ارايه‌ي يك مدل تركيبي برنامه‌ريزي توليد ـ كنترل موجودي غير قطعي زنجيره‌ي تامين حلقه‌بسته

Production Planning Model for Closed-Loop Supply Chain with Uncertainty and Inventory Analysis

در اين پژوهش يك مدل غير قطعي زنجيره‌ي تامين با حلقه‌ي بسته، توسعه داده شده است. تابع هدف در اين مدل، بيشينه‌سازي سود كل زنجيره با در نظر گرفتن هزينه‌هاي توليد، بازتوليد، برگشت، موجودي، برون‌سپاري (توليد، بازتوليد، برگشت)، دورريز و بازيافت است. به‌دليل در نظر گرفتن هزينه‌هاي موجودي، مدل مذكور چنددوره‌يي است و سعي شده نسبت به مدل‌هاي قبل كمي به واقعيت نزديك‌تر ‌باشد. براي يافتن جواب بهينه در اين مدل، از روش الگوريتم ژنتيك استفاده شده است. در انتها نيز چندين مثال عددي با اين روش حل شده كه نتايج آن بيان‌گر كارايي خوب رويكرد پيشنهادي و قابليت‌ها و فوايد آن در تصميم‌گيري‌هاي ميان‌مدت است.

Green supply chain management (GSCM) has been growing in recent years with interest from both academia and industry. A preponderance of special issues devoted to this topic in leading operations and supply chain management (SCM) journals attests to this trend. The closed loop supply chain (CLSC) is a branch of GSCM which includes recycling and remanufacturing activities for reasons of environment protection. CLSC has attracted growing attention in recent years, due to the scarcity of natural resources and increased environmental concerns. In this paper, a multi period model is proposed which contains production planning and inventory control in a closed loop system. This model is developed from the work of Shi et al. (2011), which considers return flow and uncertainty for production planning in a closed loop system. In the proposed model, we have added inventory control, pricing, land filling, recycling and out sourcing (manufacturing, remanufacturing and return) to the former model. Since this model considers inventory control, it should be a multi period model. For decreasing the complexity of the model, in this paper, it is assumed that only one product can be produced. But, it can be used for multi products too. It is assumed that a percentage of used products can be remanufactured but all can be recycled. The genetic algorithm (GA) is suggested for solving the proposed model. Some random examples are solved by GA for illustrating the proposed model and the solving approach. For evaluating the solving approach, one of the modelʹs constraints is eliminated and the example solved without that constraint. Hence, the upper limit of the objective function is defined. The results show that the solving approach has a maximum average error of 3.59 percent. Note that the upper limit of the objective function is infeasible for the original example. Hence, GA is an appropriate approach for solving the proposed model.