arXiv subjects

Faisal Alkaabneh

Publications and source records attributed to Faisal Alkaabneh.

3 recordsLinked to original sources

arXiv2024

Routing mobile health clinics: An integrated routing and resupply plan based on synchronization

As an important means of providing medical services in developing countries and remote areas, Mobile Health Clinics (MHCs) focus on distributing medical supplies and providing basic health needs to underserved communities. In this paper, we propose a new model for the mobile health clinics with resupply from a truck and heterogeneous demand. In addition to the traditional routing decisions, our model also establishes en-route resupply plan. Adding the en-route resupply plan adds complexities to this problem as more constraints need to be added to accommodate for the synchronization between a fleet of MHCs and a resupply truck. We model this problem as a vehicle routing problem with multiple synchronization constraints and heterogeneous demand (VRPMSC-HD) and formulate a mixed integer linear programming (MILP). We propose a metaheuristic based on adaptive large neighborhood search (ALNS) with new operators to solve large-scale instances of the model. To demonstrate the value of the synchronization approach, we compare the total distance traveled by the mobile health clinics and the resupply truck and the arrival of the last mobile health clinic to the depot against a model where mobile health clinics are allowed to perform multiple trips to resupply from the depot. Our results reveal that despite the increase of 6.79% in traveled distance under the synchronization approach, the reduction in the latest arrival time is 16.06% on average. Implying that the utilization of the fleet of mobile health clinics can be significantly improved at the cost of extra traveling time when combining the traveling time of all the mobile health clinics and the resupply truck.

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arXiv2023

Adaptive Large Neighborhood Search Metaheuristic for Vehicle Routing Problem with Multiple Synchronization Constraints and Multiple Trips

This work is motivated by solving a problem faced by big agriculture companies implementing precision agriculture operations for spraying practices using two types of operators, namely a tender tanker and a fleet of sprayers. We model this problem as a vehicle routing problem with multiple synchronization constraints and multiple trips with the objective of minimizing the waiting time of the sprayers and the total routing distance of the sprayers. The resulting mixed integer programming model that we develop is hard to solve using a commercial solver, owing to the dependencies caused by the spatio-temporal synchronization between the two operators. To solve large-scale instances effectively, we present an adaptive large neighborhood search metaheuristic that uses an intensive local search mechanism. We conduct an extensive computational analysis to assess the effectiveness of our solution approach and gain managerial insights into the problem by analyzing various models. The proposed metaheuristic yields high-quality solutions quickly, with an overall average improvement of 5.61% over what is implemented in practice implying significant savings in time and cost.

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arXiv2023

Matheuristic for Vehicle Routing Problem with Multiple Synchronization Constraints and Variable Service Time

This paper considers an extension of the vehicle routing problem with synchronization constraints and introduces the vehicle routing problem with multiple synchronization constraints and variable service time. This important problem is motivated by a real-world problem faced by one of the largest agricultural companies in the world providing precision agriculture services to their clients who are farmers and growers. The solution to this problem impacts the performance of farm spraying operations and can help design policies to improve spraying operations in large-scale farming. We propose a Mixed Integer Programming (MIP) model for this challenging problem, along with problem-specific valid inequalities. A three-phase powerful matheuristic is proposed to solve large instances enhanced with a novel local search method. We conduct extensive numerical analysis using realistic data. Results show that our matheuristic is fast and efficient in terms of solution quality and computational time compared to the state-of-the-art MIP solver. Using real-world data, we demonstrate the importance of considering an optimization approach to solve the problem, showing that the policy implemented in practice overestimates the costs by 15-20%. Finally, we compare and contrast the impact of various decision-maker preferences on several key performance metrics by comparing different mathematical models.

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