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017
Publications

2022

The multi-product inventory-routing problem with pickups and deliveries: Mitigating fluctuating demand via rolling horizon heuristics

Authors
Neves Moreira, F; Almada Lobo, B; Guimaraes, L; Amorim, P;

Publication
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW

Abstract

2022

Explaining the Labor Share: Automation Vs Labor Market Institutions

Authors
Guimaraes, L; Gil, PM;

Publication
LABOUR ECONOMICS

Abstract
We propose a simple model to assess the evolution of the US labor share and how automation affects employment. In our model, heterogeneous firms may choose a manual technology and hire a worker subject to matching frictions. Alternatively, they may choose an automated technology and produce using only machines (robots). Our model suggests that automation reduces the labor share but increases employment and wages. Furthermore, our model suggests that labor market institutions are unlikely to have played a major role in the fall of the US labor share after 1987. Instead, technological factors are a more promising candidate.

2021

Improving picking performance at a large retailer warehouse by combining probabilistic simulation, optimization, and discrete-event simulation

Authors
Amorim Lopes, M; Guimaraes, L; Alves, J; Almada Lobo, B;

Publication
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH

Abstract
Distribution warehouses are a critical part of supply chains, representing a nonnegligible share of the operating costs. This is especially true for unautomated, labor-intensive warehouses, partially due to time-consuming activities such as picking up items or traveling. Inventory categorization techniques, as well as zone storage assignment policies, may help in improving operations, but may also be short-sighted. This work presents a three-step methodology that uses probabilistic simulation, optimization, and event-based simulation (SOS) to analyze and experiment with layout and storage assignment policies to improve the picking performance. In the first stage, picking performance is estimated under different storage assignment policies and zone configurations using a probabilistic model. In the second stage, a mixed integer optimization model defines the overall warehouse layout by selecting the configuration and storage assignment policy for each zone. Finally, the optimized layout solution is tested under demand uncertainty in the third, final simulation phase, through a discrete-event simulation model. The SOS methodology was validated with three months of operational data from a large retailer's warehouse, successfully illustrating how it may be successfully used for improving the performance of a distribution warehouse.

2021

Product line selection of fast-moving consumer goods

Authors
Andrade, X; Guimaraes, L; Figueira, G;

Publication
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE

Abstract
The fast-moving consumer goods sector relies on economies of scale. However, its assortments have been overextended as a means of market share appropriation and top-line growth. This paper studies the selection of the optimal set of products for fast-moving consumer goods producers to offer, as there is no previous model for product line selection that satisfies the requirements of the sector. Our mixed-integer programming model combines a multi-category attraction model with a capacitated lot-sizing problem, shared setups and safety stock. The multi-category attraction model predicts how the demand for each product responds to changes within the assortment. The capacitated lot-sizing problem allows us to account for the indirect production costs associated with different assortments. As seasonality is prevalent in consumer goods sales, the production plan optimally weights the trade-off between stocking finished goods from a long run with performing shorter runs with additional setups. Finally, the safety stock extension addresses the effect of the demand uncertainty associated with each assortment. With the computational experiments, we assess the value of our approach using data based on a real case. Our findings suggest that the benefits of a tailored approach are at their highest in scenarios typical fast-moving consumer goods industry: when capacity is tight, demand exhibits seasonal patterns and high service levels are required. This also occurs when the firm has a strong competitive position and consumer price-sensitivity is low. By testing the approach in two real-world instances, we show that this decision should not be made based on the current myopic industry practices. Lastly, our approach obtains profits of up to 9.4% higher than the current state-of-the-art models for product line selection. © 2020 Elsevier Ltd

2021

Resource definition and allocation for a multi-asset portfolio with heterogeneous degradation

Authors
Dias, L; Leitao, A; Guimaraes, L;

Publication
RELIABILITY ENGINEERING & SYSTEM SAFETY

Abstract
When making long-term plans for their asset portfolios, decision-makers have to define a priori a maintenance budget that is to be shared among the several assets and managed throughout the planning period. During the planning period, the a priori budget is then allocated by managers to different operation and maintenance interventions ensuring the overall performance of the system. Because asset degradation is stochastic, a considerable amount of uncertainty is associated with this problem. Hence, to define a robust budget, it is essential to account for several degradation scenarios pertaining to the individual condition of each asset. This paper presents a novel mathematical formulation to tackle this problem in a heterogeneous multiasset portfolio. The proposed mathematical model was formulated as a mixed-integer programming two-stage stochastic optimization model with mean-variance constraints to minimize the number of scenarios with an insufficient budget. A Gamma process was used to model the condition of each individual asset while taking into consideration different technological features and operating conditions. We compared the solutions obtained with our model to alternative practices in a set of generated instances covering different types of multi-asset portfolios. This comparison allowed us to explore the value of modeling uncertainty and how it affects the generated solutions. The proposed approach led to gains in performance of up to 50% depending on the level of uncertainty. Furthermore, the model was validated using real-world data from a utility company working with portfolios of power transformers. The results obtained showed that the company could reduce costs by as much as 40%. Further conclusions showed that the cost-saving potential was higher in asset portfolios in worse condition and that defining a priori operation and maintenance interventions led to worse results. Finally, the results showcased how different decision-maker risk-levels affect the value of taking uncertainty into account.

Supervised
thesis

2021

Developing Marketing Analytics Tools in a Non-Profit Organization

Author
Manuel Carlos Oliveira Rocha

Institution
UP-FEUP

2021

A Framework for Asset Management System in Healthcare Services

Author
Nathássia Marques Trevizani

Institution
UP-FEUP

2021

Data Visualization to Support Assortment Planning in Community Pharmacies

Author
Eduardo Miguel Silva Pereira

Institution
UP-FEUP

2021

Conceptualization and design of an Asset Performance Management solution within a Manufacturing Execution System

Author
Rui Pedro Teixeira da Silveira Soares Morais

Institution
UP-FEUP

2021

Neural Network Verification using Why3

Author
António Manuel Carvalho Gonçalves

Institution
UM