A review of discrete-time optimization models for tactical production planning

This is an Accepted Manuscript of an article published in International Journal of Production Research on 27 Mar 2014, available online: http://doi.org/10.1080/00207543.2014.899721[EN] This study presents a review of optimization models for tactical production planning. The objective of this research is to identify streams and future research directions in this field based on the different classification criteria proposed. The major findings indicate that: (1) the most popular production-planning area is master production scheduling with a big-bucket time-type period; (2) most of the considered limited resources correspond to productive resources and, to a lesser extent, to inventory capacities; (3) the consideration of backlogs, set-up times, parallel machines, overtime capacities and network-type multisite configuration stand out in terms of extensions; (4) the most widely used modelling approach is linear/integer/mixed integer linear programming solved with exact algorithms, such as branch-and-bound, in commercial MIP solvers; (5) CPLEX, C and its variants and Lindo/Lingo are the most popular development tools among solvers, programming languages and modelling languages, respectively; (6) most works perform numerical experiments with random created instances, while a small number of works were validated by real-world data from industrial firms, of which the most popular are sawmills, wood and furniture, automobile and semiconductors and electronic devices.This study has been funded by the Universitat Politècnica de València projects: ‘Material Requirement Planning Fourth Generation (MRPIV)’ (Ref. PAID-05-12) and ‘Quantitative Models for the Design of Socially Responsible Supply Chains under Uncertainty Conditions. Application of Solution Strategies based on Hybrid Metaheuristics’ (PAID-06-12).Díaz-Madroñero Boluda, FM.; Mula, J.; Peidro Payá, D. (2014). A review of discrete-time optimization models for tactical production planning. International Journal of Production Research. 52(17):5171-5205. doi:10.1080/00207543.2014.899721S51715205521

Supply chain operational transport planning by using an interactive fuzzy multi-objective linear programming approach

[EN] In this paper, we propose a new fuzzy multi-objective linear programming model (FMOLP) for the supply chain operational transport planning problem that considers simultaneously the fuzziness in the aspiration levels and uncertainty in some critical parameters such as transport capacity levels. We also present an interactive solution methodology to convert this FMOLP model into an auxiliary crisp single-objective linear model and to find a preferred compromise solution in an interactive fashion. We validated the proposed model and the solution methodology with a real-world automobile supply chain. The experimental results indicate that the proposed approach outperforms the heuristic decision-making procedure applied in the automobile supply chain under study.[ES] En este trabajo se propone un modelo de programación lineal fuzzy multiobjetivo (PLFMO) para la planificación operativa del transporte que considera simultáneamente la borrosidad en los niveles de aspiración del planificador y en ciertos parámetros críticos como son los niveles de capacidad del transporte. Asimismo, se presenta una metodología de resolución para convertir el modelo de PLFMO en un modelo monoobjetivo lineal auxiliar equivalente y encontrar una solución de compromiso de forma interactiva. Se validan el modelo y la metodología de resolución en una cadena de suministro CS real del sector del automóvil. Por último, los resultados obtenidos muestran la mejora aportada por el modelo propuesto respecto al procedimiento heurístico para la toma de decisiones empleado actualmente en la CS.Este trabajo está financiado por el Proyecto Nacional del Ministerio de Ciencia e Innovación (MiCiNN) del Gobierno Español titulado: Tecnología de producción basada en la realimentación de decisiones de planificación de producción, transporte y descargas y el rediseño de almacenes en cadena de suministro (REVOLUTION) (Ref. DPI2010-19977).Díaz-Madroñero Boluda, FM.; Peidro Payá, D.; Mula, J. (2012). Planificación operativa del transporte en una cadena de suministro mediante un enfoque interactivo de programación lineal fuzzy multiobjetivo. Dirección y Organización. (46):31-44. http://hdl.handle.net/10251/35961S31444

Solving the time capacitated arc routing problem under fuzzy and stochastic travel and service times

[EN] Stochastic, as well as fuzzy uncertainty, can be found in most real-world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper we propose a fuzzy simheuristic to solve the Time Capacitated Arc Routing Problem (TCARP) when the nature of the travel time can either be deterministic, stochastic or fuzzy. The main goal is to find a solution (vehicle routes) that minimizes the total time spent in servicing the required arcs. However, due to uncertainty, other characteristics of the solution are also considered. In particular, we illustrate how reliability concepts can enrich the probabilistic information given to decision-makers. In order to solve the aforementioned optimization problem, we extend the concept of simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated into the CARP. In order to test our approach, classical CARP instances have been adapted and extended so that customers' demands become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones. In particular, our fuzzy simheuristic is capable of generating new best-known solutions for the stochastic versions of some instances belonging to the tegl, tcarp, val, and rural benchmarks.Spanish Ministry of Science, Grant/Award Number: PID2019-111100RB-C21/AEI/10.13039/501100011033; Barcelona Council and the "la Caixa" Foundation under the framework of the Barcelona Science Plan 2020-2023, Grant/Award Number: 21S09355-001; Generalitat Valenciana,Grant/Award Number: PROMETEO/2021/065Martín, XA.; Panadero, J.; Peidro Payá, D.; Pérez Bernabeu, E.; Juan-Pérez, ÁA. (2023). Solving the time capacitated arc routing problem under fuzzy and stochastic travel and service times. Networks. 82(4):318-335. https://doi.org/10.1002/net.2215931833582

Solving the time capacitated arc routing problem under fuzzy and stochastic travel and service times

Stochastic, as well as fuzzy uncertainty, can be found in most real-world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper we propose a fuzzy simheuristic to solve the Time Capacitated Arc Routing Problem (TCARP) when the nature of the travel time can either be deterministic, stochastic or fuzzy. The main goal is to find a solution (vehicle routes) that minimizes the total time spent in servicing the required arcs. However, due to uncertainty, other characteristics of the solution are also considered. In particular, we illustrate how reliability concepts can enrich the probabilistic information given to decision-makers. In order to solve the aforementioned optimization problem, we extend the concept of simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated into the CARP. In order to test our approach, classical CARP instances have been adapted and extended so that customers' demands become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones. In particular, our fuzzy simheuristic is capable of generating new best-known solutions for the stochastic versions of some instances belonging to the tegl, tcarp, val, and rural benchmarks.This work has been partially supported by the Spanish Ministry of Science (PID2019-111100RB-C21/AEI/10.13039/01100011033), as well as by the Barcelona Council and the “laCaixa” Foundation under the framework of the Barcelona Science Plan 2020-2023 (grant21S09355-01) and Generalitat Valenciana (PROMETEO/2021/065).Peer ReviewedPostprint (published version

ArtificialIntelligence with Open AI Gym and Ray RLlib for Interactive Learning of Material Requirements Planning

[EN] Material requirements planning (MRP) is a process whose purpose is to guarantee the flow of materials into production, ensuring that each of the necessary materials is received in the required quantity and on the required date. As a problem, MRP involves products, bills of materials and components, inventory, purchase orders, and production orders, among other input variables. All this abundant dataset intervening in the solution configures a combinatorial optimisation problem of great complexity. Indeed, the MRP belongs to the group of NP-hard problems, since the time required to calculate the optimal solution, in terms of computability, is of non-polynomial order so that it increases exponentially as the volume of data increases. In real-world environments, the problem reaches such a magnitude that it usually becomes intractable for exact approximation methods. This paper proposes the use of Open AI Gym y RLlib, two well-known frameworks for deep reinforcement learning (DRL), to carry out MRP simulation practices based on the project-based learning (PBL) teaching technique, in the educational context of the Master's Degree in Industrial Organisation and Logistics (MUIOL) currently taught at the Alcoy Campus of the Universitat Politècnica de València (UPV). The contribution of this study is twofold: i) it brings artificial intelligence closer to the teaching context, and ii) it provides a reference for developing teaching materials for the study of MRP.[ES] La planificación de requerimiento de materiales, actividad conocida como MRP por sus siglas en inglés, es un proceso cuyo propósito es garantizar el flujo de materiales en la producción, asegurando que cada uno de los materiales necesarios sea recibido en la cantidad y fecha requeridas. Como problema, el MRP involucra productos, listas de materiales y componentes, inventario, pedidos de compra, y ordenes de producción, entre otras variables de entrada. Todo este abundante conjunto de datos interviniendo en la solución configura un problema de optimización combinatoria de gran complejidad. En efecto, el MRP pertenece al grupo de los problemas NP-hard, ya que el tiempo requerido para calcular la solución óptima, en términos de computabilidad, es de orden no polinomial, de modo que aumenta de forma exponencial frente a incrementos en el volumen de datos. En entornos reales el problema alcanza tal dimensión que, por lo general, se vuelve intratable para los métodos exactos de aproximación. Este artículo propone el empleo de Open AI Gym y Ray RLlib, dos conocidos marcos de trabajo de aprendizaje por refuerzo profundo (ARP), para la realización de prácticas de simulación del MRP sobre la base de la técnica docente del aprendizaje basado en proyectos (ABP), en el contexto educativo del Máster Universitario en Ingeniería de Organización y Logística (MUIOL) que, actualmente, se imparte en el Campus de Alcoy de la Universitat Politècnica de València (UPV). La contribución de este estudio es doble: i) aproxima la inteligencia artificial al contexto de la enseñanza; y ii) proporciona una referencia para desarrollar materiales didácticos para el estudio del MRP.Serrano Ruiz, JC.; Peidro Payá, D.; Mula Bru, J.; Poler Escoto, R. (2022). Inteligencia Artificial con Open AI Gym y Ray RLlib para el Aprendizaje Interactivo de la Planificación de Requerimiento de Materiales. Editorial Universitat Politècnica de València. 1338-1349. https://doi.org/10.4995/INRED2022.2022.158771338134

Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model

Traditionally, supply chain planning problems consider variables with uncertainty associated with uncontrolled factors. These factors have been normally modelled by complex methodologies where the seeking solution process often presents high scale of difficulty. This work presents the fuzzy set theory as a tool to model uncertainty in supply chain planning problems and proposes the particle swarm optimisation (PSO) metaheuristics technique combined with a backward calculation as a solution method. The aim of this combination is to present a simple effective method to model uncertainty, while good quality solutions are obtained with metaheuristics due to its capacity to find them with satisfactory computational performance in complex problems, in a relatively short time period.This research is partly supported by the Spanish Ministry of Economy and Competitiveness projects 'Methods and models for operations planning and order management in supply chains characterised by uncertainty in production due to the lack of product uniformity' (PLANGES-FHP) (Ref. DPI2011-23597) and 'Operations design and Management of Global Supply Chains' (GLOBOP) (Ref. DPI2012-38061-C02-01); by the project funded by the Polytechnic University of Valencia entitled 'Quantitative Models for the Design of Socially Responsible Supply Chains under Uncertainty Conditions. Application of Solution Strategies based on Hybrid Metaheuristics' (PAID-06-12); and by the Ministry of Science, Technology and Telecommunications, government of Costa Rica (MICITT), through the incentive program of the National Council for Scientific and Technological Research (CONICIT) (contract No FI-132-2011).Grillo Espinoza, H.; Peidro Payá, D.; Alemany Díaz, MDM.; Mula, J. (2015). Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model. International Journal of Bio-Inspired Computation. 7(3):157-169. https://doi.org/10.1504/IJBIC.2015.069557S1571697

Introducción al Programa de Desarrollo de Ejecutivos del simulador GlobalDNA

Guía introductoria sobre el programa de formación de ejecutivos dentro del simulador empresarial GlobalDNAhttps://media.upv.es/player/?id=19cd9580-4624-11e7-9b33-83cdd974e088Peidro Payá, D. (2017). Introducción al Programa de Desarrollo de Ejecutivos del simulador GlobalDNA. http://hdl.handle.net/10251/82606DE

Transportation planning with modified S-curve membership functions using an interactive fuzzy multi-objective approach

In this paper, we consider the transportation planning decision (TPD) problem with fuzzy goals, available supply and forecast demand. An interactive method is designed for solving the multi-objective TPD problem where the fuzzy data are represented by modified S-curve membership functions. The proposed method attempts to simultaneously minimize the total production and transportation costs and the total delivery time with reference to budget constraints and available supply, machine capacities at each source, as well as forecast demand and warehouse space constraints at each destination. An interactive fuzzy approach is applied to solve the multi-objective TPD problem and to find a preferred compromise solution. Finally, the performance of S-curve membership functions that represent uncertainty goals and constraints in TPD problems with linear membership functions in an industrial case is compared. © 2010 Elsevier B.V. All rights reserved.This work has been funded part by the Spanish Ministry of Science and Technology project: 'Simulation and evolutionary computation and fuzzy optimization models of transportation and production planning processes in a supply chain. Proposal of collaborative planning supported by multi-agent systems, Integration in a decision system, Applications' (EVOLUTION) (Ref. DPI2007-65501) and part by the Spanish Ministry of Science and Innovation project: 'Production technology based on the feedback from production, transport and unload planning and the redesign of warehouses decisions in the supply chain' (REVOLUTION) (Ref. DPI2010-19977).Peidro Payá, D.; Vasant, P. (2011). Transportation planning with modified S-curve membership functions using an interactive fuzzy multi-objective approach. Applied Soft Computing. 11(2):2656-2663. https://doi.org/10.1016/j.asoc.2010.10.014S2656266311

Política Monetaria. Estabilidad de Precios

En el siguiente objeto de aprendizaje se define en qué consiste la política monetaria y la estabilidad de precios, así como su relación con la inflación.https://polimedia.upv.es/visor/?id=df35a4e0-c838-11ea-a6d2-35fb6681846eAndrés Navarro, B.; Peidro Payá, D. (2021). Política Monetaria. Estabilidad de Precios. http://hdl.handle.net/10251/167942DE

Propuesta de modelización de la gestión de la cola de actividades decisionales en el marco del modelo DGRAI

En este artículo se presenta una propuesta de extensión de los procedimientos simulados del Modelo DGRAI, enfocando la problemática de la gestión de las colas de actividades decisionales de los recursos humanos partícipes en el Sistema Decisional de una