This paper presents a framework for reverse logistics aimed at managing reusable items within supplier-buyer relationships to promote sustainability and reduce environmental impact. In this model, the supplier produces and inspects items, shipping only perfect items to buyers, while recycling or disposing of imperfect ones. Returned items from consumers are categorized as either reusable or damaged at a collection center. The concept of a circular economy encourages the return and refilling of reusable items, while damaged items are recycled. Additionally, the model incorporates carbon emissions considerations across production, storage, transportation, and landfilling, emphasizing the importance of environmental factors. To evaluate the sustainability and economic efficiency of the supply chain network, both Stackelberg and Nash equilibrium strategies are employed. The paper provides a mathematical framework based on lemmas to analyze the impact of the network and promote sustainable supply chain practices. In this cycle, consumers use the items and eventually discard them. To support a zero-waste policy, the supplier labels the bottles with barcodes to identify used items upon collection. The supplier has two different rates at which they purchase used bottles from consumers. Refilled bottles are sent back for reuse, while damaged bottles are either repurposed as raw materials or disposed of. The research paper aims to develop a mathematical model that determines the buyer's cycle time and the number of deliveries from the supplier to the buyer, ensuring that the buyer's demand is met without shortages.

This research investigated how data privacy practices may impact reverse logistics in the automotive engineering sector, particularly by examining whether environmental efficiency plays a mediating role. This research uses the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT) to understand how data privacy-driven processes support environmental practices and reverse logistics optimization. Primary research is done through structured questionnaires of automotive engineering professionals. The partial least squares structural equation modeling (PLS-SEM) approach tested the relationships amongst data privacy, environmental efficiency, and reverse logistics. However, the results further clarify how key intermediate outcomes, after all, improved environmental efficiency, affected by robust data privacy practices, may enhance reverse logistics processes. The nexus of data privacy and environmental efficiency highlights the critical need to embed respect for private sector information into logistics strategies that achieve superior business performance and also protect corporate sustainability. The findings suggested that environmental consequences must be considered in the flexibility of data-privacy measures with important strategic implications for firms operating in a complex and more environmentally conscious market. This study makes a novel contribution to the extant literature by empirically detecting how environmental efficiency mediates data privacy practices and reverse logistics. These findings will be useful for industry practitioners to use data privacy to enable sustainable logistics management of business operations within automotive engineering.

The purpose of this study is to analyze the relationship between the performance of Reverse Logistics and Green Supply Chain Management, to analyze the relationship between Green Procurement Aspects and the performance of Green Supply Chain Management. This research method is quantitative, the analysis of research data uses the partial least square structural equation model (SEM-PLS) with a statistical data processing tool, namely SmartPLS 4.0 software. Research data was obtained by distributing online questionnaires through social media designed using a Likert scale of 7. Respondents in this study were 670 SMEs owners in Java Island, Indonesia. The stages of data analysis are validity test, reliability test and significance test or hypothesis test. The results of this study indicate that Reverse Logistics has a positive and significant effect on the performance of Green Supply Chain Management, Green Procurement Aspects has a positive and significant effect on the performance of Green Supply Chain Management. The novelty of this study is the relationship model between Reverse Logistics variables, the performance of Green Supply Chain Management and Green Procurement in SMEs which was not found in previous studies. Culinary SMEs are expected to be able to participate in supporting environmentally sound development. This is because the concept of Green Supply Chain Management (GSCM) is a concept that aims to minimize the negative impact of an organization and its supply chain on the environment related to climate change, pollution and resources that are not too large. In order to support GSCM, it is necessary to evaluate the extent to which this concept is carried out by Culinary SMEs. By conducting this evaluation, it is hoped that the constraints and obstacles faced by SMEs in carrying out GSCM can be identified. For this reason, it is necessary to have support from related parties, in this case the government, to conduct socialization and counseling and assistance in implementing GSCM.

Nowadays, companies are recognizing their primordial roles and responsibilities towards the protection of the environment and save the natural resources. They are focusing on some contemporary activities such as Reverse Logistics which is economically and environmentally viable. However, the integration of such an initiative needs flows restructuring and supply chain management in order to increase sustainability and maximize profits. Under this background, this paper addresses an inventory control model for a reverse logistics system that deals with two separated types of demand, for new products and remanufactured products, with different selling prices. The model consists of a single shared machine between production and remanufacturing operations, while the machine is subject to random failures and repairs. Three stock points respectively for returns, new products and remanufactured products are investigated. Meanwhile, in this paper, a modeling of the problem with Discrete-Event simulation using Arena® was conducted. Regarding the purpose of finding, a near-optimal inventory control policy that minimizes the total cost, an optimization of the model based on Tabu Search and Genetic Algorithms was established. Computational examples and sensitivity analysis were performed in order to compare the results and the robustness of each proposed algorithm. Then the results of the two methods were compared with those of OptQuest® optimization tool.

This study aims to analyze the contribution of Green Manufacturing and Green Distribution on improving the performance of Green Supply Chain Management (GSCM) through Reverse Logistics. The development of industry and increasing consumer concern for the environment as well as issues regarding the concept of an environmentally sound industry have forced industries to adjust in line with the GSCM concept. To make the program a success, Green Manufacturing, Green Distribution, and Reverse Logistics are assumed to be supporting the implementation process. This study uses quantitative methods, with the number of samples taken randomly as many as 70 people. The analysis was carried out using the Path Analysis method. Hypothesis testing was carried out in two stages, namely Structural Model-1 and Structural Model-2 testing to obtain each path coefficient number. The results of the study conclude that there is the contribution of Green Manufacturing, Green Distribution, and Reverse Logistics on the success of GSM implementation so that companies must always pay attention to the facilities and related policies to improve the performance of those variables.

Closed-loop supply chain management is an effective and efficient solution for a set of activities to retrieve a product from a customer and improve its value or to dispose it. Today, designing and planning a closed-loop chain is an inevitable but difficult task. In this research, a scenario-based modeling approach is presented by considering both forward and reverse flows as a closed-loop supply chains in steel industry. The proposed study also develops a multi-product and multi-period model based on a mixed integer linear programming (MILP) approach for profit maximization. The study also considers uncertainty in the amount of raw material, processing, storage and distribution of several products flow. Uncertainty is associated with the quantity and quality of the products in the reverse flow, which are directly affected by customers and sorting centers, respectively. Finally, the model is deployed in Steel industry with real data. The results show that by increasing the quality level of returned products the need for raw materials is reduced and the total profit of the supply chain is increased.

The objective of this paper is to develop an integrated production inventory model for reworkable items with exponential demand rate. This is a three-layer supply chain model with perspectives of supplier, producer and retailer. Supplier delivers raw material to the producer and finished goods to the retailer. We consider perfect and imperfect quality products, product reliability and reworking of imperfect items. After screening, defective items reworked at a cost just after the regular manufacturing schedule. At the beginning, the manufacturing system starts produce perfect items, after some time the manufacturing system can undergo into “out-of-control” situation from “in-control” situation, which is controlled by reverse logistic technique. This paper deliberates the effects of business strategies like optimum order size of raw material, exponential demand rate, production rate is demand dependent, idle times and reverse logistics for an integrated marketing system. Mathematica is used to develop the optimal solution of production rate and raw material order for maximum expected average profit. A numerical example and sensitivity analysis is illustrated to validate the model.

This paper presents a unified multi items general inventory model for integrated production of new items and remanufacturing of returned and defected items for a finite planning horizon. In this paper, a production model that takes into account learning, instantaneous deterioration rate and inflation is proposed. In addition, we also consider that the holding cost is a non-negative, non-decreasing and continuous function of time. In this model, the preservation technology is used to reduce the rate of product deterioration. A theory is developed to find the optimal solution of the proposed model; it is then exemplified with the help of several numerical examples. An efficient solution procedure is also provided to find the optimal strategy. Finally, sensitivity of the optimal solution to changes in the values of different parameters of the system and the convexities of the cost functions are also studied and represented through the graphs.

Based on studying organizational structure of Construction Green Supply Chain Management (CGSCM), a mathematical programming model of CGSCM was proposed. The model aimed to maximize the aggregate profits of normalized construction logistics, the reverse logistics and the environmental performance. Numerical experiments show that the proposed approach can improve the aggregate profit effectively. In addition, return ratio, subsidies from governmental organizations, and environmental performance were analyzed for CGSCM performance. Herein, the proper return, subsidy and control strategy could optimize construction green supply chain.

Reverse logistics plays a very critical role in the overall strategy of a business and hence need to be very effective in meeting its objectives. Studies have come up with various insights to optimize reverse logistics arrangements within a specific industry or a sector, but presently there is no study which provides an approach to share knowledge drawn out of reverse logistics arrangements, across dissimilar industries and sectors. Such a study is significant because the response to a reverse logistics arrangement is not uniform in an industry or sector in all the countries, due to different market maturity levels, dissimilar consumer behaviour, and the state of the economy itself. Therefore, the purpose of this paper is to provide a guide for logistics planners through which they can utilize the learning outcomes that emerge from dissimilar industries or sectors within the same economy also. The research findings show that the reverse logistics arrangements can be categorised into various types on the basis of origin and reason for return. It is shown that the products with dissimilar characteristics can be grouped together into six types depending on the common supply chain member interests. Further, the reverse logistics arrangements change from one type to another as a product moves across its life stages. It highlights an approach using which the knowledge drawn from a reverse logistics type in one sector/industry can be applied to the same type in another sector/industry, by focusing on the product types, whose return share similar supply chain member interests. Logistics network planners can apply the insights that have emerged from this analysis to effectively design reverse logistics channels.