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.

Customers' growing concern for environmentally friendly goods and services has created a competitive and environmentally responsible business scenario. This global awareness of a green environment has motivated several researchers and companies to work on reducing carbon emissions and sustainable supply chain management. This study explores a sustainable supply chain system in the context of an imperfect flexible production system with a single manufacturer and multiple competitive retailers. It aims to reduce the carbon footprints of the developed system through uncertain human learning. Three carbon regulation policies are designed to control carbon emissions caused by various supply chain activities. Despite the retailers being competitive in nature, the smart production system with a sustainable supply chain and two-level screening reduces carbon emissions effectively with maximum profit. Obtained results explore the significance of uncertain human learning, and the total profit of the system increases to 0.039% and 2.23%, respectively. A comparative study of the model under different carbon regulatory policies shows a successful reduction in carbon emissions (beyond 20%), which meets the motive of this research.

Remanufacturing industry gives an opportunity to rework defective products from a production system and make them useful again. When an industry remanufactures multiple similar types of products, every type of product goes through the same procedure repetitively. Repetition of the same procedure for similar products causes the overuse of a machine. This study investigates a flexible production system to reduce the overuse of machines for repetitive tasks. A two-stage flexible production system is considered where the common parts of multiple products are produced and remanufactured in the Stage 1. Continuing from Stage 1, the rest product-specific production of each product and remanufacturing processes are completed in Stage 2. Transportation of products uses a multiple delivery policy. This study aims to optimize the cycle time for the production process along with the production rate for Stages 1 and 2. The model is solved by a classical optimization technique and numerical results find the minimum cost of the remanufacturing system. A linear along with non-linear relationship effect of the shared-production process on the production cost are discussed. Results show that the two-stage production system with a shared-production process is cost-efficient and reduce the cycle time.

A long-run manufacturing system can experience machine breakdown at any time for various reasons such as unskilled labor or outdated machinery technology. In an integrated green inventory model, the produced green products cannot all be perfect throughout a cycle, particularly when machines malfunction. Therefore, an inspection policy is introduced to clean the production process from unusable defect products, the correctness of which depends on the discussion of the inspected errors. The perfect products detected via the inspection process are delivered to the retailer as well as the market. To transport green products, it is essential to control the capacity of the containers and the quantities of green products transported per batch. In this study, the greenhouse gas equivalence factor of CO ${}_2$ emissions is calculated for all green products' manufacturing and transportation mediums. These types of energies are used in the manufacturing process: electricity, natural gas, and coal. Whereas within transportation, four transportation modes are considered: railways, roadways, airways, and waterways. The retailer can agree to transport their inventories to the customers' house according to their requirement by requiring a third-party local agency via outsourcing criteria. The model solves the problem of CO ${}_2$ emissions through production and transportation within the machine breakdown.

Currently, most countries are moving towards digitalization, and their energy consumption is increasing daily. Thus, power networks face major challenges in controlling energy consumption and supplying huge amounts of electricity. Again, using excessive power reduces the stored fossil fuels and affects the environment in terms of $\mathrm{C}{\mathrm{O}}_2$ emissions. Keep these issues in mind; this study focuses on energy-efficient products in an energy supply chain management model under credit sales, variable production, and stochastic demand. Here, the manufacturer grants a credit period for the retailer to get more orders; thus, the order quantity is related to the credit period envisaged in this model. Considering such components, supply chain members can reduce negative environmental impacts and significant energy consumption, achieve optimal results and avoid drastic financial losses. Additionally, including a credit period increases the possibility of default risk, for which a certain interest is charged. The marginal reduction cost for limiting carbon emissions, flexible production to meet fluctuating demand, and continuous investment to improve product quality are considered here. The global optimality of system profit function and decision variables (credit period, quality improvement, and production rate) is ensured through the classical optimization method. Interpretive sensitivity analyses and numerical investigations are performed to validate the proposed model. The results demonstrate that the idea of credit sales, flexible production, and quality improvement increases total system profit by $28.64\mathrm{\%}$ and marginal reduction technology reduces $\mathrm{C}{\mathrm{O}}_2$ emissions up to $4.01\mathrm{\%}$.

The proposed study described the application of innovative technology to solve the issues in a supply chain model due to the players' unreliability. The unreliable manufacturer delivers a percentage of the ordered quantity to the retailer, which causes shortages. At the same time, the retailer provides wrong information regarding the amount of the sales of the product. Besides intelligent technology, a single setup multiple unequal increasing delivery transportation policy is applied in this study to reduce the holding cost of the retailer. A consumed fuel and electricity-dependent carbon emission cost are used for environmental sustainability. Since the industries face problems with smooth functioning in each of its steps for unreliable players, the study is proposed to solve the unpredictable player problem in the supply chain. The robust distribution approach is utilized to overcome the situation of unknown lead time demand. Two metaheuristic optimization techniques, genetic algorithm (GA) and particle swarm optimization (PSO) are used to optimize the total cost. From the numerical section, it is clear the PSO is $0.32$ % more beneficial than GA to obtain the minimum total cost of the supply chain. The discussed case studies show that the applied single-setup-multi-unequal-increasing delivery policy is $0.62$ % beneficial compared to the single-setup-single-delivery policy and $0.35$ % beneficial compared to the single-setup-multi-delivery policy. The sensitivity analysis with graphical representation is provided to explain the result clearly.

The current study focuses on a two-echelon supply chain for a reliable retailer, an unreliable manufacturer, and selling price-dependent demand. Due to an unreliable manufacturer and transportation hazards, shortages arise, which negatively impact the reputation of the retailer. Moreover, customers are more conscious of the environment, as a result, most of the industry focuses on the production of green products. To reduce the holding cost of the retailer, a fuel consumption-based single-setup-multi-unequal-increasing-delivery policy was utilized in this current study. With this transportation policy, the number of shipments increases, which directly increases carbon emissions and transportation hazards. To protect the environment, the green level of the product is enhanced through some investments. The demand varies with the price of the product as well as with the level of the greenness of the product. Due to uncertain demand, the rate of the production is treated as controllable. A classical optimization technique and distribution-free approach have been utilized to obtain the optimum solution and the optimized system profit. To prove the applicability, the study is illustrated numerically and graphically via a well-explained analysis of sensitivity. The study proves that single-setup-multi-unequal-increasing delivery policy is $0.62\mathrm{\%}$ beneficial compared to single-setup-single-delivery policy and $0.35\mathrm{\%}$ better than the single-setup-multi-delivery policy.

Social activities, economic benefits, and environmental friendly approach are very much essential for a sustainable production system. This is widely observed during the Covid-19 pandemic situation. The demand for essential goods in the business sector is always changing due to different unavoidable situations. The proposed study introduces a variable demand for controlling the fluctuating demand. However, a reworking of produced imperfect products makes the production model more profitable. Partial outsourcing of the good quality products has made the production system more popular and profitable. Separate holding cost for the reworked and produced products are very helpful idea for the proposed model. Moreover, consumption of energy during various purpose are considered. Separate green investment make the model more sustainable and eco-friendly. The main focus of the model is to find the maximum profit through considering optimum value of lot size quantity, average selling price, and green investment. The classical optimization technique is utilized here for optimizing the solution theoretically. The use of concave 3D graphs, different examples, and sensitivity analyses are considered here. Furthermore, managerial insights from this study can be used for industry improvement.