57,030 research outputs found

    Modeling a complex production line using virtual cells

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    This chapter presents modeling and simulation of a complex multistage multiproduct production line with four closed loop networks configuration, which also act as a virtual cell. This allows for a greater understanding of the functions within the production line through the simplification of the production flow with the addition of buffers between the cells. Virtual cells are crucial in this instance due to the dynamic configuration, which could help production system designers in optimizing the complex configuration of production

    Production planning under dynamic product environment: a multi-objective goal programming approach

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    Production planning is a complicated task that requires cooperation among multiple functional units in any organization. In order to design an efficient production planning system, a good understanding of the environment in terms of customers, products and manufacturing processes is a must. Although such planning exists in the company, it is often incorrectly structured due to the presence of multiple conflicting objectives. The primary difficulty in modern decision analysis is the treatment of multiple conflicting objectives. A formal decision analysis that is capable of handling multiple conflicting goals through the use of priorities may be a new frontier of management science. The objective of this study is to develop a multi objective goal programming (MOGP) model to a real-life manufacturing situation to show the trade-off between different some times conflicting goals concerning customer, product and manufacturing of production planning environment. For illustration, two independent goal priority structures have been considered. The insights gained from the experimentation with the two goal priority structures will guide and assist the decision maker for achieving the organizational goals for optimum utilization of resources in improving companies competitiveness. The MOGP results of the study are of very useful to various functional areas of the selected case organization for routine planning and scheduling. Some of the specific decision making situations in this context are: (i). the expected quality costs and production costs under identified product scenarios, (ii).under and over utilization of crucial machine at different combinations of production volumes, and (iii). the achievement of sales revenue goal at different production volume combinations. The ease of use and interpretation make the proposed MOGP model a powerful communication tool between top and bottom level managers while converting the strategic level objectives into concrete tactical and operational level plans.

    Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

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    Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

    DeSyRe: on-Demand System Reliability

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    The DeSyRe project builds on-demand adaptive and reliable Systems-on-Chips (SoCs). As fabrication technology scales down, chips are becoming less reliable, thereby incurring increased power and performance costs for fault tolerance. To make matters worse, power density is becoming a significant limiting factor in SoC design, in general. In the face of such changes in the technological landscape, current solutions for fault tolerance are expected to introduce excessive overheads in future systems. Moreover, attempting to design and manufacture a totally defect and fault-free system, would impact heavily, even prohibitively, the design, manufacturing, and testing costs, as well as the system performance and power consumption. In this context, DeSyRe delivers a new generation of systems that are reliable by design at well-balanced power, performance, and design costs. In our attempt to reduce the overheads of fault-tolerance, only a small fraction of the chip is built to be fault-free. This fault-free part is then employed to manage the remaining fault-prone resources of the SoC. The DeSyRe framework is applied to two medical systems with high safety requirements (measured using the IEC 61508 functional safety standard) and tight power and performance constraints
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