https://www.econbiz.de/archiv1/2010/107672_sequencing_mixed_model.pdf

Sequencing mixed-model assembly lines: Survey, classification and model critique

Industrial Resources in the design of Reconfigurable Manufacturing Systems for aerospace: A systematic literature review

Variability in products is driven by the customer and pushes the manufacturer to offer product variants by mass customization. Companies that offer product variety while maintaining competitive cost and quality will gain a competitive edge over other companies in today's market. As the automobile industry adapts to the mass customization strategy, it would require the ability to conduct early design, development, and manufacturing trade-offs among competing objectives. An analytical approach is then required to manage the complexity and the risk associated with this environment. This chapter will present a set of simulation-based methodologies for measuring complexity. The developed methodologies will assist designers in analyzing and mitigating the risks associated with product variety and its impact on manufacturing complexity.

Manufacturing Complexity Analysis: A Simulation-Based Methodology

In this paper, we deal with the cyclic scheduling problem. More precisely, we consider the cyclic job shop with assembly tasks. Such a problem is made of several jobs, each job consisting of tasks (assembly/disassembly tasks and transformation tasks) being assigned to machines in a cyclic way. This kind of scheduling problem is well fitted to medium and large production demands, since the cyclic behavior can avoid the scheduling of the whole tasks by considering only a small temporal window (cycle). Thus, cyclic scheduling is a heuristic to solve the scheduling problems whose complexity is NP-hard in the general case. Many methods have been proposed to solve the cyclic scheduling problem. Among them, we focus on the mathematical programming approach. We will propose here a mathematical model for cyclic scheduling with assembly tasks and Work-In-Process minimization, and we illustrate this approach with an example from literature.

/pdf/a-mathematical-model-for-cyclic-scheduling-with-assembly-24tgyf34i6.pdf

A mathematical model for cyclic scheduling with assembly tasks and Work-In-Process minimization

The discrete event simulation is one of the most powerful tools available for modeling and problem analysis. Frequently it is used for planning, designing and controlling complex systems in several segments. The logistic system area has also applied it extensively to analyse the logistic capacities, evaluate the impacts of changes in the productive processes, test different configurations in the allocation of limited resources, determine operation parameters, validate new projects, among other applications. Thus, the growth of the simulation awoke the interest of the companies specialized in software development. This fact resulted in several options with different characteristics and costs. Because of this, it was identified the necessity of a selection method for discrete event simulation software dedicated to the resolution of logistic problems. The available works in Literature only approach the choice of softwares for generic purposes. To fill in this gap, a set of criteria that address the specific necessities of the development of models dedicated to the analysis of logistic systems was proposed. These criteria formed the base of the proposed method. It has incorporated the best characteristics of the works identified in Literature. Its first phase generated a list with softwares with widely recognized efficiency and simplicity that supported the essential criteria through the application of a scoring model. Its second phase used the Analytic Hierarchy Process (AHP) which is recommended for the decision taking based on multiple criteria because it allows the identification and correction of the inconsistencies generated by the human judgment. Two test problems have also been proposed for the development of applications in logistic systems and performance of experiments during the second phase. Arena, ProModel and @Risk, extensively used for education and research in the Polytechnical School of the University of São Paulo, were evaluated during the practical application of the proposed method. The results confirmed the perception acquired during the second phase of the method that Arena and ProModel present similar characteristics. They presented a discrete difference in their total score. The several scenes developed in the sensitivity analysis also ratified this trend.

/pdf/metodo-para-avaliacao-e-selecao-de-softwares-de-simulacao-de-15r1nf0rrr.pdf

Método para avaliação e seleção de softwares de simulação de eventos discretos aplicados à análise de sistemas logísticos.

An Australian automotive component company plans to assemble and deliver seats to customer on just-in-time basis. The company management has decided to model operations of the seat plant to help them make decisions on capital investment and labour requirements. There are four different areas in seat assembly and delivery areas. Each area is modeled independently to optimise its operations. All four areas are then combined into one model called the plant model to model operations of seat plant from assembly to delivery. Discrete event simulation software is used to model the assembly operations of seat plant.

/pdf/modeling-a-just-in-time-plant-using-sequencing-algorithms-3ovqetrdwz.pdf

Modeling a Just-In-Time Plant Using Sequencing Algorithms

An Australian automotive component company plans to assemble and deliver seats to customer on a just-in-time basis. It plans to assemble various seat types on one assembly line. Mixed-model sequencing is very important if a company has to assemble seats in a just-in-time environment. Toyota Motor Company's goal chasing algorithm I and a user defined algorithm are used to sequence seats on the assembly line. Discrete event simulation software is used to model the assembly operations of the seat plant. Both algorithms are programmed to generate a sequence for the seat plant. Model results show both algorithms can sequence seats on the assembly line and each algorithm has its advantages and disadvantages.

/pdf/sequencing-production-on-an-assembly-line-using-goal-chasing-17pitijbb4.pdf

Sequencing production on an assembly line using goal chasing and user defined algorithm

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Modeling a Just-In-Time Plant Using Sequencing Algorithms

Sequencing production on an assembly line using goal chasing and user defined algorithm