On the dynamic allocation of assets subject to failure and replenishment

Ford, Stephen and Glazebrook, Kevin and Jacko, Peter (2021) On the dynamic allocation of assets subject to failure and replenishment. PhD thesis, Lancaster University.

[thumbnail of 2021fordphd]
Text (2021fordphd)
2021fordphd.pdf - Published Version
Available under License None.

Download (1MB)


Problems of the dynamic allocation of assets subject to both failure and replenishment are common. We consider a problem inspired by naval search, where unmanned aerial vehicles are required to search an area of ocean for targets. The vehicles will require refuelling or rearming; this is represented by the aspects of failure and replenishment. Similar models can arise from considering problems of search and rescue, environmental monitoring, or project management. We formulate several versions of the problem, initially using the framework of a Markov decision process, bearing in mind trade-offs between real-world fidelity and mathematical tractability. We first consider models where rewards are gained independently from different tasks, before moving on to consider a specific kind of dependence in the rewards. We use a variety of mathematical techniques, including restless bandits, to formulate near-optimal policies for a slew of models. We consider and investigate the various policies through comprehensive computational modelling. For the independent case, we find that a Whittle index policy is extremely close to optimal while being computationally efficient. For the dependent formulation, we create a class of policies guaranteed to contain the optimal, parameterise the space, then choose the best from a limited set of parameters, augmenting with a single step of policy improvement. We close with some thoughts about what we have learned, considerations about applying the results presented in this thesis, and a discussion of intensifications and extensions we did not have time to consider.

Item Type:
Thesis (PhD)
ID Code:
Deposited By:
Deposited On:
04 May 2021 08:20
Last Modified:
16 Jul 2024 05:55