6,805 research outputs found

    Survey of dynamic scheduling in manufacturing systems

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    A reusable iterative optimization software library to solve combinatorial problems with approximate reasoning

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    Real world combinatorial optimization problems such as scheduling are typically too complex to solve with exact methods. Additionally, the problems often have to observe vaguely specified constraints of different importance, the available data may be uncertain, and compromises between antagonistic criteria may be necessary. We present a combination of approximate reasoning based constraints and iterative optimization based heuristics that help to model and solve such problems in a framework of C++ software libraries called StarFLIP++. While initially developed to schedule continuous caster units in steel plants, we present in this paper results from reusing the library components in a shift scheduling system for the workforce of an industrial production plant.Comment: 33 pages, 9 figures; for a project overview see http://www.dbai.tuwien.ac.at/proj/StarFLIP

    Periodic Review, Push Inventory Policies for Remanufacturing

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    Sustainability has become a major issue in most economies, causing many leading companies to focus on product recovery and reverse logistics. This research is focused on product recovery, and in particular on production control and inventory management in the remanufacturing context. We study a remanufacturing facility that receives a stream of returned products according to a Poisson process. Demand is uncertain and also follows a Poisson process. The decision problems for the remanufacturing facility are when to release returned products to the remanufacturing line and how many new products to manufacture. We assume that remanufactured products are as good as new. In this paper, we employ a "push" policy that combines these two decisions. It is well known that the optimal policy parameters are difficult to find analytically; therefore, we develop several heuristics based on traditional inventory models. We also investigate the performance of the system as a function of return rates, backorder costs and manufacturing and remanufacturing lead times; and we develop approximate lower and upper bounds on the optimal solution. We illustrate and explain some counter-intuitive results and we test the performance of the heuristics on a set of sample problems. We find that the average error of the heuristics is quite low.inventory;reverse logistics;remanufacturing;environment;heuristics

    Techniques for the Fast Simulation of Models of Highly dependable Systems

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    With the ever-increasing complexity and requirements of highly dependable systems, their evaluation during design and operation is becoming more crucial. Realistic models of such systems are often not amenable to analysis using conventional analytic or numerical methods. Therefore, analysts and designers turn to simulation to evaluate these models. However, accurate estimation of dependability measures of these models requires that the simulation frequently observes system failures, which are rare events in highly dependable systems. This renders ordinary Simulation impractical for evaluating such systems. To overcome this problem, simulation techniques based on importance sampling have been developed, and are very effective in certain settings. When importance sampling works well, simulation run lengths can be reduced by several orders of magnitude when estimating transient as well as steady-state dependability measures. This paper reviews some of the importance-sampling techniques that have been developed in recent years to estimate dependability measures efficiently in Markov and nonMarkov models of highly dependable system

    On the alignment of lot sizing decisions in a remanufacturing system in the presence of random yield

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    In the area of reverse logistics, remanufacturing has been proven to be a valu- able option for product recovery. In many industries, each step of the products’ recovery is carried out in lot sizes which leads to the assumption that for each of the different recovery steps some kind of fixed costs prevail. Furthermore, holding costs can be observed for all recovery states of the returned product. Although several authors study how the different lot sizes in a remanufacturing system shall be determined, they do not consider the specificity of the remanufacturing process itself. Thus, the disassembly operations which are always neglected in former analyses are included in this contribution as a specific recovery step. In addition, the assumption of deterministic yields (number of reworkable compo- nents obtained by disassembly) is extended in this work to study the system behavior in a stochastic environment. Three different heuristic approaches are presented for this environment that differ in their degree of sophistication. The least sophisticated method ignores yield randomness and uses the expected yield fraction as certainty equivalent. As a numerical experiment shows, this method already yields fairly good results in most of the investigated problem instances in comparison to the other heuristics which incorporate yield uncertainties. How- ever, there exist instances for which the performance loss between the least and the most sophisticated heuristic amounts to more than 6%.reverse logistics, remanufacturing, lot sizing, disassembly, random yield
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