Un problema de programación de la producción en células de fabricación que incluye almacenes

En este trabajo se presenta un problema específico de programación de la producción flow-shop de interés práctico. El sistema de fabricación está configurado como una célula de fabricación y en el planteamiento del problema se consideran los almacenes de materias primas y de productos terminados. El desempeño de la programación se evalúa de una manera multiobjetivo, considerando el tiempo total de producción (makespan) y la tardanza total (tardiness). Se propone una formulación matemática para el problema. Además, se presenta una estrategia meta-heurística para resolver eficientemente dicho problema y obtener soluciones de buena calidad en un tiempo computacional razonable. El procedimiento aplicado se basa en una adaptación de la metaheurística de recocido simulado. Se generaron conjuntos de problemas para evaluar el método propuesto, obteniendo soluciones óptimas o casi óptimas en tiempos significativamente menores que los requeridos por el enfoque de optimización resuelto mediante CPLEX. Además, el algoritmo fue probado con problemas de mayor tamaño, para evaluar su comportamiento en espacios de búsqueda más extensos.Sociedad Argentina de Informática e Investigación Operativ

A smart algorithm for multi-criteria optimization of model sequencing problem in assembly lines

Assembly Lines (ALs) are used for mass production as they offer lots of advantages over other production systems in terms of lead time and cost. The advent of mass customization has forced the manufacturing industries to update to Mixed-Model Assembly Lines (MMALs) but at the cost of increased complexity. In the real world, industries need to determine the sequence of models based on various conflicting performance measures/criteria. This paper investigates the Multi-Criteria Model Sequencing Problem (MC-MSP) using a modified simulation integrated Smart Multi-Criteria Nawaz, Enscore, and Ham (SMC-NEH) algorithm. To address the multiple criteria, a modified simulation integrated Smart Multi-Criteria Nawaz, Enscore, and Ham (SMC-NEH) algorithm was developed by integrating a priori approach with NEH algorithm. Discrete Event Simulation (DES) was used to evaluate each solution. A mathematical model was developed for three criteria: flow time, makespan and idle time. Further, to validate the effectiveness of the proposed SMC-NEH a case study and Taillard's benchmark instances were solved and a Multi-Criteria Decision-Making (MCDM) analysis was performed to compare the performance of the proposed SMC-NEH algorithm with the traditional NEH algorithm and its variants. The results showed that the proposed SMC-NEH algorithm outperformed the others in optimizing the conflicting multi-criteria problem

Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems

Multi-objective optimisation problems have seen a large impulse in the last decades. Many new techniques for solving distinct variants of multi-objective problems have been proposed. Production scheduling, as with other operations management fields, is no different. The flowshop problem is among the most widely studied scheduling settings. Recently, the Iterated Greedy methodology for solving the single-objective version of the flowshop problem has produced state-of-the-art results. This paper proposes a new algorithm based on Iterated Greedy technique for solving the multi-objective permutation flowshop problem. This algorithm is characterised by an effective initialisation of the population, management of the Pareto front, and a specially tailored local search, among other things. The proposed multi-objective Iterated Greedy method is shown to outperform other recent approaches in comprehensive computational and statistical tests that comprise a large number of instances with objectives involving makespan, tardiness and flowtime. Lastly, we use a novel graphical tool to compare the performances of stochastic Pareto fronts based on Empirical Attainment Functions. © 2011 Elsevier Ltd.The authors are indebted to the anonymous referees for their helpful comments which have helped in improving an earlier version of this manuscript. This work is partially funded by the Spanish Ministry of Science and Innovation, under the projects "SMPA - Advanced Parallel Multiobjective Sequencing: Practical and Theoretical Advances" with reference DPI2008-03511/DPI. The authors should also thank 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 and the Polytechnic University of Valencia, for the project PPAR with reference 3147.Minella, GG.; Ruiz García, R.; Ciavotta, M. (2011). Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems. Computers and Operations Research. 38(11):1521-1533. https://doi.org/10.1016/j.cor.2011.01.010S15211533381

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

Balancing labor requirements in a manufacturing environment

“This research examines construction environments within manufacturing facilities, specifically semiconductor manufacturing facilities, and develops a new optimization method that is scalable for large construction projects with multiple execution modes and resource constraints. The model is developed to represent real-world conditions in which project activities do not have a fixed, prespecified duration but rather a total amount of work that is directly impacted by the level of resources assigned. To expand on the concept of resource driven project durations, this research aims to mimic manufacturing construction environments by allowing a non-continuous resource allocation to project tasks. This concept allows for resources to shift between projects in order to achieve the optimal result for the project manager. Our model generates a novel multi-objective resource constrained project scheduling problem. Specifically, two objectives are studied; the minimization of the total direct labor cost and the minimization of the resource leveling. This research will utilize multiple techniques to achieve resource leveling and discuss the advantage each one provides to the project team, as well as a comparison of the Pareto Fronts between the given resource leveling and cost minimization objective functions. Finally, a heuristic is developed utilizing partial linear relaxation to scale the optimization model for large scale projects. The computation results from multiple randomly generated case studies show that the new heuristic method is capable of generating high quality solutions at significantly less computational time”--Abstract, page iv

Secuenciación de máquinas con necesidad de ajustes y recursos adicionales.

[ES] En esta tesis doctoral se estudia el problema de secuenciación de máquinas paralelas no relacionadas con necesidad de ajustes y recursos adicionales asignados en los ajustes. En este problema, se tiene un grupo de tareas (también llamadas trabajos), donde cada una debe ser procesada en una de las máquinas paralelas disponibles. Para procesar una tarea después de otra en la misma máquina, se debe hacer un ajuste en la máquina. Se asume que estos ajustes deben ser realizados por un recurso adicional limitado (por ejemplo, operarios). En esta tesis doctoral se estudian dos variantes del problema planteado: 1) considerando el problema con el único objetivo de minimizar el tiempo máximo de finalización de todos los trabajos (makespan), y 2) considerando el problema multi-objetivo minimizando simultáneamente el makespan y el consumo máximo de recursos adicionales. Inicialmente, se realiza una completa revisión bibliográfica sobre estudios relacionados con el problema planteado. En esta revisión se detecta que, a pesar de existir numerosos estudios de secuenciación de máquinas paralelas, no muchos de estos estudios tienen en cuenta recursos adicionales. Posteriormente, para introducir el problema a estudiar antes de plantear métodos de resolución, se realiza una breve explicación de los principales problemas de secuenciación de máquinas paralelas. El problema de un solo objetivo está clasificado como NP-Hard. Por ello, para abordar su resolución se han diseñado e implementado heurísticas y metaheurísticas siguiendo dos enfoques diferentes. Para el primer enfoque, que ignora la información sobre el consumo de recursos adicionales en la fase constructiva, se adaptan dos de los mejores algoritmos existentes en la literatura para el problema de máquinas paralelas con ajustes sin necesidad de recursos adicionales. En el segundo enfoque, que sí tiene en cuenta la información sobre el consumo de recursos adicionales en la fase constructiva, se proponen nuevos algoritmos heurísticos y metaheurísticos para resolver el problema. Tras analizar los resultados de los experimentos computacionales realizados, concluimos que hay diferencias entre los dos enfoques, siendo significativamente mejor el enfoque que tiene en cuenta la información sobre los recursos adicionales. Al igual que en el caso de un solo objetivo, la complejidad del problema multi-objetivo obliga a presentar algoritmos heurísticos o metaheurísticos para resolverlo. En esta tesis se presenta un nuevo algoritmo metaheurístico multi-objetivo eficiente para encontrar buenas aproximaciones a la frontera de Pareto del problema. Además, se adaptaron otros tres algoritmos que han mostrado buenos resultados en diferentes estudios de problemas de secuenciación de máquinas multi-objetivo. Después de realizar experimentos computacionales exhaustivos, concluimos que el nuevo algoritmo propuesto en esta tesis es significativamente mejor que los otros tres algoritmos existentes, y que se han adaptado para resolver este problema.[CAT] En aquesta tesi doctoral s'estudia el problema de seqüenciació de màquines paral·leles no relacionades amb necessitat d'ajustos i recursos addicionals assignats en els ajustos. En aquest problema, es tenen un grup de tasques (també anomenades treballs), on cadascuna ha de ser processada en una de les màquines paral·leles disponibles. Per processar una tasca després d'una altra en la mateixa màquina, s'ha de fer un ajustament en la màquina. S'assumeix que aquests ajustos en les màquines per a processar una tasca després del processament d'una altra, han de ser realitzats per un recurs addicional limitat (per exemple, operaris). En aquesta tesi doctoral s'estudien dos variants al problema plantejat: 1) considerant el problema com l'únic objectiu de minimitzar el temps màxim de finalització de tots els treballs (makespan), i 2) considerant el problema multi-objectiu minimitzant simultàniament el makespan i el consum màxim de recursos addicionals. Inicialment, es realitza una completa revisió bibliogràfica sobre estudis relacionats amb el problema plantejat. En esta revisió es detecta que, tot i existir nombrosos estudis de seqüenciació de màquines paral·leles, hi ha molts pocs que tenen en compte recursos addicionals. Posteriorment, per introduir el problema a estudiar abans de plantejar mètodes de resolució, es realitza una breu explicació dels principals problemes de seqüenciació de màquines paral·leles. El problema d'un sol objectiu està classificat com NP-Hard. Per això, per abordar la seua resolució s'han dissenyat i implementat heurístiques y metaheurístiques seguint dos enfocs diferents. El primer enfoc ignora la informació sobre el consum de recursos en la fase constructiva, adaptant dos dels millors algoritmes existents en la literatura per al problema de seqüenciació de màquines paral·leles amb ajustaments sense necessitat de recursos. Per al segon enfoc si es té en compte la informació sobre el consum de recursos en la fase constructiva. Després d'analitzar els resultats dels experiments computacionals realitzats, concloem que hi ha diferencies entre els dos enfocs, sent significativament millor l'enfoc que té en compte la informació sobre el recursos. De la mateixa manera que en el cas d'un sol objectiu, la complexitat del problema multi-objectiu obliga a presentar algoritmes heurístics o metaheurístics per a resoldre-ho. En aquesta tesi es presenta un nou algoritme metaheurístic multi-objectiu eficient per trobar bones aproximacions a la frontera de Pareto del problema. A més, es van adaptar altres tres algoritmes que han mostrat bons resultats en diferents estudis de problemes de seqüenciació de màquines multi-objectiu. Després de realitzar experiments computacionals exhaustius, concloem que el nou algoritme proposat en aquesta tesi és significativament millor que els altres tres algoritmes existents i que s'han adaptat per resoldre aquest problema.[EN] In this thesis we study the unrelated parallel machine scheduling problem with setup times and additional limited resources in the setups. In this problem, we have a group of tasks (also called jobs), where each one must be processed on one of the available parallel machines. To process one job after another on the same machine, a setup must be made on the machine. It is assumed that these setups on machines must be made by a limited additional resource (eg, operators). In this thesis two variants of the problem are studied: 1) considering the problem with the objective of minimizing the maximum completion time of all jobs (makespan), and 2) considering the multi-objective problem, minimizing the makespan and the maximum consumption of additional resources. Initially, a complete literature review is carried out on studies related to the problem addressed in this thesis. This review finds that despite numerous parallel machine scheduling studies, there are very few that take into account additional resources. Subsequently, to introduce the problem addressed before proposing resolution methods, a brief explanation of the main parallel machines scheduling problems is made. The problem with a single objective is classified as NP-Hard. Therefore, to solve it, heuristics and metaheuristics have been designed and implemented following two different approaches. For the first approach, which ignores the information on the consumption of resources in the construction phase, two of the best algorithms existing in the literature for the problem of parallel machines with setups without additional resources are adapted. For the second approach, which does take into account information on the consumption of resources in the construction phase, new heuristic and metaheuristic algorithms are proposed to solve the problem. Following the results of the computational experiments, we conclude that there are differences between the two approaches, the approach that takes into account the information on resources being significantly better. As in the case of a single objective, the complexity of the multi-objective problem requires the formulation of heuristic or metaheuristic algorithms to solve it. In this thesis, a new efficient multi-objective metaheuristic algorithm is presented to find good approximations to the Pareto front of the problem. In addition, three other algorithms that have shown good results in different studies of multi-objective machine scheduling problems were adapted. After carrying out exhaustive computational experiments, we concluded that the new algorithm proposed in this thesis is significantly better than the other three adapted algorithms.Yepes Borrero, JC. (2020). Secuenciación de máquinas con necesidad de ajustes y recursos adicionales [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/158742TESI

Inteligencia computacional en la programación de la producción con recursos adicionales

[ES] En esta Tesis Doctoral se aborda el problema del taller de flujo de permutación considerando recursos adicionales renovables, que es una versión más realista del clásico problema de taller de flujo de permutación, muy estudiado en la literatura. La inclusión de los recursos ayuda a acercar el mundo académico-científico al mundo real de la industria. Se ha realizado una completa revisión bibliográfica que no se ha limitado a problemas del taller de flujo, sino que han revisado problemas similares del ámbito de scheduling que consideren recursos. En esta revisión, no se han encontrado en la literatura artículos para el problema concreto que se estudia en esta tesis. Por ello, la aportación principal de esta Tesis Doctoral es el estudio por primera vez de este problema y la propuesta y adaptación de métodos para su resolución. Inicialmente, el problema se modeliza a través de un modelo de programación lineal entera mixta (MILP). Dada la complejidad del problema, el MILP es capaz de resolver instancias de un tamaño muy pequeño. Por ello, es necesario adaptar, diseñar e implementar heurísticas constructivas y metaheurísticas para obtener buenas soluciones en un tiempo de computación razonable. Para evaluar la eficacia y eficiencia de los métodos propuestos, se generan instancias de problemas partiendo de los conjuntos más utilizados en la literatura para el taller de flujo de permutación. Se utilizan estas instancias propuestas tanto para calibrar los distintos métodos como para evaluar su rendimiento a través de experimentos computacionales masivos. Los experimentos muestran que las heurísticas propuestas son métodos sencillos que consiguen soluciones factibles de una forma muy rápida. Para mejorar las soluciones obtenidas con las heurísticas y facilitar el movimiento a otros espacios de soluciones, se proponen tres metaheurísticas: un método basado en búsqueda local iterativa (ILS), un método voraz iterativo (IG) y un algoritmo genético con búsqueda local (HGA). Todos ellos utilizan las heurísticas propuestas más eficaces como solución o soluciones iniciales. Las metaheurísticas obtienen las mejores soluciones utilizando tiempos de computación razonables, incluso para las instancias de mayor tamaño. Todos los métodos han sido implementados dentro de la plataforma FACOP (Framework for Applied Combinatorial Optimization Problems). Dicha plataforma es capaz de incorporar nuevos algoritmos de optimización para problemas de investigación operativa relacionados con la toma de decisiones de las organizaciones y está diseñada para abordar casos reales en empresas. El incorporar en esta plataforma todas las metodologías propuestas en esta Tesis Doctoral, acerca el mundo académico al mundo empresarial.[CA] En aquesta Tesi Doctoral s'aborda el problema del taller de flux de permutació considerant recursos addicionals renovables, que és una versió més realista del clàssic problema de taller de flux de permutació, molt estudiat a la literatura. La inclusió dels recursos ajuda a apropar el món acadèmic-científic al món real de la indústria. S'ha realitzat una revisió bibliogràfica completa que no s'ha limitat a problemes del taller de flux, sinó que ha revisat problemes similars de l'àmbit de scheduling que considerin recursos. En aquesta revisió, no s'ha trobat a la literatura articles per al problema concret que s'estudia en aquesta tesi. Per això, l'aportació principal d'aquesta Tesi Doctoral és l'estudi per primera vegada d'aquest problema i la proposta i l'adaptació de mètodes per resoldre'ls. Inicialment, el problema es modelitza mitjançant un model de programació lineal sencera mixta (MILP). Donada la complexitat del problema, el MILP és capaç de resoldre instàncies d'un tamany molt petita. Per això, cal adaptar, dissenyar i implementar heurístiques constructives i metaheurístiques per obtenir bones solucions en un temps de computació raonable. Per avaluar l'eficàcia i l'eficiència dels mètodes proposats, es generen instàncies de problemes partint dels conjunts més utilitzats a la literatura per al taller de flux de permutació. S'utilitzen aquestes instàncies proposades tant per calibrar els diferents mètodes com per avaluar-ne el rendiment a través d'experiments computacionals massius. Els experiments mostren que les heurístiques proposades són mètodes senzills que aconsegueixen solucions factibles de manera molt ràpida. Per millorar les solucions obtingudes amb les heurístiques i facilitar el moviment a altres espais de solucions, es proposen tres metaheurístiques: un mètode basat en cerca local iterativa (ILS), un mètode voraç iteratiu (IG) i un algorisme genètic híbrid (HGA). Tots ells utilitzen les heurístiques proposades més eficaces com a solució o solucions inicials. Les metaheurístiques obtenen les millors solucions utilitzant temps de computació raonables, fins i tot per a les instàncies més grans. Tots els mètodes han estat implementats dins de la plataforma FACOP (Framework for Applied Combinatorial Optimization Problems). Aquesta plataforma és capaç d'incorporar nous algorismes d'optimització per a problemes de recerca operativa relacionats amb la presa de decisions de les organitzacions i està dissenyada per abordar casos reals a empreses. El fet d'incorporar en aquesta plataforma totes les metodologies proposades en aquesta Tesi Doctoral, apropa el món acadèmic al món empresarial.[EN] In this Doctoral Thesis, the permutation flowshop problem is addressed considering additional renewable resources, which is a more realistic version of the classic permutation flowshop problem, widely studied in the literature. The inclusion of resources helps to bring the academic-scientific world closer to the real world of industry. A complete bibliographic review has been carried out that has not been limited to flow shop problems, but has reviewed similar problems in the scheduling field that consider resources. In this review, no articles have been found in the literature for the specific problem studied in this thesis. Therefore, the main contribution of this Doctoral Thesis is the study for the first time of this problem and the proposal and adaptation of methods for its resolution. Initially, the problem is modeled through a mixed integer linear programming (MILP) model. Given the complexity of the problem, the MILP is capable of solving very small instances. Therefore, it is necessary to adapt, design and implement constructive heuristics and metaheuristics to obtain good solutions in a reasonable computation time. In order to evaluate the effectiveness and efficiency of the proposed methods, problem instances are generated starting from the sets most used in the literature for the permutation flowshop. These proposed instances are used both to calibrate the different methods and to evaluate their performance through massive computational experiments. Experiments show that proposed heuristics are simple methods that achieve feasible solutions very quickly. To improve the solutions obtained with the heuristics and facilitate movement to other solution spaces, three metaheuristics are proposed: a method based on iterated local search (ILS), an iterative greedy method (IG) and a hybrid genetic algorithm (HGA). All of them use the most effective proposed heuristics as initial solution or solutions. Metaheuristics get the best solutions using reasonable computation times, even for the largest instances. All the methods have been implemented within the FACOP platform (Framework for Applied Combinatorial Optimization Problems). Said platform is capable of incorporating new optimization algorithms for operational research problems related to decision-making in organizations and it is designed to address real cases in companies. Incorporating in this platform all the methodologies proposed in this Doctoral Thesis, brings the academic world closer to the business world.Alfaro Fernández, P. (2023). Inteligencia computacional en la programación de la producción con recursos adicionales [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19889

Innovative hybrid MOEA/AD variants for solving multi-objective combinatorial optimization problems

Orientador : Aurora Trinidad Ramirez PozoCoorientador : Roberto SantanaTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 16/12/2016Inclui referências : f. 103-116Resumo: Muitos problemas do mundo real podem ser representados como um problema de otimização combinatória. Muitas vezes, estes problemas são caracterizados pelo grande número de variáveis e pela presença de múltiplos objetivos a serem otimizados ao mesmo tempo. Muitas vezes estes problemas são difíceis de serem resolvidos de forma ótima. Suas resoluções tem sido considerada um desafio nas últimas décadas. Os algoritimos metaheurísticos visam encontrar uma aproximação aceitável do ótimo em um tempo computacional razoável. Os algoritmos metaheurísticos continuam sendo um foco de pesquisa científica, recebendo uma atenção crescente pela comunidade. Uma das têndencias neste cenário é a arbordagem híbrida, na qual diferentes métodos e conceitos são combinados objetivando propor metaheurísticas mais eficientes. Nesta tese, nós propomos algoritmos metaheurísticos híbridos para a solução de problemas combinatoriais multiobjetivo. Os principais ingredientes das nossas propostas são: (i) o algoritmo evolutivo multiobjetivo baseado em decomposição (MOEA/D framework), (ii) a otimização por colônias de formigas e (iii) e os algoritmos de estimação de distribuição. Em nossos frameworks, além dos operadores genéticos tradicionais, podemos instanciar diferentes modelos como mecanismo de reprodução dos algoritmos. Além disso, nós introduzimos alguns componentes nos frameworks objetivando balancear a convergência e a diversidade durante a busca. Nossos esforços foram direcionados para a resolução de problemas considerados difíceis na literatura. São eles: a programação quadrática binária sem restrições multiobjetivo, o problema de programação flow-shop permutacional multiobjetivo, e também os problemas caracterizados como deceptivos. Por meio de estudos experimentais, mostramos que as abordagens propostas são capazes de superar os resultados do estado-da-arte em grande parte dos casos considerados. Mostramos que as diretrizes do MOEA/D hibridizadas com outras metaheurísticas é uma estratégia promissora para a solução de problemas combinatoriais multiobjetivo. Palavras-chave: metaheuristicas, otimização multiobjetivo, problemas combinatoriais, MOEA/D, otimização por colônia de formigas, algoritmos de estimação de distribuição, programação quadrática binária sem restrições multiobjetivo, problema de programação flow-shop permutacional multiobjetivo, abordagens híbridas.Abstract: Several real-world problems can be stated as a combinatorial optimization problem. Very often, they are characterized by the large number of variables and the presence of multiple conflicting objectives to be optimized at the same time. These kind of problems are, usually, hard to be solved optimally, and their solutions have been considered a challenge for a long time. Metaheuristic algorithms aim at finding an acceptable approximation to the optimal solution in a reasonable computational time. The research on metaheuristics remains an attractive area and receives growing attention. One of the trends in this scenario are the hybrid approaches, in which different methods and concepts are combined aiming to propose more efficient approaches. In this thesis, we have proposed hybrid metaheuristic algorithms for solving multi-objective combinatorial optimization problems. Our proposals are based on (i) the multi-objective evolutionary algorithm based on decomposition (MOEA/D framework), (ii) the bio-inspired metaheuristic ant colony optimization, and (iii) the probabilistic models from the estimation of distribution algorithms. Our algorithms are considered MOEA/D variants. In our MOEA/D variants, besides the traditional genetic operators, we can instantiate different models as the variation step (reproduction). Moreover, we include some design modifications into the frameworks to control the convergence and the diversity during their search (evolution). We have addressed some important problems from the literature, e.g., the multi-objective unconstrained binary quadratic programming, the multiobjective permutation flowshop scheduling problem, and the problems characterized by deception. As a result, we show that our proposed frameworks are able to solve these problems efficiently by outperforming the state-of-the-art approaches in most of the cases considered. We show that the MOEA/D guidelines hybridized to other metaheuristic components and concepts is a powerful strategy for solving multi-objective combinatorial optimization problems. Keywords: meta-heuristics, multi-objective optimization, combinatorial problems, MOEA/D, ant colony optimization, estimation of distribution algorithms, unconstrained binary quadratic programming, permutation flowshop scheduling problem, hybrid approaches

Ergonomic risk and cycle time minimization for the U-shaped worker assignment assembly line balancing problem: A multi-objective approach

[EN] Workers still perform the bulk of operations in the manufacturing industry. The consideration of the assignment of workers and the reduction of ergonomic risks in U-shaped assembly lines is of paramount importance. However, the objectives of efficient task and worker assignment and a reduction in ergonomic risks are not usually correlated. Moreover, there is limited research in the existing literature into multi-objective approaches in U-shaped assembly lines. We formulate a U-shaped assembly worker assignment and balancing problem to simultaneously minimize cycle times and ergonomic risks. In addition, and due to its simplicity and successful results in flow shop scheduling problems, a Restarted Iterated Pareto Greedy algorithm is designed to optimize both objectives. In this algorithm, a problem-specific heuristic-based initialization is extended to improve the initial solution. Two precedence-based greedy and local search phases are developed to exploit the space around the current solution. Finally, a restart mechanism is proposed to help the algorithm escape from local optima. Comprehensive computational results, supported by detailed statistical analyses, suggest that the proposed multi-objective algorithm outperforms existing methods on a large number of benchmark instances.The authors would like to thank the anonymous reviewers for their helpful comments and constructive suggestions. This work is supported by National Natural Science Foundation of China (No. 51875421, No. 51875420). Ruben Ruiz is partly 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.Zhang, Z.; Tang, Q.; Ruiz García, R.; Zhang, L. (2020). Ergonomic risk and cycle time minimization for the U-shaped worker assignment assembly line balancing problem: A multi-objective approach. Computers & Operations Research. 118:1-15. https://doi.org/10.1016/j.cor.2020.104905S115118Akyol, S. D., & Baykasoğlu, A. (2016). A multiple-rule based constructive randomized search algorithm for solving assembly line worker assignment and balancing problem. Journal of Intelligent Manufacturing, 30(2), 557-573. doi:10.1007/s10845-016-1262-6Akyol, S. D., & Baykasoğlu, A. (2016). ErgoALWABP: a multiple-rule based constructive randomized search algorithm for solving assembly line worker assignment and balancing problem under ergonomic risk factors. Journal of Intelligent Manufacturing, 30(1), 291-302. doi:10.1007/s10845-016-1246-6Alavidoost, M. H., Babazadeh, H., & Sayyari, S. T. (2016). An interactive fuzzy programming approach for bi-objective straight and U-shaped assembly line balancing problem. Applied Soft Computing, 40, 221-235. doi:10.1016/j.asoc.2015.11.025Alavidoost, M. H., Tarimoradi, M., & Zarandi, M. H. F. (2015). Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems. Applied Soft Computing, 34, 655-677. doi:10.1016/j.asoc.2015.06.001Araújo, F. F. B., Costa, A. M., & Miralles, C. (2012). Two extensions for the ALWABP: Parallel stations and collaborative approach. International Journal of Production Economics, 140(1), 483-495. doi:10.1016/j.ijpe.2012.06.032Aryanezhad, M. B., Kheirkhah, A. S., Deljoo, V., & Mirzapour Al-e-hashem, S. M. J. (2008). Designing safe job rotation schedules based upon workers’ skills. The International Journal of Advanced Manufacturing Technology, 41(1-2), 193-199. doi:10.1007/s00170-008-1446-0Avikal, S., Jain, R., Mishra, P. K., & Yadav, H. C. (2013). A heuristic approach for U-shaped assembly line balancing to improve labor productivity. Computers & Industrial Engineering, 64(4), 895-901. doi:10.1016/j.cie.2013.01.001Battini, D., Calzavara, M., Otto, A., & Sgarbossa, F. (2016). The Integrated Assembly Line Balancing and Parts Feeding Problem with Ergonomics Considerations. IFAC-PapersOnLine, 49(12), 191-196. doi:10.1016/j.ifacol.2016.07.594Battini, D., Faccio, M., Persona, A., & Sgarbossa, F. (2011). New methodological framework to improve productivity and ergonomics in assembly system design. International Journal of Industrial Ergonomics, 41(1), 30-42. doi:10.1016/j.ergon.2010.12.001Bautista, J., Batalla-García, C., & Alfaro-Pozo, R. (2016). Models for assembly line balancing by temporal, spatial and ergonomic risk attributes. European Journal of Operational Research, 251(3), 814-829. doi:10.1016/j.ejor.2015.12.042Baykasoglu, A. (2006). Multi-rule Multi-objective Simulated Annealing Algorithm for Straight and U Type Assembly Line Balancing Problems. Journal of Intelligent Manufacturing, 17(2), 217-232. doi:10.1007/s10845-005-6638-yBaykasoğlu, A., Demirkol Akyol, S., & Demirkan, B. (2017). An Excel-based program to teach students quick ergonomic risk assessment techniques with an application to an assembly system. Computer Applications in Engineering Education, 25(3), 489-507. doi:10.1002/cae.21816Baykasoglu, A., Tasan, S. O., Tasan, A. S., & Akyol, S. D. (2017). Modeling and solving assembly line design problems by considering human factors with a real-life application. Human Factors and Ergonomics in Manufacturing & Service Industries, 27(2), 96-115. doi:10.1002/hfm.20695Blum, C., & Miralles, C. (2011). On solving the assembly line worker assignment and balancing problem via beam search. Computers & Operations Research, 38(1), 328-339. doi:10.1016/j.cor.2010.05.008Borba, L., & Ritt, M. (2014). A heuristic and a branch-and-bound algorithm for the Assembly Line Worker Assignment and Balancing Problem. Computers & Operations Research, 45, 87-96. doi:10.1016/j.cor.2013.12.002Bortolini, M., Faccio, M., Gamberi, M., & Pilati, F. (2017). Multi-objective assembly line balancing considering component picking and ergonomic risk. Computers & Industrial Engineering, 112, 348-367. doi:10.1016/j.cie.2017.08.029Botti, L., Mora, C., & Regattieri, A. (2017). Integrating ergonomics and lean manufacturing principles in a hybrid assembly line. Computers & Industrial Engineering, 111, 481-491. doi:10.1016/j.cie.2017.05.011Bukchin, Y., & Raviv, T. (2018). Constraint programming for solving various assembly line balancing problems. Omega, 78, 57-68. doi:10.1016/j.omega.2017.06.008Costa, A. M., & Miralles, C. (2009). Job rotation in assembly lines employing disabled workers. International Journal of Production Economics, 120(2), 625-632. doi:10.1016/j.ijpe.2009.04.013Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017Ding, J.-Y., Song, S., Gupta, J. N. D., Zhang, R., Chiong, R., & Wu, C. (2015). An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem. Applied Soft Computing, 30, 604-613. doi:10.1016/j.asoc.2015.02.006Fattahi, A., Elaoud, S., Sadeqi Azer, E., & Turkay, M. (2013). A novel integer programming formulation with logic cuts for the U-shaped assembly line balancing problem. International Journal of Production Research, 52(5), 1318-1333. doi:10.1080/00207543.2013.832489Grunert da Fonseca, V., Fonseca, C.M., Hall, A.O., 2001. Inferential performance assessment of stochastic optimisers and the attainment function. 1993, 213–225.Guo, Z. X., Wong, W. K., Leung, S. Y. S., Fan, J. T., & Chan, S. F. (2008). A Genetic-Algorithm-Based Optimization Model for Solving the Flexible Assembly Line Balancing Problem With Work Sharing and Workstation Revisiting. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(2), 218-228. doi:10.1109/tsmcc.2007.913912Hatami, S., Ruiz, R., & Andrés-Romano, C. (2015). Heuristics and metaheuristics for the distributed assembly permutation flowshop scheduling problem with sequence dependent setup times. International Journal of Production Economics, 169, 76-88. doi:10.1016/j.ijpe.2015.07.027Hazır, Ö., & Dolgui, A. (2015). A decomposition based solution algorithm for U-type assembly line balancing with interval data. Computers & Operations Research, 59, 126-131. doi:10.1016/j.cor.2015.01.010Hignett, S., & McAtamney, L. (2000). Rapid Entire Body Assessment (REBA). Applied Ergonomics, 31(2), 201-205. doi:10.1016/s0003-6870(99)00039-3HOFFMANN, T. R. (1990). Assembly line balancing: a set of challenging problems. International Journal of Production Research, 28(10), 1807-1815. doi:10.1080/00207549008942835Karhu, O., Kansi, P., & Kuorinka, I. (1977). Correcting working postures in industry: A practical method for analysis. Applied Ergonomics, 8(4), 199-201. doi:10.1016/0003-6870(77)90164-8Knowles, J.D., Thiele, L., Zitzler, E., 2006. A tutorial on the performance assessment of stochastic multiobjective optimizers. TIK-Report 214.López-Ibáñez, M., Paquete, L., & Stützle, T. (2006). Hybrid Population-Based Algorithms for the Bi-Objective Quadratic Assignment Problem. Journal of Mathematical Modelling and Algorithms, 5(1), 111-137. doi:10.1007/s10852-005-9034-xLi, M., Tang, Q., Zheng, Q., Xia, X., & Floudas, C. A. (2017). Rules-based heuristic approach for the U-shaped assembly line balancing problem. Applied Mathematical Modelling, 48, 423-439. doi:10.1016/j.apm.2016.12.031Li, Z., Tang, Q., & Zhang, L. (2017). Two-sided assembly line balancing problem of type I: Improvements, a simple algorithm and a comprehensive study. Computers & Operations Research, 79, 78-93. doi:10.1016/j.cor.2016.10.006Liles, D. H., Deivanayagam, S., Ayoub, M. M., & Mahajan, P. (1984). A Job Severity Index for the Evaluation and Control of Lifting Injury. Human Factors: The Journal of the Human Factors and Ergonomics Society, 26(6), 683-693. doi:10.1177/001872088402600608McAtamney, L., & Nigel Corlett, E. (1993). RULA: a survey method for the investigation of work-related upper limb disorders. Applied Ergonomics, 24(2), 91-99. doi:10.1016/0003-6870(93)90080-sMiltenburg, G. J., & Wijngaard, J. (1994). The U-line Line Balancing Problem. Management Science, 40(10), 1378-1388. doi:10.1287/mnsc.40.10.1378Minella, G., Ruiz, R., & Ciavotta, M. (2011). Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems. Computers & Operations Research, 38(11), 1521-1533. doi:10.1016/j.cor.2011.01.010Miralles, C., García-Sabater, J. P., Andrés, C., & Cardós, M. (2008). Branch and bound procedures for solving the Assembly Line Worker Assignment and Balancing Problem: Application to Sheltered Work centres for Disabled. Discrete Applied Mathematics, 156(3), 352-367. doi:10.1016/j.dam.2005.12.012Moreira, M. C. O., Miralles, C., & Costa, A. M. (2015). Model and heuristics for the Assembly Line Worker Integration and Balancing Problem. Computers & Operations Research, 54, 64-73. doi:10.1016/j.cor.2014.08.021Mutlu, Ö., Polat, O., & Supciller, A. A. (2013). An iterative genetic algorithm for the assembly line worker assignment and balancing problem of type-II. Computers & Operations Research, 40(1), 418-426. doi:10.1016/j.cor.2012.07.010Nourmohammadi, A., Zandieh, M., & Tavakkoli-Moghaddam, R. (2013). An imperialist competitive algorithm for multi-objective U-type assembly line design. Journal of Computational Science, 4(5), 393-400. doi:10.1016/j.jocs.2012.09.001OCCHIPINTI, E. (1998). OCRA: a concise index for the assessment of exposure to repetitive movements of the upper limbs. Ergonomics, 41(9), 1290-1311. doi:10.1080/001401398186315Oksuz, M. K., Buyukozkan, K., & Satoglu, S. I. (2017). U-shaped assembly line worker assignment and balancing problem: A mathematical model and two meta-heuristics. Computers & Industrial Engineering, 112, 246-263. doi:10.1016/j.cie.2017.08.030Otto, A., & Battaïa, O. (2017). Reducing physical ergonomic risks at assembly lines by line balancing and job rotation: A survey. Computers & Industrial Engineering, 111, 467-480. doi:10.1016/j.cie.2017.04.011Otto, A., & Scholl, A. (2011). Incorporating ergonomic risks into assembly line balancing. European Journal of Operational Research, 212(2), 277-286. doi:10.1016/j.ejor.2011.01.056Pan, Q.-K., & Ruiz, R. (2014). An effective iterated greedy algorithm for the mixed no-idle permutation flowshop scheduling problem. Omega, 44, 41-50. doi:10.1016/j.omega.2013.10.002Pereira, J. (2018). The robust (minmax regret) assembly line worker assignment and balancing problem. Computers & Operations Research, 93, 27-40. doi:10.1016/j.cor.2018.01.009Polat, O., Kalayci, C. B., Mutlu, Ö., & Gupta, S. M. (2015). A two-phase variable neighbourhood search algorithm for assembly line worker assignment and balancing problem type-II: an industrial case study. International Journal of Production Research, 54(3), 722-741. doi:10.1080/00207543.2015.1055344Rabbani, M., Kazemi, S. M., & Manavizadeh, N. (2012). Mixed model U-line balancing type-1 problem: A new approach. Journal of Manufacturing Systems, 31(2), 131-138. doi:10.1016/j.jmsy.2012.02.002Ramezanian, R., & Ezzatpanah, A. (2015). Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem. Computers & Industrial Engineering, 87, 74-80. doi:10.1016/j.cie.2015.04.017Ritt, M., Costa, A. M., & Miralles, C. (2015). The assembly line worker assignment and balancing problem with stochastic worker availability. International Journal of Production Research, 54(3), 907-922. doi:10.1080/00207543.2015.1108534Şahin, M., & Kellegöz, T. (2017). An efficient grouping genetic algorithm for U-shaped assembly line balancing problems with maximizing production rate. Memetic Computing, 9(3), 213-229. doi:10.1007/s12293-017-0239-0Schaub, K., Caragnano, G., Britzke, B., & Bruder, R. (2013). The European Assembly Worksheet. Theoretical Issues in Ergonomics Science, 14(6), 616-639. doi:10.1080/1463922x.2012.678283Shin, W., & Park, M. (2017). Ergonomic interventions for prevention of work-related musculoskeletal disorders in a small manufacturing assembly line. International Journal of Occupational Safety and Ergonomics, 25(1), 110-122. doi:10.1080/10803548.2017.1373487Sungur, B., & Yavuz, Y. (2015). Assembly line balancing with hierarchical worker assignment. Journal of Manufacturing Systems, 37, 290-298. doi:10.1016/j.jmsy.2014.08.004Talbot, F. B., Patterson, J. H., & Gehrlein, W. V. (1986). A Comparative Evaluation of Heuristic Line Balancing Techniques. Management Science, 32(4), 430-454. doi:10.1287/mnsc.32.4.430Tan, K. C., Yang, Y. J., & Goh, C. K. (2006). A distributed Cooperative coevolutionary algorithm for multiobjective optimization. IEEE Transactions on Evolutionary Computation, 10(5), 527-549. doi:10.1109/tevc.2005.860762Tasgetiren, M. F., Kizilay, D., Pan, Q.-K., & Suganthan, P. N. (2017). Iterated greedy algorithms for the blocking flowshop scheduling problem with makespan criterion. Computers & Operations Research, 77, 111-126. doi:10.1016/j.cor.2016.07.002Tiacci, L., & Mimmi, M. (2018). Integrating ergonomic risks evaluation through OCRA index and balancing/sequencing decisions for mixed model stochastic asynchronous assembly lines. Omega, 78, 112-138. doi:10.1016/j.omega.2017.08.011Vilà, M., & Pereira, J. (2014). A branch-and-bound algorithm for assembly line worker assignment and balancing problems. Computers & Operations Research, 44, 105-114. doi:10.1016/j.cor.2013.10.016WATERS, T. R., PUTZ-ANDERSON, V., GARG, A., & FINE, L. J. (1993). Revised NIOSH equation for the design and evaluation of manual lifting tasks. Ergonomics, 36(7), 749-776. doi:10.1080/00140139308967940Yorke, J., 2017. Henry Ford – Master of flow.Zacharia, P. T., & Nearchou, A. C. (2016). A population-based algorithm for the bi-objective assembly line worker assignment and balancing problem. Engineering Applications of Artificial Intelligence, 49, 1-9. doi:10.1016/j.engappai.2015.11.007Zaman, T., Paul, S. K., & Azeem, A. (2012). Sustainable operator assignment in an assembly line using genetic algorithm. International Journal of Production Research, 50(18), 5077-5084. doi:10.1080/00207543.2011.63676