76 research outputs found
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
Overview on: sequencing in mixed model flowshop production line with static and dynamic context
In the present work a literature overview was given on solution techniques considering basic as well as more advanced and consequently more complex arrangements of mixed model flowshops. We first analyzed the occurrence of setup time/cost; existing solution techniques are mainly focused on permutation sequences. Thereafter we discussed objectives resulting in the introduction of variety of methods allowing resequencing of jobs within the line. The possibility of resequencing within the line ranges from 1) offline or intermittent buffers, 2) parallel stations, namely flexible, hybrid or compound flowshops, 3) merging and splitting of parallel lines, 4) re-entrant flowshops, to 5) change job attributes without physically interchanging the position.
In continuation the differences in the consideration of static and dynamic demand was studied. Also intermittent setups are possible, depending on the horizon and including the possibility of resequencing, four problem cases were highlighted: static, semi dynamic, nearly dynamic and dynamic case.
Finally a general overview was given on existing solution methods, including exact and approximation methods. The approximation methods are furthermore divided in two cases, know as heuristics and methaheuristic
Heuristic for flow shop sequencing with separated and sequence independent setup times
This paper deals with the permutation flow shop scheduling problem with separated and sequence-independent machine setup times. A heuristic method with the objective of minimizing the total time to complete the schedule is introduced. The proposed heuristic is based on a structural property of this scheduling problem, which provides an upper bound on the idle time of the machines between the completion of the setup task and the beginning of job processing. Experimental results show that the new heuristic outperforms two existing ones.(CNPq) National Council for Scientific and Technological Developmen
An estimation of distribution algorithm for lot-streaming flow shop problems with setup times
Lot-streaming flow shops have important applications in different industries including textile, plastic,
chemical, semiconductor and many others. This paper considers an n-job m-machine lot-streaming
flow shop scheduling problem with sequence-dependent setup times under both the idling and noidling
production cases. The objective is to minimize the maximum completion time or makespan. To
solve this important practical problem, a novel estimation of distribution algorithm (EDA) is proposed
with a job permutation based representation. In the proposed EDA, an efficient initialization scheme
based on the NEH heuristic is presented to construct an initial population with a certain level of quality
and diversity. An estimation of a probabilistic model is constructed to direct the algorithm search
towards good solutions by taking into account both job permutation and similar blocks of jobs.
A simple but effective local search is added to enhance the intensification capability. A diversity
controlling mechanism is applied to maintain the diversity of the population. In addition, a speed-up
method is presented to reduce the computational effort needed for the local search technique and the
NEH-based heuristics. A comparative evaluation is carried out with the best performing algorithms
from the literature. The results show that the proposed EDA is very effective in comparison after
comprehensive computational and statistical analyses.This research is partially supported by the National Science Foundation of China (60874075, 70871065), and Science Foundation of Shandong Province in China under Grant BS2010DX005, and Postdoctoral Science Foundation of China under Grant 20100480897. Ruben Ruiz is partially funded by the Spanish Ministry of Science and Innovation, under the project "SMPA-Advanced Parallel Multiobjective Sequencing: Practical and Theoretical Advances" with reference DPI2008-03511/DPI and by the IMPIVA-Institute for the Small and Medium Valencian Enterprise, for the project OSC with references IMIDIC/2008/137, IMIDIC/2009/198 and IMIDIC/2010/175.Pan, Q.; Ruiz García, R. (2012). An estimation of distribution algorithm for lot-streaming flow shop problems with setup times. Omega. 40(2):166-180. https://doi.org/10.1016/j.omega.2011.05.002S16618040
NEW HEURISTICS FOR THE NO-WAIT FLOWSHOP WITH SEQUENCE-DEPENDENT SETUP TIMES PROBLEM
In this paper, we address the problem of scheduling jobs in a no-wait flowshop with sequence-dependent setup times with the objective of minimizing the makespan and the total flowtime. As this problem is well-known for being NP-hard, we present two new constructive heuristics in order to obtain good approximate solutions for the problem in a short CPU time, named GAPH and QUARTS. GAPH is based on a structural property for minimizing makespan and QUARTS breaks the problem in quartets in order to minimize the total flowtime. Experimental results demonstrate the superiority of the proposed approachs over three of the best-know methods in the literature: BAH and BIH, from Bianco, Dell´Olmo and Giordani (1999) and TRIPS, by Brown, McGarvey and Ventura (2004)
Solving no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines and rework time by the adjusted discrete Multi Objective Invasive Weed Optimization and fuzzy dominance approach
Purpose: Adjusted discrete Multi-Objective Invasive Weed Optimization (DMOIWO) algorithm, which uses fuzzy dominant approach for ordering, has been proposed to solve No-wait two-stage flexible flow shop scheduling problem.
Design/methodology/approach: No-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times and probable rework in both stations, different ready times for all jobs and rework times for both stations as well as unrelated parallel machines with regards to the simultaneous minimization of maximum job completion time and average latency functions have been investigated in a multi-objective manner. In this study, the parameter setting has been carried out using Taguchi Method based on the quality indicator for beater performance of the algorithm.
Findings: The results of this algorithm have been compared with those of conventional, multi-objective algorithms to show the better performance of the proposed algorithm. The results clearly indicated the greater performance of the proposed algorithm.
Originality/value: This study provides an efficient method for solving multi objective no-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times, probable rework in both stations, different ready times for all jobs, rework times for both stations and unrelated parallel machines which are the real constraints.Peer Reviewe
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
Permutation Flow Shop via Simulated Annealing and NEH
Permutation Flow Shop Scheduling refers to the process of allocating operations of jobs to machines such that an operation starts to process on machine j only after the processing completes in j-1machine. At a time a machine can process only one operation and similarly a job can have only one operation processed at a time. Finding a schedule that minimizes the overall completion times for Permutation Flow Shop problems is NP-Hard if the number of machines is greater than 2. Sowe concentrates on approaches with approximate solutions that are good enough for the problems. Heuristics is one way to find the approximate solutions for a problem. For our thesis, we have used two heuristics - NEH and Simulated Annealing, both individually and in a combined form, to find the solutions for Permutation Flow Shop problems. We have compared NEH and Simulated Annealing algorithm based on result and execution time and also compared the combined algorithm with existing ones. Standard benchmarks are used to evaluate the performances of the implemented algorithm
Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems
[EN] Industrial production scheduling problems are challenges that researchers have been trying to solve for decades. Many practical scheduling problems such as the hybrid flowshop are ATP-hard. As a result, researchers resort to metaheuristics to obtain effective and efficient solutions. The traditional design process of metaheuristics is mainly manual, often metaphor-based, biased by previous experience and prone to producing overly tailored methods that only work well on the tested problems and objectives. In this paper, we use an Automatic Algorithm Design (AAD) methodology to eliminate these limitations. AAD is capable of composing algorithms from components with minimal human intervention. We test the proposed MD for three different optimization objectives in the hybrid flowshop. Comprehensive computational and statistical testing demonstrates that automatically designed algorithms outperform specifically tailored state-of-the-art methods for the tested objectives in most cases.Pedro Alfaro-Fernandez and Ruben Ruiz are partially supported by the Spanish Ministry of Science, Innovation, and Universities, under the project "OPTEP-Port Terminal Operations Optimization" (No. RTI2018-094940-B-I00) financed with FEDER funds and under grants BES-2013-064858 and EEBB-I-15-10089. This work was supported by the COMEX project (P7/36) within the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office. Thomas Stiitzle acknowledges support from the Belgian F.R.S.-FNRS, of which he is a Research Director.Alfaro-Fernandez, P.; Ruiz García, R.; Pagnozzi, F.; Stützle, T. (2020). Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems. 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BALANCING TRADE-OFFS IN ONE-STAGE PRODUCTION WITH PROCESSING TIME UNCERTAINTY
Stochastic production scheduling faces three challenges, first the inconsistencies among key performance indicators (KPIs), second the trade-offs between the expected return and the risk for a portfolio of KPIs, and third the uncertainty in processing times. Based on two inconsistent KPIs of total completion time (TCT) and variance of completion times (VCT), we propose our trade-off balancing (ToB) heuristic for one-stage production scheduling. Through comprehensive case studies, we show that our ToB heuristic with preference =0.0:0.1:1.0 efficiently and effectively addresses the three challenges. Moreover, our trade-off balancing scheme can be generalized to balance a number of inconsistent KPIs more than two. Daniels and Kouvelis (DK) proposed a scheme to optimize the worst-case scenario for stochastic production scheduling and proposed the endpoint product (EP) and endpoint sum (ES) heuristics to hedge against processing time uncertainty. Using 5 levels of coefficients of variation (CVs) to represent processing time uncertainty, we show that our ToB heuristic is robust as well, and even outperforms the EP and ES heuristics on worst-case scenarios at high levels of processing time uncertainty. Moreover, our ToB heuristic generates undominated solution spaces of KPIs, which not only provides a solid base to set up specification limits for statistical process control (SPC) but also facilitates the application of modern portfolio theory and SPC techniques in the industry
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