در این تحقیق مدلی برای یک زنجیره تامین حلقه بسته پایدار با در نظرگرفتن ملاحظات اقتصادی، اجتماعی و زیست محیطی به همراه ریسک ناشی از عدم قطعیت در پارامترها ارائه میگردد که برای مدلسازی این مسئله از برنامه ریزی تصادفی استفاده شده و همچنین با استفاده از مقیاس ارزش در معرض ریسک شرطی به اندازهگیری ریسکها پرداخته میشود. هدف این مدل ماکسیمم کردن سود طراحی شبکه، کاهش نرخ بیکاری و افزایش فرصت های شغلی ناشی از احداث مراکزو مینیمم کردن کربن تولید شده حاصل از حملو نقلهای درون شبکه، مراکز تولید، بازیافت، تعمیر، تولید مجدد میباشد. از دیگر اهداف این مسئله میتوان به مینیمم کردن ریسک ناشی از عدم قطعیت در هزینه حملونقل و تقاضای مشتری نیز اشاره نمود. در پایان جهت نشان دادن کارایی مدل، مثالی تحت شرایط قطعیت و عدم قطعیت با معیار اندازهگیری ریسک حل نموده و جوابهای بهینه پارتو آنها با هم مقایسه میگردد. نتایج این تحقیق بیانگر این مطلب میباشد که با افزایش ریسک، سود ناشی از شبکه زنجیره تامین کاهش یافته و جهت مواجه شدن با ریسک می بایست هزینه بیشتری را متحمل گردید.
عنوان مقاله [English]
Multi-objective optimization model for risk assessment in the supply chain of a closed close loop under uncertainty conditions in parameters: Using a Constrained Risk Value Approach (CVaR)
In this research, a model for a sustainable closed-loop supply chain with economic, social and environmental considerations, along with the risk arising from uncertainty in parameters, is presented. Stochastic programming has been used for modeling this problem and also using the scale of value Exposure to conditional risk is measured by risk. The aim of this model is to maximize network design benefits, reduce unemployment and increase job opportunities resulting from the construction of facilities and minimize the production of carbon produced through intranets, production centers, recycling, repair, re-production. Other goals include minimizing the risk posed by uncertainty in transportation costs and customer demand. In the end, in order to demonstrate the efficiency of the model, an example is solved with certainty and uncertainty with the risk measurement criterion, and the pareto optimal solutions are compared. Results show that, with increasing risk, the profit from the supply chain network has decreased and should be costlier to face the risk.
Today, the necessity and importance of corporate responsibility and the social impact of companies have led managers and planners to give special attention to these aspects in their organization's missions, visions, and strategies. Corporate social responsibility encompasses the influence of a company's activities on various social groups, including employee rights, workplace safety, favorable working conditions, and job creation, among others. Furthermore, the significance of environmental standards and organizations' efforts to reduce pollution and promote efficient waste management and recycling practices have become crucial for organizational success, considering legal requirements and customer expectations. In recent years, the integration of reverse logistics, social responsibility, and environmental objectives in supply chain management has gained increasing attention due to factors such as resource reduction, pollution mitigation, environmental pressures, customer demands, and transportation costs in a competitive market. This integration, known as the closed-loop supply chain network, aims to ensure sustainability. Additionally, risk management within the supply chain has become a vital concern for supply chain management, considering the uncertainties prevailing in the global economy and trends such as increased outsourcing and advancements in information technology. The growing interest in achieving sustainability as an effective strategy for addressing challenges in the global supply chain has led to extensive research in the field of sustainable closed-loop supply chain management. However, previous studies in this area have lacked a comprehensive measure for assessing risk. Therefore, it is essential to address this issue, which involves considering stability goals in a closed-loop supply chain alongside risk management in uncertain conditions. The necessity for such research is evident, given the complexity of global supply chains and the increased vulnerability and risk exposure faced by organizations.
Materials and Methods
Given the existing gaps in the literature and the presence of uncertainty in real-world data, a mathematical model was proposed to help decision-makers reduce risk by considering identified risks and utilizing a comprehensive and effective risk measurement scale. In the designed model and forward network, suppliers are responsible for procuring raw materials. The manufactured products are then delivered to the market's customers through distributor networks. In the reverse flow of products, returned items are categorized into two groups: separable and non-separable products, after collection and inspection. Products that can be disassembled are sent to separation centers where they are transformed into components. The components are further divided into recoverable and non-recoverable categories. Non-recoverable components are transferred to disposal centers for safe disposal, while recoverable components are sent to inspection, cleaning, and sorting centers. After inspection and cleaning, the products are classified into repairable, remanufacturable, and recyclable groups. In the remanufacturing process, reusable components, after inspection, cleaning, and sorting, are sent to factories based on the production center's capacity. They are then combined with other parts to create new products that reenter the distribution cycle. In the recycling process, separated recyclable components are transported to recycling centers for direct production of raw materials, based on the capacity of the recycling centers, after collection and inspection.
Discussion and Results
Model 1 represents the initial approach, where scenario analysis for future conditions is not utilized, and the average values of uncertain parameters are taken into account. On the other hand, Model 2 incorporates various scenarios of future conditions. It is a linear model that considers possible future conditions as well. Model 1 exhibits lower costs compared to Model 2. The predictability of this problem arises from the fact that the risk associated with future market conditions was largely disregarded in Model 1. However, in Model 2, the consideration of introduced triple conditions for possible future outcomes necessitates a higher cost. Nevertheless, this higher cost brings us closer to real-world approximation and facilitates better decision-making in supply chain management when confronted with risks.
In this article, we conducted a literature review on the topic of risk models in supply chains and identified existing gaps. We found that most of the work in this field has certain weaknesses. Firstly, the focus has primarily been on risks in conventional and single-objective supply chains, neglecting the consideration of new risks and uncertainties that may arise in sustainable supply chains. To address this, we proposed a model for risk management in sustainable closed-loop supply chains. Secondly, we noticed that most of the existing studies lack a suitable and effective scale for measuring risk, particularly in the design of sustainable closed-loop supply chains. Drawing from the financial literature, we introduced the CVaR scale to fill this gap. Lastly, we developed and analyzed a model based on research gaps, using a case study in the home appliance industry as an example. The examination of the model's results, along with comparisons to real-world outcomes and previous research, validates the credibility of the proposed model.