Developing and solving an integrated model for production routing in sustainable closed-loop supply chain
توسعه و حل یک مدل یکپارچه برای مسیر یابی تولید در زنجیره تأمین حلقه بسته پایدار-2021
Social and environmental sustainability has gained increasing importance in today’s complex supply chains. Accordingly, an integrated model for production routing in the sustainable closed-loop supply chain is presented in the current study. A three-objective mathematical model is also proposed to minimize supply chain costs, maximize social responsibility or social beneﬁts, and ﬁnally, minimize environmental emissions. Sample trial problems are solved in three groups of the small, medium, and large size using the BCO algorithm. To prove the efﬁciency of this algorithm, its results are compared with the results using the NSGA-II algorithm in terms of comparative metrics such as quality, diversity, and spacing, as well as the runtime to the solution. According to the results, in all cases, the BCO algorithm outperformed the NSGA-II algorithm as it achieved more qualitative and near-optimal solutions. Also, the diversity metric values showed that the BCO algorithm is stronger in the exploration and extraction of the solution feasible region. The results of the metric of spacing and runtime to solution also showed that the NSGA-II algorithm achieves the solution in lower runtime than the BCO algorithm and searches solutions space in a more uniform manner.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Closed-loop supply chain | Sustainability | Production routing
Conceptual MINLP approach to the development of a CO2 supply chain network – Simultaneous consideration of capture and utilization process flowsheets
رویکرد مفهومی MINLP برای توسعه یک شبکه زنجیره تامین CO2 - در نظر گرفتن همزمان صفحه های جریان فرآیند ضبط و استفاده-2021
A large fraction of anthropogenic CO2 emissions comes from large point sources such as power plants, petroleum refineries, and large industrial facilities. A significant decrease of these CO2 emissions can be achieved with CO2 capture, utilization, and storage (CCUS) technologies. This study proposes a conceptually simplified model for the optimization of combined CO2 supply networks and capture and utilization technologies by the mixed-integer non-linear programming (MINLP) approach. The objective is to maximize the profit of CCUS technologies, considering chemisorption using methyl-diethanolamine (MDEA) as a capture technology and conversion of CO2 to CH3OH as a utilization technology. Additionally, avoided tax from reduced CO2 emissions is considered as a revenue. A hypothetical case study of five larger point sources of CO2 was investigated, namely coal power plants, biogas plant, aluminium production plant and two cement plants. Two scenarios were considered: i) Scenario A considering different values of the CO2 tax, and ii) Scenario B considering different flue gas flowrates at different values of the CO2 tax. The results show the potential of model-based optimization in reducing the amount of CO2 in the atmosphere by CCUS technology. Furthermore, the results in Scenario A show that CCUStechnology is only profitable if the price of CO2 emissions is higher than 110 €/t emitted CO2. Moreover, the results in Scenario B show that both the profit and the production of CH3OH depend to a large extent on the flue gas flow.
KEYWORDS: Point sources of CO2 | Carbon capture | Storage and utilization (CCUS) | Supply network optimization | Process optimization | MINLP approach
Data Driven Robust Optimization for Handling Uncertainty in Supply Chain Planning Models
بهینه سازی قوی مبتنی بر داده ها برای مدیریت عدم قطعیت در مدل های برنامه ریزی زنجیره تامین-2021
While addressing supply chain planning under uncertainty, Robust Optimization (RO) is regarded as an efficient and tractable method. As RO involves calculation of several statistical moments or maximum / minimum values involving the objective functions under realizations of these uncertain parameters, the accuracy of this method significantly depends on the efficient techniques to sample the uncertainty parameter space with limited amount of data. Conventional sampling techniques, e.g. box/budget/ellipsoidal, work by sampling the uncertain parameter space inefficiently, often leading to inaccuracies in such estimations. This paper proposes a methodology to amalgamate machine learning and data analytics with RO, thereby making it data-driven. A novel neuro fuzzy clustering mechanism is implemented to cluster the uncertain space such that the exact regions of uncertainty are optimally identified. Subsequently, local density based boundary point detection and Delaunay triangulation based boundary construction enable intelligent Sobol based sampling to sample the uncertain parameter space more accurately. The proposed technique is utilized to explore the merits of RO towards addressing the uncertainty issues of product demand, machine uptime and production cost associated with a multiproduct, and multisite supply chain planning model. The uncertainty in supply chain model is thoroughly analysed by carefully constructing examples and its case studies leading to large scale mixed integer linear and nonlinear programming problems which were efficiently solved in GAMS framework. Demonstration of efficacy of the proposed method over the box, budget and ellipsoidal sampling method through comprehensive analysis adds to other highlights of the current work.
Keywords: Uncertainty Modelling | Supply chain Management | Data driven Robust Optimization | Neuro Fuzzy Clustering | Multi-Layered Perceptron
A big data based architecture for collaborative networks: Supply chains mixed-network
یک معماری مبتنی بر داده های بزرگ برای شبکه های مشارکتی: شبکه های مخلوط شبکه های تأمین-2021
Nowadays, the world knows a high-speed development and evolution of technologies, vulnerable economic environments, market changes, and personalised consumer trends. The issue and challenge related to enterprises networks design are more and more critical. These networks are often designed for short terms since their strategies must be competitive and better adapted to the environment, social and economical changes. As a solution, to design a flexible and robust network, it is necessary to deal with the trade-off between conflicting qualitative and quantitative criteria such as cost, quality, delivery time, and competition, etc. To this end, using Big Data (BD) as emerging technology will enhance the real performances of these kinds of networks. Moreover, even if the literature is rich with BD models and frameworks developed for a single supply chain network (SCN), there is a real need to scale and extend these BD models to networked supply chains (NSCs). To do so, this paper proposes a BD architecture to drive a mixed-network of SCs that collaborate in serial and parallel fashions. The collaboration is set up by sharing their resources, capabilities, competencies, and information to imitate a unique organisation. The objective is to increase internal value to their shareholders (where value is seen as wealth) and deliver better external value to the end-customer (where value represents customer satisfaction). Within a mixed-network of SCs, both values are formally calculated considering both serial and parallel networks configurations. Besides, some performance factors of the proposed BD architecture such as security, flexibility, robustness and resilience are discussed.
Keywords: Big data architecture | Collaborative networks | Enterprises network | Supply chain network | Flexibility | Robustness
Ocular Biometric Characteristics Measured by Swept-Source Optical Coherence Tomography in Individuals Undergoing Cataract Surgery
مشخصات بیومتریک چشم اندازه گیری شده توسط توموگرافی انسجام نوری منبع جارو در افراد تحت عمل جراحی آب مروارید-2021
PURPOSE: To study the distribution of ocular biometric parameters utilizing a swept-source optical coherence tomography (SS-OCT) biometer in adult candidates for cataract surgery.
Design: A retrospective cross-sectional study
METHODS: SETTING: A single-center analysis of consecutive eyes measured with the IOLMaster 700 SS-OCT biometer at a large tertiary medical center between February 2018 and June 2020.
RESULTS: 3836 eyes of 3836 patients were included in the study. The mean age was 72.3±12.8 years and 53% were females. The mean biometric values were: total corneal power (44.17±1.70D), total corneal astigmatism (TCA) (1.11±0.87D), mean posterior keratometry (- 5.87±0.26D), posterior corneal astigmatism (-0.26±0.15D), axial length (AL) (23.95±1.66mm), anterior chamber depth (ACD) (3.18±0.42mm), lens thickness (LT) (4.49±0.47mm); white-towhite distance (WTW) (11.92±0.44mm), central corneal thickness (CCT) (0.54 ± 0.04mm), angle alpha (0.49±0.17mm), and angle kappa (0.34±0.17mm). There were sex-related differences in all biometric parameters with the exception of LT (P=.440), angle kappa (P=.216), and corneal astigmatism (P=.103). Biometric parameters demonstrated correlations between AL, WTW distance, ACD, and LT (P<.001). Age correlated with all parameters (P<.001), with the exception of CCT and posterior keratometry. Angle alpha and angle kappa magnitudes also correlated (P<.001). The prevalence of patients with TCA ≥0.75D, 1.0D and 1.5D were 59.1%, 43.4% and22.6%,respectively.
CONCLUSIONS: Age significantly correlated with most of the biometric parameters and significant differences between sexes were noted. Furthermore, the high prevalence of TCA and relatively large angle alpha and angle kappa magnitudes were noted among subjects. These data can be relevant in planning local and national health economics.
Nonlinear analysis and active management of production-distribution in nonlinear supply chain model using sliding mode control theory
تحلیل غیرخطی و مدیریت فعال تولید-توزیع در مدل غیر خطی زنجیره تامین با استفاده از تئوری کنترل حالت کشویی-2021
This paper deals with system dynamics approach for dynamical behaviors and control synthesis of supply chain system by utilizing three-stage production-distribution model. The presented approach offers systematic tools for determining fundamental relationships between multi-echelons in the supply chain dynamics by using eigenvalues, bifurcation, and time history investigation. By exploring system dynamics on time series analysis, it is found that system performance has suffered severely from the bullwhip effect under impacts of model uncertainties and perturbed demand. The novel fractional-order sliding mode control algorithm has been presented based on adaptation mechanism, ensuring that the shipment flows are robustly stable in supply chain networks against disruptions. This is a smarter way of getting sufficient strength to sustain existing competitive market for mitigating the risks and improving the supply chain performance. The system stability has been thoroughly analyzed by using Routh-Hurwitz criterion and Lyapunov theory. Extensive numerical simulations have been conducted to obtain insights into the system behaviors and to validate effectiveness of active control policies by matching the shipment sent to customer demand, ensuring supply chains resilience. Finally, it is found that the presented approach can help decision-makers develop more efficient supply chain management system against severe market disruptions.
Keywords: System dynamics | Supply chain management | Production-distribution model | Fractional order | Sliding mode control | Adaptive law
Biometric traits of onion ( Allium cepa L:) exposed to 137Cs and 243Am under hydroponic cultivation
صفات بیومتریک پیاز (Allium cepa L:) در معرض 137 درجه سانتیگراد و 243 آمپر در زیر کشت هیدروپونیک-2021
≈ ≈≈ ≈To elucidate the features of bioaccumulation and phytotoxic effects of long-lived artificial radionuclides, a hydroponic experiment was carried out with the cultivation of onion (Allium cepa L.) in low-mineralized solutions spiked with 137Cs (250 kBq L—1) or 243Am (9 kBq L—1). After the 27-day growth period, 70% of 137Cs and 14% of 243Am were transferred from the solutions to onion biomass with transfer factor values 400 and 80, respectively. Since the bioaccumulation of both radionuclides mainly took place in the roots of onion (77% 137Csand 93% 243Am of the total amount in biomass), edible organs – bulbs and leaves – were protected to some extent from radioactive contamination. At the same time, the incorporation of the radionuclides into the root tissues caused certain changes in their biometric (geometric and mass) traits, which were more pronounced under the243Am-treatment of onion. Exposure to 243Am significantly reduced the number, length, and total surface area of onion roots by 1.3–2.6 times. Under the influence of 137Cs, the dry-matter content in roots decreased by 1.3 times with a corresponding increase in the degree of hydration of the root tissues. On the whole, the data obtained revealed the specific features of 137Cs and 243Am behaviour in “hydroponic solution – plant” system and suggested that biometric traits of onion roots could be appropriate indicators of phyto(radio)toxicity.
Keywords: Radionuclides | Bioaccumulation | Root uptake | Transfer factor | Root–to–shoot translocation | Phytotoxicity
Strategies for ensuring required service level for COVID-19 herd immunity in Indian vaccine supply chain
راهکارهایی برای اطمینان از سطح خدمات مورد نیاز برای مصونیت گله COVID-19 در زنجیره تأمین واکسن هند-2021
Post COVID-19 vaccine development, nations are now getting ready to face another challenge: how to effectively distribute vaccines amongst the masses to quickly achieve herd immunity against the infection. According to some experts, herd immunity for COVID-19 can be achieved by inoculating 67% of the population. India may find it difficult to achieve this service level target, owing to several infrastructural deficiencies in its vaccine supply chain. Effect of these deficiencies is to cause frequent lead time disruptions. In this context, we develop a novel modelling approach to identify few nodes, which require additional inventory allocations (strategic inventory reserves) to ensure minimum service level (67%) under the possibility of lead time disruptions. Later, through an illustrative case study on distribution of Japanese Encephalitis vaccine, we identify conditions under which strategic inventory reserve policy cannot be practically implemented to meet service level targets. Nodes fulfilling these conditions are termed as critical nodes and must be overhauled structurally to make the implementation of strategic inventory policy practically viable again. Structural overhauling may entail installation of better cold storage facilities, purchasing more quality transport vans, improving reliability of transport network, and skills of cold storage manager by training. Ideally, conditions for identifying critical nodes for COVID-19 vaccine distribution must be derived separately by substituting COVID-19 specific parametric values in our model. In the absence of the required data for COVID-19 scenario, JE specific criteria can be used heuristically to identify critical nodes and structurally overhaul them later for efficiently achieving service level targets.
Keywords: Humanitarian logistics (O) | Structural resilience | COVID-19 | Lead time disruption | Herd immunity | Vaccine supply chain
بهینه سازی شرایط فرآیند تولید کربن فعال بسیار متخلخل از ضایعات پوست خرما به منظور حذف آلاینده های موجود در آب
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 32
در این مطالعه ، فرآیند تهیه کربن فعال بسیار متخلخل (AC) از پوست خرما از طریق روش سطح پاسخ، بهینه سازی شد. شرایط بهینه آماده سازی AC از طریق روش ترکیبی تجزیه حرارتی با فعال سازی شیمیایی با استفاده از اسید فسفریک در حدود 3 ساعت زمان فعال سازی ، 400 درجه سانتیگراد درجه حرارت فعال سازی و 40وزنی برای مقدار عامل فعال بدست آمد. بالاترین مقادیر سطح خاص و تعداد ید تحت شرایط بهینه عبارتند از902 متر مربع در گرم و 983 میلی گرم در گرم، که تخلخل بسیار بالای ساختار AC را تأیید می کند. همچنین AC آماده به دلیل مساحت زیاد و وجود گروههای عملکردی اسیدی در سطح آن ، توانایی چشمگیری در از بین بردن آلاینده های مختلف از جمله آرسنیک (V) ، متیلن آبی ، متیل نارنجی و کوئرستین داشت. سرانجام ، شاخص تجاری محاسباتی در حدود 451 مترمربع در هر واحد مواد به دست آمد که کاربرد پوست خرما را به عنوان یک پیش درآمد ارزان قیمت و امیدوار کننده برای آماده سازی تجاری AC تأیید می کند.
واژه های کلیدی: پوست خرما | روش سطح پاسخ | سطح خاص | شماره ید | کوئرستین
|مقاله ترجمه شده|
Improving the sustainability of a reverse supply chain system under demand uncertainty by using postponement strategies
بهبود پایداری سیستم زنجیره تأمین معکوس تحت عدم اطمینان تقاضا با استفاده از استراتژی های به تعویق انداختن-2021
In recent decades, issues of resource depletion and waste piling have grown at an alarming rate, which are happening in the cases of product wastes with significant residual values, such as e-waste. To address these issues, stakeholders have focused to develop a reverse supply chain (RSC) system that can sustain profitable takeback, reuse, and recycling operations in the long-term. Such a system requires efficiency in handling complex operations involving various players while being responsive to demand uncertainty and changes. One way in realizing these capabilities is by incorporating postponement concepts to the integrated RSC network, allowing the delay of operations susceptible to demand uncertainty. This study pioneers the formulation of a two-stage stochastic mixed-integer model of a multi-player RSC with speculation-postponement strategies. The sample average approximation method is used to solve and verify the proposed model that has an uncertain demand. Various speculation-postponement strategies, namely, disassembly, reconditioning, and reassembly strategies are developed to configure forecast and demand-driven RSC operations, including the purchasing, product takeback, production planning, inven- tory, and item speculation decisions. Numerical examples of the notebook computer RSC demonstrate that utilizing the right operation postponement can increase the network’s flexibility, allowing better economic performances even under high demand uncertainty risks and stricter environmental regula- tions. In various cases, the RSC performs better with speculation-postponement strategies than without postponement strategy, demonstrating the proposed model’s superiority. This study can provide insight to decision-makers to improve RSC sustainability through postponement. Moreover, the model is generic and can be applied to other products as well.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Reverse supply chain | Reuse | Recycling | Postponement | Demand uncertainty