Bicriteria scheduling of a two-machine flowshop with sequence-dependent setup times

The official published version of the article can be found at the link below.A two-machine flowshop scheduling problem is addressed to minimize setups and makespan where each job is characterized by a pair of attributes that entail setups on each machine. The setup times are sequence-dependent on both machines. It is shown that these objectives conflict, so the Pareto optimization approach is considered. The scheduling problems considering either of these objectives are NP-hard , so exact optimization techniques are impractical for large-sized problems. We propose two multi-objective metaheurisctics based on genetic algorithms (MOGA) and simulated annealing (MOSA) to find approximations of Pareto-optimal sets. The performances of these approaches are compared with lower bounds for small problems. In larger problems, performance of the proposed algorithms are compared with each other. Experimentations revealed that both algorithms perform very similar on small problems. Moreover, it was observed that MOGA outperforms MOSA in terms of the quality of solutions on larger problems.Partial Funding from EPSRC under grant EP/D050863/1

Balancing and Sequencing of Mixed Model Assembly Lines

Assembly lines are cost efficient production systems that mass produce identical products. Due to customer demand, manufacturers use mixed model assembly lines to produce customized products that are not identical. To stay efficient, management decisions for the line such as number of workers and assembly task assignment to stations need to be optimized to increase throughput and decrease cost. In each station, the work to be done depends on the exact product configuration, and is not consistent across all products. In this dissertation, a mixed model line balancing integer program (IP) that considers parallel workers, zoning, task assignment, and ergonomic constraints with the objective of minimizing the number of workers is proposed. Upon observing the limitation of the IP, a Constraint Programming (CP) model that is based on CPLEX CP Optimizer is developed to solve larger assembly line balancing problems. Data from an automotive OEM are used to assess the performance of both the MIP and CP models. Using the OEM data, we show that the CP model outperforms the IP model for bigger problems. A sensitivity analysis is done to assess the cost of enforcing some of the constraint on the computation complexity and the amount of violations to these constraints once they are disabled. Results show that some of the constraints are helpful in reducing the computation time. Specifically, the assignment constraints in which decision variables are fixed or bounded result in a smaller search space. Finally, since the line balance for mixed model is based on task duration averages, we propose a mixed model sequencing model that minimize the number of overload situation that might occur due to variability in tasks times by providing an optimal production sequence. We consider the skip-policy to manage overload situations and allow interactions between stations via workers swimming. An IP model formulation is proposed and a GRASP solution heuristic is developed to solve the problem. Data from the literature are used to assess the performance of the developed heuristic and to show the benefit of swimming in reducing work overload situations

Balancing of mixed-model parallel U-shaped assembly lines considering model sequences

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this record.As a consequence of increasing interests in customised products, mixed-model lines have become the most significant components of today’s manufacturing systems to meet surging consumer demand. Also, U-shaped assembly lines have been shown as the intelligent way of producing homogeneous products in large quantities by reducing the workforce need thanks to the crossover workstations. As an innovative idea, we address the mixed-model parallel U-shaped assembly line design which combines the flexibility of mixed-model lines with the efficiency of U-shaped lines and parallel lines. The multi-line stations utilised in between two adjacent lines provide extra efficiency with the opportunity of assigning tasks into workstations in different combinations. The new line configuration is defined and characterised in details and its advantages are explained. A heuristic solution approach is proposed for solving the problem. The proposed approach considers the model sequences on the lines and seeks efficient balancing solutions for their different combinations. An explanatory example is also provided to show the sophisticated structure of the studied problem and explain the running mechanism of the proposed approach. The results of the experimental tests and their statistical analysis indicated that the proposed line design requires fewer number of workstations in comparison with independently balanced mixed-model U-lines

Reactive approach for automating exploration and exploitation in ant colony optimization

Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. Exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is an alternative technique to maintain the dynamism of the mechanics. However, ACO-based reactive search technique has three (3) problems. First, the memory model to record previous search regions did not completely transfer the neighborhood structures to the next iteration which leads to arbitrary restart and premature local search. Secondly, the exploration indicator is not robust due to the difference of magnitudes in distance matrices for the current population. Thirdly, the parameter control techniques that utilize exploration indicators in their feedback process do not consider the problem of indicator robustness. A reactive ant colony optimization (RACO) algorithm has been proposed to overcome the limitations of the reactive search. RACO consists of three main components. The first component is a reactive max-min ant system algorithm for recording the neighborhood structures. The second component is a statistical machine learning mechanism named ACOustic to produce a robust exploration indicator. The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. The performance of RACO is evaluated on traveling salesman and quadratic assignment problems and compared with eight metaheuristics techniques in terms of success rate, Wilcoxon signed-rank, Chi-square and relative percentage deviation. Experimental results showed that the performance of RACO is superior than the eight (8) metaheuristics techniques which confirmed that RACO can be used as a new direction for solving optimization problems. RACO can be used in providing a dynamic exploration and exploitation mechanism, setting a parameter value which allows an efficient search, describing the amount of exploration an ACO algorithm performs and detecting stagnation situations

Minimisation des retards dans le séquencement des véhicules sur une ligne d'assemblage multi modèles

National audienceDans cet article, nous considérons le problème du séquencement sur une ligne d'assemblage à transport continu de véhicules industriels. Pour équilibrer au mieux la charge dynamique, nous proposons de minimiser les retards à l'issue de chaque véhicule. Nous proposons une formalisation par un modèle de type programmation linéaire. Le modèle est testé sur des instances du cas d'étude de l'usine de montage de Renault Trucks à Bourg en Bresse

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

CSP Dinámico: Un algoritmo dinámico para la resecuenciación en un almacén de líneas en paralelo

[EN] This paper shows the model used for resequencing where a selectivity bank consisting of parallel line segments is applied to reshuffle a given initial sequence and rule violations are to be minimized.[ES] En el presente trabajo se muestra el modelo utilizado para la resecuenciación en una línea de ensamblaje con mezcla de modelos donde un almacén con configuración en líneas en paralelo se utiliza para resecuenciar y minimizar la violación de restricciones de la línea de montaje.El presente trabajo se ha desarrollado gracias a la ayuda DPI2010-18243 del Ministerio de Ciencia e Innovación del Gobierno de España dentro del programa de Proyectos de Investigación Fundamental no orientada, con el título "Coordinación de operaciones en redes de suministro/demanda ajustadas, resilientes a la incertidumbre: modelos y algoritmos para la gestion de la incertidumbre y la complejidad". Este trabajo también ha sido financiado parcialmente a partir del proyecto DPI2011-27633 y título “Programacion de produccion en cadenas de suministro sincronizadas multietapa con ensamblajes/desensamblajes con renovacion constante de productos en un contexto de innovación”Valero-Herrero, M.; Molina Morte, P. (2013). CSP Dinámico: Un algoritmo dinámico para la resecuenciación en un almacén de líneas en paralelo. Working Papers on Operations Management. 4(1):23-33. https://doi.org/10.4995/wpom.v4i1.1234SWORD233341Inman, R. R., & Schmeling, D. M. (2003). Algorithm for agile assembling-to-order in the automotive industry. International Journal of Production Research, 41(16), 3831-3848. doi:10.1080/00207540310001595792Jayaraman, A., Narayanaswamy, R., & Gunal, A. K. (1997). A Sortation System Model, in Simulation Conference, 1997., Proceedings of the 1997 Winter, pp. 866-871.Kittithreerapronchai, O. & Anderson, C. (2003). Do ants paint trucks better than chickens? Markets versus response thresholds for distributed dynamic scheduling, in Evolutionary Computation, 2003. CEC '03. The 2003 Congress on, pp. 1431-1439.Valero-Herrero, M., Garcia-Sabater, J. P., & Maheut, J. (2011b). An approach to the real circumstances of the car sequencing problem, in 41st International Conference on Computers and Industrial Engineering

New matrix methodology for algorithmic transparency in assembly line balancing using a genetic algorithm

© 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/This article focuses on the Mixed-Model Assembly Line Balancing single-target problem of type 2 with single-sided linear assembly line configurations, which is common in the industrial environment of small and medium-sized enterprises (SMEs). The main objective is to achieve Algorithmic Transparency (AT) when using Genetic Algorithms for the resolution of balancing operation times. This is done by means of a new matrix methodology that requires working with product functionalities instead of product references. The achieved AT makes it easier for process engineers to interpret the obtained solutions using Genetic Algorithms and the factors that influence decisions made by algorithms, thereby helping in the later decision-making process. Additionally, through the proposed new matrix methodology, the computational cost is reduced with respect to the stand-alone use of Genetic Algorithms. The AT produced using the new matrix methodology is validated through its application in an industry-based paradigmatic example.Peer ReviewedPostprint (published version