Abstract
Maternal mortality is one of the leading causes of death among child-bearing women in Africa. One of the major challenges facing women in rural areas in Africa is delivering their babies safely, thereby curtailing excessive bleeding during birth. This paper focuses on optimizing costs related to delivery of blood using drone technology by determining how much blood is shipped from the distribution center to each destination center to help minimize total shipping costs. We envisage that drone-aided healthcare services can reduce the shipping costs associated with transporting blood from the point of origin to the centers. This paper also examines a classical optimization problem referred to as the transportation model in which the supply depends on the demand in the various facilities in rural Rwanda. Our study focuses from the start of drone operations in Rwanda in 2018. Using this model, we observe that data is critical for the success of any facility location analysis. Our preliminary results show that the associated travel from the second distribution point of origin to all destination centers (except the third destination) is optimal since the quantity of blood transported from the same point of origin to the same centers is zero. The results of the integer linear programming show optimality. Drone-aided networks using a mathematical optimization model and a simulation analysis help in understanding transportation costs associated with transporting blood in Rwanda so as to reduce maternal mortality.
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Notes
- 1.
Agent-based models support a ‘what-if’ analysis which allows the impact of unexpected perturbations to be studied (Iturriza et al. 2018).
- 2.
Church and ReVelle’s proposed method for answering this question is using a binary optimization model known as the Maximal Covering Location Model for identifying locations for public access defibrillators.
- 3.
- 4.
The importance of doing so has been described by Jacobsen (1990) who points out that formulating a problem incorrectly (e.g., failing to account for important problem factors) is likely to be far more important than whether or not you obtain an optimal or sub-optimal solution to a particular problem’s formulation (Daskin 2013).
- 5.
This study follows Snyder and Daskin (2004) who formulated reliability models based on both the P-median problem (PMP) and the uncapacitated fixed-charge location problem (UFLP) and presented an optimal Lagrangian relaxation algorithm to solve them.
- 6.
Objective space is the space spanned by the objective function values corresponding to each point in the solution space.
- 7.
Solution space is a set of all feasible solutions to a problem.
- 8.
Good data on customer demand, distances, costs, and other relevant inputs are essential for developing credible solutions and recommendations.
- 9.
The planning process includes identifying the problem; analysis; communication and decisions; and implementation (Daskin 2013). The process of identifying goals, actors, objectives, constraints, and options is of considerable value in and of itself, even if no model is solved.
- 10.
Models and data should be tested by using them to represent existing conditions. This process is known as calibration and it enables us to compare the model’s predictions with measured and actual performance. For example, we can compare the predicted or modeled average response time for a drone system with the actual average response time. If the two numbers are close enough, we may be willing to accept both the validity of the model and the data (Daskin 2013). This then leads to actionable recommendations for policy and program design based on a simulated study and its findings.
- 11.
With the recent launch of a similar technological advancement in Ghana, it will be good to understand how these technological systems work following the introduction of more products like vaccines. More research is needed in this regard to further understand what impact the addition of more products like vaccines for treating ailments can have on patients’ well-being.
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Ikelu, C.I., Ezin, E.C. (2020). Cost-effectiveness of Optimizing a Network of Drone-Aided Healthcare Services in Rural Rwanda. In: Das, G., Johnson, R. (eds) Rwandan Economy at the Crossroads of Development. Frontiers in African Business Research. Springer, Singapore. https://doi.org/10.1007/978-981-15-5046-1_8
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