دانلود مقاله ISI انگلیسی شماره 46804
ترجمه فارسی عنوان مقاله

شبیه سازی ترکیبی و الگوریتم هیوریستیک بر اساس MIP برای برنامه ریزی تولید و توزیع در صنعت نوشابه

عنوان انگلیسی
Hybrid simulation and MIP based heuristic algorithm for the production and distribution planning in the soft drink industry
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
46804 2014 15 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Manufacturing Systems, Volume 33, Issue 3, July 2014, Pages 385–399

ترجمه کلمات کلیدی
مدیریت زنجیره تامین - برنامه ریزی تولید و توزیع - برنامه ریزی خطی مخلوط عدد صحیح - رویکرد ترکیبی - شبیه سازی - هیوریستیک نورد افق
کلمات کلیدی انگلیسی
Supply chain management; Production and distribution planning; Mixed integer linear programming; Hybrid approach; Simulation; Rolling horizon heuristics
پیش نمایش مقاله
پیش نمایش مقاله  شبیه سازی ترکیبی و الگوریتم هیوریستیک بر اساس MIP برای برنامه ریزی تولید و توزیع در صنعت نوشابه

چکیده انگلیسی

In this paper, we present a hybrid solution methodology combining simulation and mixed integer programming (MIP) based Fixed and Optimize heuristic to solve the considered problem. First, MIP based Fix and Relax (F&R), Fix and Optimize (F&O) heuristics are proposed. The solution quality and performance of the proposed heuristics are analyzed with the randomly generated demand figures for the three granularity categories and various capacity load scenarios. Computational performances of these heuristic procedures are compared with the standard MIP results. The computational experiments carried out on a large set of instances have shown that the F&O heuristic algorithm provides good quality solutions in a reasonable amount of time. Second, simulation model is introduced to represent the problem with stochastic machine failures. Hybrid methodology combining the MIP based F&O heuristic and simulation model is implemented. The optimization model uses an F&O heuristic to determine the production and delivered quantity. Subsequently the simulation model is applied to capture the uncertainty in the production rate. Numerical studies from the data which have a tight production capacity and high demand granularity demonstrate that the developed hybrid approach is capable of solving real sized instance within a reasonable amount of time and demonstrate the applicability of the proposed approach.