in practice remains scant. This opinion paper argues that effectiveness increase realized by such approaches can be marginal due to triviality of planning problems, external constraints, and information losses. Cost increases, on the other hand, can be substantial. These include costs of implementation and use, data gathering, and mismatches with organizational cultures. Though such costs are a key concern for humanitarian organizations, OR/MS studies typically consider effectiveness measures only. We argue a paradigm shift towards cost-effectiveness maximization and increasing the strength of the presented evidence is needed and discuss corresponding future research needs

Prioritizing Replenishments of the Forward Reserve

Having sufficient inventories in the piece-picking area of a warehouse is an essential condition for warehouse operations. This requires a timely replenishment of the products from a reserve area in case they could run out of stock. In this paper we develop analytical models to arrive at priority rules for these replenishments in case replenishments and order picking are done simultaneously because of time pressure. This problem was observed in a warehouse of a large cosmetics firm. The priority rules are compared by means of simulation and regression. Finally we present the results o

Toward Elimination of Infectious Diseases with Mobile Screening Teams

I n pursuit of Sustainable Development Goal 3 “Ensure healthy lives and promote well-being for all at all ages,” considerable global effort is directed toward elimination of infectious diseases in general and Neglected Tropical Diseases in particular. For various such diseases, the deployment of mobile screening teams forms an important instrument to reduce prevalence toward elimination targets. There is considerable variety in planning methods for the deployment of these mobile teams in practice, but little understanding of their effectiveness. Moreover, there appears to be little understanding of the relationship between the number of mobile teams and progress toward the goals. This research considers capacity planning and deployment of mobile screening teams for one such neglected tropical disease: Human African trypanosomiasis (HAT, or sleeping sickness). We prove that the deployment problem is strongly NP-Hard and propose three approaches to find (near) optimal screening plans. For the purpose of practical implementation in remote rural areas, we also develop four simple policies. The performance of these methods and their robustness is benchmarked for a HAT region in the Democratic Republic of Congo (DRC). Two of the four simple practical policies yield near optimal solutions, one of which also appears robust against parameter impreciseness. We also present a simple approximation of prevalence as a function of screening capacity, which appears rather accurate for the case study. While the results may serve to more effectively allocate funding and deploy mobile screening capacity, they also indicate that mobile screening may not suffice to achieve HAT eliminatio

Site Visit Frequency Policies for Mobile Family Planning Services

Improving access to family planning services is key to achieving many of the United Nations sustainable development goals. To scale up access in remote areas and urban slums, many developing countries deploy mobile family planning teams that visit “outreach sites” several times per year. Visit frequencies have a significant effect on the total number of clients served and hence the impact of the outreach program. Using a large dataset of visits in Madagascar, Uganda and Zimbabwe, our study models the relationship between the number of clients seen during a visit and the time since the last visit and uses this model to analyse the characteristics of optimal frequencies. We use the latter to develop simple frequency policies for practical use, prove bounds on the worst-case optimality gap, and test the impact of the policies with a simulation model. Our main finding is that despite the complexity of the frequency optimisation problem, simple policies yield near-optimal results. This holds even when few data are available and when the relationship between client volume and the time since the last visit is misspecified or substantially biased. The simulation for Uganda shows a potential increase in client numbers of between 7% and 10%, which corresponds to more than 12,000 additional families to whom family planning services could be provided.

Forecasting Human African Trypanosomiasis Prevalences from Population Screening Data Using Continuous Time Models

To eliminate and eradicate gambiense human African trypanosomiasis (HAT), maximizing the effectiveness of active case finding is of key importance. The progression of the epidemic is largely influenced by the planning of these operations. This paper introduces and analyzes five models for predicting HAT prevalence in a given village based on past observed prevalence levels and past screening activities in that village. Based on the quality of prevalence level predictions in 143 villages in Kwamouth (DRC), and based on the theoretical foundation underlying the models, we consider variants of the Logistic Model—a model inspired by the SIS epidemic model—to be most suitable for predicting HAT prevalence levels. Furthe