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About

Currently, I am a researcher at INESC TEC and PhD student in Industrial Engineering and Management at FEUP. My research focuses on development of mathematical models and solutions methods to solve industrial problems under uncertainty, such as lot-sizing and scheduling problems, supplier selection and vehicle routing problems. I also worked in the development of supply chain and production planning optimization software and in some practical projects to optimize distribution networks for grocery retail stores and for a telecommunication company. 

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Publications

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.

2016

Supplier selection in the processed food industry under uncertainty

Authors
Amorim, P; Curcio, E; Almada Lobo, B; Barbosa Povoa, APFD; Grossmann, IE;

Publication
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Abstract
This paper addresses an integrated framework for deciding about the supplier selection in the processed food industry under uncertainty. The relevance of including tactical production and distribution planning in this procurement decision is assessed. The contribution of this paper is three-fold. Firstly, we propose a new two-stage stochastic mixed-integer programming model for the supplier selection in the process food industry that maximizes profit and minimizes risk of low customer service. Secondly, we reiterate the importance of considering main complexities of food supply chain management such as: perishability of both raw materials and final products; uncertainty at both downstream and upstream parameters; and age dependent demand. Thirdly, we develop a solution method based on a multi-cut Benders decomposition and generalized disjunctive programming. Results indicate that sourcing and branding actions vary significantly between using an integrated and a decoupled approach. The proposed multi-cut Benders decomposition algorithm improved the solutions of the larger instances of this problem when compared with a classical Benders decomposition algorithm and with the solution of the monolithic model.