• Energy Research

Abstract Global warming due to excessive use of fossil fuels has driven researchers to focus on sustainable energy sources for the future. For clean production systems, biofuel is expanding the domain of renewable and sustainable energy supplies. An efficient and sustainable supply chain plays a pivotal role in ensuring this supply. In this research, crop residuals in different agricultural zones, transportation for shipment of residual biomass as well as biofuel, multiple biorefineries, and multiple market centers are considered. The expense of the resources, the yield of residual biomass in agricultural zones, and the demand of market centers are represented by fuzzy numbers as they are assumed to be uncertain. The carbon emissions cost at all stages of the supply chain was also incorporated into this model. This objective of this study is to develop a supply chain model that minimizes the total cost of a second generation biofuel supply chain and location-allocation for agricultural zones and biorefineries to meet the uncertain demand for market centers. Two numerical examples are analyzed, and the results proves that the cost of biofuel production in biorefineries contributed 52.16%, which is a major proportion of the total cost. In the entire supply chain, the transportation sector is the foremost source of carbon emissions in an environment with 88.50% of the total carbon emissions. The results confirms that the proposed model is viable for designing second generation biofuel supply chains under uncertainty. Significant managerial insights of this research are also described to better express the efficiency of the model.

Nowadays, many industries are focusing on automation in manufacturing for high production and good quality to meet the needs of customers in a short period of time. This trend has produced a forward shift in technology in the form of advancement, which ultimately increases energy demand. For that reason, researchers have started working on sustainable development associated with cleaner-energy policies to avoid increasing energy consumption for enhanced manufacturing technology in developed countries. The other important issue affecting our world is global warming, which is the result of greenhouse gas emissions. That is the reason, renewable energies like solar energy have dramatically increased during recent years to compensate for the energy demand and reduced carbon footprint for cleaner production. This paper considers a supply chain management of automobile part manufacturing industry with suppliers to optimize the production quantity with multiple objectives i.e., minimizing the total cost of production including minimum quantity lubrication is a first objective, reduction of the carbon footprint is the second, and minimizing the cost of energy considering renewable energy is the last objective. This study considers a situation, where imperfect quality items are managed and controlled by the suppliers as outsourcing operations. A weighted goal programming methodology is utilized to solve the proposed mathematical model including sustainable suppliers. Sensitivity analysis of the model is performed for different scenarios with respect to the energy utilization. The optimal result of minimum production cost and carbon emissions is the evidence of successful pragmatic application in automobile industry. The results validate the model to provide the basis for sustainability in supply chain environment considering manufacturer and suppliers.

<abstract><p>Production of defective products is a very general phenomenon. But backorder and shortages occur due to this defective product, and it hampers the manufacturer's reputation along with customer satisfaction. That is why, these outsourced products supply, a portion of required products for in-line production. This study develops a flexible production model that reworks repairable defective products and outsources products to prevent backlogging. A percentage of total in-line production is defective products, which is random, and those defective products are repairable. A green investment helps the reworking process, which has a direct impact on the market demand for products. A classical optimization solves the profit maximization model, and a numerical method proves the global optimal solutions. Sensitivity analysis, managerial insights, and discussions provide the highlights and decision-making strategies for the applicability of this model.</p></abstract>

The phenomena of global warming have increased the frequency of natural disasters. These disasters generate thousands of tons of waste and cause loss of human lives, environmental damages, and economic losses every year. Currently, disaster response policies are reactive in nature to bring the community back to normal routine. However, increased resilience against future disasters can be achieved by working on long-term planning and setting goals for ecological, economic, and social sustainability in disaster response policies. Keeping in view the importance of the considered issue, this study proposes a large-scale disaster waste management supply chain model, considering economic aspect via total waste processing, environmental aspect by greenhouse gas emissions from disaster waste processing, and social aspect by job opportunities generated during waste processing. To demonstrate the applicability of the proposed supply chain model, numerical experiments are performed on a large-scale case problem. Results show that there is a strong trade-off among the dimensions of sustainability. If decision makers want to achieve higher satisfaction levels against environmental and social objectives, the operational cost of waste management will increase accordingly. Numerical studies obtain the results in accordance with the values of the confidence level of decision makers and coefficient of compensation decided by the managers which also provides the flexibility for the decision makers of developing countries to obtain preferred compromised solution in accordance with their own preferences for the dimensions of sustainability during disaster waste management operation.

This paper formulates an integrated inventory model that allows Stackelberg game policy for optimizing joint total cost of a vendor and buyer system. After receiving the lot, the buyer commences an inspection process to determine the defective items. All defective items the buyer sends to vendor during the receiving of the next lot. Due to increasing number of shipments fixed and variable transportation, as well as carbon emissions, are considered, which makes the model sustainable integrated model forever. To reduce the setup cost for the vendor, a discrete setup reduction is considered for maximization more profit. The players of the integrated model are with unequal power (as leader and follower) and the Stackelberg game strategy is utilized to solve this model for obtaining global optimum solution over the finite planning horizon. An illustrative numerical example is given to understand this model clearly.

Increasing energy demand and the fast depletion of fossil fuels have prompted the quest for sustainable energy sources. Biodiesel is a potential fossil fuel replacement that can be used in engines without modification. However, the commercial feasibility of biodiesel production is a major challenge. A resilient and cost-efficient biodiesel supply chain network is essential for commercialization. In addition, disruption risks arising from operational downtime, labor strikes, natural disasters, and uncertainty embedded in the data compromise the effectiveness of tactical and strategic level supply chain planning. In line with these requirements, an animal fat-based biodiesel supply chain model that reduces the total system cost and accounts for both disruption and operational risks is proposed. The proposed model determines the optimal production–distribution quantities and supports facility location and capacity decisions against multiple supply and demand interruption scenarios. A novel interactive solution technique, robust possibilistic flexible programming, which enables decision-makers to incorporate flexibility into model constraints, has been introduced. Furthermore, a p-measure constraint that ensures the lowest cost under disruption scenarios is used to control network reliability. A real-world case study is used to assess the suggested model and solution technique's applicability. The findings demonstrate a tradeoff between system reliability and nominal cost, showing that with a marginal increase in overall cost, the decisions can be secured against an uncertain environment. Biodiesel producers and distributors, as well as investors and regulators, may potentially benefit from the proposed model.

Optimal lot sizing is the primary tool applied by lean practitioners to reduce inconsistency in the manufacturing system to cut down inventories, which are often considered as a type of waste in the lean culture. Managers attempt to consider environmental impacts of the manufacturing system and find ways to reduce these effects while making efforts to achieve environmental protection. From a sustainability standpoint, carbon emissions are the major source of environmental contamination and degradation. In this context, this research provides an economic production quantity model with uncertain demand and process information in a multistage manufacturing process. This imperfect manufacturing process produces defective products at an uncertain rate, and is reworked to convert them into perfect quality products and reduce wastages. To control this uncertainty in the manufacturing process, the decomposition principle and the signed distance method of fuzzy theory are applied. The manufacturing process is analyzed with regard to environmental concerns, and a sustainable lot size is obtained through an interactive Weighted Fuzzy Goal Programming (WFGP) approach for the simultaneous achievement of economic and environmental sustainability. An experimental study is performed to verify the practical implication of the model, and results are evaluated through a sensitivity analysis. Important managerial insights and graphical illustrations are provided to elaborate the model.