دانلود مقاله ISI انگلیسی شماره 88788
ترجمه فارسی عنوان مقاله

یک مدل برنامه ریزی تولید انبوه پایدار برای صنایع فرایند شیمیایی

عنوان انگلیسی
A sustainable aggregate production planning model for the chemical process industry
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
88788 2018 38 صفحه PDF
منبع

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

Journal : Computers & Operations Research, Volume 94, June 2018, Pages 154-168

ترجمه کلمات کلیدی
تولید پایدار، برنامه ریزی کامل سیستم پشتیبانی تصمیم، شبکه های عصبی صنعت فرایند شیمیایی،
کلمات کلیدی انگلیسی
Sustainable production; Aggregate planning; Decision support system; Queuing networks; Chemical process industry;
پیش نمایش مقاله
پیش نمایش مقاله  یک مدل برنامه ریزی تولید انبوه پایدار برای صنایع فرایند شیمیایی

چکیده انگلیسی

Process industries typically involve complex manufacturing operations and thus require adequate decision support for aggregate production planning (APP). In this paper, we focus on two relevant features of APP in process industry operations: (i) sustainable operations planning involving multiple alternative production modes/routings with specific production-related carbon emission and the social dimension of varying operating rates, (ii) integrated campaign planning with the operational level in order to anticipate production mix/volume/routing decisions on campaign lead times and WIP inventories as well as the impact of variability originating from a stochastic manufacturing environment. We focus on the issue of multi-level chemical production processes and highlight the mutual trade-offs along the triple bottom line concerning economic, environmental and social factors. To this end, production-related carbon emission and overtime working hours are considered as externalized factors as well as internalized ones in terms of resulting costs. A hierarchical decision support tool is presented that combines a deterministic linear programming model and an aggregate stochastic queuing network model. The approach is exemplified at a case example from the chemical industry to illustrate managerial insights and methodological benefits of our approach.