In modern times, customers are increasingly aware of the environmental risks posed by the premature expiration of smart products. To safeguard the environment, companies have embraced green technology when procuring products. As a result, it is challenging for business managers to capture the market by offering the best quality products at a reasonable price, regardless of the economic situation. This paper presents a production model incorporating reverse logistics to identify defective products. The model involves learning through production and utilizes green technologies. Additionally, a portion of the assembled products is remanufactured after being received from consumers. The remanufactured items are screened and distributed to markets. Both new and remanufactured products are sold to the market based on their quality in the first and second markets, respectively. To reduce product spoilage, manufacturers employ green technology like liquid cooling technology. The numerical results demonstrate that by investing in liquid cooling technology, the production store can reduce spoilage items by 8.50%, a positive environmental outcome regarding waste reduction, and due to the learning effect, the total cost can decrease by 1.44%. The paper includes numerical and sensitivity analyses accompanied by graphs.

Industries face many challenges when emergencies arise. In emergency, there is an increasing demand for self-administered products that are easy to use. The decay rate of these products decreases with time. Moreover, the lack of disposal of used products increases waste and carbon emissions. By observing the scenario, this study develops a closed-loop supply chain management that considers the collection and remanufacturing of used products. The manufacturing rate is linear and the demand is ramp-type and carbon emissions dependent. The model is solved by a classical optimization and calculates the optimal total cost. The results show that the retailer can handle a shortage situation when the demand becomes stable (Case 2) and the total cost increases with the production rate. A sensitivity analysis shows the changes in the total cost with respect to the parameters.

One of the most successful ways to get the word out about a product’s popularity across all types of customers is through advertising. It has a valuable direct influence on increasing product demand. The supply chain model is developed for manufacturer and retailer, where advertisements are dependent on demand. The advertisement rate has been considered a function that has enhanced at a diminishing rate concerning time, although the growth rate slowed. During the manufacturing cycle, the market’s demand is a function of advertisement, and the customer’s demand is a linear function of time. The production rate exceeds the demand rate during manufacturing and remanufacturing; shortages are not faced. It involves a manufacturing/remanufacturing process that quickly delivers consumer products and less waste. To keep the environment clean, the cost of carbon emissions is incorporated into the manufacturer’s and supplier’s holding and degrading costs. The model’s primary purpose is to minimize the overall cost of manufacturing and remanufacturing. The overall cost during the manufacturing cycle is higher than that during the remanufacturing cycle. This study confirms that the increasing cost of advertising provides the continuous increasing value of the total cost. A numerical example is provided, graphical representation and sensitivity analysis determine the function’s behavior and test the model.

Running the business smoothly for protecting the environment is a significant challenge, on which industries are trying something to do at their level best. Reverse logistics play an important role in system design by reducing environmental consequences and increasing economic and social impacts. Given the recent fluctuations of the market, the production cost and ordering cost are considered triangular fuzzy numbers in this study. Customers' demand is met at the right time, and there is no shortage of items; thus, attention can be paid to two warehouses of a retailer. The setup costs Purchasing costs and deterioration costs of this system are affected by the learning effects, which lead to a decrease in the total cost. Inflation is a significant problem in the market because manufacturing, remanufacturing, and retailers are all affected. This study proposes a reverse logistics system model so that customers can resolve their complaints about defective items and carbon emissions under two warehouses. Numerical results show that the fuzzy model is more economically beneficial than the crisp model, finds that the crisp and fuzzy model saw a difference of 0.34% in total cost. Two numerical examples illustrate this study, and a sensitivity analysis is performed using tables and graph.

Due to the environment of corona virus pandemic, a huge problem arises in the market, due to which the retailer keeps his items lying, and those items start to deteriorate. He is unable to get the new goods from suppliers and get a shortage. We established an optimal policy for mathematical inventory model for decaying items under preservation technology (PT) with learning effect. This model is starting with partially backlogging shortage. The investment in Preservation technology is used so that the items deposited with the retailer do not deteriorate. In this model learning effect and preservation technology plays a very important role. Which the help of the total cost is reduced and maintains the quality of the environment. We show that the total cost is a convex function. Finally, some figures are presented to highlight the numerical examples and sensitivity results. And performed using the Mathematicia-9.0 software.