arXiv subjects

Rosario Paradiso

Publications and source records attributed to Rosario Paradiso.

4 recordsLinked to original sources

arXiv2026

An exact algorithm for vehicle routing problems with temporal dependency constraints

Temporal dependencies between customer visits, such as synchronization constraints, pose a fundamental challenge in vehicle routing. These dependencies, which arise in applications such as home healthcare routing, aircraft scheduling, and technician routing, introduce inter-route constraints that make the resulting problems significantly harder to solve. We present an exact solution method for vehicle routing problems with temporal dependencies capable of handling all types of temporal dependencies studied in the literature, unlike most existing approaches that target specific subclasses. Our approach is based on a fragment-based formulation in which routes are represented as sequences of a new type of fragment, designed to handle temporal dependency constraints. This formulation is solved via a price-cut-and-enumerate algorithm that computes a lower bound using alternating column-and-row generation, obtains an initial upper bound, and iteratively refines both bounds through fragment enumeration and branch-and-cut, supported by several new classes of valid inequalities. Computational experiments show that our method significantly outperforms state-of-the-art benchmark methods and is able to solve previously intractable instances while covering a wider range of temporal dependencies.

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arXiv2025

Stochastic Programming for Dynamic Temperature Control of Refrigerated Road Transport

Temperature control in refrigerated delivery vehicles is critical for preserving product quality, yet existing approaches neglect critical operational uncertainties, such as stochastic door opening durations and heterogeneous initial product temperatures. We propose a framework to optimize cooling policies for refrigerated trucks on fixed routes by explicitly modeling these uncertainties while capturing all relevant thermodynamic interactions in the trailer. To this end, we integrate high-fidelity thermodynamic modeling with a multistage stochastic programming formulation and solve the resulting problem using stochastic dual dynamic programming. In cooperation with industry partners and based on real-world data, we set up computational experiments that demonstrate that our stochastic policy consistently outperforms the best deterministic benchmark by 35% on average while being computationally tractable. In a separate analysis, we show that by fixing the duration of temperature violations, our policy operates with up to $40$\% less fuel than deterministic policies. Our results demonstrate that pallet-level thermal status information is the single most crucial information in the problem and can be used to significantly reduce temperature violations. Knowledge of the timing and length of customer stops is the second most important factor and, together with detailed modeling of thermodynamic interactions, can be used to further significantly reduce violations. Our analysis of the optimal stochastic cooling policy reveals that preemptive cooling before a stop is the key element of an optimal policy. These findings highlight the value of sophisticated control strategies in maintaining the quality of perishable products while reducing the carbon footprint of the industry and improving operational efficiency.

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arXiv2024

The role of individual compensation and acceptance decisions in crowdsourced delivery

One of the recent innovations in urban distribution is crowdsourced delivery, where deliveries are made by occasional drivers who wish to utilize their surplus resources (unused transport capacity) by making deliveries in exchange for some compensation. The potential benefits of crowdsourced delivery include reduced delivery costs and increased flexibility (by scaling delivery capacity up and down as needed). The use of occasional drivers poses new challenges because (unlike traditional couriers) neither their availability nor their behavior in accepting delivery offers is certain. The relationship between the compensation offered to occasional drivers and the probability that they will accept a task has been largely neglected in the scientific literature. Therefore, we consider a setting in which compensation-dependent acceptance probabilities are explicitly considered in the process of assigning delivery tasks to occasional drivers. We propose a mixed-integer nonlinear model that minimizes the expected delivery costs while identifying optimal assignments of tasks to a mix of professional and occasional drivers and their compensation. We propose an exact two-stage solution algorithm that allows to decompose compensation and assignment decisions for generic acceptance probability functions and show that the runtime of this algorithm is polynomial under mild conditions. Finally, we also study a more general case of the considered problem setting, show that it is NP-hard and propose an approximate linearization scheme of our mixed-integer nonlinear model. The results of our computational study show clear advantages of our new approach over existing ones. They also indicate that these advantages remain in dynamic settings when tasks and drivers are revealed over time and in which case our method constitutes a fast, yet powerful heuristic.

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arXiv2022

Exact and Approximate Schemes for Robust Optimization Problems with Decision Dependent Information Discovery

Uncertain optimization problems with decision dependent information discovery allow the decision maker to control the timing of information discovery, in contrast to the classic multistage setting where uncertain parameters are revealed sequentially based on a prescribed filtration. This problem class is useful in a wide range of applications, however, its assimilation is partly limited by the lack of efficient solution schemes. In this paper we study two-stage robust optimization problems with decision dependent information discovery where uncertainty appears in the objective function. The contributions of the paper are twofold: (i) we develop an exact solution scheme based on a nested decomposition algorithm, and (ii) we improve upon the existing K-adaptability approximate by strengthening its formulation using techniques from the integer programming literature. Throughout the paper we use the orienteering problem as our working example, a challenging problem from the logistics literature which naturally fits within this framework. The complex structure of the routing recourse problem forms a challenging test bed for the proposed solution schemes, in which we show that exact solution method outperforms at times the K-adaptability approximation, however, the strengthened K-adaptability formulation can provide good quality solutions in larger instances while significantly outperforming existing approximation schemes even in the decision independent information discovery setting. We leverage the effectiveness of the proposed solution schemes and the orienteering problem in a case study from Alrijne hospital in the Netherlands, where we try to improve the collection process of empty medicine delivery crates by co-optimizing sensor placement and routing decisions.

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