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Detalhes

014
Publicações

2021

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

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

Publicação
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

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

Publicação
Omega (United Kingdom)

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

Autores
Dias, L; Leitao, A; Guimaraes, L;

Publicação
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.

2019

Solving a large multi-product production-Routing problem with delivery time windows

Autores
Neves Moreira, F; Almada Lobo, B; Cordeau, JF; Guimaraes, L; Jans, R;

Publicação
Omega

Abstract

2019

Tackling perishability in multi-level process industries

Autores
Wei, WC; Amorim, P; Guimaraes, L; Almada Lobo, B;

Publicação
International Journal of Production Research

Abstract
The classical multi-level lot-sizing and scheduling problem formulations for process industries rarely address perishability issues, such as limited shelf lives of intermediate products. In some industries, ignoring this specificity may result in severe losses. In this paper, we start by extending a classical multi-level lot-sizing and scheduling problem formulation (MLGLSP) to incorporate perishability issues. We further demonstrate that with the objective of minimising the total costs (purchasing, inventory and setup), the production plans generated by classical models are often infeasible under a setting with perishable products. The model distinguishes different perishability characteristics of raw materials, intermediates and end products according to various industries. Finally, we provide quantitative insights on the importance of considering perishability for different production settings when solving integrated production planning and scheduling problems. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

Teses
supervisionadas

2020

Modelo de Custeio e Investimento para uma Nova Linha de Montagem numa Empresa de Packaging de Semicondutores

Autor
Luís Pedro Silva Manso-Preto Rodrigues

Instituição
UP-FEUP

2020

Reestruturação do Supply Network Plan numa Empresa de Painéis Derivados de Madeira

Autor
Rui Fernando Teixeira da Silva Macedo

Instituição
UP-FEUP

2020

Self-adaptive optimization algorithms using machine learning for the capacitated vehicle routing problem

Autor
André João Pereira Alves de Sousa

Instituição
UP-FEUP

2020

Optimizing O&M plans for flexible hydropower systems

Autor
Xavier Tarrio Fernandes

Instituição
UP-FEUP

2020

MRP Model Conception for an Automated Material Handling and Storage Systems Industry

Autor
João Nuno Alvim de Castro Ferreira dos Santos

Instituição
UP-FEUP