Blood and its products are considered one of the most critical medical needs. Factors such as irregular supply and demand, lack of technology to produce blood and dependence on donation, different types of blood, perishability, and mortality of people in the event of a blood shortage have increased the importance of blood in people’s lives. To address these challenges, this paper presents a mix integer linear programming model for a blood supply chain network based on sustainable development goals under uncertainty that includes collecting, processing, holding, and delivering various blood products. More specifically, a multi-objective, multi-period, and multi-product mathematical programming model is proposed to minimize total costs, minimize environmental damage, and maximize social effects resulting from the implementation of the network. The proposed model can help managers in hospitals and the blood transfusion organizations in making important strategic, tactical, and operational decisions such as where to locate blood facilities, the flow of different products between facilities, assigning donor groups to blood facilities, and inventory control decisions. First, a robust possibilistic programming approach is utilized to deal with the uncertainty of the mathematical model. Second, an interactive fuzzy programming method is used to solve the proposed model. The efficacy of the mathematical model is evaluated using real data from the Shiraz metropolitan area in Iran. Finally, the mathematical model’s behavior was analyzed using sensitivity analyses on the main factors. The experimental results show the efficiency of the model in the satisfaction of demand and the reduction of waste.

In today’s complex and fast-paced world, supply chain management, especially in the medical equipment sector, has become one of the fundamental challenges for organizations. Selecting appropriate suppliers who can meet various indicators such as quality, agility, resilience, and circular economy is of great importance. This study aims to evaluate and prioritize suppliers of medical equipment (knee prostheses). Initially, key supplier evaluation indicators were identified and categorized into four main dimensions: resilience, agility, circular economy, and general indicators. Then, using the fuzzy best–worst method (FBWM), the weight of each indicator was calculated to determine their relative importance in supplier evaluation. Finally, by employing the fuzzy data envelopment analysis (FDEA) method, suppliers were prioritized based on the weighted indicators. The research findings indicate that the significant categories, in order of importance, include resilience, generality, agility, and circular economy. Additionally, among the main indicators for evaluating crisis management suppliers, robustness and quality are paramount. The results show that in the field of assessing medical equipment suppliers, resilience and related quality indicators hold greater significance. This multi-criteria approach helps organizations to improve the efficiency and sustainability of their supply chains through a more comprehensive evaluation of their suppliers, ultimately ensuring customer satisfaction and long-term success. The findings of this study can serve as an effective guide for managers in selecting strategic suppliers in the medical equipment field.

In today’s complex business environment, highlighted by challenges like the COVID-19 outbreak, it is crucial to enhance supply chain resilience and agility. Additionally, the fourth industrial revolution and digital transformation necessitate the integration of advanced technologies into supply chains. Moreover, the importance of sustainability, encompassing environmental and social factors, has become increasingly prominent across various sectors. The dairy industry, particularly in chain stores, stands out as particularly sensitive to adopting these paradigms. In this regard, the main goal of this study is to design a model to evaluate suppliers of dairy products in chain stores, which is done using data-driven methods. Due to the expansion of the available data volume and the higher accuracy of the data compared to the intuition of experts, in this study, a new data-driven approach to evaluate and predict the performance of suppliers is presented. In the first step, according to the collected data, clustering is done with the K-means algorithm, and then, using the random forest algorithm, the evaluation and prediction model of the suppliers’ performance is designed. The random forest algorithm, with an accuracy of 92%, has outperformed the K-nearest neighbors (KNN) and artificial neural network (ANN) algorithms. In order to increase the accuracy of the model, the principal component analysis (PCA) algorithm is used, and by applying this algorithm, the accuracy of the model reached 98%. Additionally, the Shapley additive explanation (SHAP) algorithm was used to conduct a sensitivity analysis of the features influencing supplier evaluation. The findings indicate that delivery speed, product quality, and the supplier’s financial capability have the greatest impact. In summary, the main innovation of this study is the development of a data-driven multi-combination model for supplier evaluation.

Hydrogen production by natural gas reforming is a mature and well-established method. The process uses methane (CH4), which is present in natural gas, to make hydrogen via thermal processes such as methane steam reforming, partial oxidation, and autothermal reforming. For a wide range of chemical processes, radial flow tubular reactors (RFTRs) offer several advantages over conventional axial flow tubular reactors (AFTRs). Their ability to improve catalyst efficiency, reduce pressure drop, and enable efficient heat transfer makes them a valuable tool for a variety of catalytic chemical processes. This paper presents a study on the modeling and optimization of an autothermal reactor with a radial flow configuration. The reactor is designed to operate under specific conditions, and its performance is optimized by developing a mathematical model that accurately describes the behavior of the system. A combination of experimental data and theoretical calculations is used to develop the model. Genetic algorithm is employed to optimize the reformer’s performance by varying various parameters, such as the feed temperature and the ratio of feed components. The optimization increases the hydrogen production rate by 11% and the methane conversion by 5%, and the optimal temperature profile along the catalyst bed is studied by changing feed specifications.

This study investigates the catalytic performance of the multi-shell Co@C(Z-d)@Void-SiO2@CeO2 catalyst in Fischer–Tropsch synthesis (FTS) under optimized operating conditions. Using response surface methodology (RSM) with a central composite design (CCD), we examined the effects of four key operating parameters—pressure, temperature, gas hourly space velocity (GHSV), and the H2/CO feed ratio—on three critical response variables: CO conversion (XCO), methane selectivity (SC1), and selectivity for heavier hydrocarbons (SC5+). Polynomial models for each response were developed based on experimental data and evaluated through analysis of variance (ANOVA). The optimization revealed that the ideal conditions for maximizing XCO (78.36%) and SC5+ (97.15%) while minimizing SC1 (3.11%) are a pressure of 25 bar, temperature of 230 °C, GHSV of 800 h−1, and a H2/CO ratio of 1.6. These findings provide clear guidance on how to achieve higher efficiency in FTS, with a desirability of 0.914 under the optimal conditions.

This study examines a supply chain optimization model for perishable products, addressing the challenge of demand uncertainty through a hybrid approach that combines blockchain technology with multi-criteria decision-making (MCDM) methods, specifically the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The model aims to address dual objectives: reducing waste and managing inventory effectively. A crucial aspect of the model is the selection of suppliers, which is vital for mitigating risks related to product failures and delivery issues. Supplier performance management is incorporated to ensure that vendor choices meet various performance criteria. To account for demand uncertainty, the study employs chance constraint programming (CCP) to develop optimization models that incorporate uncertainties and randomness. The research evaluates two scenarios: one with blockchain technology and one without. Blockchain enhances information sharing between manufacturers and retailers, thereby reducing demand uncertainty and improving forecast reliability. The model is validated through a real-world case study of a dairy product manufacturer, with results obtained using the CPLEX solver. Findings reveal that blockchain implementation significantly reduces costs associated with holding, shortages, and production while also improving overall inventory management. Sensitivity analysis is performed to assess the impact of confidence levels on model performance, showing that higher confidence levels result in reduced costs. The study demonstrates the potential of combining blockchain, TOPSIS, and chance constraint programming to enhance supply chain efficiency and suggests future research directions, including the integration of sustainability factors into green production systems.

Pharmaceutical waste management has become one of the major challenges in reverse logistics (RL) over the past decade. As drugs are classified as hazardous materials, their uncontrolled disposal poses significant risks to both the environment and public health. One potential solution is the recycling of surplus (unused) drugs by the citizens. This research aims to implement a RL system for surplus drugs and prevent the release of hazardous pharmaceutical waste into the environment. By leveraging Digital Transformation, the study proposes solutions to encourage citizens to sell their surplus drugs and participate more actively in implementing RL of drugs. In this regard, this research focuses on the proper collection and management of surplus drugs, particularly strategic drugs, by outsourcing this task to a Third-Party Reverse Logistics Provider (3PRLP). In this system, the government plays a crucial role in overseeing RL management. The primary objective is to recover raw materials and safely eliminate hazardous waste through a circular economy approach. To achieve this, 3PRLP companies utilize a three-channel platform powered by information technologies, including geographic information system (GIS), Cloud Computing (CC), and blockchain. Citizens can use these technologies to register and request the sale of surplus drugs. The study introduces a bi-level model with a mixed-integer nonlinear programming structure to optimize a multi-objective sustainable model at the upper level and minimize the costs associated with drug collection at the lower level. In addition, the research determines optimal drug pricing based on the Generalized Axiom of Revealed Preference, and presents a new demand function based on the Cobb–Douglas production function, incorporating relevant risks. The Lagrangian relaxation method is employed to address load-balancing issues and calculate CC costs. Applying this model allows for the optimization of energy consumption in cloud centers. In addition, the model helps develop the concept of the circular economy and achieve sustainability in the RL of drugs through purchasing citizens’ surplus drugs and recycling. Furthermore, the proposed approach can lead to substantial currency savings for the country and ensure a more efficient supply of essential drugs, particularly for patients with special needs. In the numerical analysis, the large-size instances of the multi-objective model are solved using a linearization method. Examining the Pareto front using the ε-constraint method shows the high correlation between the base price of purchasing drugs from citizens by 3PRLPs and the price offered by the government to 3PRLPs.

Although there is a growing focus on issues and challenges related to the implementation of sustainable development goals (SDGs), their importance and priority need to be studied in order to have better planning and policymaking on sustainable development. The purpose of this paper is to categorize and prioritize SDGs based on importance–performance analysis (IPA) from the perspective of Iran. The calculation of SDGs’ importance is considered based on the knowledge and expertise of 11 Iranian experts in the field of sustainable development. By using a paired comparison questionnaire, the data of this part of the research was collected and processed by the DEMATEL-based analytic network process (DANP) technique. Based on IPA results, The SDGs are categorized and prioritized into four levels. “Zero hunger,” “Industry, Innovation, and Infrastructure,” and “Reducing inequalities” are known as the most important goals to be pursued in Iran and policymakers need to pay more consideration to these goals in setting their plans and policies. Policymakers and executive managers of sustainable development can potentially use the comprehensive framework introduced in this research to stream SDGs in their country’s national plans. Based on the current SDGs scene reflected by the results of this research, valuable insights could be extracted to guide future national development plans, institutional focus, and government policies. In this research, the capabilities of modeling and prioritizing SDGs with a DANP-IPA combined approach are well demonstrated.

The horticulture industry has a special role in the health of society due to its direct impact on the food security in society, so it is one of the important industries that affect the lives of all people. Also, due to the increase in the growth of the world population, the need for food is increasing day by day, so it is necessary to pay special attention to the horticulture industry to reduce the rate of hunger in the world, which causes various problems. This study focuses on the routing of vehicles to carry different kinds of horticultural products considering their shelf life to keep the stability and quality of these products at the highest level. This aim has been achieved through presenting a comprehensive multi-period MILP model for different types of horticultural products and various types of vehicles in the horticultural supply chain. A case study in European Union is also presented to analyze the efficacy of model. The outcome of this research could be proliferative for managers, decision makers, and policy designers of horticulture supply chain since it proffers avenues of sustainable production enhancement.

Surplus renewable generation is usually considerable part of total energy and has a determinative role on energy efficiency and overall cost. This excess electricity is uncertain variable and cannot be used to charge the battery or supply the load in the microgrid (MG) islanded mode. To reduce waste of renewable energy and improve energy efficiency, this paper presents the new demand response mechanism and robust optimization approach under renewable uncertainty for an islanded MG. The presented methodology performs a centralized power management, demand shifting, and load shedding to manage battery energy and absorb surplus renewable generation under different uncertainties and scenarios. The presented centralized robust energy management strategy with presented demand response (DR) mechanism helps to significantly reduce waste of renewable energy and increase total energy efficiency. It is shown that by using presented method, the energy efficiency increases up to 14.6% for the high renewable generation scenario and enhances 6.5% for low generation scenario than simple DR without using excess electricity. As well, the operation cost reduces up to 35.7 ($) for the high renewable generation scenario and 19.6 ($) for low generation scenario per day than simple DR without using surplus renewable generation. Finally, the performance of the proposed robust optimization approach is compared with the Homer optimization software.

Vendor-managed inventory (VMI) policies within integrated supply chain management (SCM) represent a robust approach that effectively addresses demand, quality, and inventory management, encompassing the sharing of information and data between vendors and retailers. However, in the context of perishable products, timely inventory management becomes crucial, as its success hinges significantly on product quality, which is an area that has been relatively unexplored in the VMI literature. One emerging and efficient method to tackle VMI for perishable products is the deployment of Internet of Things (IoT) devices throughout the entire supply chain. These devices enable real-time tracking and tracing of product quality, encompassing manufacturing processes. Consequently, this study aims to develop a model for selecting the most suitable IoT devices for managing perishable products in supply chains. Despite its significance, the problem of retailer selection based on critical criteria in VMI has not been thoroughly investigated to date. This paper offers optimal policies to enhance production planning and minimize waste for suppliers by harnessing the advantages of VMI and IoT. To validate the proposed model, a case study involving real data from the food supply chain is examined.

Today, protecting the environment, considering the idea of sustainability, using natural resources, and considering long-term interests are among the most crucial factors in organizing and overseeing agriculture in any nation’s economy. Afghanistan is a dry and landlocked country where agriculture contributes significantly to employment and the people’s economy to attain both profitability and environmental sustainability, the use of mathematical programming models plays a significant role. In this research, the fuzzy goal programming method is considered to optimize the cropping pattern in three scenarios including profit maximization (fuzzy 1), sustainable development of agriculture and optimal use of water resources (fuzzy 2), and simultaneous achievement of the goals mentioned above (fuzzy 3). The data required for this research was obtained through a field survey of the completion of 928 questionnaires. The results obtained from the fuzzy goal model (3) that simultaneously considers economic and environmental goals are significant. The removal of five products (paddy, melon, watermelon, onion, and cabbage) from the cropping pattern results in a decrease of 29.01% of the total cultivated area compared to the current model in Nangarhar province. The implementation of this model shows a 0.67% increase in gross margin, equivalent to $6.90 million. The reduction in the consumption of agricultural inputs, including chemical fertilizers, agricultural pesticides, and irrigation water, by 16.92%, 30.69%, and 39.97%, respectively, aligns with sustainable agricultural practices aimed at minimizing environmental impact and promoting resource efficiency. By decreasing the use of chemical fertilizers, pesticides, and irrigation water, farmers can contribute to reducing potential environmental pollution and conserving water resources.

The higher education sector plays a crucial role in sustainable development by educating the next generation of academic professionals. Thus, this study aimed to assess the environmental impact through life cycle assessment (LCA) of the operational phase of Kharazmi University’s Karaj campus. The LCA was conducted using a process-based method and a gate-to-gate system boundary for the university’s 100-year operational period, with a functional unit of “1 day.” SimaPro 9 software, incorporating methods from the Center of Environmental Science at Leiden University (CML), BEE + , and impact methods, was utilized for analysis. Results revealed that daily operations at Kharazmi University generate an average of 39,679.48 kg of CO2 equivalent (global warming potential), 205.6 kg of SO2 equivalent (acidification potential), and 40.5 kg of equivalent (eutrophication). Environmental damage indicators included 0.025 DALY (damage to human health), 8915.7 PDF × m2 × year (damage to ecosystem quality), 37,413.7 kg of CO2 equivalent (climate change), and a demand for 609,690.7 MJ of primary energy (damage to natural resources). Among the university’s activities, electricity usage, natural gas consumption, solid waste generation, wastewater production, and the consumption of meat, rice, acetone, toluene, xylene, diethyl ether, ethanol, tri-dichloromethane, and hydrogen peroxide in labs were identified as the major contributors to environmental pollution. The most sensitive parameters in the LCA were electricity consumption, wastewater production, red meat consumption, and solid waste generation. Further assessment with five different scenarios of electricity consumption revealed that implementing photovoltaic panels (PV) for all lighting applications across campus could reduce environmental damage by nearly 24% and pollution impact by about 9 to 24%. This study serves as a foundational step toward achieving the United Nations Sustainable Development Goals (SDGs) in Iran.

Agricultural sustainability is crucial for the economy of the Nijrab District in Kapisa Province, as it heavily relies on agriculture. However, there are growing concerns that the overuse and destruction of the environment may jeopardize human societies’ quality of life and health, highlighting the need for further investigation into the underlying factors and their impacts. This study aims to determine the sustainability of agriculture in the Nijrab District. This will be achieved by examining three main economic, ecological, and social aspects. The required data for this research was collected from various sources, such as the Department of Environment, the Department of Promotion, and the Department of Agriculture, Livestock, and Irrigation. Additionally, questionnaires were completed by 264 farmers and 20 experts. The analytical hierarchy process (AHP) method was utilized to evaluate agricultural sustainability. According to expert assessments, the economic aspect was identified as the most significant factor, contributing 55.8% to overall agricultural sustainability, followed by the environmental (32%) and the social (12.2%). Profit, health status, and pesticides were the most influential factors in agricultural sustainability, with weights of 0.473, 0.358, and 0.253, respectively. Kalan Dara was identified as the most sustainable valley, with a score of 0.219, while Qaus Dara was the most unsustainable valley, with a score of 0.1. The sensitivity analysis indicated that altering the sustainability criteria and sub-criteria weights did not substantially change agricultural sustainability. Afghanistan’s policymakers must prioritize policies promoting agricultural sustainability to ensure food security, economic growth, and environmental protection.

Agricultural planning is a critical task that requires assessing numerous factors ranging from soil and terrain to socioeconomic markets and infrastructure. Assessing the ecological suitability of land for agricultural cultivation is a complicated procedure. It needs special environmental data and the knowledge of GIS experts to process and evaluate them. To improve the planning and management of irrigated lands, the present study aims to establish a model for the evaluation of ecological capability using a geometric mean approach. The proposed model was tested against several established methods, including the model of Iranian Ecology using Boolean logic, the weighted linear combination (WLC) technique, and the arithmetic average, using the normalized difference vegetation index (NDVI). Results showed the proposed model, calibrated with the geometric mean, was the most accurate (overall accuracy = 71% and kappa coefficient = 0.54) among the methods tested. In contrast, the Iranian ecological model (overall accuracy = 37.5% and kappa coefficient = 0.17) and the arithmetical mean approach (overall accuracy = 57.44% and kappa coefficient = 0.02) showed the least accuracy. The use of geometric mean assessment provides a high degree of flexibility in the identification of areas of agricultural use. Due to its simplicity and high accuracy, this model can serve as a framework for the assessment of ecological capability in other similar regions. In summary, this research highlights the importance of utilizing advanced models and technologies to enhance agricultural planning and management, ultimately leading to better crop production and a sustainable future.

Human errors in power plants can have a significant impact on sustainability. Sustainability in the context of power plants involves ensuring the long-term viability of energy generation while simultaneously minimizing adverse environmental, social, and economic impacts. Human errors in the control and operation of power plants can result in energy losses, reducing the overall efficiency of the plant. This research aims to enhance organizational decision-making by identifying and evaluating key factors affecting human error probability (HEP) and their relationships in power plants. The study uses the cross-impact matrix multiplication applied to classification (MICMAC) method to identify key factors, dependencies, and interconnections influencing HEP. By recognizing and understanding these dependencies, managers can make informed decisions and implement appropriate adjustments to organizational conditions and personnel. Based on case study results, six sub-factors are identified as having the highest level of influence on HEP: the operating procedures, skills and experiences of personnel, ergonomics, the interruption of tasks, repetitiveness and simplicity of the task, and education and training plan. The insights gained from the research can be used to enhance understanding and implement effective strategies to mitigate the impact of human error, leading to improvements in sustainability within power plants.

Advanced technologies are gaining more attention in every industry sector. Therefore, to develop a logistics network that can adjust to effectively manage inventories for managing logistics while maximizing profit for all systems involved. The main objective of this research is to calculate the number of products to be dispatched at different intervals within a logistics network, aiming to minimize the overall costs of a reverse warehouse system called an automatic reverse storage system (ARWS). A mathematical model is presented to optimize the total cost and the delay in transporting rankings in a warehouse system network, considering that some scenarios are uncertain with capacity. To address the mathematical approach for both standard and extensive sizes, various meta-heuristics algorithms are utilized within the MATLAB software, and the outcomes are determined with the globally optimal solution to handle better responses to several scenarios to allocate items to shelves and complete orders and routing. The results indicate that the suggested algorithm performs well, with the total quantities sent to the warehouse equal to those derived from the precise solution. Additionally, the value of the objective function decreases with an increase in the number of iterations.

Integrated production and maintenance planning optimizes efficiency and productivity by coordinating schedules. Investigating this planning can improve operational efficiency, reduce costs, and enhance productivity. It reduces equipment breakdowns, minimizes downtime and delays, and facilitates better resource allocation, thereby lowering costs and enhancing cash flow. On the other hand, human error can significantly affect maintenance operations, reducing performance. This paper introduces a novel mathematical model aimed at cost minimization through the optimization of preventive maintenance (PM) operation planning, production scheduling, and the consideration of human error. Unlike prior research, this research accounts for the influence of human error on both the reduction coefficient of equipment virtual age and associated costs. Besides, this paper categorizes the costs linked to maintenance operations into two distinct groups. The results help decision-makers implement optimal production and maintenance operations in organizations, taking human error into account. Optimal and integrated maintenance and production planning that takes into account human error can have a significant impact on sustainability in several ways. The model is tested in the real world and validated using the sensitivity analysis method. The results suggest that the optimal human error probability, based on its costs, is equal to 0.00005. This finding encourages decision-makers to identify sources of human error and develop proactive measures to optimize performance. Overall, the model can help organizations optimize production and maintenance operations, reduce costs, and improve performance.

Solar systems’ outlet temperature plays a key role in the sustainability of power generation in solar-assisted power plants. Increasing this temperature to its maximum possible level would increase the contribution of solar energy to power generation. Control systems are meant to maintain this temperature at a desired level by adjusting the mass flow rate circulating in the solar unit. The designed controller for the plant can directly affect the expenses associated with the solar unit since the system can respond significantly faster to different working loads. This paper attempts to reduce the operating time of a combined power plant using a non-linear control system. Along with applying a control system to the plant, an exergoeconomic analysis is also performed to obtain the expenses of various fluid flow streams of the plant while occupying it with a controller. Energy and exergy analyses are also carried out to evaluate the thermal and exergetic efficiencies of the studied plant. After obtaining the solar unit’s operating time, efficiencies, and expenses associated with the carbon capture and power generation, an optimum working condition for the plant is determined. Multi-objective optimization using the particle swarm optimization (PSO) algorithm is run to minimize the system’s operating time and costs and to maximize efficiencies. Results indicate that the optimal power plant can get as much as 51.61%, 29.79%, 6.27 ($/Gj), 226.73 ($/Gj), and 25.57 (min) for exergy efficiencies, energy efficiencies, power production, carbon capture costs, and solar system operation time, respectively.

Supply chain optimization, bolstered by additive manufacturing (AM) capabilities, holds the key to a streamlined and effective manufacturing process. By seamlessly integrating AM into supply chains, enterprises can expedite lead times, drive down expenses, and enhance operational flexibility. In this context, this paper introduces a pioneering bi-objective optimization model tailored to tackle the challenges of sustainable supply chains within the realm of AM. In a novel departure, this study fills a critical research gap by delving into sustainable supply chains through the lens of AM. The overarching objective is to minimize supply chain costs while mitigating production waste and air pollution. This paper underscores the strategic amalgamation of additive and traditional manufacturing (TM) techniques, which collectively pave the way for cost efficiencies and environmental benefits. To realize these goals, this study presents a meticulously designed mixed integer linear programming (MILP) model, thoughtfully constructed using linearization techniques. This model serves as the foundation for crafting a sustainable bi-objective supply chain framework seamlessly encompassing AM capabilities. This exploration spans diverse scenarios, systematically analyzing production waste and air pollution rates across a spectrum of conditions, ranging from minimum to maximum values. Findings unequivocally highlight the potential of the combined AM and TM approach to curtail waste, minimize environmental emissions, and concurrently reduce inventory and transportation costs. Throughout this investigation, this study stresses the pivotal significance of weighing environmental and economic costs during the evaluation of production scenarios, empowering decision-makers to make informed choices that align with corporate objectives. Ultimately, this study not only offers a comprehensive understanding of AM’s impact on supply chains but also serves as a practical guide for supply chain industry decision-makers. By optimizing production and transportation processes, stakeholders can achieve substantial cost savings while upholding standards of quality and operational efficiency.

Sustainability has become a significant business issue, and efforts to achieve a sustainable supply chain have been intensely considered. In order to manage a sustainable supply chain, it is essential to choose the appropriate suppliers and assign the right amount of orders among them. Uncertainty about future demand makes these matters a substantial concern, and despite their importance, it has received much less attention from researchers in supplier selection and order allocation problems. In this regard, this paper presents a three-stage method for sustainable supplier selection and order allocation. In the first stage, fuzzy AHP and fuzzy TOPSIS were used to weight the criteria and rank the sustainable suppliers, and suppliers with acceptable sustainability performance were selected. In the second stage, the future value of demand is forecasted by polynomial regression (PR). In the third stage, a mathematical programming model was formulated considering a novel quality standard policy. Efficient solutions were obtained by solving a novel multi-objective, multi-period stochastic mixed-integer model utilizing LP-metric. Also, a real-world case study for a small business is presented to validate the performance of the proposed method. A sensitivity analysis reveals the effect of changes in demand, suppliers’ capacity, purchasing costs, and quality policy.

One of the most common and successful approaches to integrated supply chain management (SCM) is vendor-managed inventory (VMI). One of the technologies that has been widely used recently to share information in the VMI is blockchain technology (BT). Given that many factors, such as scalability and adoption cost, play a role in obtaining the optimal number of transactions, it is of vital importance to account for them in the VMI. Since the VMI strategy aims for a long-term relationship between the vendor and the retailer and highly affects the supply chain’s total cost, the vendor must pay more attention in selecting retailers. Another contribution of this study is to consider the issue of environmental pollution generated by inventory holding, ordering, set-up, and transportation operations, which has not been thoroughly investigated in the existing literature. This study uses a green two-echelon multi-product, multiple-vendor, and multiple-retailer supply chain with a hybrid of multi-criteria decision-making (MCDM) methods, and a multi-objective programming under the VMI policy is developed. This paper examines BT for supply chain management by accounting for the most impactful criteria of BT implementation in retailer selection and optimization. For this purpose, this paper applies the Bayesian best–worst approach (BWM) as one of the MCDM techniques. The obtained weights are then plugged into the model as the inputs of the proposed model. Finally, the efficiency of the presented method is verified through a case study with actual data collected from the electronic supply chain.

Sustainable transportation has become a central issue for scholars around the world. It is now well established that the transportation sector is one of the few industrial segments escalating greenhouse gas emissions. On the other hand, Tehran as one of the most populous cities in the world has been constantly confronting environmental challenges, particularly the motor vehicles’ pollution. Therefore, the objectives of this study were to (1) determine the optimal capacity of slaughterhouses, (2) identify the best routes for transporting, and (3) reduce the number of vehicles used, transportation costs, and greenhouse gas emission costs. The model is presented in two phases that solved using the general algebraic modeling system (GAMS) software for data set of the real instance. The results of phase 1 indicated that using the designed transportation model might reduce transportation costs by 0.3 and 9.8%. In addition, as slaughterhouse capacity increased, the average cost per period decreased by 8.5%. The results of phase 2 demonstrated that using the designed transportation model might reduce costs of transportation for 3 days by 29.56, 27.91, and 32.58%, respectively. In addition, the costs of greenhouse gas emissions would decrease by 17.29, 22.82, and 23.08%, respectively.

Selecting the suppliers in a green supply chain (GSC) improves supply chain capabilities by considering environmental policies. On the other hand, considering the development of technology and intelligence of the Internet of Things (IoT) and their help to meet goals better, it is essential to study them in this area. So, it is crucial to identify the influential factors of the IoT in selecting a green supplier and find its most important criteria for further monitoring and control. This paper aims to illustrate the ability of four different combinatorial multi-criteria decision-making (CMCDM) techniques in determining the best supplier in the rubber GSC. The suppliers are weighted using the fuzzy hierarchical analysis (FAHP) method, then ranked using four methods: VIKOR, TOPSIS, ELECTERE, and WASPAS. Then, their ranks are compared with each other. Eventually, Spearman’s rank correlation was examined to compare CMCDM methods. The results indicate that there is a similar ranking between all four CMCDM methods. Finally, it was found the second supplier is the best alternative for rubber companies looking for environmentally friendly suppliers. Also, FAHP-ELECTERE and FAHP-WASPAS methods have a high correlation with each other. The developed method can help decision-makers to make prompt decisions with less environmental pollution, which helps to achieve sustainable performance in the entire supply chain.