arXiv subjects

Pedro Cesar Lopes Gerum

Publications and source records attributed to Pedro Cesar Lopes Gerum.

3 recordsLinked to original sources

arXiv2026

Dynamic Driver Allocation Under Latent Demand Regimes: Indexability of a Partially Observed Markov Decision Process

Quick-commerce dark stores dispatch e-grocery orders within 15 to 30 minutes, so operators such as Getir, Glovo, and GoPuff must commit drivers before orders arrive. Demand follows a latent regime that persists across hours, while unfulfilled orders spill forward as a compounding backlog. This decision binds whether drivers are employed on fixed shifts or drawn from a gig platform whose incentives are set ahead of the hour, yet existing models do not learn the regime as orders arrive. We formulate the single-store problem as a partially observable Markov decision process in which the firm infers the regime from realized orders. We show that optimal staffing rises with backlog and prove the single-store problem is indexable, a property open for multi-action partially observed problems in general. We then extend the framework to a driver pool shared across stores through a Lagrangian relaxation that decouples the network into per-store subproblems. The result is a two-level allocation policy that prices the value of tracking demand in real time and ranks stores by a provably valid priority index. On 2021 to 2022 data from the European firm SuperGlovo, which staffs full-time drivers to comply with Spain's Rider's Law, belief updating adds 10.9\%, about \$188K across 27 stores, over the same program with the belief held fixed. The gain concentrates in the stores whose regimes are most persistent, observable before deployment. Online allocation reduces to a table lookup and a ranked list with a cutoff price.

math.OC↗

arXiv2025

Alignment Games

This paper introduces alignment games, a new class of zero-sum games modeling strategic interventions where effectiveness depends on alignment with an underlying hidden state. Motivated by operational problems in medical diagnostics, economic sanctions, and resource allocation, this framework features two players, a Hider and a Searcher, who choose subsets of a given space. Payoffs are determined by their misalignment (symmetric difference), explicitly modeling the trade-off between commission errors (unnecessary action) and omission errors (missed targets), given by a cost function and a penalty function, respectively. We provide a comprehensive theoretical analysis, deriving closed-form equilibrium solutions that contain interesting mathematical properties based on the game's payoff structure. When cost and penalty functions are unequal, optimal strategies are consistently governed by cost-penalty ratios. On the unit circle, optimal arc lengths are direct functions of this ratio, and in discrete games, optimal choice probabilities are proportional to element-specific ratios. When costs are equal, the solutions exhibit rich structural properties and sharp threshold behaviors. On the unit interval, this manifests as a geometric pattern of minimal covering versus maximal non-overlapping strategies. In discrete games with cardinality constraints, play concentrates on the highest-cost locations, with solutions changing discontinuously as parameters cross critical values. Our framework extends the theory of geometric and search games and is general enough that classical models, such as Matching Pennies, emerge as special cases. These results provide a new theoretical foundation for analyzing the strategic tension between comprehensive coverage and precise targeting under uncertainty.

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arXiv2019

Modelagem de um Problema de Dimensionamento de Lotes com Demanda Variavel e Deterministica e Efeitos de Learning e Forgetting

The main goal of this paper was to analyze the importance that the effects of learning and forgetting might have in a lot-sizing problem. It assumes that the learning curve and the economies of scale are present in several industries yet are, in most cases, not considered when dealing with a lot-sizing problem. The importance of the effects was demonstrated and quantified, showing that there is still space for developments in this field. However, as the problem becomes quadratic, there is a possibility that the current algorithms are not able to solve the problem to optimality. Thus, future improvements in the algorithms may further improve the results. However, the overall results found with current algorithms show that the contribution of a discount from a learning curve can be very considerable, even if it is a minimal amount.

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