constrained programming
در نشریات گروه صنایع-
Hubs are facilities to collect arrange and distribute commodities in telecommunication networks, cargo delivery systems, etc. In this paper, an uncapacitated phub center problem in case of single allocation and also multiple allocation will be introduced in which travel times or transportation costs are considered as fuzzy parameters. Then, by proposing new methods, the presented problem is converted to deterministic mixed integer programming (MIP) problems where these methods will be obtained through the implementation of the possibility theory and fuzzy chance-constrained programming. Both possibility and necessity measures are considered separately in the proposed new methods. Finally, the proposed methods are applied on the popular CAB data set. The computational results of our study show that these methods can be implemented for the phub center problem with uncertain frameworks.Keywords: phub center, fuzzy chance, constrained programming, Uncertainty, hub location
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Determination a sequence of extracting ore is one of the most important problems in mine annual production scheduling. Production scheduling affects mining performance especially in a poly-metallic open pit mine with considering the imposed operational and physical constraints mandated by high levels of reliability in relation to the obtained actual results. One of the important operational constraints for optimization is the uniformity of metallic minerals grade after the blending process. This constraint directly affects the performance of the mineral processing plant. The sequence of extracting ore is usually determined by the order of pushbacks, which should be mined. Metallic minerals’ grade in each pushback is stochastic in nature that comes from some statistical errors committed by the sampling. In such situations, decision making about the order of pushbacks for extraction as an exact defined process is not possible. Moreover, the decision-maker should maximize the total Net Present Value NPV as the main objective of mining operations by considering the high performance of mineral processing plant. To deal with such cases, this research proposes a model based on the chance-constrained one-sided goal-programming and the obtained results from this procedure confirms the model’s reliability and correctness.
Keywords: ORE blending, Poly, metallic open pit mine, Chance, constrained programming, Goal, programming -
با توجه به وجود ابهام در فرآیند مالی پروژه ها، مفاهیم فازی مبنای این مقاله را به خود اختصاص داده است و از برنامه ریزی شانس و روش شبیه سازی آنیلینگ به عنوان وسیله ای جهت تعیین ارزش خالص فعلی چند پروژه و درنهایت انتخاب اقتصادی ترین آنها بهره گرفته شده است. در این مقاله، هزینه های سرمایه گذاری و فرآیند مالی خالص سالیانه به صورت فازی براساس مقدار اعتبار درنظر گرفته شد. مدل با استفاده از شبیه سازی آنیلینگ حل و سپس جهت اعتبارسنجی با نتایج حاصل از شبیه سازی فازی براساس الگوریتم ژنتیک و روش شاخه و حد مقایسه گردید. نتایج نشان دهنده این موضوع است که الگوریتم ژنتیک در ابعاد کوچک در مقایسه با شبیه سازی آنیلینگ، دارای خطای کمتر و نتیجه بهتری است.لازم بذکر است که تمامی الگوریتم ها (شبیه سازی فازی و آنیلینگ) با استفاده از نرم افزار MTLAB R2009a در کامپیوتر شخصی با، 2.66GHz نوشته شده اندکلید واژگان: بودجه بندی سرمایه ای، ارزش خالص فعلی، برنامه ریزی شانس، شبیه سازی فازی، شبیه سازی آنیلینگInternational Journal of Industrial Engineering & Production Management, Volume:21 Issue: 3, 2010, P 94With the existence of ambiguity in the financial processes of projects, fuzzy concepts are being implemented into the foundation and essence of the current article. Authors have employed chance constrained programming and simulated annealing as appropriate tools for determining the net present worth value of several projects. At the end, the most economical project was chosen. In this article, the investment expenses and annual net financial processes are considered fuzzy taking the credibility definition into consideration. The proposed model is then solved by simulated annealing program and to check its credibility we have compared the results of our model with the results obtained by the Genetic Algorithm as well as the Branch and Bound routine. The results indicate that for small sized problems, Genetic Algorithm has worked relatively with less error amounts and produces better results. All developed algorithms such as simulated annealing and fuzzy simulation are coded in MATLAB R2009a environment and on the personal computer with266GHZ.Keywords: Capital Budgeting, net present value, Chance, Constrained Programming, Fuzzy Simulation, Simulated Annealing
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