Performance evaluation of the remanufacturing system prone to random failure and repair

Implementation of new environmental legislation and public awareness has increased the responsibility of manufacturers. Remanufacturing has been applied in many industries and sectors since its introduction. However, only 10% to 20% of the returned products pass through the remanufacturing process, and the remaining products are disposed in the landfills. Uncertainties like high failure rates of the servers, buffer capacities, and inappropriate preventive maintenance policies would be responsible for most of the delays in remanufacturing operations. In this paper, a simulation-based experimental methodology is used to determine the optimal preventive maintenance frequency and buffer allocation in a remanufacturing line. Moreover, an estimated relationship between preventive maintenance frequency and Mean Time Between Failure (MTBF), is presented to determine the best preventive maintenance frequency. The solution approach is applied to computer remanufacturing industry. Analysis of variance (ANOVA), and regression analysis are performed to denote the most influential factors to remanufacturing cycle time (performance measures). A case study is used to show the applicability of the modelling approach in assessing and improving the cycle time, and the profit of a remanufacturing line . Managerial insights are highlighted to support managers and decision-makers in their quest for more efficient and smooth operation of the remanufacturing system

Multi-objective analysis of the buffer allocation problem with simulation meta-models and a hybrid metaheuristic

[EN] This article presents a multi-objective formulation of the buffer allocation problem (BAP) in a serial-parallel production line, which aims to maximize the throughput rate and minimize the total cost of the allocation of buffers. Three case studies involving operating conditions are analyzed: reliable, unreliable and reprocesses. Process times, times between failures and repair times, consider distribution functions: Exponential, Normal and Weibull. The evaluation method used in this document implies simulation meta-models constructed from experiment designs and production line simulations. On the other hand, the optimization method implemented is a hybrid metaheuristic of Genetic Algorithms and Simulated Annealing. The results report the allocation of buffers in the case studies, their impact on the objectives and the computational efficiency of the proposed hybrid algorithm.[ES] Este artículo presenta una formulación multi-objetivo del problema de asignación del buffer (BAP, por sus siglas en inglés) en una línea de producción paralela en serie, que pretende maximizar la tasa promedio de producción y minimizar el costo total de la asignación de buffers. Se analizan tres casos de estudio que involucran condiciones de operación: confiables, no confiables y reprocesos. Los tiempos de proceso, tiempos entre fallas y tiempos de reparación, consideran funciones de distribución: Exponencial, Normal y Weibull. El método de evaluación empleado en este documento, implica meta-modelos de simulación construidos a partir de diseños de experimentos y simulaciones de la línea de producción; por su parte, el método de optimización implementado, es una metaheurística híbrida de Algoritmos Genéticos (AG) y Recocido Simulado (RS). Los resultados reportan la asignación de buffers en los casos de estudio, su impacto en los objetivos y la eficiencia computacional del algoritmo híbrido propuesto.Se agradece al Consejo Nacional de Ciencia y Tecnología (CONACYT) por el financiamiento de esta investigación con número de registro CVU: 375571; y al Tecnológico Nacional de México / Instituto Tecnológico de Celaya, por el apoyo brindado. Finalmente, un reconocimiento a Juana Cinthia Lizbeth Nava Torres, Rafael Paniagua Soto y Juan Pablo Gallardo Ochoa por su ayuda en la fase de programación.Hernández-Vázquez, JO.; Hernández-González, S.; Hernández-Vázquez, JI.; Jiménez-García, JA.; Hernández-Ripalda, MD. (2022). Análisis multi-objetivo del problema de asignación del buffer con meta-modelos de simulación y una metaheurística híbrida. Revista Iberoamericana de Automática e Informática industrial. 19(2):221-232. https://doi.org/10.4995/riai.2021.1573122123219

An Optimization of Multi-product Assembly Lines Using Simulation and Multi-Objective Programming Approach

This paper investigates unreliable multi-product assembly lines with mixed (serial-parallel) layout model in which machines failures and repairing probabilities are considered. The aim of this study is to develop a multi-objective mathematical model consisting the maximization of the throughput rate of the system and the minimization of the total cost of reducing mean processing times and the total buffer capacities with respect to the optimal values of the mean processing time of each product in each workstation and the buffer capacity between workstations. For this purpose, in order to configure the structure of the mathematical model, Simulation, Design of Experiments and Response Surface Methodology are used and to solve it, the meta-heuristic algorithms including Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Non-Dominated Ranked Genetic Algorithm (NRGA) are implemented. The validity of the multi-objective mathematical model and the application of the proposed methodology for solving the model is examined on a case study. Finally, the performance of the algorithms used in this study is evaluated. The results show that the proposed multi-objective mathematical model is valid for optimizing unreliable production lines and has the ability to achieve optimal (near optimal) solutions in other similar problems with larger scale and more complexity.IntroductionA production line consists of a sequence of workstations, in each of which parts are processed by machines. In this setup, each workstation includes a number of similar or dissimilar parallel machines, and a buffer is placed between any two consecutive workstations. In production lines, the buffer capacity and processing time of machinery have a significant impact on the system's performance. The presence of buffers helps the system to maintain production despite possible conditions or accidents, such as machinery failure or changes in processing time. Previous research has investigated production lines without any possibility of machinery failure, referred to as "safe production lines." However, in real production lines, machinery failure is inevitable. Therefore, several studies have focused on "uncertain production lines,"assuming the existence of a probability of failure in a deterministic or exponential distribution. This research examines uncertain production lines with a combined layout, resulting from the combination of parallel deployment of machines within each workstation, if necessary, and serial deployment of workstations. The objective of this research is to determine the optimal values (or values close to optimal) of the average processing time of each product in each workstation, as well as the volume of buffers, as decision variables. The approach aims to maximize the system's output while minimizing the costs associated with reducing the processing time of workstations and minimizing the total volume of buffers between stations. Moreover, simulation can be applied without interrupting the production line or consuming significant resources. In this research, due to the high cost and time involved, implementing the proposed changes on the system is not cost-effective for investigating the changes in the production system's output rate. Therefore, the simulation technique has been utilized to optimize the production line.Research methodThe present study aims to develop a multi-objective mathematical model, based on simulation, to optimize multi-product production lines. In the first step, the structure of the multi-objective mathematical model is defined, along with the basic assumptions. To adopt a realistic approach in the model structure, the simulation technique has been employed to address the first objective function, which is maximizing the output rate of the production line. To achieve this, the desired production system is simulated. The design of experiments is used to generate scenarios for implementation in the simulated model, and the response surface methodology is utilized to analyze the relationship between the input variables (such as the average processing time of each product type in each workstation and the buffer volume between stations) and the response variable (production rate).ResultsTo implement the proposed methodology based on the designed multi-objective programming model, a case study of a three-product production line with 9 workstations and 8 buffers was conducted. Subsequently, to compare the performance of the optimization algorithms, five indicators were used: distance from the ideal solution, maximum dispersion, access rate, spacing, and time. For this purpose, 30 random problems, similar to the mathematical model of the case study, were generated and solved. Based on the results obtained, both algorithms exhibited similar performance in all indices, except for the maximum dispersion index.ConclusionsIn this article, the structure of a multi-objective mathematical model was sought in uncertain multi-product production lines with the combined arrangement of machines in series-parallel (parallel installation of machines in workstations if needed and installation of workstations in series). The objective was to determine the optimal values of the average processing time of each type of product in each workstation and the buffer volume of each station, with the goals of maximizing the production rate, minimizing the costs resulting from reducing the processing time, and the total volume of inter-station buffers simultaneously. To investigate the changes in the output rate of the production system, due to the high cost and time, it was deemed not cost-effective to implement the proposed changes on the system. Therefore, the combination of simulation techniques, design of experiments, and response surface methodology was used to fit the relevant metamodel. In the proposed approach of this research, taking a realistic view of production line modeling, the probability of machinery failure, as well as the possibility of repairability and return to the system, were considered in the form of statistical distribution functions. Additionally, all time parameters, including the arrival time between the parts, the start-up time of all the machines, the processing time, the time between two failures, and the repair time of the machines, were non-deterministic and subject to statistical distributions. Finally, to solve the structured mathematical model, two meta-heuristic algorithms (NSGA-II) and (NRGA) were considered

Performance Evaluation of Remanufacturing Systems

Implementation of new environmental legislation and public awareness has increased the responsibility on manufacturers. These responsibilities have forced manufacturers to begin remanufacturing and recycling of their goods after they are disposed or returned by customers. Ever since the introduction of remanufacturing, it has been applied in many industries and sectors. The remanufacturing process involves many uncertainties like time, quantity, and quality of returned products. Returned products are time sensitive products and their value drops with time. Thus, the returned products need to be remanufactured quickly to generate the maximum revenue. Every year millions of electronic products return to the manufacturer. However, only 10% to 20% of the returned products pass through the remanufacturing process, and the remaining products are disposed in the landfills. Uncertainties like failure rate of the servers, buffer capacity and inappropriate preventive maintenance policy would be highly responsible the delays in remanufacturing. In this thesis, a simulation based experimental methodology is used to determine the optimal preventive maintenance frequency and buffer allocation in a remanufacturing line, which will help to reduce the cycle time and increase the profit of the firm. Moreover, an estimated relationship between preventive maintenance frequency and MTBF (Mean Time Between Failure) is presented to determine the best preventive maintenance frequency for any industry. The solution approach is applied to a computer remanufacturing and a cell phone remanufacturing industry. Analysis of variance and regression analysis are performed to denote the influential factors in the remanufacturing line, and optimization is done by using the regression techniques and ANOVA results

A Linear Programming Approach for Performance Evaluation of Multi-Type Production Lines Applied in Manufacturing Strategies Comparison

This research work develops an analytical approach to calculate the cycle time for each type of products in a multi-type serial production line with finite intermediate buffers and stochastic processing times. I consider the stochastic variables follow a certain distribution with mean and variance. The basic idea is to solve a linear programming approach modeling a production line operating with batch sized arrivals of different types of products and the cycle time can be found based on the batch restriction. A simulation model is created to test the relevance of the analytical approach and validate the proposed method\u27s correctness. Scenarios such as switching processing from one product type to another without setup, with setup, and failure and repair are considered separately and comprehensive experiments combining these scenarios together are conducted as well. The failure and repair situation is stochastic as well. Experiment results are shown to validate the efficiency of the method. Periodic sampling approach is explored and considered while tackling these manufacturing strategies