Development and Numerical Optimization of a System of Integrated Agents for Serial Production Lines

In modern high-volume industries, the serial production line (SPL) is of growing importance due to the inexorable increase in the complexity of manufacturing systems and the associated production costs. Optimal decisions regarding buffer size and the selection of components when designing and implementing an SPL can be difficult, often requiring complex analytical models, which can be difficult to conceive and construct. Here, we propose a model to evaluate and optimize the design of an SPL, integrating numerical simulation with artificial intelligence (AI). Numerous studies relating to the design of SPL systems have been published, but few have considered the simultaneous consideration of a number of decision variables. Indeed, the authors have been unable to locate in the published literature even one work that integrated the selection of components with the optimization of buffer sizes into a single framework. In this research, a System of Integrated Agents Numerical Optimization (SIGN) is developed by which the SPL design can be optimized. A SIGN consists of a components selection system and a decision support system. A SIGN aids the selection of machine tools, buffer sizes, and robots via the integration of AI and simulations. Using a purpose-developed interface, a user inputs the appropriate SPL parameters and settings, selects the decision-making and optimization techniques to use, and then displays output results. It will be implemented in open-source software to broaden the impact of the SIGN and extend its influence in industry and academia. It is expected that the results of this research project will significantly influence open-source manufacturing system design and, consequently, industrial and economic development

Throughput enhancement with parallel redundancy in multi-product flow line system

We develop a new analytic approximation method to replace a set of parallel machines by an equivalent machine in a series-parallel flow line with finite buffer. We develop our method based on discrete state Markov chain. The proposed technique replaces a set of parallel machines at a work centre by an equivalent machine in order to obtain a traditional flow line with machines in series separated by intermediate buffers. We derive equations for the parameters of the equivalent machine when it operates in isolation as well as in flow line. The existing analytic methods for series-parallel systems can tract only lines with a maximum of two machines in series and a buffer in-between them. The method we propose in this thesis can be used in conjunction with an approximation method or simulation to solve flow lines of any length. We also model and evaluate the performance of series-parallel systems manufacturing more than one product types with predefined sequence and lot size. We address this issue for a considerable longer flow line system with finite buffer which is common in industry. We consider the set-up time of the machines as the product type changes, deterministic processing times and operation dependent failures of the machines. We analyze the effects of buffer and number of machines in parallel on the performance of series-parallel systems

Adaptive policy of buffer allocation and preventive maintenance actions in unreliable production lines

Abstract The buffer allocation problem is an NP-hard combinatorial optimization problem, and it is an important design problem in manufacturing systems. The research proposed in this paper concerns a product line consisting of n unreliable machines with n − 1 buffers and a preventive maintenance policy. The focus of the research is to obtain a better trade-off between the buffer level and the preventive maintenance actions. This paper proposes a dynamic control of the buffers' level and the interval between two consecutive preventive actions. The set of the parameter of the proposed policy allows choosing the reduction in the costs or the increment of the throughput rate. A simulation model is developed to test the proposed model to the solution proposed in the literature. The proposed policy leads to better results in terms of total costs reduction keeping high production rate, while the design of a fixed level of buffer works better for lower production rates required

Modeling and Performance Evaluation of Multistage Serial Manufacturing Systems with Rework Loops and Product Polymorphism

This paper studies multistage serial manufacturing systems with the integrated consideration of machine failures, process defects, multiple rework loops, etc. In particular, multiple rework loops and product polymorphism lead to a more complex conversion of internal material flows, and therefore it's difficult to model and analyse such manufacturing systems. A modular modeling method based on Generalized Stochastic Petri Nets (GSPN) is presented to characterize the material flows, it is capable of representing the processing differences resulting from product polymorphism comparing with traditional Markov model or Queuing network model. By analysing the model, the processing ratio of each workstation is inferred. Using 2M1B (two-machine and one-buffer) Markov cell model as the building blocks, which is obtained based on the GSPN models for their isomorphism, an overlapping decomposition method is then developed for evaluating the performance of the multistage serial systems with rework loops. Numerical experiments and a case study of a powertrain assembly line illustrate the efficiency of the proposed method

Hybrid metaheuristic approach GA-SA for the buffer allocation problem that minimizes the work in process in open serial production lines

[EN] The Buffer Allocation Problem (BAP) is a problem of combinatorial NP-Hard optimization in the design of production lines. This consists of defining the allocation of storage places (buffers) within a production line, in order to maximize the efficiency of the process. The methods of optimization have been reported with greater success in recent years are metaheuristic techniques. In this work, a hybrid approach is proposed that uses the metaheuristic techniques of Genetic Algorithms (GA) and Simulated Annealing (SA), with the objective of determining the required buffers that minimize the average work in process (WIP) in open serial production lines M/M/1/K. The evaluation is carried out with an analytical method of decomposition. The results obtained demonstrate the computational efficiency of the proposed hybrid algorithm with respect to a simple SA or GA.[ES] El problema de asignación del buffer (BAP, por sus siglas en inglés) es clasificado como un problema de optimización combinatorio NP-Duro en el diseño de las líneas de producción. Éste consiste en definir la asignación de lugares de almacenamiento (buffers) dentro de una línea de producción, con el fin de aumentar al máximo la eficiencia del proceso. Los métodos de optimización que han sido reportados con mayor éxito en los últimos años son las técnicas metaheurísticas. En este trabajo, se propone un enfoque híbrido que utiliza las técnicas metaheurísticas de: Algoritmos Genéticos (AG) y Recocido Simulado (RS), con el objetivo de determinar los buffers requeridos que minimicen el promedio de inventario en proceso (WIP, por sus siglas en inglés) en líneas de producción abiertas en serie M/M/1/K. La evaluación se realiza con un método analítico de descomposición. Los resultados obtenidos demuestran la eficiencia computacional del algoritmo híbrido propuesto con respecto a un RS o AG estándar.Se agradece al Consejo Nacional de Ciencia y Tecnología (CONACYT) por el financiamiento de esta investigación con número de registro CVU: 375571.Hernández-Vázquez, JO.; Hernández-González, S.; Jiménez-García, JA.; Hernández-Ripalda, MD.; Hernández-Vázquez, JI. (2019). Enfoque híbrido metaheurístico AG-RS para el problema de asignación del buffer que minimiza el inventario en proceso en líneas de producción abiertas en serie. Revista Iberoamericana de Automática e Informática. 16(4):447-458. https://doi.org/10.4995/riai.2019.10883SWORD447458164Amiri, M., & Mohtashami, A. (2011). Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm. 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An Analytical Approach to Cycle Time Evaluation in an Unreliable Multi-Product Production Line with Finite Buffers

This thesis develops an analytical approximation method to measure the performance of a multi-product unreliable production line with finite buffers between workstations. The performance measure used in this thesis is Total Cycle Time. The proposed approximation method generalizes the processing times to relax the variation of product types in a multi-product system. A decomposition method is then employed to approximate the production rate of a multi-product production line. The decomposition method considers generally distributed processing times as well as random failure and repair. A GI/G/1/N queuing model is also applied to obtain parameters such as blocking and starving probabilities that are needed for the approximation procedure. Several numerical experiments under different scenarios are performed, and results are validated by simulation models in order to assess the accuracy and strength of the approximation method. Consequent analysis and discussion of the results is also presented

Evaluation of Pull Production Control Strategies Under Uncertainty: An Integrated Fuzzy Ahp-Topsis Approach

Purpose: Just-In-Time (JIT) production has continuously been considered by industrial practitioners and researchers as a leading strategy for the yet popular Lean production. Pull Production Control Policies (PPCPs) are the major enablers of JIT that locally control the level of inventory by authorizing the production in each station. Aiming to improve the PPCPs, three authorization mechanisms: Kanban, constant-work-in-process (ConWIP), and a hybrid system, are evaluated by considering uncertainty. Design/methodology/approach: Multi-Criteria Decision Making (MCDM) methods are successful in evaluating alternatives with respect to several objectives. The proposed approach of this study applies the fuzzy set theory together with an integrated Analytical Hierarchy Process (AHP) and a Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. Findings: The study finds that hybrid Kanban-ConWIP pull production control policies have a better performance in controlling the studied multi-layer multi-stage manufacturing and assembly system. Practical implications: To examine the approach a real case from automobile electro-mechanical part production industry is studied. The production system consists of multiple levels of manufacturing, feeding a multi-stage assembly line with stochastic processing times to satisfy the changing demand. Originality/value: This study proposes the integrated Kanban-ConWIP hybrid pull control policies and implements several alternatives on a multi-stage and multi-layer manufacturing and assembly production system. An integrated Fuzzy AHP TOPSIS method is developed to evaluate the alternatives with respect to several JIT criteriaPeer Reviewe

MAXIMIZING THROUGHPUT USING DYNAMIC RESOURCE ALLOCATION AND DISCRETE EVENT SIMULATION

This research studies a serial two stage production system with two flexible servers which can be dynamically assigned to either station. This is modeled using discrete event simulation and more specifically the Arena software package by Rockwell. The goal is to determine dynamic allocation policies based upon the inventory level at each station to maximize the throughput of finished goods out of the system. This model adds to previous work by including actual switching time. The effect of the pre-emptive resume assumption is gauged, and the effectiveness of the OptQuest optimization package is also tested. Studies are conducted to determine the throughput of the system using easily implementable heuristics including when workers are together and separate. Additionally, the effect of buffer allocation and buffer sizing are studied, and it is shown that buffer allocation is not sensitive to changes in buffer ratio as long as there is buffer space available at each station while adding buffer space has a diminishing rate of return