187 research outputs found
Energy Efficient Manufacturing Scheduling: A Systematic Literature Review
The social context in relation to energy policies, energy supply, and
sustainability concerns as well as advances in more energy-efficient
technologies is driving a need for a change in the manufacturing sector. The
main purpose of this work is to provide a research framework for
energy-efficient scheduling (EES) which is a very active research area with
more than 500 papers published in the last 10 years. The reason for this
interest is mostly due to the economic and environmental impact of considering
energy in production scheduling. In this paper, we present a systematic
literature review of recent papers in this area, provide a classification of
the problems studied, and present an overview of the main aspects and
methodologies considered as well as open research challenges
A survey of scheduling problems with setup times or costs
Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Flowshop with additional resources during setups: Mathematical models and a GRASP algorithm
[EN] Machine scheduling problems arise in many production processes, and are something that needs to be consider when optimizing the supply chain. Among them, flowshop scheduling problems happen when a number of jobs have to be sequentially processed by a number of machines. This paper addressees, for the first time, the Permutation Flowshop Scheduling problem with additional Resources during Setups (PFSR-S). In this problem, in addition to the standard permutation flowshop constraints, each machine requires a setup between the processing of two consecutive jobs. A number of additional and scarce resources, e.g. operators, are needed to carry out each setup. Two Mixed Integer Linear Programming formulations and an exact algorithm are proposed to solve the PFSR-S. Due to its complexity, these approaches can only solve instances of small size to optimality. Therefore, a GRASP metaheuristic is also proposed which provides solutions for much larger instances. All the methods designed for the PFSR-S in this paper are computationally tested over a benchmark of instances adapted from the literature. The results obtained show that the GRASP metaheuristic finds good quality solutions in short computational times.Juan C. Yepes-Borrero acknowledges financial support by Colfuturo under program Credito-Beca grant number 201503877 and from ElInstituto Colombiano de Credito Educativo y Estudios Tecnicos en el Exterior - ICETEX under program Pasaporte a la ciencia - Doctor-ado, Foco-reto pais 4.2.3, grant number 3568118. This research hasbeen partially supported by the Agencia Estatal de Investigacion (AEI)and the European Regional Development's fund (ERDF): PID2020-114594GB-C21; Regional Government of Andalusia: projects FEDER-US-1256951, AT 21_00032, and P18-FR-1422; Fundacion BBVA: project Netmeet Data (Ayudas Fundacion BBVA a equipos de investigacioncientifica 2019). The authors are partially supported by Agencia Valenciana de la Innovacion (AVI) under the project ireves (innovacionen vehiculos de emergencia sanitaria): una herramienta inteligente dedecision'' (No. INNACC/2021/26) partially financed with FEDER funds(interested readers can visit http://ireves.upv.es), and by the Spanish Ministry of Science and Innovation under the project OPRES-RealisticOptimization in Problems in Public Health'' (No. PID2021-124975OB-I00), partially financed with FEDER funds. Part of the authors aresupported by the Faculty of Business Administration and Managementat Universitat Politecnica de ValenciaYepes-Borrero, JC.; Perea, F.; Villa Juliá, MF.; Vallada Regalado, E. (2023). Flowshop with additional resources during setups: Mathematical models and a GRASP algorithm. Computers & Operations Research. 154. https://doi.org/10.1016/j.cor.2023.10619215
The Distributed and Assembly Scheduling Problem
Tesis por compendio[EN] Nowadays, manufacturing systems meet different new global challenges and
the existence of a collaborative manufacturing environment is essential to face
with. Distributed manufacturing and assembly systems are two manufacturing
systems which allow industries to deal with some of these challenges. This
thesis studies a production problem in which both distributed manufacturing
and assembly systems are considered. Although distributed manufacturing
systems and assembly systems are well-known problems and have been extensively
studied in the literature, to the best of our knowledge, considering
these two systems together as in this thesis is the first effort in the literature.
Due to the importance of scheduling optimization on production performance,
some different ways to optimize the scheduling of the considered problem are
discussed in this thesis.
The studied scheduling setting consists of two stages: A production and an
assembly stage. Various production centers make the first stage. Each of these
centers consists of several machines which are dedicated to manufacture jobs.
A single assembly machine is considered for the second stage. The produced
jobs are assembled on the assembly machine to form final products through a
defined assembly program.
In this thesis, two different problems regarding two different production
configurations for the production centers of the first stage are considered.
The first configuration is a flowshop that results in what we refer to as the
Distributed Assembly Permutation Flowshop Scheduling Problem (DAPFSP).
The second problem is referred to as the Distributed Parallel Machine and
Assembly Scheduling Problem (DPMASP), where unrelated parallel machines
configure the production centers. Makespan minimization of the product on the
assembly machine located in the assembly stage is considered as the objective
function for all considered problems.
In this thesis some extensions are considered for the studied problems
so as to bring them as close as possible to the reality of production shops.
In the DAPFSP, sequence dependent setup times are added for machines in
both production and assembly stages. Similarly, in the DPMASP, due to
technological constraints, some defined jobs can be processed only in certain
factories.
Mathematical models are presented as an exact solution for some of the
presented problems and two state-of-art solvers, CPLEX and GUROBI are
used to solve them. Since these solvers are not able to solve large sized
problems, we design and develop heuristic methods to solve the problems. In
addition to heuristics, some metaheuristics are also designed and proposed to
improve the solutions obtained by heuristics. Finally, for each proposed problem,
the performance of the proposed solution methods is compared through
extensive computational and comprehensive ANOVA statistical analysis.[ES] Los sistemas de producción se enfrentan a retos globales en los que el concepto
de fabricación colaborativa es crucial para poder tener éxito en el entorno
cambiante y complejo en el que nos encontramos. Una característica de los sistemas
productivos que puede ayudar a lograr este objetivo consiste en disponer
de una red de fabricación distribuida en la que los productos se fabriquen en
localizaciones diferentes y se vayan ensamblando para obtener el producto
final. En estos casos, disponer de modelos y herramientas para mejorar el
rendimiento de sistemas de producción distribuidos con ensamblajes es una
manera de asegurar la eficiencia de los mismos.
En esta tesis doctoral se estudian los sistemas de fabricación distribuidos
con operaciones de ensamblaje. Los sistemas distribuidos y los sistemas con
operaciones de ensamblaje han sido estudiados por separado en la literatura.
De hecho, no se han encontrado estudios de sistemas con ambas características
consideradas de forma conjunta.
Dada la complejidad de considerar conjuntamente ambos tipos de sistemas
a la hora de realizar la programación de la producción en los mismos, se ha
abordado su estudio considerando un modelo bietápico en la que en la primera
etapa se consideran las operaciones de producción y en la segunda se plantean
las operaciones de ensamblaje.
Dependiendo de la configuración de la primera etapa se han estudiado dos
variantes. En la primera variante se asume que la etapa de producción está
compuesta por sendos sistemas tipo flowshop en los que se fabrican los componentes
que se ensamblan en la segunda etapa (Distributed Assembly Permutation
Flowshop Scheduling Problem o DAPFSP). En la segunda variante
se considera un sistema de máquinas en paralelo no relacionadas (Distributed
Parallel Machine and Assembly Scheduling Problem o DPMASP). En ambas
variantes se optimiza la fecha de finalización del último trabajo secuenciado
(Cmax) y se contempla la posibilidad que existan tiempos de cambio (setup)
dependientes de la secuencia de trabajos fabricada. También, en el caso
DPMASP se estudia la posibilidad de prohibir o no el uso de determinadas
máquinas de la etapa de producción.
Se han desarrollado modelos matemáticos para resolver algunas de las
variantes anteriores. Estos modelos se han resuelto mediante los programas
CPLEX y GUROBI en aquellos casos que ha sido posible. Para las instancias
en los que el modelo matemático no ofrecía una solución al problema se han
desarrollado heurísticas y metaheurísticas para ello.
Todos los procedimientos anteriores han sido estudiados para determinar
el rendimiento de los diferentes algoritmos planteados. Para ello se ha realizado
un exhaustivo estudio computacional en el que se han aplicado técnicas
ANOVA.
Los resultados obtenidos en la tesis permiten avanzar en la comprensión
del comportamiento de los sistemas productivos distribuidos con ensamblajes,
definiendo algoritmos que permiten obtener buenas soluciones a este tipo de
problemas tan complejos que aparecen tantas veces en la realidad industrial.[CA] Els sistemes de producció s'enfronten a reptes globals en què el concepte de
fabricació col.laborativa és crucial per a poder tindre èxit en l'entorn canviant
i complex en què ens trobem. Una característica dels sistemes productius
que pot ajudar a aconseguir este objectiu consistix a disposar d'una xarxa de
fabricació distribuïda en la que els productes es fabriquen en localitzacions
diferents i es vagen acoblant per a obtindre el producte final. En estos casos,
disposar de models i ferramentes per a millorar el rendiment de sistemes de
producció distribuïts amb acoblaments és una manera d'assegurar l'eficiència
dels mateixos.
En esta tesi doctoral s'estudien els sistemes de fabricació distribuïts amb
operacions d'acoblament. Els sistemes distribuïts i els sistemes amb operacions
d'acoblament han sigut estudiats per separat en la literatura però, en allò
que es coneix, no s'han trobat estudis de sistemes amb ambdós característiques
conjuntament. Donada la complexitat de considerar conjuntament ambdós
tipus de sistemes a l'hora de realitzar la programació de la producció en els
mateixos, s'ha abordat el seu estudi considerant un model bietàpic en la que
en la primera etapa es consideren les operacions de producció i en la segona es
plantegen les operacions d'acoblament.
Depenent de la configuració de la primera etapa s'han estudiat dos variants.
En la primera variant s'assumix que l'etapa de producció està composta per
sengles sistemes tipus flowshop en els que es fabriquen els components que
s'acoblen en la segona etapa (Distributed Assembly Permutation Flowshop
Scheduling Problem o DAPFSP). En la segona variant es considera un sistema
de màquines en paral.lel no relacionades (Distributed Parallel Machine and
Assembly Scheduling Problem o DPMASP). En ambdós variants s'optimitza
la data de finalització de l'últim treball seqüenciat (Cmax) i es contempla la
possibilitat que existisquen temps de canvi (setup) dependents de la seqüència
de treballs fabricada. També, en el cas DPMASP s'estudia la possibilitat de
prohibir o no l'ús de determinades màquines de l'etapa de producció.
S'han desenvolupat models matemàtics per a resoldre algunes de les variants
anteriors. Estos models s'han resolt per mitjà dels programes CPLEX
i GUROBI en aquells casos que ha sigut possible. Per a les instàncies en
què el model matemàtic no oferia una solució al problema s'han desenrotllat
heurístiques i metaheurísticas per a això. Tots els procediments anteriors han
sigut estudiats per a determinar el rendiment dels diferents algoritmes plantejats.
Per a això s'ha realitzat un exhaustiu estudi computacional en què s'han
aplicat tècniques ANOVA.
Els resultats obtinguts en la tesi permeten avançar en la comprensió del
comportament dels sistemes productius distribuïts amb acoblaments, definint
algoritmes que permeten obtindre bones solucions a este tipus de problemes
tan complexos que apareixen tantes vegades en la realitat industrial.Hatami, S. (2016). The Distributed and Assembly Scheduling Problem [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/64072TESISCompendi
Native metaheuristics for non-permutation flowshop scheduling
The most general flowshop scheduling problem is also addressed in the literature as non-permutation flowshop
(NPFS). Current processors are able to cope with the combinatorial complexity of (n!)exp m. NPFS scheduling by
metaheuristics. After briefly discussing the requirements for a manufacturing layout to be designed and
modeled as non-permutation flowshop, a disjunctive graph (digraph) approach is used to build native
solutions. The implementation of an Ant Colony Optimization (ACO) algorithm has been described in detail;
it has been shown how the biologically inspired mechanisms produce eligible schedules, as opposed to most
metaheuristics approaches, which improve permutation solutions. ACO algorithms are an example of native
non-permutation (NNP) solutions of the flowshop scheduling problem, opening a new perspective on building
purely native approaches. The proposed NNP-ACO has been assessed over existing native approaches
improving most makespan upper bounds of the benchmark problems from Demirkol et al. (1998)
Spatial-temporal data modelling and processing for personalised decision support
The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less
Keywords
Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio
Spatial-temporal data modelling and processing for personalised decision support
The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less
Keywords
Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio
Theoretical and Computational Research in Various Scheduling Models
Nine manuscripts were published in this Special Issue on “Theoretical and Computational Research in Various Scheduling Models, 2021” of the MDPI Mathematics journal, covering a wide range of topics connected to the theory and applications of various scheduling models and their extensions/generalizations. These topics include a road network maintenance project, cost reduction of the subcontracted resources, a variant of the relocation problem, a network of activities with generally distributed durations through a Markov chain, idea on how to improve the return loading rate problem by integrating the sub-tour reversal approach with the method of the theory of constraints, an extended solution method for optimizing the bi-objective no-idle permutation flowshop scheduling problem, the burn-in (B/I) procedure, the Pareto-scheduling problem with two competing agents, and three preemptive Pareto-scheduling problems with two competing agents, among others. We hope that the book will be of interest to those working in the area of various scheduling problems and provide a bridge to facilitate the interaction between researchers and practitioners in scheduling questions. Although discrete mathematics is a common method to solve scheduling problems, the further development of this method is limited due to the lack of general principles, which poses a major challenge in this research field
Energy aware hybrid flow shop scheduling
Only if humanity acts quickly and resolutely can we limit global warming' conclude more than 25,000 academics with the statement of SCIENTISTS FOR FUTURE. The concern about global warming and the extinction of species has steadily increased in recent years
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