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About

Eduardo Curcio is a researcher at INESC TEC, and an invited professor at FEUP. He holds a Ph.D. degree in Engineering and Industrial Management from FEUP. His research focuses on the development of mathematical models and solutions methods to solve industrial problems under uncertainty, such as lot-sizing and scheduling, supply chain planning and vehicle routing problems. He has published in international journals such as Computers and Operations Research, European Journal of Operational Research, and International Journal of Production Economics. He has also been involved in several practical projects in areas of production planning, scheduling and logistics.

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Publications

2020

A robust optimization approach for the vehicle routing problem with selective backhauls

Authors
Santos, MJ; Curcio, E; Mulati, MH; Amorim, P; Miyazawa, FK;

Publication
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW

Abstract
The Vehicle Routing Problem with Selective Backhauls (VRPSB) aims to minimize the total routing costs minus the total revenue collected at backhaul customers. We explore a VRPSB under uncertain revenues. A deterministic VRPSB is formulated as a mixed-integer programming problem and two robust counterparts are derived. A novel method to estimate the probabilistic bounds of constraint violation is designed. A robust metaheuristic is developed, requiring little time to obtain feasible solutions with average gap of 1.40%. The robust approach studied demonstrates high potential to tackle the problem, requiring similar computing effort and maintaining the same tractability as the deterministic modeling.

2019

A Benders Decomposition Algorithm for the Berth Allocation Problem

Authors
Barbosa, F; Oliveira, JF; Carravilla, MA; Curcio, EF;

Publication
Springer Proceedings in Mathematics and Statistics

Abstract
In this paper we present a Benders decomposition approach for the Berth Allocation Problem (BAP). Benders decomposition is a cutting plane method that has been widely used for solving large-scale mixed integer linear optimization problems. On the other hand, the Berth Allocation Problem is a NP-hard and large-scale problem that has been gaining relevance both from the practical and scientific points of view. In this work we address the discrete and dynamic version of the problem, and develop a new decomposition approach and apply it to a reformulation of the BAP based on the Heterogeneous Vehicle Routing Problem with Time Windows (HVRPTW) model. In a discrete and dynamic BAP each berth can moor one vessel at a time, and the vessels are not all available to moor at the beginning of the planning horizon (there is an availability time window). Computational tests are run to compare the proposed Benders Decomposition with a state-of-the-art commercial solver. © 2019, Springer Nature Switzerland AG.

2018

A computational study of the general lot-sizing and scheduling model under demand uncertainty via robust and stochastic approaches

Authors
Alem, D; Curcio, E; Amorim, P; Almada Lobo, B;

Publication
COMPUTERS & OPERATIONS RESEARCH

Abstract
This paper presents an empirical assessment of the General Lot-Sizing and Scheduling Problem (GLSP) under demand uncertainty by means of a budget-uncertainty set robust optimization and a two-stage stochastic programming with recourse model. We have also developed a systematic procedure based on Monte Carlo simulation to compare both models in terms of protection against uncertainty and computational tractability. The extensive computational experiments cover different instances characteristics, a considerable number of combinations between budgets of uncertainty and variability levels for the robust optimization model, as well as an increasing number of scenarios and probability distribution functions for the stochastic programming model. Furthermore, we have devised some guidelines for decision-makers to evaluate a priori the most suitable uncertainty modeling approach according to their preferences.

2018

Adaptation and approximate strategies for solving the lot-sizing and scheduling problem under multistage demand uncertainty

Authors
Curcio, E; Amorim, P; Zhang, Q; Almada Lobo, B;

Publication
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS

Abstract
This work addresses the lot-sizing and scheduling problem under multistage demand uncertainty. A flexible production system is considered, with the possibility to adjust the size and the schedule of lots in every time period based on a rolling-horizon planning scheme. Computationally intractable multistage stochastic programming models are often employed on this problem. An adaptation strategy to the multistage setting for two-stage programming and robust optimization models is proposed. We also present an approximate heuristic strategy to address the problem more efficiently, relying on multistage stochastic programming and adjustable robust optimization. In order to evaluate each strategy and model proposed, a Monte Carlo simulation experiment under a rolling-horizon scheme is performed. Results show that the strategies are promising in solving large-scale problems: the approximate strategy based on adjustable robust optimization has, on average, 6.72% better performance and is 7.9 times faster than the deterministic model.

2016

Defining the Best distribution Network for Grocery Retail Stores

Authors
Amorim, P; Martins, S; Curcio, E; Almada Lobo, B;

Publication
ERCIM NEWS

Abstract
Large food retailers have to deal with a complex distribution network with multiple distribution centres, different temperature requirements, and a vast range of store formats. This project used an optimization-simulation approach to help food retailer Sonae MC make the best decisions regarding product-warehouse-outlet assignment, product delivery modes planning and fleet sizing.