787 research outputs found
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).DÃaz-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
Global solar irradiation prediction using a multi-gene genetic programming approach
This is the author accepted manuscript. The final version is available from AIP Publishing via the DOI in this record.In this paper, a nonlinear symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for a data-driven modelling between the dependent and the independent variables. The technique is applied for modelling the measured global solar irradiation and validated through numerical simulations. The proposed modelling technique shows improved results over the fuzzy logic and artificial neural network (ANN) based approaches as attempted by contemporary researchers. The method proposed here results in nonlinear analytical expressions, unlike those with neural networks which is essentially a black box modelling approach. This additional flexibility is an advantage from the modelling perspective and helps to discern the important variables which affect the prediction. Due to the evolutionary nature of the algorithm, it is able to get out of local minima and converge to a global optimum unlike the back-propagation (BP) algorithm used for training neural networks. This results in a better percentage fit than the ones obtained using neural networks by contemporary researchers. Also a hold-out cross validation is done on the obtained genetic programming (GP) results which show that the results generalize well to new data and do not over-fit the training samples. The multi-gene GP results are compared with those, obtained using its single-gene version and also the same with four classical regression models in order to show the effectiveness of the adopted approach
An integrated approach for remanufacturing job shop scheduling with routing alternatives.
Remanufacturing is a practice of growing importance due to increasing environmental awareness and regulations. However, the stochastic natures inherent in the remanufacturing processes complicate its scheduling. This paper undertakes the challenge and presents a remanufacturing job shop scheduling approach by integrating alternative routing assignment and machine resource dispatching. A colored timed Petri net is introduced to model the dynamics of remanufacturing process, such as various process routings, uncertain operation times for cores, and machine resource conflicts. With the color attributes in Petri nets, two types of decision points, recovery routing selection and resource dispatching, are introduced and linked with places in CTPN model. With time attributes in Petri nets, the temporal aspect of recovery operations for cores as well as the evolution dynamics in cores\u27 operational stages is mathematically analyzed. A hybrid meta-heuristic algorithm embedded scheduling strategy over CTPN is proposed to search for the optimal recovery routings for worn cores and their recovery operation sequences on workstations, in minimizing the total production cost. The approach is demonstrated through the remanufacturing of used machine tool and its effectiveness is compared against another two cases: baseline case with fixed recovery process routings and case 2 using standard SA/MST
MRP IV: Planificación de requerimientos de materiales cuarta generación. Integración de la planificación de la producción y del transporte de aprovisionamiento
Tesis por compendioEl sistema de planificación de requerimientos de materiales o MRP (Material Requirement
Planning), desarrollado por Orlicky en 1975, sigue siendo en nuestros dÃas y, a pesar de sus
deficiencias identificadas, el sistema de planificación de la producción más utilizado por las
empresas industriales. Las evoluciones del MRP se vieron reflejadas en el sistema MRPII
(Manufacturing Resource Planning), que considera restricciones de capacidad productiva, MRPIII
(Money Resource Planning), que introduce la función de finanzas; y la evolución comercial del
mismo en el ERP (Enterprise Resource Planning), que incorpora modularmente todas las funciones
de la empresa en un único sistema de decisión, cuyo núcleo central es el MRP. Los desarrollos
posteriores de los sistemas ERP han incorporado las nuevas tecnologÃas de la información y
comunicaciones. Asimismo, éstos se han adaptado al contexto económico actual caracterizado por la
globalización de los negocios y la deslocalización de los proveedores desarrollando otras funciones
como la gestión de la cadena de suministro o del transporte, entre otros. Por otro lado, existen
muchos trabajos en la literatura académica que han intentado resolver algunas de las debilidades del
MRP tales como la optimización de los resultados, la consideración de la incertidumbre en
determinados parámetros, el inflado de los tiempos de entrega, etc. Sin embargo, tanto en el ámbito
comercial como en el cientÃfico, el MRP y sus variantes se centran en el requerimiento de los
materiales y en la planificación de las capacidades de producción, lo que es su desventaja principal
en aquellas cadenas de suministro donde existe una gran deslocalización de los proveedores de
materias primas y componentes. En estos entornos, la planificación del transporte adquiere un
protagonismo fundamental, puesto que los elevados costes y las restricciones logÃsticas suelen hacer
subóptimos e incluso infactibles los planes de producción propuestos, siendo la re-planificación
manual una práctica habitual en las empresas. Esta tesis doctoral propone un modelo denominado
MRPIV, que considera de forma integrada las decisiones de la planificación de materiales,
capacidades de recursos de producción y el transporte, con las restricciones propias de este último,
tales como diferentes modos de recogida (milk-run, camión completo, rutas) en la cadena de
suministro con el objetivo de evitar la suboptimización de estos planes que en la actualidad se
generan usualmente de forma secuencial e independiente. El modelo propuesto se ha validado en una
cadena de suministro del sector del automóvil confirmando la reducción de costes totales y una
planificación más eficiente del transporte de los camiones necesarios para efectuar el
aprovisionamiento.DÃaz-Madroñero Boluda, FM. (2015). MRP IV: Planificación de requerimientos de materiales cuarta generación. Integración de la planificación de la producción y del transporte de aprovisionamiento [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48524TESISCompendi
Supervisory model predictive control of building integrated renewable and low carbon energy systems
To reduce fossil fuel consumption and carbon emission in the building sector,
renewable and low carbon energy technologies are integrated in building energy
systems to supply all or part of the building energy demand. In this research, an
optimal supervisory controller is designed to optimize the operational cost and the
CO2 emission of the integrated energy systems. For this purpose, the building
energy system is defined and its boundary, components (subsystems), inputs and
outputs are identified. Then a mathematical model of the components is obtained.
For mathematical modelling of the energy system, a unified modelling method is
used. With this method, many different building energy systems can be modelled
uniformly. Two approaches are used; multi-period optimization and hybrid model
predictive control. In both approaches the optimization problem is deterministic, so
that at each time step the energy consumption of the building, and the available
renewable energy are perfectly predicted for the prediction horizon. The controller
is simulated in three different applications. In the first application the controller is
used for a system consisting of a micro-combined heat and power system with an
auxiliary boiler and a hot water storage tank. In this application the controller
reduces the operational cost and CO2 emission by 7.31 percent and 5.19 percent
respectively, with respect to the heat led operation. In the second application the
controller is used to control a farm electrification system consisting of PV panels, a
diesel generator and a battery bank. In this application the operational cost with
respect to the common load following strategy is reduced by 3.8 percent. In the
third application the controller is used to control a hybrid off-grid power system
consisting of PV panels, a battery bank, an electrolyzer, a hydrogen storage tank
and a fuel cell. In this application the controller maximizes the total stored energies
in the battery bank and the hydrogen storage tank
Planning and Scheduling Optimization
Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development
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