24 research outputs found
The distributed assembly permutation flowshop scheduling problem
Nowadays, improving the management of complex supply chains is a key to become competitive in the twenty-first century global market. Supply chains are composed of multi-plant facilities that must be coordinated and synchronised to cut waste and lead times. This paper proposes a Distributed Assembly Permutation Flowshop Scheduling Problem (DAPFSP) with two stages to model and study complex supply chains. This problem is a generalisation of the Distributed Permutation Flowshop Scheduling Problem (DPFSP). The first stage of the DAPFSP is composed of f identical production factories. Each one is a flowshop that produces jobs to be assembled into final products in a second assembly stage. The objective is to minimise the makespan. We present first a Mixed Integer Linear Programming model (MILP). Three constructive algorithms are proposed. Finally, a Variable Neighbourhood Descent (VND) algorithm has been designed and tested by a comprehensive ANOVA statistical analysis. The results show that the VND algorithm offers good performance to solve this scheduling problem.Ruben Ruiz is partially supported by the Spanish Ministry of Science and Innovation, under the project 'RESULT - Realistic Extended Scheduling Using Light Techniques' with reference DPI2012-36243-C02-01. Carlos Andres-Romano is partially supported by the Spanish Ministry of Science and Innovation, under the project 'INSAMBLE' - Scheduling at assembly/disassembly synchronised supply chains with reference DPI2011-27633.Hatami, S.; Ruiz GarcÃa, R.; Andrés Romano, C. (2013). The distributed assembly permutation flowshop scheduling problem. International Journal of Production Research. 51(17):5292-5308. https://doi.org/10.1080/00207543.2013.807955S529253085117Basso, D., Chiarandini, M., & Salmaso, L. (2007). Synchronized permutation tests in replicated designs. Journal of Statistical Planning and Inference, 137(8), 2564-2578. doi:10.1016/j.jspi.2006.04.016Biggs, D., De Ville, B., & Suen, E. (1991). A method of choosing multiway partitions for classification and decision trees. Journal of Applied Statistics, 18(1), 49-62. doi:10.1080/02664769100000005Chan, F. T. S., Chung, S. H., Chan, L. Y., Finke, G., & Tiwari, M. K. (2006). Solving distributed FMS scheduling problems subject to maintenance: Genetic algorithms approach. Robotics and Computer-Integrated Manufacturing, 22(5-6), 493-504. doi:10.1016/j.rcim.2005.11.005Chan, F. T. S., Chung, S. H., & Chan, P. L. Y. (2006). Application of genetic algorithms with dominant genes in a distributed scheduling problem in flexible manufacturing systems. International Journal of Production Research, 44(3), 523-543. doi:10.1080/00207540500319229Liao, C.-J., & Liao, L.-M. (2008). Improved MILP models for two-machine flowshop with batch processing machines. Mathematical and Computer Modelling, 48(7-8), 1254-1264. doi:10.1016/j.mcm.2008.01.001Framinan, J. M., & Leisten, R. (2003). An efficient constructive heuristic for flowtime minimisation in permutation flow shops. Omega, 31(4), 311-317. doi:10.1016/s0305-0483(03)00047-1Gao, J., & Chen, R. (2011). A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem. International Journal of Computational Intelligence Systems, 4(4), 497-508. doi:10.1080/18756891.2011.9727808Hansen, P., & Mladenović, N. (2001). Variable neighborhood search: Principles and applications. European Journal of Operational Research, 130(3), 449-467. doi:10.1016/s0377-2217(00)00100-4Hariri, A. M. A., & Potts, C. N. (1997). A branch and bound algorithm for the two-stage assembly scheduling problem. European Journal of Operational Research, 103(3), 547-556. doi:10.1016/s0377-2217(96)00312-8Jia, H. Z., Fuh, J. Y. H., Nee, A. Y. C., & Zhang, Y. F. (2002). Web-based Multi-functional Scheduling System for a Distributed Manufacturing Environment. Concurrent Engineering, 10(1), 27-39. doi:10.1177/1063293x02010001054Jia, H. Z., Nee, A. Y. C., Fuh, J. Y. H., & Zhang, Y. F. (2003). Journal of Intelligent Manufacturing, 14(3/4), 351-362. doi:10.1023/a:1024653810491Jia, H. Z., Fuh, J. Y. H., Nee, A. Y. C., & Zhang, Y. F. (2007). Integration of genetic algorithm and Gantt chart for job shop scheduling in distributed manufacturing systems. Computers & Industrial Engineering, 53(2), 313-320. doi:10.1016/j.cie.2007.06.024Kass, G. V. (1980). An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics, 29(2), 119. doi:10.2307/2986296Lee, C.-Y., Cheng, T. C. E., & Lin, B. M. T. (1993). Minimizing the Makespan in the 3-Machine Assembly-Type Flowshop Scheduling Problem. Management Science, 39(5), 616-625. doi:10.1287/mnsc.39.5.616Morgan, J. N., & Sonquist, J. A. (1963). Problems in the Analysis of Survey Data, and a Proposal. Journal of the American Statistical Association, 58(302), 415-434. doi:10.1080/01621459.1963.10500855Pan, Q.-K., & Ruiz, R. (2012). Local search methods for the flowshop scheduling problem with flowtime minimization. European Journal of Operational Research, 222(1), 31-43. doi:10.1016/j.ejor.2012.04.034Potts, C. N., Sevast’janov, S. V., Strusevich, V. A., Van Wassenhove, L. N., & Zwaneveld, C. M. (1995). The Two-Stage Assembly Scheduling Problem: Complexity and Approximation. Operations Research, 43(2), 346-355. doi:10.1287/opre.43.2.346Ruiz, R., & Stützle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033-2049. doi:10.1016/j.ejor.2005.12.009Ruiz, R., ÅžerifoÄŸlu, F. S., & Urlings, T. (2008). Modeling realistic hybrid flexible flowshop scheduling problems. Computers & Operations Research, 35(4), 1151-1175. doi:10.1016/j.cor.2006.07.014Ruiz, R., & Andrés-Romano, C. (2011). Scheduling unrelated parallel machines with resource-assignable sequence-dependent setup times. The International Journal of Advanced Manufacturing Technology, 57(5-8), 777-794. doi:10.1007/s00170-011-3318-2Stafford, E. F., Tseng, F. T., & Gupta, J. N. D. (2005). Comparative evaluation of MILP flowshop models. Journal of the Operational Research Society, 56(1), 88-101. doi:10.1057/palgrave.jors.2601805Tozkapan, A., Kırca, Ö., & Chung, C.-S. (2003). A branch and bound algorithm to minimize the total weighted flowtime for the two-stage assembly scheduling problem. Computers & Operations Research, 30(2), 309-320. doi:10.1016/s0305-0548(01)00098-3Tseng, F. T., & Stafford, E. F. (2008). New MILP models for the permutation flowshop problem. Journal of the Operational Research Society, 59(10), 1373-1386. doi:10.1057/palgrave.jors.260245
Job shop estocástico con minimización del valor esperado del maximum lateness
The drawbacks that programming in job -shop environment imply, refer to a notorious difficulty for its resolution due to its
NP-hard nature. However, the research has grown in the late years because of its constant use in manufacturing industries.
According to studies, most of the research has approached the job shop scheduling through a deterministic approach.
Nevertheless, real industrial environments are subject to random events as: machinery faults, maintenance duration,
processing duration, enlistment times, availability times, among many others. In this project, a stochastic job shop that
minimizes the expected maximum lateness is addressed. The problem consider sequence dependent setup times, and the
stochastic events are machine breakdowns. To solve the problem a simheuristic approach is proposed. The simheuristic
Hybridizes a tabu search algorithm with a Monte Carlo simulation.
The problem was solved in three phases: Firstly, a mixed integer linear programming model was designed for the
deterministic counterpart of the JSSP studied. Secondly, the meta-heuristic tabu search was designed to solving large
instances of the deterministic problem. Thirdly, the simheuristic was designed and implemented to minimize the expected
maximum lateness value, considering stochastic machine breakdowns.
For the simheuristic designing, stochastic variables were generated: times between failures and repair times, following
exponential and log-normal distributions. To generate their respective parameters [expected value (μ) and standard deviation
(σ)], the mean time to repair was found (MTTR Mean Time to Repair), out of the total mean time between breakdowns.
Four different variation coefficient values were proposed (0%, 5%, 10% and 15%), them being: 0% for the deterministic
case and 5%, 10% and 15% for stochastic events, to calculate the (σ) in log-normal distribution. On the other hand, a
simulation was performed to calculate the expected objective function. The simheuristic was firstly parametrized through
an experimental design considering different tabu list sizes and number of iterations without improvement.
With the generated parametrization, another computational experiment was executed for a total of 554 instances of different
sizes. First, the performance of the simheuristic, for small instances, was evaluated in comparison with the simulation of
optimal solutions obtained with the mathematical model. Results show that the simheuristic improves the results of
simulations of the model in a 37% for 4x4 instances and in an 11% for 6x6 instances, demonstrating that the simheuristic is
better than a deterministic mathematical model simulated. Additionally, the simheuristic performance was evaluated, for
large instances, in comparison with the simulation of EDD dispatching rule sequences. Results show that the average
improvement is 28% in log-normal distribution and 10% for exponential distribution.Ingeniero (a) IndustrialAdministrador (a) de EmpresasPregrad
Cost Factor Focused Scheduling and Sequencing: A Neoteric Literature Review
The hastily emergent concern from researchers in the application of scheduling and sequencing has urged the necessity for analysis of the latest research growth to construct a new outline. This paper focuses on the literature on cost minimization as a primary aim in scheduling problems represented with less significance as a whole in the past literature reviews. The purpose of this paper is to have an intensive study to clarify the development of cost-based scheduling and sequencing (CSS) by reviewing the work published over several parameters for improving the understanding in this field. Various parameters, such as scheduling models, algorithms, industries, journals, publishers, publication year, authors, countries, constraints, objectives, uncertainties, computational time, and programming languages and optimization software packages are considered. In this research, the literature review of CSS is done for thirteen years (2010-2022). Although CSS research originated in manufacturing, it has been observed that CSS research publications also addressed case studies based on health, transportation, railway, airport, steel, textile, education, ship, petrochemical, inspection, and construction projects. A detailed evaluation of the literature is followed by significant information found in the study, literature analysis, gaps identification, constraints of work done, and opportunities in future research for the researchers and experts from the industries in CSS
A general Framework for Utilizing Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A concise overview
Sustainable development has emerged as a global priority, and industries are
increasingly striving to align their operations with sustainable practices.
Parallel machine scheduling (PMS) is a critical aspect of production planning
that directly impacts resource utilization and operational efficiency. In this
paper, we investigate the application of metaheuristic optimization algorithms
to address the unrelated parallel machine scheduling problem (UPMSP) through
the lens of sustainable development goals (SDGs). The primary objective of this
study is to explore how metaheuristic optimization algorithms can contribute to
achieving sustainable development goals in the context of UPMSP. We examine a
range of metaheuristic algorithms, including genetic algorithms, particle swarm
optimization, ant colony optimization, and more, and assess their effectiveness
in optimizing the scheduling problem. The algorithms are evaluated based on
their ability to improve resource utilization, minimize energy consumption,
reduce environmental impact, and promote socially responsible production
practices. To conduct a comprehensive analysis, we consider UPMSP instances
that incorporate sustainability-related constraints and objectives
Machine scheduling using the Bees algorithm
Single-machine scheduling is the process of assigning a group of jobs to a machine. The jobs are arranged so that a performance measure, such as the total processing time or the due date, may be optimised. Various swarm intelligence techniques as well as other heuristic approaches have been developed for machine scheduling. Previously, the Bees Algorithm, a heuristic optimisation procedure that mimics honeybee foraging, was successfully employed to solve many problems in continuous domains. In this thesis, the Bees Algorithm is presented to solve various single-machine scheduling benchmarks, all of which, chosen to test the performance of the algorithm, are NP-hard and cannot be solved to optimality within polynomially-bounded time. To apply the Bees Algorithm for machine scheduling, a new neighbourhood structure is defined. Several local search algorithms are combined with the Bees Algorithm.
This work also introduces an enhanced Bees Algorithm. Several additional features are considered to improve the efficiency of the algorithm such as negative selection, chemotaxis, elimination and dispersal which is similar to the ‘site abandonment’ strategy used in the original algorithm, and neighbourhood change. A different way to deploy neighbourhood procedures is also presented.
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Three categories of machine scheduling problems, namely, single machine with a common due date, total weighted tardiness, and total weighted tardiness with sequence-dependent setup are used to test the enhanced Bees Algorithm’s performance. The results obtained compare well with those produced by the basic version of the algorithm and by other well-known techniques
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 caracterÃstica 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 caracterÃsticas
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 ofrecÃa una solución al problema se han
desarrollado heurÃsticas y metaheurÃsticas 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 caracterÃstica 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 caracterÃstiques
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 bietà pic 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 mà quines 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 mà quines de l'etapa de producció.
S'han desenvolupat models matemà tics 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 instà ncies en
què el model matemà tic no oferia una solució al problema s'han desenrotllat
heurÃstiques i metaheurÃsticas 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
Recommended from our members
A methodology for scheduling jobs in a flexible flowshop with sequence dependent setup times and machine skipping
A flexible flowshop, comprised of one or more stages having unrelated parallel machines, is investigated in this research. Unrelated parallel machines can perform the same function but have different capacity or capability. Since this problem is motivated by industry research, dynamic job release times and dynamic machine availability times have been considered. Each job considered in this research can have different weight and due date. Sequence-dependent setup times of jobs further add to the complexity of the research. Machine skipping is yet another innate feature of this research that allows jobs to skip one or more stages depending upon customer's demand or budgetary constraints. The objective of this research is to minimize the sum of the weighted tardiness of all jobs released within the planning horizon.
The research problem is modeled as a mixed (binary) integer-linear programming model and is shown to be strictly NP-hard. Being strongly NP-hard, industry size problems cannot be solved using an implicit enumeration technique within a reasonable computation time. Cognizant of the challenges in solving industry-size problems, we use the tabu-search-based heuristic solution algorithm to find optimal/near optimal solutions. Five different initial solution finding mechanisms, based on dispatching rules, have been developed, to initiate the search. The five initial solution finding mechanisms (IS1-IS5) have been used in conjunction with the six tabu-search-based heuristics (TS1-TS6) to
solve the problems in an effective and efficient manner. The applicability of the search algorithm on an example problem has been demonstrated. The tabu-search based heuristics are tested on seven small problems and the quality of their solutions is compared to the optimal solutions obtained by the branch-and-bound technique. The evaluations show that the tabu-search based heuristics are capable of obtaining solutions of good quality within a much shorter computation time. The best performer among these heuristics recorded a percentage deviation of only 2.19%.
The performance of the tabu-search based heuristics is compared by conducting a statistical experiment that is based on a randomized complete block design. Three sizes of problem structures ranging from 9 jobs to 55 jobs are used in the experiment. The results of the experiment suggest that no IS finding mechanism or TS algorithm contributed to identifying a better quality solution (i.e a lower TWT) for all three problem instances (i.e. small, medium and large). In other words, no IS finding mechanism or TS algorithm could statistically outperform others. In absence of a distinct outperformer, TS1 with short-term memory and fixed TLS are recommended for all problem instances. When comparing the efficiency of the search algorithm, the results of the experiment show that IS1, which refers to the EDD (earliest due date) method, is recommended as the initial solution generation method for small problem sizes. The EDD method is capable of obtaining an initial solution that helps the tabu-search based heuristic to get to the final solution within a short time. TS1 is recommended as the tabu-search based heuristic for large problems, in order to save on time. TS1 is also recommended to solve small and medium problem structures in absence of a statistically proven outperformer