Peidro PayĆ”, David

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Now showing 1 - 10 of 27
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    Publication
    Transportation planning with modified S-curve membership functions using an interactive fuzzy multi-objective approach
    (Elsevier, 2011-03) Peidro PayĆ”, David; Vasant, P.; Departamento de OrganizaciĆ³n de Empresas; Centro de InvestigaciĆ³n en GestiĆ³n e IngenierĆ­a de ProducciĆ³n; Escuela PolitĆ©cnica Superior de Alcoy; Ministerio de EducaciĆ³n y Ciencia; Ministerio de Ciencia e InnovaciĆ³n
    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. Ā© 2010 Elsevier B.V. All rights reserved.
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    Fuzzy multi-objective optimisation for master planning in a ceramic supply chain
    (Taylor & Francis, 2012) Peidro PayĆ”, David; Mula Bru, Josefa; Alemany DĆ­az, MarĆ­a del Mar; Lario Esteban, Francisco Cruz; Departamento de OrganizaciĆ³n de Empresas; Centro de InvestigaciĆ³n en GestiĆ³n e IngenierĆ­a de ProducciĆ³n; Escuela TĆ©cnica Superior de IngenierĆ­a Industrial; Escuela PolitĆ©cnica Superior de Alcoy; Ministerio de Ciencia e InnovaciĆ³n
    In this paper, we consider the master planning problem for a centralised replenishment, production and distribution ceramic tile supply chain. A fuzzy multi-objective linear programming (FMOLP) approach is presented which considers the maximisation of the fuzzy gross margin, the minimisation of the fuzzy idle time and the minimisation of the fuzzy backorder quantities. By using an interactive solution methodology to convert this FMOLP model into an auxiliary crisp single-objective linear model, a preferred compromise solution is obtained. For illustration purposes, an example based on modifications of real-world industrial problems is used.
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    A tabu search approach for production and sustainable routing planning decisions for inbound logistics in an automotive supply Chain
    (Springer International Publishing, 2015) Peidro PayĆ”, David; DĆ­az-MadroƱero Boluda, Francisco Manuel; Mula Bru, Josefa; NavalĆ³n DavĆ³, Abraham; Departamento de OrganizaciĆ³n de Empresas; Centro de InvestigaciĆ³n en GestiĆ³n e IngenierĆ­a de ProducciĆ³n; Escuela PolitĆ©cnica Superior de Alcoy; Universitat PolitĆØcnica de ValĆØncia
    [EN] In this paper, a mixed-integer mathematical programming model is proposed to address a production and routing problem related to inbound logistics processes in supply chains environments. This model is also enriched with sustainable issues related to routing decisions by introducing additional fuel consumption and pollutants emissions calculations into the objective function. For the solution methodology, a two-phase decoupled solution procedure based on exact algorithms for the production model and a tabu search algorithm for the routing model is adopted. Results of computational experiments performed with a realworld automotive supply chain confirm the efficiency of the proposed solution method in terms of total cost, fuel consumptions and CPU time.
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    Solving the uncapacitated facility location problem under uncertainty: a hybrid tabu search with path-relinking simheuristic approach
    (Springer-Verlag, 2024-04) Peidro PayĆ”, David; MartĆ­n, Xabier A.; Panadero, Javier; Juan PĆ©rez, Ɓngel Alejandro; Departamento de OrganizaciĆ³n de Empresas; Departamento de EstadĆ­stica e InvestigaciĆ³n Operativa Aplicadas y Calidad; Centro de InvestigaciĆ³n en GestiĆ³n e IngenierĆ­a de ProducciĆ³n; Escuela PolitĆ©cnica Superior de Alcoy; Generalitat Valenciana; AGENCIA ESTATAL DE INVESTIGACION; Universitat PolitĆØcnica de ValĆØncia
    [EN] The uncapacitated facility location problem (UFLP) is a well-known combinatorial optimization problem that finds practical applications in several fields, such as logistics and telecommunication networks. While the existing literature primarily focuses on the deterministic version of the problem, real-life scenarios often involve uncertainties like fluctuating customer demands or service costs. This paper presents a novel algorithm for addressing the UFLP under uncertainty. Our approach combines a tabu search metaheuristic with path-relinking to obtain near-optimal solutions in short computational times for the determinisitic version of the problem. The algorithm is further enhanced by integrating it with simulation techniques to solve the UFLP with random service costs. A set of computational experiments is run to illustrate the effectiveness of the solving method.
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    Solving NP-Hard Challenges in Logistics and Transportation under General Uncertainty Scenarios Using Fuzzy Simheuristics
    (MDPI AG, 2023-12) Juan PĆ©rez, Ɓngel Alejandro; Rabe, Markus; Ammouriova, Majsa; Panadero, Javier; Peidro PayĆ”, David; Riera, Daniel; Departamento de OrganizaciĆ³n de Empresas; Departamento de EstadĆ­stica e InvestigaciĆ³n Operativa Aplicadas y Calidad; Centro de InvestigaciĆ³n en GestiĆ³n e IngenierĆ­a de ProducciĆ³n; Escuela PolitĆ©cnica Superior de Alcoy; Generalitat Valenciana; AGENCIA ESTATAL DE INVESTIGACION
    [EN] n the field of logistics and transportation (L&T), this paper reviews the utilization of simheuristic algorithms to address NP-hard optimization problems under stochastic uncertainty. Then, the paper explores an extension of the simheuristics concept by introducing a fuzzy layer to tackle complex optimization problems involving both stochastic and fuzzy uncertainties. The hybrid approach combines simulation, metaheuristics, and fuzzy logic, offering a feasible methodology to solve large-scale NP-hard problems under general uncertainty scenarios. These scenarios are commonly encountered in L&T optimization challenges, such as the vehicle routing problem or the team orienteering problem, among many others. The proposed methodology allows for modeling various problem componentsĀæincluding travel times, service times, customersĀæ demands, or the duration of electric batteriesĀæas deterministic, stochastic, or fuzzy items. A cross-problem analysis of several computational experiments is conducted to validate the effectiveness of the fuzzy simheuristic methodology. Being a flexible methodology that allows us to tackle NP-hard challenges under general uncertainty scenarios, fuzzy simheuristics can also be applied in fields other than L&T.
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    Fuzzy estimations and systems dynamics for improving manufacturing orders in VMI supply chains
    (Springer Verlag, 2014-02-16) Campuzano BolarĆ­n, Francisco; Mula Bru, Josefa; Peidro PayĆ”, David; Departamento de OrganizaciĆ³n de Empresas; Centro de InvestigaciĆ³n en GestiĆ³n e IngenierĆ­a de ProducciĆ³n; Escuela PolitĆ©cnica Superior de Alcoy; Universitat PolitĆØcnica de ValĆØncia
    [EN] In this chapter, we evaluate the behavior of fuzzy estimations of demand for releasing manufacturing orders in a Vendor-Managed Inventory (VMI) supply chain, which is based on a collaborative deal between retailer and manufacturer, and focuses on the interchange of information about demand and inventory levels. The supply chain considered consists of an end consumer, a retailer and a manufacturer. A system dynamics model with fuzzy estimations of demand has been constructed for supply chain simulation. Fuzzy numbers are used to model fuzzy estimations of demand. With a numerical example, we show that the bullwhip effect can be effectively reduced at the level where fuzzy orders exist and that the fill rate reached improves at the retailer level.
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    Solving the time capacitated arc routing problem under fuzzy and stochastic travel and service times
    (John Wiley & Sons, 2023-12) MartĆ­n, Xabier A.; Panadero, Javier; Peidro PayĆ”, David; PĆ©rez Bernabeu, Elena; Juan PĆ©rez, Ɓngel Alejandro; Departamento de OrganizaciĆ³n de Empresas; Departamento de EstadĆ­stica e InvestigaciĆ³n Operativa Aplicadas y Calidad; Centro de InvestigaciĆ³n en GestiĆ³n e IngenierĆ­a de ProducciĆ³n; Escuela PolitĆ©cnica Superior de Alcoy; GENERALITAT VALENCIANA; AJUNTAMENT DE BARCELONA; Ministerio de Ciencia e InnovaciĆ³n
    [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.
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    Optimization models for supply chain production planning under fuzziness
    (Springer Verlag, 2014) Mula Bru, Josefa; Peidro PayĆ”, David; Poler Escoto, RaĆŗl; Departamento de OrganizaciĆ³n de Empresas; Centro de InvestigaciĆ³n en GestiĆ³n e IngenierĆ­a de ProducciĆ³n; Escuela PolitĆ©cnica Superior de Alcoy; Universitat PolitĆØcnica de ValĆØncia
    [EN] The aim of this chapter is to propose diverse fuzzy mathematical programming models based on the fuzzy set theory for supply chain production planning in a multi-product, multi-plant environment with fuzziness and capacity constraints. The proposed models consider fuzziness in demand and at the aspiration level of total costs. The main contribution of this chapter to the field of fuzzy sets is a practical application of flexible programming approaches (or fuzzy constraints) in linear programming with diverse aggregation schemes to the model originally proposed by McDonald and Karimi (1997)for supply chain production planning.
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    Reinforcement learning applied to production planning and control
    (Taylor & Francis, 2023-08-18) Esteso Ɓlvarez, Ana; Peidro PayĆ”, David; Mula Bru, Josefa; DĆ­az-MadroƱero Boluda, Francisco Manuel; Departamento de OrganizaciĆ³n de Empresas; Centro de InvestigaciĆ³n en GestiĆ³n e IngenierĆ­a de ProducciĆ³n; Escuela TĆ©cnica Superior de IngenierĆ­a Industrial; Escuela PolitĆ©cnica Superior de Alcoy; Escuela de Doctorado; GENERALITAT VALENCIANA; AGENCIA ESTATAL DE INVESTIGACION; European Regional Development Fund; COMISION DE LAS COMUNIDADES EUROPEA
    [EN] The objective of this paper is to examine the use and applications of reinforcement learning (RL) techniques in the production planning and control (PPC) field addressing the following PPC areas: facility resource planning, capacity planning, purchase and supply management, production scheduling and inventory management. The main RL characteristics, such as method, context, states, actions, reward and highlights, were analysed. The considered number of agents, applications and RL software tools, specifically, programming language, platforms, application programming interfaces and RL frameworks, among others, were identified, and 181 articles were sreviewed. The results showed that RL was applied mainly to production scheduling problems, followed by purchase and supply management. The most revised RL algorithms were model-free and single-agent and were applied to simplified PPC environments. Nevertheless, their results seem to be promising compared to traditional mathematical programming and heuristics/metaheuristics solution methods, and even more so when they incorporate uncertainty or non-linear properties. Finally, RL value-based approaches are the most widely used, specifically Q-learning and its variants and for deep RL, deep Q-networks. In recent years however, the most widely used approach has been the actor-critic method, such as the advantage actor critic, proximal policy optimisation, deep deterministic policy gradient and trust region policy optimisation.
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    Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model
    (Inderscience, 2015) Grillo Espinoza, Hanzel; Peidro PayĆ”, David; Alemany DĆ­az, MarĆ­a del Mar; Mula Bru, Josefa; Departamento de OrganizaciĆ³n de Empresas; Centro de InvestigaciĆ³n en GestiĆ³n e IngenierĆ­a de ProducciĆ³n; Escuela TĆ©cnica Superior de IngenierĆ­a Industrial; Escuela PolitĆ©cnica Superior de Alcoy; Ministerio de Ciencia e InnovaciĆ³n; Universitat PolitĆØcnica de ValĆØncia; Ministerio de EconomĆ­a y Competitividad; Consejo Nacional para Investigaciones CientĆ­ficas y TecnolĆ³gicas, Costa Rica
    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.