1,663 research outputs found

    Capacity Planning and Resource Allocation in Assembly Systems Consisting of Dedicated and Reconfigurable Lines

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    AbstractCompanies with diverse product portfolio often face capacity planning problems due to the diversity of the products and the fluctuation of the order stream. High volume products can be produced cost-efficiently in dedicated assembly lines, but the assembly of low-volume products in such lines involves high idle times and operation costs. Reconfigurable assembly lines offer reasonable solution for the problem; however, it is still complicated to identify the set of products which are worth to assemble in such a line instead of dedicated ones. In the paper a novel method is introduced that supports the long-term decision to relocate the assembly of a product with decreasing demand from a dedicated to a reconfigurable line, based on the calculated investment and operational costs. In order to handle the complex aspects of the planning problem a new approach is proposed that combines discrete-event simulation and machine learning techniques. The feasibility of the approach is demonstrated through the results of an industrial case study

    Capacity management of modular assembly systems

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    Companies handling large product portfolio often face challenges that stem from market dynamics. Therefore, in production management, efficient planning approaches are required that are able to cope with the variability of the order stream to maintain the desired rate of production. Modular assembly systems offer a flexible approach to react to these changes, however, there is no all-encompassing methodology yet to support long and medium term capacity management of these systems. The paper introduces a novel method for the management of product variety in assembly systems, by applying a new conceptual framework that supports the periodic revision of the capacity allocation and determines the proper system configuration. The framework has a hierarchical structure to support the capacity and production planning of the modular assembly systems both on the long and medium term horizons. On the higher level, a system configuration problem is solved to assign the product families to dedicated, flexible or reconfigurable resources, considering the uncertainty of the demand volumes. The lower level in the hierarchy ensures the cost optimal production planning of the system by optimizing the lot sizes as well as the required number of resources. The efficiency of the proposed methodology is demonstrated through the results of an industrial case study from the automotive sector. © 2017 The Society of Manufacturing Engineer

    Configuration of a Customized Product

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    The chapter discusses problems of the product configuration process and application of chosen methods to represent the knowledge related to this process. One of the most important issues in product life-cycle management is to identify customer needs and combine them with product’s technical and trade characteristics. The main tasks related to product configuration are focused on identifying the most suitable product to a particular customer, product decomposition, and estimating product characteristics. In the presented approach, identification of customer needs was discussed, and a product decomposition method was presented. The quality function deployment (QFD) method was suggested to be applied as a product and production process data integration tool, where engineering characteristics of a product are combined with its trade characteristics

    ASlib: A Benchmark Library for Algorithm Selection

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    The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. Demonstrating the breadth and power of our platform, we describe a set of example experiments that build and evaluate algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.Comment: Accepted to be published in Artificial Intelligence Journa

    Evaluating a computational support tool for set-based configuration of production systems:Results from an industrial case

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    This paper describes research conducted in the context of an industrial case dealing with the design of re configurable cellular manufacturing systems. Reconfiguring such systems represents a complex task due to the interdependences between the constituent subsystems. A novel computational tool was developed to support the production engineers in (sub) system configuration by enabling to consider multiple alternative configurations simultaneously. The tool was tested by applying it in two realistic system engineering problems and conducting interviews to evaluate its effects. The prototype was found to be an effective and efficient approach to support exploring evaluating and selecting sets of system configurations. The findings suggest that the approach is applicable in practice and represents a means to strategically leverage the flexibility in production system design as well as to improve the efficiency of the engineering process. Hence further research could examine if the approach is useful in additional systems engineering domains

    Co-Evolution of Product Families and Assembly Systems.

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    To remain competitive in the midst of global competition and rapidly changing consumer tastes, manufacturers increased the amount of product variety they offer to the market and their responsiveness to changing market needs. Product families and reconfigurable manufacturing systems (RMS) enable manufacturers to cost effectively supply high product variety and to be responsive. However, there is a lack of systematic methods for the joint design of product families and RMSs. Co-evolution of product families and assembly systems is introduced as a new method for the joint design and reconfiguration of product families and assembly systems within and across product generations. There are two main phases in the co-evolution methodology. The first phase involves the joint design of a product family and assembly system in the first generation. The second phase involves the joint evolution of the product family and assembly system between product generations. For co-evolution, models are required for: (i) the joint design of a product family and its corresponding assembly system, (ii) the evolution of the product family within the constraints of an assisting assembly system, and (iii) the reconfiguration of the assembly system. Methodologies are introduced for the first and third problems in this dissertation. Two non-linear integer programming (INLP) formulations are developed for the problem of the concurrent design of a single generation of a product family and assembly system. The objective of the first formulation is to maximize the efficiency of the assembly system and minimize the oversupply of functionality. The objective of the second formulation is to maximize profits. Genetic algorithms are introduced for the solution of these problems. The assembly system reconfiguration planning (ASRP) problem is also formulated as INLP. Genetic algorithm and dynamic programming procedures are introduced for the solution of this problem. In addition, an algorithm for generating all the possible assembly system configurations is introduced. Examples are used to demonstrate how the methods for co-evolution introduced in this dissertation can lead to reduced costs and increased responsiveness to market changes.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60824/1/abryan_1.pd

    CHANGE-READY MPC SYSTEMS AND PROGRESSIVE MODELING: VISION, PRINCIPLES, AND APPLICATIONS

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    The last couple of decades have witnessed a level of fast-paced development of new ideas, products, manufacturing technologies, manufacturing practices, customer expectations, knowledge transition, and civilization movements, as it has never before. In today\u27s manufacturing world, change became an intrinsic characteristic that is addressed everywhere. How to deal with change, how to manage it, how to bind to it, how to steer it, and how to create a value out of it, were the key drivers that brought this research to existence. Change-Ready Manufacturing Planning and Control (CMPC) systems are presented as the first answer. CMPC characteristics, change drivers, and some principles of Component-Based Software Engineering (CBSE) are interwoven to present a blueprint of a new framework and mind-set in the manufacturing planning and control field, CMPC systems. In order to step further and make the internals of CMPC systems/components change-ready, an enabling modeling approach was needed. Progressive Modeling (PM), a forward-looking multi-disciplinary modeling approach, is developed in order to modernize the modeling process of today\u27s complex industrial problems and create pragmatic solutions for them. It is designed to be pragmatic, highly sophisticated, and revolves around many seminal principles that either innovated or imported from many disciplines: Systems Analysis and Design, Software Engineering, Advanced Optimization Algorisms, Business Concepts, Manufacturing Strategies, Operations Management, and others. Problems are systemized, analyzed, componentized; their logic and their solution approaches are redefined to make them progressive (ready to change, adapt, and develop further). Many innovations have been developed in order to enrich the modeling process and make it a well-assorted toolkit able to address today\u27s tougher, larger, and more complex industrial problems. PM brings so many novel gadgets in its toolbox: function templates, advanced notation, cascaded mathematical models, mathematical statements, society of decision structures, couplers--just to name a few. In this research, PM has been applied to three different applications: a couple of variants of Aggregate Production Planning (APP) Problem and the novel Reconfiguration and Operations Planning (ROP) problem. The latest is pioneering in both the Reconfigurable Manufacturing and the Operations Management fields. All the developed models, algorithms, and results reveal that the new analytical and computational power gained by PM development and demonstrate its ability to create a new generation of unmatched large scale and scope system problems and their integrated solutions. PM has the potential to be instrumental toolkit in the development of Reconfigurable Manufacturing Systems. In terms of other potential applications domain, PM is about to spark a new paradigm in addressing large-scale system problems of many engineering and scientific fields in a highly pragmatic way without losing the scientific rigor
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