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Publicações

Publicações por Marco Costa Silva

2020

Solution algorithms for minimizing the total tardiness with budgeted processing time uncertainty

Autores
Silva, M; Poss, M; Maculan, N;

Publicação
European Journal of Operational Research

Abstract

2021

A Branch-Price-And-Cut Algorithm for Stochastic Crowd Shipping Last-Mile Delivery with Correlated Marginals

Autores
Silva, M; Pedroso, JP; Viana, A; Klimentova, X;

Publicação
21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, ATMOS 2021, September 9-10, 2021, Lisbon, Portugal (Virtual Conference).

Abstract
We study last-mile delivery with the option of crowd shipping, where a company makes use of occasional drivers to complement its vehicle's fleet in the activity of delivering products to its customers. We model it as a data-driven distributionally robust optimization approach to the capacitated vehicle routing problem. We assume the marginals of the defined uncertainty vector are known, but the joint distribution is difficult to estimate. The presence of customers and available occasional drivers can be random. We adopt a strategic planning perspective, where an optimal a priori solution is calculated before the uncertainty is revealed. Therefore, without the need for online resolution performance, we can experiment with exact solutions. Solving the problem defined above is challenging: not only the first-stage problem is already NP-Hard, but also the uncertainty and potentially the second-stage decisions are binary of high dimension, leading to non-convex optimization formulations that are complex to solve. We propose a branch-price-and-cut algorithm taking into consideration measures that exploit the intrinsic characteristics of our problem and reduce the complexity to solve it.

2022

Deep Reinforcement Learning for Crowdshipping Last-Mile Delivery with Endogenous Uncertainty

Autores
Silva, M; Pedroso, JP;

Publicação
MATHEMATICS

Abstract
In this work, we study a flexible compensation scheme for last-mile delivery where a company outsources part of the activity of delivering products to its customers to occasional drivers (ODs), under a scheme named crowdshipping. All deliveries are completed at the minimum total cost incurred with their vehicles and drivers plus the compensation paid to the ODs. The company decides on the best compensation scheme to offer to the ODs at the planning stage. We model our problem based on a stochastic and dynamic environment where delivery orders and ODs volunteering to make deliveries present themselves randomly within fixed time windows. The uncertainty is endogenous in the sense that the compensation paid to ODs influences their availability. We develop a deep reinforcement learning (DRL) algorithm that can deal with large instances while focusing on the quality of the solution: we combine the combinatorial structure of the action space with the neural network of the approximated value function, involving techniques from machine learning and integer optimization. The results show the effectiveness of the DRL approach by examining out-of-sample performance and that it is suitable to process large samples of uncertain data, which induces better solutions.

2009

Outsourcing de TI e redefinição do papel da subsidiária: um estudo comparativo entre as subsidiárias brasileira e indiana de uma multinacional americana

Autores
Silva, MAd;

Publicação
JISTEM Journal of Information Systems and Technology Management

Abstract

2022

Min-Sup-Min Robust Combinatorial Optimization with Few Recourse Solutions

Autores
Arslan, AN; Poss, M; Silva, M;

Publicação
INFORMS JOURNAL ON COMPUTING

Abstract
In this paper, we consider a variant of adaptive robust combinatorial optimization problems where the decision maker can prepare K solutions and choose the best among them upon knowledge of the true data realizations. We suppose that the uncertainty may affect the objective and the constraints through functions that are not necessarily linear. We propose a new exact algorithm for solving these problems when the feasible set of the nominal optimization problem does not contain too many good solutions. Our algorithm enumerates these good solutions, generates dynamically a set of scenarios from the uncertainty set, and assigns the solutions to the generated scenarios using a vertex p-center formulation, solved by a binary search algorithm. Our numerical results on adaptive shortest path and knapsack with conflicts problems show that our algorithm compares favorably with the methods proposed in the literature. We additionally propose a heuristic extension of our method to handle problems where it is prohibitive to enumerate all good solutions. This heuristic is shown to provide good solutions within a reasonable solution time limit on the adaptive knapsack with conflicts problem. Finally, we illustrate how our approach handles nonlinear functions on an all-or-nothing subset problem taken from the literature.

2018

k-Adaptive Routing for the Robust Network Loading Problem

Autores
Silva, M; Poss, M; Maculan, N;

Publicação
Electronic Notes in Discrete Mathematics

Abstract
We experiment an alternative routing scheme for the Robust Network Loading problem. Named k-adaptive, it is based on the fact that the decision-maker chooses k second-stage solutions and then commits to one of them only after realization of the uncertainty. This routing scheme, with its corresponding k-partition of the uncertainty set, is dynamically defined under an iterative method to sequentially improve the solution. The method has an inherent characteristic of multiplying the number of variables and constraints after each iteration, so that additional measures are introduced in the solution strategy in order to control its tractability. We compare our k-adaptive results with the ones obtained through other routing schemes and also verify the effectiveness of the methods utilized using several realistic instances from SNDlib. © 2018

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