F.-Javier Heredia - Academic web site

Publication Type	Funded research projects
Year of Publication	2020
Authors	F.-Javier Heredia
Type of participation	Leader
Duration	06/2020-12/2020
Funding organization	Top Cable
Partners	-
Project code	C-11622
Key Words	research; one dimensional cutting stock; private; project; manufacturing
Abstract	L’empresa Top Cable, líder en la fabricació de cables elèctrics, es planteja aplicar una millora en el seu ERP de gestió del magatzem, afegint-hi un nou mòdul que optimitzi i doni suggeriments a la logística del magatzem tenint en compte les noves arribades i l’stock que hi hagi present.
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On April 2020 a research project called Optimització de Reserves de Cable en Bobines has been settled with the company Top Cable,  a international leader in cable technology. The purpose of the project is to engage a study to asses the  suitability of mathematical optimization techniques to improve the procedure they currently apply to the fulfillment of the customer's orders. In this first part of the project a multiperiod extension of the one-dimensional cutting stock problem is going to be formulated to solve a instance of the company's demand supply problem.

Publication Type	Conference Paper
Year of Publication	2018
Authors	Daniel Ramón-Lumbierres; F.-Javier Heredia; Robert Gimeno Feu; Julio Consola; Román Buil Giné
Conference Name	23th International Symposium on Mathematical Programming
Conference Date	01-06/07/2018
Conference Location	Bordeaux
Editor	Mathematical Optimization Society (MOS)
Type of Work	contributed presentation
Key Words	research; supply chain; postponment; multistage stochastic programming
Abstract	Supply chain management has been widely developed through the evolution of manufacturing, distribution, forecasting and customer behavior, encouraging the introduction of postponement strategies in its various forms. At these strategies, semi-finished goods are stored in certain operations of the chain, called decoupling points, waiting for the placement of demand orders, which trigger production flows from decoupling points to the remainder operations. Such a design problem facing the speculation/postponement paradigm must intrinsically include elements that "unveil" demand orders when they are placed, that is, the modelling approach should keep demand orders as random variables until their placement, when they are disclosed. This work proposes a multi-stage stochastic programming model that decides the optimal allocation of decoupling points, as well as a process selection among alternative designs for any general supply chain case, where the stochastic parameters, demands by period and product, are represented through a scenario tree, which is in turn generated using the forecasting. Both a risk-neutral model and a risk-aversion approach with stochastic dominance constraints are presented and solved with multi-stage instances of test cases based on real manufacturing problems defined in collaboration with the Accenture consultancy company.
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Publication Type	Conference Paper
Year of Publication	2018
Authors	F.-Javier Heredia
Conference Name	Maths for Industry 4.0
Conference Date	19/02/2018
Conference Location	Barcelona
Type of Work	Round table
Key Words	research,; industrial mathematics; industry 4.0; BGSMath
Abstract	“Maths for Industry 4.0” will showcase how academic excellence at BGSMath is helping companies becoming digital. Join us to learn about successful collaborative initiatives, such as industrial doctoral theses, as well as the range of expertise you could benefit from. The workshop will be closed by a round table on Data Analytics. Our experts will discuss common challenges and trends across sectors, and how mathematical creativity enable solutions for supply chain, risk management, control and monitoring. This activity belongs to the Mobile Week Barcelona and it’s an open space for reflexion on digital transformation through art, science and technology.
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The Barcelona Graduate School of Mathematics (BGSMath ) organized last February 19 2018 the workshop "Maths for Industry 4.0 " to showcase how academic excellence at BGSMath is helping companies becoming digital through several successful collaborative initiatives, such as industrial doctoral theses or consultancy and development projects of the BGSMaths's research groups in Data Science and Optimization. The workshop will be closed by the round table "Optimising data analytics for industry 4.0" where I was invited to participate as expert in supply chain optimization. This activity is embedded into the Mobile Week Barcelona and it's an open space for reflexion on digital transformation through art, science and technology. More photos of the event at this link .

Publication Type	Tesis de Grau i Màster // BSc and MSc Thesis
Year of Publication	2018
Authors	Sandra Orozco Martín
Director	Roman Buil; F.-Javier Heredia; Elena Fernández
Tipus de tesi	MSc Thesis
Titulació	Interuniversity Master in Statistics and Operations Research UPC-UB
Centre	Faculty of Mathematics and Statistics
Data defensa	19/01/2018
Nota // mark	10 MH (A+)
Key Words	teaching; VRP; metaheuristics; MSc Thesis
Abstract	In thisThesis, a variant of the Vehicle Routing Problem is considered where products are distributed from a depot to multiple retail stores using capacitated vehicles and not only the transportation cost but also the lost sales resulting from stock-outs at each location are minimized. Given that the real demand of a particular product at some location during a given time period can only be rougthly estimated, a deterministic and dynamic solution is proposed. This solution is divided into two phases: (i) an overnight optimization determines the set of retailers assigned to each vehicle and computes the optimal routes using the available information; (ii) a dynamic model is used separately on each vehicle in order to reoptimize its path according to the new information made available during the execution of the routes. Two exact formulations and two metaheuristics are proposed for the first phase; an exact formulation is implemented for the second phase. Moreover, an end-to-end solution is developed through the implementation of a visualization tool in R Shiny, which uses MySQL databases and shell calls to AMPL to simulate the process of a whole day.
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A new result of the research project Digitalizing Supply Chain Strategy with 3D Printing with Accenture TechLabs and Center for Analytics has been presented in the Manufacturing Engineering Society International Conference 2017 , MESIC 2017. The study compares different possible production strategies for a product (via conventional technologies and Additive Manufacturing) and assesses the degree of postponement that it would be recommended in order to meet a certain demand distribution. The problem solving is calculated by a program containing a stochastic mathematical model which incorporates extensive information on costs and lead times for the required manufacturing operations. This study was published in Procedia Manufacturing.  

Publication Type	Proceedings Article
Year of Publication	2017
Authors	J. Minguella-Canela; A. Muguruza; D.R. Lumbierres: F.-Javier Heredia; R. Gimeno; P. Guo; M. Hamilton; K.Shastry, S.Webb
Conference Name	Manufacturing Engineering Society International Conference 2017, MESIC 201
Series Title	Procedia Manufacturing
Volume	13
Pagination	754-761
Conference Start Date	18/06/2017
Publisher	Elsevier
Conference Location	Vigo, Spain
Editor	Jorge Salguero, Enrique Ares
ISSN Number	2351-9789
Key Words	Additive Manufacturing; Ultra-postponement; Supply Chain; research; paper
Abstract	The best-selling products manufactured nowadays are made in long series along rigid product value chains. Product repetition and continuous/stable manufacturing is seen as a chance for achieving economies of scale. Nevertheless, these speculative strategies fail to meet special customer demands, thus reducing the effective market share of a product in a range. Additive Manufacturing technologies open promising product customization opportunities; however, to achieve it, it is necessary to delay the production operations in order to incorporate the customer’s inputs in the product materialization. The study offered in the present paper compares different possible production strategies for a product (via conventional technologies and Additive Manufacturing) and assesses the degree of postponement that it would be recommended in order to meet a certain demand distribution. The problem solving is calculated by a program containing a stochastic mathematical model which incorporates extensive information on costs and lead times for the required manufacturing operations.
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DOI	https://doi.org/10.1016/j.promfg.2017.09.181
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Publication Type	Conference Paper
Year of Publication	2017
Authors	J. Minguella-Canela; A. Muguruza; D.R. Lumbierres; F.-Javier Heredia; R. Gimeno; P. Guo; M. Hamilton; K. Shastry; S. Webb
Conference Name	Manufacturing Engineering Society International Conference 2017, MESIC 2017, 28-30
Conference Date	28-30/07/2017
Publisher	Elsevier
Conference Location	Vigo, Spain
Type of Work	Contributed presentation
Key Words	research; Additive Manufacturing; Ultra-postponement; Supply Chain; stochastic programming
Abstract	The best-selling products manufactured nowadays are made in long series along rigid product value chains. Product repetition and continuous/stable manufacturing is seen as a chance for achieving economies of scale. Nevertheless, these speculative strategies fail to meet special customer demands, thus reducing the effective market share of a product in a range. Additive Manufacturing technologies open promising product customization opportunities; however, to achieve it, it is necessary to delay the production operations in order to incorporate the customer’s inputs in the product materialization. The study offered in the present paper compares different possible production strategies for a product (via conventional technologies and Additive Manufacturing) and assesses the degree of postponement that it would be recommended in order to meet a certain demand distribution. The problem solving is calculated by a program containing a stochastic mathematical model which incorporates extensive information on costs and lead times for the required manufacturing operations.
URL	Click Here
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