Boletín de Investigación N°10 - Departamento de Industrias USM

BOLETÍN DE INVESTIGACIÓN GESTIÓN DE OPERACIONES Pág.19-47

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BOLETÍN DE INVESTIGACIÓN

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l Boletín de Investigación es una herramienta comunicacional cuyo campo de acción será exclusivamente interno, y su objetivo es poder contribuir a la visibilización de los proyectos de investigación a cargo de nuestros académicos, desde las distintas áreas de donde estos se agrupan. Es por ello que, en esta edición, nos enfocaremos en la presentación del Área de Gestión de Operaciones, incluyendo el resumen de los últimos trabajos a cargo de los investigadores del Departamento de Industrias.

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OPERATIONS MANAGEMENT STUDY AREA

DESCRIPTION

CHAIR OPERATIONS AREA RaĂşl Stegmaier Bravo

The Operations Area of the Department of Industrial Engineering cultivates, transfers and generates relevant knowledge in the field of Operations Management, which contributes to the training of our students and improves the decision making and management of organizations form a pertinent vision of Industrial Engineering.

This is a management field that deals with establishing actions for the generation of the value proposal of organizations and that it is materialized through the delivery of goods or services to customers, safeguarding the effective and efficient use or resources in the fulfillment of the purposes of the organizations. The focus of the area in the Department of Industries integrates the management of the supply chain, that is, the management of the flow of goods and services from its origin to the final customer, as well as the management of the operational risks associated (Operational Risk Management) to the performance of assets and support processes of the value proposal.

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FULL TIME OPERATIONS AREA MEMBERS MG. RAÚL STEGMAIER B. raul.stegmaier@usm.cl • Lean management, maintenance policies, asset management.

• Reliability engineering, risk analysis.

• Decision analysis, productivity and efficiency analysis, simulation.

• Mining and manufacturing, transportation.

DR. VÍCTOR ALBORNOZ S. victor.albornoz@usm.cl • Distribution and logistics.

• Integer optimization, mixer integer optimization, optimization under uncertainty.

• Agriculture.

DR. PABLO ESCALONA R. pablo.escalona@usm.cl • Facility location modeling, distribution and logistics, inventory control.

• Stochastic processes, non-linear optimization, optimization under uncertainty, mathematical programming.

• Manufacturing, warehousing, transportation, service systems.

DR. DAVID GODOY R. david.godoy@usm.cl Área de especialización: • Maintenance optimization, asset management.

• Reliability engineering, risk analysis, predictive maintenance, condition-based maintenance optimization.

• Stochastics processes.

• Energy, mining and manufacturing.

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MG. EDWARD JOHNS N. edward.johns@usm.cl • Supply-chain management.

• Decision analysis.

• Quality management.

DR. FREDY KRISTJANPOLLER R. fredy.kristjanpoller@usm.cl • Maintenance policies, RAM analysis (reliability, availability and maintenance).

• Reliability engineering, risk analysis.

• Simulation, mathematical modeling.

• Mining and manufacturing.

DR. RODRIGO MENA B. rodrigo.mena@usm.cl • Maintenance policies, RAM analysis (reliability, availability and maintenance).

• Reliability engineering.

• Optimization under uncertainty, algorithms and heuristics.

• Energy, power systems operations.

DRA. MÓNICA LÓPEZ C. monica.lopezc@usm.cl • Asset management, process modeling, lean management.

• Reliability engineering, fault diagnosis and condition monitoring.

• Decision analysis, measurement, productivity and efficiency analysis.

• Design of experiments, quality management, statistical quality control.

• Mining and manufacturing, critical infrastructure.

DR. PABLO VIVEROS G. pablo.viveros@usm.cl • Maintenance policies, RAM analysis (reliability, availability and maintenance), asset management.

• Reliability engineering, risk analysis.

• Simulation, performance measurement, productivity and efficiency analysis.

• Mining and manufacturing, transportation.

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USM Collaborators • Claudio Álvarez.

• Cristian Álvarez.

• Alejandro Angulo.

• Rodrigo Barraza.

• C. Cárdenas.

• R. Caviedes.

• Carlos Contreras.

• Ismael Kauak.

• Kevin Michell.

• Christopher Nikulin.

• Linco Ñanco.

• Rodrigo Ortega.

• José Luis Sáez.

• Roger Schurch • Erich Stowhas.

• Marcelo Véliz.

• Patricio Vera.

• Roberto Villalón.

• Jorge Weston.

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External Collaborators • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

Angelina Anani – Pontificia Universidad Católica de Chile, Chile.

Luis Barbera – Universidad de Sevilla, España.

Héctor Cancela – Universidad de la República, Uruguay.

Salvatore Cannella – Universidad de Catania, Italia.

Hugo Cárdenas – Pontificia Universidad Católica de Valparaíso, Chile.

Adolfo Crespo – Universidad de Sevilla, España.

Rodrigo Escobar – Pontificia Universidad Católica de Chile, Chile.

Rosa González-Ramírez – Universidad de los Andes, Chile.

Vicente González-Prida – Universidad de Sevilla, España.

Valentina Hernández – Pontificia Universidad Católica de Valparaíso, Chile.

Michael Hitch – University of Technology, Estonia.

Andrew Jardine – University of Toronto, Canadá.

Peter Knights – The University of Queensland, Australia.

Sebastián Koziolek – Wroclaw University, Polonia.

Patricio Lillo – Pontificia Universidad Católica de Chile, Chile. Álvaro Lorca – Pontificia Universidad Católica de Chile, Chile.

Fernando Mancilla-David – University of Colorado Denver, EEUU. Vladimir Marianov – Pontificia Universidad Católica de Chile, Chile.

Pablo Miranda – Universidad Andrés Bello, Chile.

Carlos Nakousi – Pontificia Universidad Católica de Chile, Chile.

Matías Negrete-Pincetic – Pontificia Universidad Católica de Chile, Chile.

Daniel Olivares – Pontificia Universidad Católica de Chile, Chile.

Elías Olivares-Benitez – Universidad Panamericana, México.

Fernando Ordóñez – Universidad de Chile, Chile.

Virna Ortíz-Araya – Universidad del Bío-Bío, Chile.

Rodrigo Pascual – Universidad de Concepción, Chile.

Eleazar Puente – Tecnológico de Monterrey, México.

F. Ríos – Pontificia Univesidad Católica de Chile, Chile.

Edmo Rodovalho – Universidade Federal de Alfenas, Brazil.

Milton Román – Universidad de Concepción, Chile.

Francisco Tapia-Ubeda – Pontificia Universidad Católica de Valparaíso, Chile.

Carlos Testuri – Universidad de la República, Uruguay.

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Contenido 26

REFEREED PUBLICATIONS “Integrated versus hierarchical approach for zone delineation and crop planning under uncertainty” – Albornoz, V. M., Véliz, M. I., Ortega, R. & Ortíz-Araya, V. Annals of Operations Research, Vol. 286, March 2020, pp 617-634. DOI: 10.1007/s10479-019-03198-y “Fleet optimization considering overcapacity and load sharing restrictions using genetic algorithms and ant colony optimization” – Kristjanpoller, F., Michell, K., Kristjanpoller, W. & Crespo, A. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 13 January 2020, pp 1-10. DOI: 10.1017/S0890060419000428 “Optimizing CBM decisions for step-down power transformers in service: Use of non-arbitrary covariate bands into condition assessment” – Godoy, D., Stegmaier, R., Jardine, A. & Ríos, F. Reliability Engineering & System Safety (Working Paper). 2019.

“A condition-based maintenance model including resource constraints in the number of inspections” – Álvarez, C., LópezCampos, M., Stegmaier, R., Mancilla-David, F., Schurch, R. & Angulo, A. IEEE Transactions on Reliability (early access), 13 December 2019, pp 1-12. DOI: 10.1109/TR.2019.2955558 “A simulation based modelling approach to jointly support and evaluate spare parts supply chain network and maintenance system” – Miranda, P. A., Tapia-Ubeda, F. J., Hernández, V., Cardenas, H. & López-Campos, M. IFAC-PapersOnLine, Vol. 52, Nº 13, 2019, pp 2231-2236. DOI: 10.1016/j.ifacol.2019.11.537 “A beinstock-zuckerberg-based algorithm for solving a networkflow formulation of the convex hull pricing problem” – Álvarez, C., Mancilla-David, F. Escalona, P. & Angulo, A. IEEE Transactions on Power Systems, 18 November 2019, pp 1-1. DOI: 10.1109/ TPWRS.2019.2953862

“Reducing mining footprint by matching haul fleet demand and route-orientes tire types” – Pascual, R., Román, M., López-Campos, M., Hitch, M. & Rodovalho, E. Journal of Cleaner Production, Vol. 227, 01 August 2019, pp 645-651. DOI: 10.1016/j.clepro.2019.04.069 “On the effect of two popular service-level measures of the design of a critical level policy for fast-moving items” – Escalona, P., Angulo, A., Weston, J. Stegmaier, R. & Kauak, I. Computers & Operations Research, Vol. 107, July 2019, pp 107-126. DOI: 10.1016/j.cor.2019.03.011 “Modeling the operation of synchronized supply chains under a collaborative structure” – López-Campos, M., Cannella, S., Miranda, P. & Stegmaier, R. Academia Revista Latinoamericana de Administración, Vol. 32, Nº 2, June 2019, pp 203-224. DOI: 10.1108/ARLA-04-2017-0090 “Prospective study using archetypes and systems dynamics” – Vera, P., Nikulin, C., López-Campos, M. & González-Ramírez, R. Academia Revista Latinoamericana de Administración, Vol. 32, Nº 2, June 2019, pp 181-202. DOI: 10.1108/ARLA-05-2017-0151 “Delineating robust rectangular management zones based on column generation algorithm” – Albornoz, V. M., Ñanco, L. J. & Sáez, J. L. Computers and Electronics ins Agriculture, Vol. 61, June 2019, pp 194-201. DOI: 10.1016/j.compag.2019.01.045 “Stochastics discrete lot-sizing with lead times for fuel supply optimization” – Testuri, C. E., Cancela, H. & Albornoz, V. M. Pesquisa Operacional, Vol. 29, Nº 1, 09 May 2019, pp 37-55. DOI: 10.1590/01017438.2019.039.01.0037 “The impact of concentrated solar power in electric power systems: A Chilean case study” – Mena, R., Escobar, R., Lorca, Á., NegretePincetic, M. & Olivares, D. Applied Energy, Vol. 235, 01 February 2019, pp 258-283. DOI: 10.1016/j.apenergy.2018.10.088

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“Resolution of reliability problems based on failure mode analysis: an integrated proposal applied to a mining case study” – Viveros, P., Nikulin, C., López-Campos, M., Villalón, R. & Crespo, A. Production Planning & Control, Vol. 29, Nº 15, 28 January 2019, pp 1225-1237. DOI: 10.1080/09537287.2018.1520293 “An asset-management oriented methodology for mine haul-fleet usage scheduling” – Nakousi, C., Pascual, R., Anani, A., Kristjanpoller, F. & Lillo, P. Reliability Engineering and System Safety, Vol. 180, December 2018, pp 226-344. DOI: 10.1016/j.ress.2018.07.034 “Graphical analysis for overall effectiveness management: A graphical method to support operation and maintenance performance assessment” – Viveros, P., Kristjanpoller, F., LópezCampos, M., Crespo, A. & Pascual, R. Quality and Reliability Engineering International, Vol. 34, Nº 8, December 2018, pp 1615-1632. DOI: 10.1002/ qre.2348 “Combined use of mathematical optimization and design of experiments for the maximization of profit in a four-echelon supply chain” – Olivares, D., Olivares-Benitez, E., Puente, E., López-Campos, M. & Miranda, P. A. Complexity, Vol. 2018. DOI: 10.1155/2018/8731027 “On the effect of inventory policies on distribution network with several demand clases” – Escalona, P., Marianov, V., Ordóñez, F. & Stegmaier, R. Transportation Research Part E: Logistics and Transportation Review, Vol. 111, March 2018, pp 229-240. DOI: 10.1016/j. tre.2017.10.019 “Decision criteria to select a suitable criticality assessment technique” – Viveros, P., Crespo, A., Barbera, L., González-Prida, V. & Kristjanpoller, F. DYNA, Vol. 94, Nº2, March 2018, pp 133-134. DOI: 10.6036/8666 “Reliability assessment methodology for massive manufacturing using multi-function equipment” – López-Campos, M., Kristjanpoller, F., Viveros, P. & Pascual, R. Complexity in Manufacturing Processes and Systems, Vol. 2018, 20 February 2018. DOI: 10.1155/2018/4084917 “Value-based optimization of replacement intervals for critical spare components” – Godoy, D., Knights, P. & Pascual, R. International Journal of Mining, Reclamation and Environment, Vol. 34, Nº 4, 2018, pp 264-272. DOI: 17480930.2017.1278660

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SELECTED PROCEEDINGS “Assessment of economic impact and management techniques for failure modes in photovoltaic systems” – Barraza, R., Caviedes, R., Cárdenas, C. & Godoy, D. Solar Word Congress 2019, November 04-07, 2019, Santiago, Chile.

“Wind farms reliability modeling for life cycle cost analysis” – Kristjanpoller, F., López-Campos, M., Viveros, P., Pascual, R., González-Prida, V. & Crespo, A. Proceedings of the 29th International European Safety and Reliability Conference – ESREL 2019, September 22-26, 2019, Hannover, Germany. DOI: 10.3850/978981-11-2724-3_0224-cd “A graphical method for diagnosing the effectiveness of a maintenance plan” – Viveros, P., Mena, R., Kristjanpoller, F., Stowhas, E., Grubessich, T., González-Prida, V. & Nikulin, C. Proceedings of the 29th International European Safety and Reliability Conference – ESREL 2019, September 22-26, 2019, Hannover, Germany. DOI: 10.3850/978-981-11-2724-2_0564-cd “Design of weekly maintenance schedule for a fleet of trains for the achievement of organizational requirements” – Grubessich, T., Stegmaier, R., Viveros, P. & Kristjanpoller, F. Proceedings of the 29th International European Safety and Reliability Conference – ESREL 2019, September 22-26, 2019, Hannover, Germany. DOI: 10.3850/978-981-11-2724-3_0556-cd “Design of performance indicators based on effective time and throughput variability. Case study in mining industry” – Grubessich, T., Stegmaier, R., Viveros, P., López-Campos, M., Krisjtanpoller, F., Nikulin, C. & Koziolek, S. Advances in Intelligent Systems and Computing, Vol. 835, pp 139-150. Proceedings of the 2nd International Conference on Intelligent Systems in Production Engineering and Maintenance – ISPEM 2018, September 17-18, 2018. DOI: 10.1007/978-3-319-97490-4_14 “Machine learning modeling for massive industrial data: Railroad peak kips prediction” – Contreras, C., López-Campos, M., Escalona, P., Stegmaier, R. & Grubessich, T. In Safety and Reliability – Safe Societis in a Changing World, pp 1139-1142. Proceedings of the 28th International European Safety and Reliability Conference – ESREL 2018, June 17-21, 2018, Trondheim, Norway. ISBN 978-08153-8682-7

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FONDECYT PROJECTS “Risk–Controlled Capacity Expansion Planning of Power Systems with High Renewable Integration”. Proyecto Fondecyt de Iniciación en Investigación 2019. Comisión Nacional de Investigación Científica y Tecnológica. Chile. Investigador responsable: Rodrigo Mena B. “A decision-making framework to optimize mine haul road predictive maintenance interventions based on complex network conditions and truck management systems”. Proyecto Fondecyt de Iniciación en Investigación 2019. Comisión Nacional de Investigación Científica y Tecnológica. Chile. Investigador responsable: David Godoy R. “Design of a reference framework for maintenance management in the context of logistics infrastructure concession contracts: a solution to guarantee the long-term condition of Chilean assets”. Proyecto Fondecyt de Iniciación en Investigación 2018.

Comisión Nacional de Investigación Científica y Tecnológica. Chile.

Investigadora responsable: Mónica López C.

PATENTS “Placa de revestimiento de molinos en procesos de molienda de minerales, que comprenda una zona de debilitamiento de fractura controlada”. Soto, F., Nikulin, C., Stegmaier, R. & Perazzo, F. Patente de invención, Registo INAPI Nº 56358, 2018

BOOKS “Gestión de activos en el sector ferroviario”. Stegmaier, R., Viveros, P., Villalón, R., González-Prida, V., Gómez, S. & Kristjanpoller, F. Editorial: Fundación Confemetal, 2018, páginas: 262. ISBN: 978-84-16671-37-3

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REFEREED PUBLICATIONS “INTEGRATED VERSUS HIERARCHICAL

“FLEET OPTIMIZATION CONSIDERING

APPROACH FOR ZONE DELINEATION AND CROP

OVERCAPACITY AND LOAD SHARING

PLANNING UNDER UNCERTAINTY”

RESTRICTIONS USING GENETIC ALGORITHMS AND ANT COLONY OPTIMIZATION”

Albornoz, Víctor1; Véliz, Marcelo1; Ortega, Rodrigo2 & Ortíz-Araya, Virna3 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Commercial Engineering, Universidad Técnica Federico Santa María, Chile.

3 Department of Business Management, Universidad del Bío-Bío, Chile.

This paper considers the problem of zone delineation management and crop planning. The problem consists of selecting which crops to plant in different management zones in order to minimize the total costs subjected to a given demand requirement. From a hierarchical point of view, the process starts by generating a partition of an agricultural field into homogeneous management zones, according to a given soil property. Then, the best crop rotation must be assigned to each management zone, applying agronomic practices in a site-specific manner in each zone. This hierarchical approach establishes two decision making levels of planning.

At each level, a two-stage stochastic optimization model is proposed, representing the uncertain behavior of a soil property and crop yields by using a finite set of scenarios.

Next, we combined them into a new two-stage stochastic program, solving an integrated approach by simultaneously determining an optimal zoning and allocation. Results from a set of evaluated instances showed the relevance of the proposed methodology and the benefits of the hierarchical approach over the integrated one.

Annals of Operations Research, Vol. 286, March 2020, pp 617-634.

DOI: 10.1007/s10479-019-03198-y

Kristjanpoller, Fredy1; Michell, Kevin1; Kristjanpoller, Werner1 & Crespo, Adolfo2 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Industrial Management, School of Engineering, Universidad de Sevilla, España.

This paper presents a fleet model explained through a complex configuration of load sharing that considers overcapacity and is based on a Life Cycle Cost (LCC) approach for cost-related decision making. By analyzing the variables needed to optimize the fleet size, which must be evaluated in combination with the Event Space Method (ESM), the solution to this problem would normally require high computing performance and long computing times. Considering this, the combined use of an integer Genetic Algorithm (GA) and the Ant Colony Optimization (ACO) method was proposed in order to determine the optimal solution. In order to analyze and highlight the added value of this proposal, several empirical simulations were performed. The results showed the potential strengths of the proposal related to its flexibility and capacity in solving large problems with a near optimal solution for large fleet size and potential real-world applications. Even larger problems can be solved this way than by using the complete enumeration approach and a non-family fleet approach. Thus, this allows for a more real solution to fleet design that also considers overcapacity, availability and a Life Cycle Cost approach. The simulations showed that the model can be solved in much less time compared to the base model, and allows for the resolution of a fleet of at least 64 trucks using GA and 130 using ACO. Thus, the proposed framework can solve real world problems, such as fleet design of mining companies, by offering a more realistic approach.

Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 13 January 2020, pp 1-10.

DOI: 10.1017/S0890060419000428

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“OPTIMIZING CBM DECISIONS FOR STEP-DOWN

“A CONDITION-BASED MAINTENANCE MODEL

POWER TRANSFORMERS IN SERVICE: USE

INCLUDING RESOURCE CONSTRAINTS IN THE

OF NON-ARBITRARY COVARIATE BANDS INTO

NUMBER OF INSPECTIONS”

CONDITION ASSESSMENT”

Godoy, David1; Stegmaier, Raúl1; Jardine, Andrew2 & Ríos, F.3 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Mechanical and Industrial Engineering, University of Toronto, Canadá.

3 Department of Electric Engineering, Pontificia Universidad Católica de Chile, Chile.

Power transformers are critical within the electrical transmission industry since they support the premise of operational continuity for the energy supply system.

Companies often monitor these assets aiming to mitigate significant performance losses and financial impacts from catastrophic events. Condition assessment is essential to predict this risk of failure, yet current practices commonly involve classical comparison to fixed thresholds that can leave substantial residual life untapped. Under a proportional hazards model, we introduced a comprehensive approach that uses non-arbitrary covariate bands in pursuit of a deeper understanding of the equipment health to benefit the most from a predictive policy. This paper presents a dynamic framework to estimate the reliability function and optimize predictive decisions for power transformers in realworld conditions. It guides the best moment to intervene, just before failure, by continuously assessing trustworthy covariate clusters and then combining with the economic consequences. Embedded as an application programming interface, the resultant adaptive health-based algorithm is capable of managing as many as variables the experts require for accurate risk evaluation and adjust the policy when needed.

Reliability Engineering & System Safety (Working Paper). 2019.

Álvarez, Claudio1; López-Campos, Mónica1; Stegmaier, Raúl1; Mancilla-David, Fernando2; Schurch, Roger3 & Angulo, Alejandro3 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2

Electrical Engineering, University of Colorado Denver, United States.

3 Department of Electrical Engineering, Universidad Técnica Federico Santa María, Chile.

This article presents a stochastic dynamic programming model for a condition-based maintenance application. The approach seeks to determine the most opportune moment to inspect and execute preventive maintenance over each component of a nonredundant system, where the number of inspections to be performed simultaneously during each period is limited due to resource constraints. The model minimizes the total maintenance cost per unit of time, considering failure, maintenance, and inspection costs. Unlike most related literature, the model proposed herein allows nonperiodic inspections; it does not require to predefine a maintenance threshold and does not necessarily connect inspections to maintenance actions. Also, the criticality of each component is not static through time, or defined beforehand, but dynamically determined according to the available resources and the risk of failure. A numerical example illustrates the performance of the proposed model in comparison to three traditional maintenance models, namely corrective maintenance, agebased maintenance, and condition-based maintenance with periodic inspections. Results suggest that the proposed model yields the best solution among the studied policies and is more efficient, with a significant reduction of 90% in inspection resources.

IEEE Transactions on Reliability (early access), 13 December 2019, pp 1-12.

DOI: 10.1109/TR.2019.2955558

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“A SIMULATION BASED MODELLING APPROACH

“A BEINSTOCK-ZUCKERBERG-BASED ALGORITHM

TO JOINTLY SUPPORT AND EVALUATE SPARE

FOR SOLVING A NETWORK-FLOW FORMULATION

PARTS SUPPLY CHAIN NETWORK AND

OF THE CONVEX HULL PRICING PROBLEM”

MAINTENANCE SYSTEM”

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Miranda, Pablo2; Tapia-Ubeda, Francisco3; Hernández, Valentina3; Cardenas, Hugo3 & López-Campos, Mónica1 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Science of Engineering, Universidad Andrés Bello, Chile.

3 Faculty of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Chile.

Álvarez, Cristian1; Mancilla-David, Fernando2; Escalona, Pablo1 & Angulo, Alejandro3 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Electrical Engineering, University of Colorado Denver, United States.

3 Department of Electrical Engineering, Universidad Técnica Federico Santa María, Chile.

This research aims at proposing a simulation-based modelling methodology to support the decision-making process related to the Spare Parts Supply Chain, which is a significant supporting system for maintenance operations in manufacturing systems. The proposed simulation-based modelling approach suits as a useful quantitative methodology to evaluate the performance of the Spare Parts Supply Chain along with its impact on the underlying Maintenance System.

The joint modelling and performance evaluation of both the Spare Parts Supply Chain and the Maintenance System allows to support the assessment and selection of policies and strategies related to Spare Parts inventories (e.g. supplying processes, transport decisions and maintenance operations).

This methodology may yield a better system costs balance between Spare Parts Supply Chain and Maintenance System, while ensuring desired system service levels. Finally, it worth to be mentioned that this proposed simulation-based approach is suitable to be embedded into a more general decision-making framework to support Spare Parts Supply Chain planning and control.

This paper studies the convex hull pricing problem in electricity markets using a network-flow-based formulation. The network represents the feasible operating region of a generating unit, and the associated flow constraints define a polyhedron with an integrality property. These facts provide modeling flexibility with respect to the inclusion of unit features and allow to obtain convex hull prices from a linear programming problem. The formulation is solved using a primal-dual approach based on the algorithm developed by Bienstock and Zuckerberg. The algorithm, together with the implemented pre-processing and initialization techniques, allows achieving lower solution times than those obtained by state-of-the-art algorithms available in commercial solvers, e.g., barrier and dual simplex. Furthermore, results suggest that the proposed formulation obtains the minimum uplift payments even when time-dependent startup costs are included, making the approach more robust than the best documented compact formulation. The paper also discusses the effect of sub-optimal prices on uplift payments by relaxing the optimality criterion of the algorithm, observing a significant impact on lost opportunity costs.

IFAC-PapersOnLine, Vol. 52, Nº 13, 2019, pp 2231-2236.

DOI: 10.1016/j.ifacol.2019.11.537

IEEE Transactions on Power Systems, 18 November 2019, pp 1-1.

DOI: 10.1109/TPWRS.2019.2953862

“REDUCING MINING FOOTPRINT BY MATCHING HAUL FLEET DEMAND AND ROUTE-ORIENTES TIRE TYPES”

Pascual, Rodrigo2; Román, Milton2; López-Campos, Mónica1; Hitch, Michael3 & Rodovalho, Edmo4 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Mechanical Engineering, Universidad de Concepción, Chile.

3 Department of Geology, University of Technology, Estonia.

4 Instituto de Ciencia e Tecnologia, Universidade Federal de Alfenas, Brazil.

Off-The-Road (OTR) tires represent an important part of the operational costs of the mining industry. Each year, a typical operation consumes hundreds of tires. In general worn and damaged tires are not reused and represent a major issue for mining sustainability. In terms of operational costs, tires are only second to fuel, translating into several USD millions per year for an average mine operation. In addition, tires affect equipment performance and availability and, consequently, put at risk the capacity of the haul fleets to deliver the production targets.

OTR tire lifespan depends on proper type selection. Each tire-type implies choosing a combination of rubber compounds and geometric specifications that are suited to road parameters. Medium and long term mining plans specify routes and production goals. In general, each route has a specific optimal tire type. The traditional approach is to consider the most demanding conditions and selecting a single tire type for the whole fleet. In such a way, truck dispatch is flexible as any truck can haul on any route. A drawback is that this one-size-fits-all policy increases tire consumption as the worst-case route sets the type of tire for the entire fleet.

The above builds an interesting case for optimizing tire selection and haul fleet usage schedule as both decisions can be relevant in search for reducing tire consumption, decrease operational costs, and assure production plan adherence. As tire type may influence haul cycle times, assignment of trucks to different routes should also be considered. This work introduces a novel methodology for setting a usage allocation plan for the haul fleet and selecting route-oriented tire types.

We test our methodology using a medium size open-pit operation in northern Chile. The case study shows a tire consumption reduction of 7.3% with respect to the traditional approach over a 5 year time span. Net present tire costs are reduced by USD 2.7 millions (−7.2%). Our methodology presents a novel approach to both reducing costs and achieving long-term production plans.

Journal of Cleaner Production, Vol. 227, 01 August 2019, pp 645-651.

DOI: 10.1016/j.clepro.2019.04.069

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“ON THE EFFECT OF TWO POPULAR SERVICE-

“MODELING THE OPERATION OF SYNCHRONIZED

LEVEL MEASURES OF THE DESIGN OF A CRITICAL

SUPPLY CHAINS UNDER A COLLABORATIVE

LEVEL POLICY FOR FAST-MOVING ITEMS”

STRUCTURE”

Escalona, Pablo1; Angulo, Alejandro2; Weston, Jorge1; Stegmaier, Raúl1 & Kauak, Ismael1 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Electrical Engineering, Universidad Técnica Federico Santa María, Chile.

This paper studies the effect of two service-level measures on the design of a critical-level policy for fast-moving items, where rationing is used to provide differentiated service levels to two classes of demand – high priority and low priority. Using the threshold-clearing mechanism under a strictly increasing non-negative demand to allocate backorders when multiple outstanding orders exist, we formulate service-level problems under type-I, fill rate, and mixed service-level constraints to determine the optimal parameters of a continuous review (Q, r) policy with constant threshold value C to rationing the low-priority class. Based on several monotone properties, we proposed global search algorithms to solve the service-level problems, which guarantee reaching the globally optimal solution for any desired level of accuracy. Further results and a computational study demonstrate how these different models fare against each other in practice.

Computers & Operations Research Vol. 107, July 2019, pp 107-126.

DOI: 10.1016/j.cor.2019.03.011

36

López-Campos, Mónica1; Cannella, Salvatore2; Miranda, Pablo3 & Stegmaier, Raúl1 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Industrial Organization, Universidad de Catania, Italia.

3 School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Chile.

The purpose of this paper is to propose and model collaboration and information exchange enabler strategies, designed to accomplish significant improvements in supply chain (SC) performance. Some of these improvements to the SC include the reduction of the bullwhip effect and increased customer and SC partner benefits. The authors propose a fully collaborative replenishment model. The study details the information flow required to implement new SC collaboration strategies, clarifying a specific strategy for information sharing involving inventory levels (on hand, in process, etc.), orders and demand forecast.

Academia Revista Latinoamericana de Administración, Vol. 32, Nº 2, June 2019, pp 203-224.

DOI: 10.1108/ARLA-04-2017-0090

“PROSPECTIVE STUDY USING ARCHETYPES AND SYSTEMS DYNAMICS”

Vera, Patricio2; Nikulin, Christopher2; López-Campos, Mónica1 & González-Ramírez, Rosa3 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Design Engineering, Universidad Técnica Federico Santa María, Chile.

3 Faculty of Engineering and Applied Science, Universidad de los Andes, Chile.

The purpose of this paper is to propose a combination of forecasting methods that enables a holistic understanding of a future situation, given certain influencing variables by a combination of real data and expert knowledge.

Academia Revista Latinoamericana de Administración, Vol. 32, Nº 2, June 2019, pp 181-202.

DOI: 10.1108/ARLA-05-2017-0151

“DELINEATING ROBUST RECTANGULAR MANAGEMENT ZONES BASED ON COLUMN GENERATION ALGORITHM” Albornoz, Víctor; Ñanco, Linco & Sáez, José L. Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

This paper considers a management zone delineation problem in a precision agriculture framework that defines a partition of a field into rectangular and homogeneous management zones according to certain vegetation or soil indexes. Modeling the problem as a robust optimization model accounts for spatial and temporal variability of the index considered, representing this variability according to a finite set of sampled realizations (scenarios). The model assumes that the complete enumeration of all the possible rectangular management zones is known. To deal with this assumption, we propose a strategy based on a column generation algorithm to exploit the structure of the model.

To solve the model and implement the strategy, an algebraic modeling language and state of the art MIP software was used.

Results from the modeling approach and the computational strategy were obtained by solving different instances of a real case-study. We show that the robust optimization model can be useful in real situations under temporal variability of the index used for delineating management zones. Results also show that the algorithmic strategy is appropriated for solving the instances considered.

Computers and Electronics ins Agriculture, Vol. 61, June 2019, pp 194-201.

DOI: 10.1016/j.compag.2019.01.045

37

“STOCHASTICS DISCRETE LOT-SIZING WITH LEAD

“THE IMPACT OF CONCENTRATED SOLAR POWER

TIMES FOR FUEL SUPPLY OPTIMIZATION”

IN ELECTRIC POWER SYSTEMS: A CHILEAN CASE STUDY”

Testuri, Carlos2; Cancela, Héctor2 & Albornoz, Víctor1 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Operational Research, Computing Institute, Universidad de la República, Uruguay.

We address the problem of expected cost minimization of meeting the uncertain fuel demand during a time planning horizon, where supply is provided by selecting discrete shipments with lead times. Due to uncertainty and the passage of time, corrective actions can be taken such as cancellation and postponement on supply of shipments with associated costs and delays. This problem is modeled as a stochastic multi-stage capacitated discrete lot-sizing problem with lead times. Computational experiments were performed on the resolution of various instances of the model for four information structures of uncertainty. The experimental optimal values and stochastic rating measures obtained show the validity and interest of the stochastic model, as well as the benefits that can be obtained with respect to a deterministic variant of the model that considers average demand.

Pesquisa Operacional, Vol. 29, Nº 1, 09 May 2019, pp 37-55.

DOI: 10.1590/0101-7438.2019.039.01.0037

Mena, Rodrigo1; Escobar, Rodrigo2; Lorca, Álvaro3,4; Negrete-Pincetic, Matías3 & Olivares, Daniel3 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Mechanical Engineering, Pontificia Universidad Católica de Chile, Chile.

3 Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Chile.

4 Department of Industrial and Systems Engineering, Pontificia Universidad Católica de Chile, Chile.

This paper presents a study about the impacts of the integration of concentrated solar power (CSP) with thermal energy storage (TES) in electric power systems. The main tool for this study is a comprehensive long-term power system capacity expansion planning model that integrates a specific module to represent the operation of CSP-TES power plants. The model determines the optimal investments on generation and transmission assets over a twenty-year planning horizon, ranging from 2018 until 2037, and employs projections for the various parameters involved (e.g. load growth, capital costs for the different generation technologies, fuels costs). One of the main features of the model is its ability to capture the hourly operational dynamics of the system through the consideration of multiple representative days for each of its investment periods. This feature allows a better understanding of the role of CSP-TES as a significant provider of flexibility to support a high penetration of variable renewable energy sources, as compared with traditional planning models based on load blocks. The model is applied to a case study for the Chilean electricity system. In order to study the impacts of CSP-TES, various scenarios of future capital costs and carbon tax levels are defined and analyzed for two market dominant CSP-TES technologies. The results show that for low CSP-TES capital costs, or high carbon taxes, the integration of CSP-TES in the system is significant towards year 2037, potentially reaching about one third of the total dispatched energy in the Chilean electric power system, yielding important operational, economic, and environmental benefits.

Applied Energy, Vol. 235, 01 February 2019, pp 258-283.

DOI: 10.1016/j.apenergy.2018.10.088

38

“RESOLUTION OF RELIABILITY PROBLEMS BASED

“AN ASSET-MANAGEMENT ORIENTED

ON FAILURE MODE ANALYSIS: AN INTEGRATED

METHODOLOGY FOR MINE HAUL-FLEET USAGE

PROPOSAL APPLIED TO A MINING CASE STUDY”

SCHEDULING”

Viveros, Pablo1; Nikulin, Christopher2; López-Campos, Mónica1; Villalón, Roberto1 & Crespo, Adolfo3 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Design Engineering, Universidad Técnica Federico Santa María, Chile.

3 Department of Industrial Management, School of Engineering, Universidad de Sevilla, España.

Nakousi, Carlos3; Pascual, Rodrigo2; Anani, Angelina3; Kristjanpoller, Fredy1 & Lillo, Patricio3 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Mechanical Engineering, Universidad de Concepción, Chile.

3 Department of Mining Engineering, Pontificia Universidad Católica de Chile, Chile.

Reliability determines, in large part, the operational productivity. Nevertheless, a frequent problem is the absence of effective mechanisms to support maintenance management. In particular, there is a need for methodologies focused on improving the detection and analysis of risks that affect reliability. This article presents a methodological proposal for the resolution of these problems, using a highimpact failure mode analysis. The methodology is based on four phases: identification of failure modes, ranking and criticality analysis of them, identification of the root cause(s) and search for highly effective solutions. Among the variety of tools that can be used, it is proposed the use of three specific tools: Criticality Analysis, which allows discrimination and ranking of phenomena and assets; Root Cause Analysis, which focuses on the identification of the real causes of the problems; and a tool for generation of effective and efficient solutions (TRIZ), which it is not usually applied to reliability problems. The proposal is applied in a mining filtration plant, identifying and classifying current problems and generating solutions.

Different complexities force mining companies to find efficient ways to respond to demand challenges and ensure longterm sustainability. It explains, in part, the increase in the use of prescriptive analytics to optimize physical-asset life-cycle costs and decrease greenhouse gas (GHG) emissions. Mining, being an asset-intensive industry, provides huge improvement opportunities. This is especially true for scheduling practices of mine haulage fleet usage in long term planning. Fleet aging implies important cost increases in maintenance and repair (M&R), and overhauls. Fleets are often heterogeneous in term of truck performance, fuel consumption and GHG emissions. Suboptimal scheduling decisions may induce severe cost over-runs and increased emissions. This paper proposes an original mixed integer programming formulation to optimize mine haulage equipment scheduling in the long term. The model considers the effects of equipment aging, fuel consumption, payload capacity and cycle times. Our formulation handles different aspects that according to author’s knowledge have not been considered in the literature as a whole: (i) joint minimization of fuel, M&R, and overhaul costs, (ii) reduction of GHG emissions, (iii) heterogeneous equipment performance metrics, (iv) increase in cycle times due to mine aging. The case study shows a cost reduction of 13% in the discounted flows associated with fuel, M&R, and overhauls in a time horizon of 10 years. This figure translates into an NPV gain of 13.1 million USD. Additionally, GHG emissions are reduced by an average of 3470 t/year or 11% overall.

Production Planning & Control, Vol. 29, Nº 15, 28 January 2019, pp 1225-1237.

DOI: 10.1080/09537287.2018.1520293

Reliability Engineering and System Safety, Vol. 180, December 2018, pp 226-344.

DOI: 10.1016/j.ress.2018.07.034

39

“GRAPHICAL ANALYSIS FOR OVERALL

“COMBINED USE OF MATHEMATICAL

EFFECTIVENESS MANAGEMENT: A GRAPHICAL

OPTIMIZATION AND DESIGN OF EXPERIMENTS

METHOD TO SUPPORT OPERATION AND

FOR THE MAXIMIZATION OF PROFIT IN A FOUR-

MAINTENANCE PERFORMANCE ASSESSMENT”

ECHELON SUPPLY CHAIN”

Viveros, Pablo1; Kristjanpoller, Fredy1; López-Campos, Mónica1; Crespo, Adolfo2 & Pascual, Rodrigo3 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Industrial Management, School of Engineering, Universidad de Sevilla, España.

3 Department of Mechanical Engineering, Universidad de Concepción, Chile.

This article presents a graphical tool for the monitoring of performance indicators called GAOEM (Graphical Analysis for Overall Effectiveness Management). GAOEM comprehends the main indicators of maintenance and operations management, and it constitutes an adaptation of the overall effectiveness index. GAOEM facilitates control and analysis by using specific indicators in a graphic panel, designed for industries with high investment in physical assets. It also facilitates an efficient reading and interpreting of the data, enriching the analysis, the search for phenomena of interest and improvement opportunities to support the decision making.

GAOEM requires 3 categories of input data, which are interventions data, time data, and production/process data.

With this information, the basic performance indicators are calculated, being these indicators fundamental for the control and monitoring of the performance level. Later, these indicators will be the basis for calculation of both partial and total performance.

GAOEM can be used as a diagnosis, analysis, control and monitoring tool for the indicators of interest at a strategic level, as well as a tool for searching of phenomena typical of a specific stage of the life cycle of assets and also of those conditions resulting from improper operation and/or maintenance, identifying their root causes.

Quality and Reliability Engineering International, Vol. 34, Nº 8, December 2018, pp 1615-1632.

DOI: 10.1002/qre.2348

40

Olivares, Daniel2; Olivares-Benitez, Eías3; Puente, Eleazar4; López-Campos, Mónica1 & Miranda, Pablo5 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Faculty of Information Technologies, Universidad de la Salle Bajío, León, México.

3 Faculty of Engineering, Universidad Panamericana, Zapopan, México.

4 Department of Engineering, Tecnológico de Monterrey, León, México.

5 School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Chile.

This paper develops a location-allocation model to optimize a four-echelon supply chain network, addressing manufacturing and distribution centers location, supplier selection and flow allocation for raw materials from suppliers to manufacturers, and finished products for end customers, while searching for system profit maximization. A fractional-factorial design of experiments is performed to analyze the effects of capacity, quality, delivery time, and interest rate on profit and system performance. The model is formulated as a mixed-integer linear programming problem and solved by using well-known commercial software. The usage of factorial experiments combined with mathematical optimization is a novel approach to address supply chain network design problems. The application of the proposed model to a case study shows that this combination of techniques yields satisfying results in terms of both its behavior and the obtained managerial insights. An ANOVA analysis is executed to quantify the effects of each factor and their interactions. In the analyzed case study, the transportation cost is the most relevant cost component, and the most relevant opportunity for profit improvement is found in the factor of quality. The proposed combination of methods can be adapted to different problems and industries.

Complexity, Vol. 2018.

DOI: 10.1155/2018/8731027

“ON THE EFFECT OF INVENTORY POLICIES

“DECISION CRITERIA TO SELECT A SUITABLE

ON DISTRIBUTION NETWORK WITH SEVERAL

CRITICALITY ASSESSMENT TECHNIQUE”

DEMAND CLASES”

Escalona, Pablo1; Marianov, Vladimir2; Ordóñez, Fernando3 & Stegmaier, Raúl1 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Chile.

3 Department of Electrical Engineering, Universidad de Chile, Chile.

This paper studies the effect of several inventory policies on the design of a distribution network for fast-moving items able to provide differentiated service levels in terms of product availability for several demand classes. We consider the distribution network design problem when the global round-up, single class allocation, local separate stock, local round-up, and critical level inventory policies are used. We show how to formulate these problems as conic quadratic mixed-integer problems and prove that the critical level policy provides the lowest cost distribution network design. Further results and a computational study show how these different models compare in practice.

Transportation Research Part E: Logistics and Transportation Review, Vol. 111, March 2018, pp 229240.

DOI: 10.1016/j.tre.2017.10.019

Viveros, Pablo1; Crespo, Adolfo2; Barbera, Luis2; GonzálezPrida, Vicente2 & Kristjanpoller, Fredy1 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Industrial Management, School of Engineering, Universidad de Sevilla, España.

El siguiente artículo presenta un criterio de decisión que responde a la problemática de qué técnica de jerarquización de activos es la más indicada para el contexto de una operación en particular basado en diversos criterios y variables. También, este artículo pretende determinar cómo aplicar criterios de decisión que determinen la criticidad de los activos. La metodología ha sido construida a partir de preguntas simples que hacen referencia a las características del contexto operacional, información disponible, y a la calificación de los técnicos. Esta se compone a partir de dos grandes grupos de preguntas. El primero apunta a diagnosticar el escenario actual y el segundo incluye un árbol de decisión potencialmente aplicable a diversas técnicas dependiendo de los requerimientos.

DYNA, Vol. 94, Nº2, March 2018, pp 133-134.

DOI: 10.6036/8666

41

“RELIABILITY ASSESSMENT METHODOLOGY FOR MASSIVE MANUFACTURING USING MULTIFUNCTION EQUIPMENT” López-Campos, Mónica1; Kristjanpoller, Fredy1; Viveros, Pablo1 & Pascual, Rodrigo2 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Mining Engineering, Pontificia Universidad Católica de Chile, Chile.

Experience reveals that reliability varies depending on the characteristics of operation. The manufacturing process based on multifunction equipment gives a usual case of variation in operating conditions. This work presents a methodology for the reliability analysis of multifunction processes, using the RCM approach, and a modification of the Universal Generating Function (UGF) under a massive manufacturing context. The result is a characterization of reliability, for each piece of equipment and for the production system. The methodology is applied in a workshop of a textile industry, where there is prior evidence that the failure behavior varies according to the type of function executed by multifunction machines.

Complexity in Manufacturing Processes and Systems, Vol. 2018, 20 February 2018.

DOI: 10.1155/2018/4084917

“VALUE-BASED OPTIMIZATION OF REPLACEMENT INTERVALS FOR CRITICAL SPARE COMPONENTS”

Godoy, David1, Knights, Peter2 & Pascual, Rodrigo3 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 School of Mechanical and Mining Engineering, The University of Queensland, Brisbane, Australia.

3 Department of Mining Engineering, Pontificia Universidad Católica de Chile, Chile.

1

For competitive industries, such as mining, value-adding demands enriched methods to efficiently manage critical equipment and spare components. These components are related to lengthy shutdowns with significant financial impact.

In this context, cost optimization is a widely used principle to schedule component replacements. However, this practice traditionally does not consider external factors of interest, such as business-market conditions, which can radically change decisions. To overcome this limitation, we have proposed a criterion based on the estimation of revenues–under several commodity price scenarios–at both the component intervention period and major shutdown time-windows. The paper presents a model to guide the decision about the best epoch to replace critical components, considering the maximization of valueadding rather than simply minimization of costs. Such optimal interval is optimized by comparing net benefits with reliability constraints from a condition-based maintenance strategy. As result, the decision-making process is enriched by quantifying the real value of postponing or accelerating the most favorable epoch to perform a replacement. The proposed value-based approach increases business competitiveness when included as a systemic part of an asset management perspective, thus obtaining higher profitability.

International Journal of Mining, Reclamation and Environment, Vol. 34, Nº 4, 2018, pp 264-272.

DOI: 17480930.2017.1278660

42

43

SELECTED PROCEEDINGS “ASSESSMENT OF ECONOMIC IMPACT AND

“WIND FARMS RELIABILITY MODELING FOR LIFE

MANAGEMENT TECHNIQUES FOR FAILURE

CYCLE COST ANALYSIS

MODES IN PHOTOVOLTAIC SYSTEMS” Barraza, Roberto2; Caviedes, R.2; Cárdenas, C.2 & Godoy, David1 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Mechanical Engineering, Universidad Técnica Federico Santa María, Chile.

The knowledge of an adequate risk management is vital at the time of making investments, especially in the photovoltaic (PV) industry, where this issue is currently under development.

Therefore, to achieve the objective of managing the risk, one must have knowledge of the different failures, which can be organized by means of a failure mode and effect analysis (FMEA) and, classified by the same, according to several criteria, such as: occurrence, time to detection, time to repair and/or replace and impact on the whole system and energy production. Crossing this information with economic data, failures modes can be ranked with an overall weighted cost.

The ranked failure modes are used by a maintenance plan to reduce the overall cost and guide the asset management.

This paper proposes a methodology to quantify the economic impact of failures experienced in a photovoltaic power system and how can maintenance plans might mitigate these costs.

Solar Word Congress 2019, November 04-07, 2019, Santiago, Chile.

Kristjanpoller, Fredy1; López-Campos, Mónica1; Viveros, Pablo1; Pascual, Rodrigo2; González-Prida, Vicente3 & Crespo, Adolfo3 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Mechanical Engineering, Universidad de Concepción, Chile.

3 Department of Industrial Management, School of Engineering, Universidad de Sevilla, España.

The present article explores the importance of developing a new methodology for the optimal sizing of a power generation system. To this aim, the proposed methodology is based on the analysis of the life cycle cost, which is composed by a set of differentiating costs. These differentiating costs will depend mainly on the variables of the equipment that make up the system, such as the cost of power generation, reliability of the equipment and the complete system, including the possibility of incorporating backup equipment. This analysis will allow to obtain an optimal number of such equipment. The size of the power generation system, in addition to assuming a minimum cost, have to fulfil the annual required power generated by the system itself. The proposed methodology consists of five stages: (i) Study of technical factors, (ii) Modelling and reliability analysis of the system, (iii) Application of the LCCA technique, (iv) Analysis of total costs and evaluation of scenarios to find the optimum n system size, complying with the requested generated power, (v) Sensitivity analysis and obtaining final results.

Proceedings of the 29th International European Safety and Reliability Conference – ESREL 2019, September 22-26, 2019, Hannover, Germany.

DOI: 10.3850/978-981-11-2724-3_0224-cd

44

“A GRAPHICAL METHOD FOR DIAGNOSING THE

“DESIGN OF WEEKLY MAINTENANCE SCHEDULE

EFFECTIVENESS OF A MAINTENANCE PLAN”

FOR A FLEET OF TRAINS FOR THE ACHIEVEMENT OF ORGANIZATIONAL REQUIREMENTS”

Viveros, Pablo1; Mena, Rodrigo1; Kristjanpoller, Fredy1; Stowhas, Erich1; Grubessich, Tomás1; González-Prida, Vicente2 & Nikulin, Christopher3 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Industrial Management, School of Engineering, Universidad de Sevilla, España.

3 Department of Design Engineering, Universidad Técnica Federico Santa María, Chile.

This article presents the general framework of development of a graphical tool for monitoring the performance indicators aimed to assess the effectiveness of a maintenance plan.

This tool integrates the main indicators of maintenance management and provides information in a clear and simple way and facilitate the identification of guidelines for the proper allocation of technical and financial resources to fulfill given maintenance and production goals. The graphical tool focuses on the diagnosis, analysis, monitoring and control of the historical data regarding maintenance and operational events, and it has been designed for driving an effective reading and interpretation of it, to enriching the assessment of the performance of the current maintenance plans, supporting a well-informed decision making process concerning the management actions to be taken as part of the maintenance policies applied on physical assets.

Proceedings of the 29th International European Safety and Reliability Conference – ESREL 2019, September 22-26, 2019, Hannover, Germany.

DOI: 10.3850/978-981-11-2724-2_0564-cd

Grubessich, Tomás; Stegmaier, Raúl; Viveros, Pablo & Kristjanpoller, Fredy.

Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

The following paper proposes a framework on how to face the challenge of designing a maintenance schedule that ensures the achievement of the requirements related to the assets of a company. The presence of internal and external factors to the organization make it necessary to adjust the way of carrying out the maintenance schedule, and the ability of the company to adapt is fundamental to comply with the maintenance goal.

Anticipating risky situations and taking actions to deal with them is of vital importance for the companies. However, carrying out this task is not easy, since it requires a high understanding of the system, analysis of external factors that affect performance, determination of changes in maintenance task, among others.

That is why this framework is presented, where the main objective is to design of weekly maintenance schedule for a fleet of trains for the achievement of organizational requirements. To achieve this, different phases are carried out, where the principals are to increase the comprehension of the system that generates the maintenance schedule, identify the key concepts behind the way in which the schedule is designed and the creation of a model that represent the function of the system. Once the created model has been validated, a series of experiments is carried out, which seeks to define the design of more adequate maintenance schedule.

Proceedings of the 29th International European Safety and Reliability Conference – ESREL 2019, September 22-26, 2019, Hannover, Germany.

DOI: 10.3850/978-981-11-2724-3_0556-cd

45

“DESIGN OF PERFORMANCE INDICATORS

“MACHINE LEARNING MODELING FOR MASSIVE

BASED ON EFFECTIVE TIME AND THROUGHPUT

INDUSTRIAL DATA: RAILROAD PEAK KIPS

VARIABILITY. CASE STUDY IN MINING INDUSTRY”

PREDICTION”

Grubessich, Tomás1; Stegmaier, Raúl1; Viveros, Pablo1; López-Campos, Mónica1; Krisjtanpoller, Fredy1; Nikulin, Christopher2 & Koziolek, Sebastián3 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Design Engineering, Universidad Técnica Federico Santa María, Chile.

3 Department of Mechanical, Wroclaw University, Polonia.

The following paper proposes a method on how to analyze productive systems to achieve performance indicators that allows to know the state of the system. In particular, the objective of the paper is to analyze performance indicators that allow to understand the state of the production line in systems that present variability conditions in the performance of their equipment, and in their operational conditions that will not allow direct calculation of the effective time. It is proposed to begin with the utilization of a methodology to increase the understanding of the system, which will generate a conceptual model that will concentrate the required knowledge through a logic structure that will ease the subsequent analysis. Then, a step by step process is proposed to define the system, its performance indicators of interest, and the most efficient and effective way to obtain those, considering the existing restrictions. Finally, system and subsystem level indicators will be obtained, which will be a representation of the real state of the process, by representing the effective times, and variable throughput. All of the above will be applied in a case study in the mining industry from Chile.

Advances in Intelligent Systems and Computing, Vol. 835, pp 139-150. Proceedings of the 2nd International Conference on Intelligent Systems in Production Engineering and Maintenance – ISPEM 2018, September 17-18, 2018.

DOI: 10.1007/978-3-319-97490-4_14

46

Contreras, Carlos2; López-Campos, Mónica1; Escalona, Pablo1; Stegmaier, Raúl1 & Grubessich, Tomás1 1 Department of Industrial Engineering, Universidad Técnica Federico Santa María, Chile.

2 Department of Physical, Universidad Técnica Federico Santa María, Chile.

The exponential growth of industrial data being generated by sensors, modern equipment and devices is pushing the service sector to use more sophisticated analytics tools that can produce useful knowledge and predict certain events, especially for those which require reducing loss through preventive maintenance. This work presents the application of big data analytics for machine learning processing through a railway company problem approach, using one of the most powerful tools for large scale data management: the open-source Apache Spark platform. The practical implications of this, are in a reliable prediction of the condition of trains before being loaded and sent to a destination.

In Safety and Reliability – Safe Societis in a Changing World, pp 1139-1142. Proceedings of the 28th International European Safety and Reliability Conference – ESREL 2018, June 17-21, 2018, Trondheim, Norway.

ISBN 978-0-8153-8682-7

47